Lightning Autoencoder

train.py

import os
from argparse import ArgumentParser

import torch
import torch.nn as nn
import torch.utils.data as data
import torchvision as tv
import torch.nn.functional as F
import lightning as L

parser = ArgumentParser()
parser.add_argument('--lr', default=0.02, type=float)
args = parser.parse_args()


# --------------------------------
# Step 1: Define a LightningModule
# --------------------------------
# A LightningModule (nn.Module subclass) defines a full *system*
# (ie: an LLM, difussion model, autoencoder, or simple image classifier).


class LitAutoEncoder(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, 128), 
            nn.ReLU(), 
            nn.Linear(128, 3),
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, 128),
            nn.ReLU(),
            nn.Linear(128, 28 * 28),
        )

    def forward(self, x):
        # in lightning, forward defines the prediction/inference actions
        embedding = self.encoder(x)
        return embedding

    def training_step(self, batch, batch_idx):
        # training_step defines the train loop. It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        print('lr: ', args.lr)
        optimizer = torch.optim.Adam(self.parameters(), lr=args.lr)
        return optimizer


# -------------------
# Step 2: Define data
# -------------------
dataset = tv.datasets.MNIST(os.getcwd(), download=True, transform=tv.transforms.ToTensor())
train, val = data.random_split(dataset, [55000, 5000])

# -------------------
# Step 3: Train
# -------------------
autoencoder = LitAutoEncoder()
trainer = L.Trainer()
trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))