How to use PyTorch Lightning to train a neural network on a custom dataset?

To use PyTorch Lightning to train a neural network on a custom dataset, you can create a custom PyTorch Dataset and use a PyTorch Lightning DataModule to load and preprocess the data. Here’s an example:

1. Define your custom PyTorch Dataset that inherits from torch.utils.data.Dataset and implements the __getitem__ and __len__ methods to return samples from your dataset.
2. Create a PyTorch Lightning DataModule that inherits from pl.LightningDataModule and implements the train_dataloader, val_dataloader and test_dataloader methods to return DataLoader objects that load batches of samples from your dataset. You can also implement any necessary data preprocessing in these methods.
3. Define your PyTorch Lightning model that inherits from pl.LightningModule and implements the training_step, validation_step, test_step and configure_optimizers methods.
4. Instantiate a pl.Trainer object and pass in your PyTorch Lightning model and DataModule, then call the fit method to start training. You can also use the validate and test methods to evaluate your model on validation and test data, respectively.

Here’s an example code snippet:

import torch
import pytorch_lightning as pl
from torch.utils.data import Dataset, DataLoader

class CustomDataset(Dataset):
    def __init__(self, data):
        self.data = data

    def __getitem__(self, idx):
        sample = self.data[idx]
        return sample

    def __len__(self):
        return len(self.data)

class CustomDataModule(pl.LightningDataModule):
    def __init__(self, train_data, val_data, test_data, batch_size):
        super().__init__()
        self.train_data = CustomDataset(train_data)
        self.val_data = CustomDataset(val_data)
        self.test_data = CustomDataset(test_data)
        self.batch_size = batch_size

    def train_dataloader(self):
        return DataLoader(self.train_data, batch_size=self.batch_size)

    def val_dataloader(self):
        return DataLoader(self.val_data, batch_size=self.batch_size)

    def test_dataloader(self):
        return DataLoader(self.test_data, batch_size=self.batch_size)

class CustomModel(pl.LightningModule):
    def __init__(self, input_size, output_size):
        super().__init__()
        self.fc = torch.nn.Linear(input_size, output_size)