lightning/pl_examples/domain_templates/generative_adversarial_net.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.
"""
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 os

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F  # noqa
from torch.utils.data import DataLoader
import torchvision
from torchvision.datasets import MNIST
import torchvision.transforms as transforms

from pl_examples import cli_lightning_logo
from pytorch_lightning.core import LightningDataModule, LightningModule
from pytorch_lightning.trainer import Trainer


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)
        validity = self.model(img_flat)

        return validity


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()

        # 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)

    @staticmethod
    def add_argparse_args(parent_parser: ArgumentParser):
        parser = ArgumentParser(parents=[parent_parser], add_help=False)
        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")

        return parser

    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, batch_idx, optimizer_idx):
        imgs, _ = batch

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

        # train generator
        if optimizer_idx == 0:
            # 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)

            # adversarial loss is binary cross-entropy
            g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
            tqdm_dict = {'g_loss': g_loss}
            self.log_dict(tqdm_dict)
            return g_loss

        # train discriminator
        if optimizer_idx == 1:
            # 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)

            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
            tqdm_dict = {'d_loss': d_loss}
            self.log_dict(tqdm_dict)

            return d_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_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)
        self.logger.experiment.add_image('generated_images', grid, self.current_epoch)


class MNISTDataModule(LightningDataModule):
    """
    >>> MNISTDataModule()  # doctest: +ELLIPSIS
    <...generative_adversarial_net.MNISTDataModule object at ...>
    """
    def __init__(self, batch_size: int = 64, data_path: str = os.getcwd(), num_workers: int = 4):
        super().__init__()
        self.batch_size = batch_size
        self.data_path = data_path
        self.num_workers = num_workers

        self.transform = transforms.Compose([transforms.ToTensor(),
                                             transforms.Normalize([0.5], [0.5])])
        self.dims = (1, 28, 28)

    def prepare_data(self, stage=None):
        # Use this method to do things that might write to disk or that need to be done only from a single GPU
        # in distributed settings. Like downloading the dataset for the first time.
        MNIST(self.data_path, train=True, download=True, transform=transforms.ToTensor())

    def setup(self, stage=None):
        # There are also data operations you might want to perform on every GPU, such as applying transforms
        # defined explicitly in your datamodule or assigned in init.
        self.mnist_train = MNIST(self.data_path, train=True, transform=self.transform)

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


def main(args: Namespace) -> None:
    # ------------------------
    # 1 INIT LIGHTNING MODEL
    # ------------------------
    model = GAN(args)

    # ------------------------
    # 2 INIT TRAINER
    # ------------------------
    # If use distubuted training  PyTorch recommends to use DistributedDataParallel.
    # See: https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel
    dm = MNISTDataModule.from_argparse_args(args)
    trainer = Trainer.from_argparse_args(args)

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


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

    # Add program level args, if any.
    # ------------------------
    # Add LightningDataLoader args
    parser = MNISTDataModule.add_argparse_args(parser)
    # Add model specific args
    parser = GAN.add_argparse_args(parser)
    # Add trainer args
    parser = Trainer.add_argparse_args(parser)
    # Parse all arguments
    args = parser.parse_args()

    main(args)