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dcgan_module.py
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dcgan_module.py
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from argparse import ArgumentParser
from typing import Any
import torch
import torch.nn as nn
from pytorch_lightning import LightningModule, Trainer, seed_everything
from torch import Tensor
from torch.utils.data import DataLoader
from pl_bolts.callbacks import LatentDimInterpolator, TensorboardGenerativeModelImageSampler
from pl_bolts.models.gans.dcgan.components import DCGANDiscriminator, DCGANGenerator
from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg
if _TORCHVISION_AVAILABLE:
from torchvision import transforms as transform_lib
from torchvision.datasets import LSUN, MNIST
else: # pragma: no cover
warn_missing_pkg("torchvision")
class DCGAN(LightningModule):
"""DCGAN implementation.
Example::
from pl_bolts.models.gans import DCGAN
m = DCGAN()
Trainer(gpus=2).fit(m)
Example CLI::
# mnist
python dcgan_module.py --gpus 1
# cifar10
python dcgan_module.py --gpus 1 --dataset cifar10 --image_channels 3
"""
def __init__(
self,
beta1: float = 0.5,
feature_maps_gen: int = 64,
feature_maps_disc: int = 64,
image_channels: int = 1,
latent_dim: int = 100,
learning_rate: float = 0.0002,
**kwargs: Any,
) -> None:
"""
Args:
beta1: Beta1 value for Adam optimizer
feature_maps_gen: Number of feature maps to use for the generator
feature_maps_disc: Number of feature maps to use for the discriminator
image_channels: Number of channels of the images from the dataset
latent_dim: Dimension of the latent space
learning_rate: Learning rate
"""
super().__init__()
self.save_hyperparameters()
self.generator = self._get_generator()
self.discriminator = self._get_discriminator()
self.criterion = nn.BCELoss()
def _get_generator(self) -> nn.Module:
generator = DCGANGenerator(self.hparams.latent_dim, self.hparams.feature_maps_gen, self.hparams.image_channels)
generator.apply(self._weights_init)
return generator
def _get_discriminator(self) -> nn.Module:
discriminator = DCGANDiscriminator(self.hparams.feature_maps_disc, self.hparams.image_channels)
discriminator.apply(self._weights_init)
return discriminator
@staticmethod
def _weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
nn.init.normal_(m.weight, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
nn.init.normal_(m.weight, 1.0, 0.02)
nn.init.zeros_(m.bias)
def configure_optimizers(self):
lr = self.hparams.learning_rate
betas = (self.hparams.beta1, 0.999)
opt_disc = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=betas)
opt_gen = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=betas)
return [opt_disc, opt_gen], []
def forward(self, noise: Tensor) -> Tensor:
"""Generates an image given input noise.
Example::
noise = torch.rand(batch_size, latent_dim)
gan = GAN.load_from_checkpoint(PATH)
img = gan(noise)
"""
noise = noise.view(*noise.shape, 1, 1)
return self.generator(noise)
def training_step(self, batch, batch_idx, optimizer_idx):
real, _ = batch
# Train discriminator
result = None
if optimizer_idx == 0:
result = self._disc_step(real)
# Train generator
if optimizer_idx == 1:
result = self._gen_step(real)
return result
def _disc_step(self, real: Tensor) -> Tensor:
disc_loss = self._get_disc_loss(real)
self.log("loss/disc", disc_loss, on_epoch=True)
return disc_loss
def _gen_step(self, real: Tensor) -> Tensor:
gen_loss = self._get_gen_loss(real)
self.log("loss/gen", gen_loss, on_epoch=True)
return gen_loss
def _get_disc_loss(self, real: Tensor) -> Tensor:
# Train with real
real_pred = self.discriminator(real)
real_gt = torch.ones_like(real_pred)
real_loss = self.criterion(real_pred, real_gt)
# Train with fake
fake_pred = self._get_fake_pred(real)
fake_gt = torch.zeros_like(fake_pred)
fake_loss = self.criterion(fake_pred, fake_gt)
disc_loss = real_loss + fake_loss
return disc_loss
def _get_gen_loss(self, real: Tensor) -> Tensor:
# Train with fake
fake_pred = self._get_fake_pred(real)
fake_gt = torch.ones_like(fake_pred)
gen_loss = self.criterion(fake_pred, fake_gt)
return gen_loss
def _get_fake_pred(self, real: Tensor) -> Tensor:
batch_size = len(real)
noise = self._get_noise(batch_size, self.hparams.latent_dim)
fake = self(noise)
fake_pred = self.discriminator(fake)
return fake_pred
def _get_noise(self, n_samples: int, latent_dim: int) -> Tensor:
return torch.randn(n_samples, latent_dim, device=self.device)
@staticmethod
def add_model_specific_args(parent_parser: ArgumentParser) -> ArgumentParser:
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument("--beta1", default=0.5, type=float)
parser.add_argument("--feature_maps_gen", default=64, type=int)
parser.add_argument("--feature_maps_disc", default=64, type=int)
parser.add_argument("--latent_dim", default=100, type=int)
parser.add_argument("--learning_rate", default=0.0002, type=float)
return parser
def cli_main(args=None):
seed_everything(1234)
parser = ArgumentParser()
parser.add_argument("--batch_size", default=64, type=int)
parser.add_argument("--dataset", default="mnist", type=str, choices=["lsun", "mnist"])
parser.add_argument("--data_dir", default="./", type=str)
parser.add_argument("--image_size", default=64, type=int)
parser.add_argument("--num_workers", default=8, type=int)
script_args, _ = parser.parse_known_args(args)
if script_args.dataset == "lsun":
transforms = transform_lib.Compose(
[
transform_lib.Resize(script_args.image_size),
transform_lib.CenterCrop(script_args.image_size),
transform_lib.ToTensor(),
transform_lib.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
dataset = LSUN(root=script_args.data_dir, classes=["bedroom_train"], transform=transforms)
image_channels = 3
elif script_args.dataset == "mnist":
transforms = transform_lib.Compose(
[
transform_lib.Resize(script_args.image_size),
transform_lib.ToTensor(),
transform_lib.Normalize((0.5,), (0.5,)),
]
)
dataset = MNIST(root=script_args.data_dir, download=True, transform=transforms)
image_channels = 1
dataloader = DataLoader(
dataset, batch_size=script_args.batch_size, shuffle=True, num_workers=script_args.num_workers
)
parser = DCGAN.add_model_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args(args)
model = DCGAN(**vars(args), image_channels=image_channels)
callbacks = [
TensorboardGenerativeModelImageSampler(num_samples=5),
LatentDimInterpolator(interpolate_epoch_interval=5),
]
trainer = Trainer.from_argparse_args(args, callbacks=callbacks)
trainer.fit(model, dataloader)
if __name__ == "__main__":
cli_main()