lightning/pl_examples/basic_examples/autoencoder.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MNIST autoencoder example.

To run:
python autoencoder.py --trainer.max_epochs=50
"""

import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader, random_split

import pytorch_lightning as pl
from pl_examples import _DATASETS_PATH, _TORCHVISION_MNIST_AVAILABLE, cli_lightning_logo
from pytorch_lightning.utilities.cli import LightningCLI
from pytorch_lightning.utilities.imports import _TORCHVISION_AVAILABLE

if _TORCHVISION_AVAILABLE:
    from torchvision import transforms
if _TORCHVISION_MNIST_AVAILABLE:
    from torchvision.datasets import MNIST
else:
    from tests.helpers.datasets import MNIST


class LitAutoEncoder(pl.LightningModule):
    """
    >>> LitAutoEncoder()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    LitAutoEncoder(
      (encoder): ...
      (decoder): ...
    )
    """

    def __init__(self, hidden_dim: int = 64):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 3),
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 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):
        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)
        return loss

    def validation_step(self, batch, batch_idx):
        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('valid_loss', loss, on_step=True)

    def test_step(self, batch, batch_idx):
        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('test_loss', loss, on_step=True)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer


class MyDataModule(pl.LightningDataModule):

    def __init__(
        self,
        batch_size: int = 32,
    ):
        super().__init__()
        dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
        self.mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
        self.mnist_train, self.mnist_val = random_split(dataset, [55000, 5000])
        self.batch_size = batch_size

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

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

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


def cli_main():
    cli = LightningCLI(LitAutoEncoder, MyDataModule, seed_everything_default=1234)
    cli.trainer.test(cli.model, datamodule=cli.datamodule)


if __name__ == '__main__':
    cli_lightning_logo()
    cli_main()