234 lines
7.2 KiB
Python
234 lines
7.2 KiB
Python
# Copyright The Lightning AI team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""To run this template just do: python generative_adversarial_net.py.
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After a few epochs, launch TensorBoard to see the images being generated at every batch:
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tensorboard --logdir default
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"""
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import math
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from argparse import ArgumentParser, Namespace
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from lightning.pytorch import cli_lightning_logo
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from lightning.pytorch.core import LightningModule
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from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
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from lightning.pytorch.trainer import Trainer
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from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
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if _TORCHVISION_AVAILABLE:
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import torchvision
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class Generator(nn.Module):
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"""
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>>> Generator(img_shape=(1, 8, 8)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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Generator(
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(model): Sequential(...)
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)
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"""
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def __init__(self, latent_dim: int = 100, img_shape: tuple = (1, 28, 28)):
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super().__init__()
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self.img_shape = img_shape
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def block(in_feat, out_feat, normalize=True):
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layers = [nn.Linear(in_feat, out_feat)]
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if normalize:
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layers.append(nn.BatchNorm1d(out_feat, 0.8))
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layers.append(nn.LeakyReLU(0.2, inplace=True))
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return layers
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self.model = nn.Sequential(
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*block(latent_dim, 128, normalize=False),
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*block(128, 256),
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*block(256, 512),
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*block(512, 1024),
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nn.Linear(1024, int(math.prod(img_shape))),
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nn.Tanh(),
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)
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def forward(self, z):
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img = self.model(z)
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return img.view(img.size(0), *self.img_shape)
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class Discriminator(nn.Module):
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"""
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>>> Discriminator(img_shape=(1, 28, 28)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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Discriminator(
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(model): Sequential(...)
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)
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"""
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def __init__(self, img_shape):
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super().__init__()
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self.model = nn.Sequential(
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nn.Linear(int(math.prod(img_shape)), 512),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Linear(512, 256),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Linear(256, 1),
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)
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def forward(self, img):
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img_flat = img.view(img.size(0), -1)
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return self.model(img_flat)
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class GAN(LightningModule):
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"""
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>>> GAN(img_shape=(1, 8, 8)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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GAN(
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(generator): Generator(
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(model): Sequential(...)
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)
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(discriminator): Discriminator(
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(model): Sequential(...)
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)
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)
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"""
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def __init__(
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self,
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img_shape: tuple = (1, 28, 28),
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lr: float = 0.0002,
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b1: float = 0.5,
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b2: float = 0.999,
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latent_dim: int = 100,
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):
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super().__init__()
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self.save_hyperparameters()
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self.automatic_optimization = False
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# networks
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self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=img_shape)
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self.discriminator = Discriminator(img_shape=img_shape)
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self.validation_z = torch.randn(8, self.hparams.latent_dim)
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self.example_input_array = torch.zeros(2, self.hparams.latent_dim)
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def forward(self, z):
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return self.generator(z)
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@staticmethod
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def adversarial_loss(y_hat, y):
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return F.binary_cross_entropy_with_logits(y_hat, y)
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def training_step(self, batch):
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imgs, _ = batch
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opt_g, opt_d = self.optimizers()
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# sample noise
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z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
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z = z.type_as(imgs)
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# Train generator
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# ground truth result (ie: all fake)
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# put on GPU because we created this tensor inside training_loop
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valid = torch.ones(imgs.size(0), 1)
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valid = valid.type_as(imgs)
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self.toggle_optimizer(opt_g)
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# adversarial loss is binary cross-entropy
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g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
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opt_g.zero_grad()
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self.manual_backward(g_loss)
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opt_g.step()
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self.untoggle_optimizer(opt_g)
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# Train discriminator
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# Measure discriminator's ability to classify real from generated samples
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# how well can it label as real?
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valid = torch.ones(imgs.size(0), 1)
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valid = valid.type_as(imgs)
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self.toggle_optimizer(opt_d)
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real_loss = self.adversarial_loss(self.discriminator(imgs), valid)
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# how well can it label as fake?
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fake = torch.zeros(imgs.size(0), 1)
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fake = fake.type_as(imgs)
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fake_loss = self.adversarial_loss(self.discriminator(self(z).detach()), fake)
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# discriminator loss is the average of these
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d_loss = (real_loss + fake_loss) / 2
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opt_d.zero_grad()
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self.manual_backward(d_loss)
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opt_d.step()
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self.untoggle_optimizer(opt_d)
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self.log_dict({"d_loss": d_loss, "g_loss": g_loss})
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def configure_optimizers(self):
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lr = self.hparams.lr
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b1 = self.hparams.b1
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b2 = self.hparams.b2
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opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
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opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
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return opt_g, opt_d
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def on_train_epoch_end(self):
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z = self.validation_z.type_as(self.generator.model[0].weight)
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# log sampled images
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sample_imgs = self(z)
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grid = torchvision.utils.make_grid(sample_imgs)
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for logger in self.loggers:
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logger.experiment.add_image("generated_images", grid, self.current_epoch)
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def main(args: Namespace) -> None:
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = GAN(lr=args.lr, b1=args.b1, b2=args.b2, latent_dim=args.latent_dim)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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# If use distributed training PyTorch recommends to use DistributedDataParallel.
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# See: https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel
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dm = MNISTDataModule()
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trainer = Trainer(accelerator="gpu", devices=1)
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# ------------------------
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# 3 START TRAINING
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# ------------------------
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trainer.fit(model, dm)
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if __name__ == "__main__":
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cli_lightning_logo()
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parser = ArgumentParser()
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# Hyperparameters
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parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
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parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
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parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of second order momentum of gradient")
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parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
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args = parser.parse_args()
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main(args)
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