5.2-part3-lightning-mlp.py

# Unit 5.2. Training a Multilayer Perceptron in PyTorch & Lightning
# Part 3. Training a Multilayer Perceptron in PyTorch using Lightning

import lightning as L
import torch
import torch.nn.functional as F
from shared_utilities import PyTorchMLP, compute_accuracy, get_dataset_loaders
from watermark import watermark


# LightningModule that receives a PyTorch model as input
class LightningModel(L.LightningModule):
    def __init__(self, model, learning_rate):
        super().__init__()

        self.learning_rate = learning_rate
        self.model = model

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        features, true_labels = batch
        logits = self(features)
        loss = F.cross_entropy(logits, true_labels)
        self.log("train_loss", loss)
        return loss  # this is passed to the optimizer for training

    def validation_step(self, batch, batch_idx):
        features, true_labels = batch
        logits = self(features)
        loss = F.cross_entropy(logits, true_labels)
        self.log("val_loss", loss, prog_bar=True)

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)
        return optimizer


if __name__ == "__main__":

    print(watermark(packages="torch,lightning", python=True))
    print("Torch CUDA available?", torch.cuda.is_available())

    train_loader, val_loader, test_loader = get_dataset_loaders()

    pytorch_model = PyTorchMLP(num_features=784, num_classes=10)
    lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)

    trainer = L.Trainer(
        max_epochs=10,
        accelerator="auto",  # set to "auto" or "gpu" to use GPUs if available
        devices="auto",  # Uses all available GPUs if applicable
    )

    trainer.fit(
        model=lightning_model,
        train_dataloaders=train_loader,
        val_dataloaders=val_loader,
    )

    train_acc = compute_accuracy(pytorch_model, train_loader)
    val_acc = compute_accuracy(pytorch_model, val_loader)
    test_acc = compute_accuracy(pytorch_model, test_loader)
    print(
        f"Train Acc {train_acc*100:.2f}%"
        f" | Val Acc {val_acc*100:.2f}%"
        f" | Test Acc {test_acc*100:.2f}%"
    )


PATH = "lightning.pt"
torch.save(pytorch_model.state_dict(), PATH)

# To load model:
# model = PyTorchMLP(num_features=784, num_classes=10)
# model.load_state_dict(torch.load(PATH))
# model.eval()