• Docs >
  • PyTorch Lightning Basic GAN Tutorial
Shortcuts

PyTorch Lightning Basic GAN Tutorial

  • Author: PL team

  • License: CC BY-SA

  • Generated: 2022-08-15T09:28:43.606365

How to train a GAN!

Main takeaways: 1. Generator and discriminator are arbitrary PyTorch modules. 2. training_step does both the generator and discriminator training.


Open in Open In Colab

Give us a ⭐ on Github | Check out the documentation | Join us on Slack

Setup

This notebook requires some packages besides pytorch-lightning.

[1]:
! pip install --quiet "pytorch-lightning>=1.4" "torch>=1.8" "torchvision" "ipython[notebook]" "torchmetrics>=0.7" "setuptools==59.5.0"
[2]:
import os

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST

PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
BATCH_SIZE = 256 if torch.cuda.is_available() else 64
NUM_WORKERS = int(os.cpu_count() / 2)
WARNING:root:Bagua cannot detect bundled NCCL library, Bagua will try to use system NCCL instead. If you encounter any error, please run `import bagua_core; bagua_core.install_deps()` or the `bagua_install_deps.py` script to install bundled libraries.

MNIST DataModule

Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial on them or see the latest release docs.

[3]:
class MNISTDataModule(LightningDataModule):
    def __init__(
        self,
        data_dir: str = PATH_DATASETS,
        batch_size: int = BATCH_SIZE,
        num_workers: int = NUM_WORKERS,
    ):
        super().__init__()
        self.data_dir = data_dir
        self.batch_size = batch_size
        self.num_workers = num_workers

        self.transform = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
            ]
        )

        self.dims = (1, 28, 28)
        self.num_classes = 10

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage=None):
        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)

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

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

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

A. Generator

[4]:
class Generator(nn.Module):
    def __init__(self, latent_dim, img_shape):
        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

B. Discriminator

[5]:
class Discriminator(nn.Module):
    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),
            nn.Sigmoid(),
        )

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

        return validity

C. GAN

A couple of cool features to check out in this example…

  • We use some_tensor.type_as(another_tensor) to make sure we initialize new tensors on the right device (i.e. GPU, CPU).

    • Lightning will put your dataloader data on the right device automatically

    • In this example, we pull from latent dim on the fly, so we need to dynamically add tensors to the right device.

    • type_as is the way we recommend to do this.

  • This example shows how to use multiple dataloaders in your LightningModule.

[6]:
class GAN(LightningModule):
    def __init__(
        self,
        channels,
        width,
        height,
        latent_dim: int = 100,
        lr: float = 0.0002,
        b1: float = 0.5,
        b2: float = 0.999,
        batch_size: int = BATCH_SIZE,
        **kwargs,
    ):
        super().__init__()
        self.save_hyperparameters()

        # networks
        data_shape = (channels, width, height)
        self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=data_shape)
        self.discriminator = Discriminator(img_shape=data_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)

    def adversarial_loss(self, y_hat, y):
        return F.binary_cross_entropy(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:

            # generate images
            self.generated_imgs = self(z)

            # log sampled images
            sample_imgs = self.generated_imgs[:6]
            grid = torchvision.utils.make_grid(sample_imgs)
            self.logger.experiment.add_image("generated_images", grid, 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)
            self.log("g_loss", g_loss, prog_bar=True)
            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
            self.log("d_loss", d_loss, prog_bar=True)
            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_validation_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)
[7]:
dm = MNISTDataModule()
model = GAN(*dm.dims)
trainer = Trainer(
    accelerator="auto",
    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
    max_epochs=5,
    callbacks=[TQDMProgressBar(refresh_rate=20)],
)
trainer.fit(model, dm)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /__w/1/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz
/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:115: UserWarning: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
  rank_zero_warn("You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.")
Extracting /__w/1/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz to /__w/1/s/.datasets/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /__w/1/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting /__w/1/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /__w/1/s/.datasets/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /__w/1/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting /__w/1/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /__w/1/s/.datasets/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /__w/1/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz
Missing logger folder: /__w/1/s/lightning_logs
Extracting /__w/1/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/1/s/.datasets/MNIST/raw

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]

  | Name          | Type          | Params | In sizes | Out sizes
----------------------------------------------------------------------------
0 | generator     | Generator     | 1.5 M  | [2, 100] | [2, 1, 28, 28]
1 | discriminator | Discriminator | 533 K  | ?        | ?
----------------------------------------------------------------------------
2.0 M     Trainable params
0         Non-trainable params
2.0 M     Total params
8.174     Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[8]:
# Start tensorboard.
%load_ext tensorboard
%tensorboard --logdir lightning_logs/

Congratulations - Time to Join the Community!

Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning movement, you can do so in the following ways!

Star Lightning on GitHub

The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool tools we’re building.

Join our Slack!

The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself and share your interests in #general channel

Contributions !

The best way to contribute to our community is to become a code contributor! At any time you can go to Lightning or Bolt GitHub Issues page and filter for “good first issue”.

Great thanks from the entire Pytorch Lightning Team for your interest !

Pytorch Lightning