lightning/examples/pytorch/domain_templates/generative_adversarial_net.py

# Copyright The Lightning AI 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.
"""To run this template just do: python generative_adversarial_net.py.

After a few epochs, launch TensorBoard to see the images being generated at every batch:

tensorboard --logdir default
"""
from argparse import ArgumentParser, Namespace

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from lightning.pytorch import cli_lightning_logo
from lightning.pytorch.core import LightningModule
from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
from lightning.pytorch.trainer import Trainer
from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE

if _TORCHVISION_AVAILABLE:
    import torchvision


class Generator(nn.Module):
    """
    >>> Generator(img_shape=(1, 8, 8))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    Generator(
      (model): Sequential(...)
    )
    """

    def __init__(self, latent_dim: int = 100, img_shape: tuple = (1, 28, 28)):
        super().__init__()
        self.img_shape = img_shape

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh(),
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *self.img_shape)
        return img


class Discriminator(nn.Module):
    """
    >>> Discriminator(img_shape=(1, 28, 28))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    Discriminator(
      (model): Sequential(...)
    )
    """

    def __init__(self, img_shape):
        super().__init__()

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        return self.model(img_flat)


class GAN(LightningModule):
    """
    >>> GAN(img_shape=(1, 8, 8))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    GAN(
      (generator): Generator(
        (model): Sequential(...)
      )
      (discriminator): Discriminator(
        (model): Sequential(...)
      )
    )
    """

    def __init__(
        self,
        img_shape: tuple = (1, 28, 28),
        lr: float = 0.0002,
        b1: float = 0.5,
        b2: float = 0.999,
        latent_dim: int = 100,
    ):
        super().__init__()
        self.save_hyperparameters()
        self.automatic_optimization = False

        # networks
        self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=img_shape)
        self.discriminator = Discriminator(img_shape=img_shape)

        self.validation_z = torch.randn(8, self.hparams.latent_dim)

        self.example_input_array = torch.zeros(2, self.hparams.latent_dim)

    def forward(self, z):
        return self.generator(z)

    @staticmethod
    def adversarial_loss(y_hat, y):
        return F.binary_cross_entropy_with_logits(y_hat, y)

    def training_step(self, batch):
        imgs, _ = batch

        opt_g, opt_d = self.optimizers()

        # sample noise
        z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
        z = z.type_as(imgs)

        # Train generator
        # ground truth result (ie: all fake)
        # put on GPU because we created this tensor inside training_loop
        valid = torch.ones(imgs.size(0), 1)
        valid = valid.type_as(imgs)

        self.toggle_optimizer(opt_g)
        # adversarial loss is binary cross-entropy
        g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
        opt_g.zero_grad()
        self.manual_backward(g_loss)
        opt_g.step()
        self.untoggle_optimizer(opt_g)

        # Train discriminator
        # Measure discriminator's ability to classify real from generated samples
        # how well can it label as real?
        valid = torch.ones(imgs.size(0), 1)
        valid = valid.type_as(imgs)

        self.toggle_optimizer(opt_d)
        real_loss = self.adversarial_loss(self.discriminator(imgs), valid)

        # how well can it label as fake?
        fake = torch.zeros(imgs.size(0), 1)
        fake = fake.type_as(imgs)

        fake_loss = self.adversarial_loss(self.discriminator(self(z).detach()), fake)

        # discriminator loss is the average of these
        d_loss = (real_loss + fake_loss) / 2

        opt_d.zero_grad()
        self.manual_backward(d_loss)
        opt_d.step()
        self.untoggle_optimizer(opt_d)

        self.log_dict({"d_loss": d_loss, "g_loss": g_loss})

    def configure_optimizers(self):
        lr = self.hparams.lr
        b1 = self.hparams.b1
        b2 = self.hparams.b2

        opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
        opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
        return opt_g, opt_d

    def on_train_epoch_end(self):
        z = self.validation_z.type_as(self.generator.model[0].weight)

        # log sampled images
        sample_imgs = self(z)
        grid = torchvision.utils.make_grid(sample_imgs)
        for logger in self.loggers:
            logger.experiment.add_image("generated_images", grid, self.current_epoch)


def main(args: Namespace) -> None:
    # ------------------------
    # 1 INIT LIGHTNING MODEL
    # ------------------------
    model = GAN(lr=args.lr, b1=args.b1, b2=args.b2, latent_dim=args.latent_dim)

    # ------------------------
    # 2 INIT TRAINER
    # ------------------------
    # If use distributed training  PyTorch recommends to use DistributedDataParallel.
    # See: https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel
    dm = MNISTDataModule()
    trainer = Trainer(accelerator="gpu", devices=1)

    # ------------------------
    # 3 START TRAINING
    # ------------------------
    trainer.fit(model, dm)


if __name__ == "__main__":
    cli_lightning_logo()
    parser = ArgumentParser()

    # Hyperparameters
    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
    parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
    parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of second order momentum of gradient")
    parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
    args = parser.parse_args()

    main(args)