210 lines
6.5 KiB
Python
210 lines
6.5 KiB
Python
"""
|
|
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
|
|
"""
|
|
import os
|
|
from argparse import ArgumentParser
|
|
from collections import OrderedDict
|
|
|
|
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 torch.utils.data import DataLoader
|
|
from torchvision.datasets import MNIST
|
|
|
|
from pytorch_lightning.core import LightningModule
|
|
from pytorch_lightning.trainer import Trainer
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
class GAN(LightningModule):
|
|
|
|
def __init__(self, hparams):
|
|
super().__init__()
|
|
self.hparams = hparams
|
|
|
|
# networks
|
|
mnist_shape = (1, 28, 28)
|
|
self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)
|
|
self.discriminator = Discriminator(img_shape=mnist_shape)
|
|
|
|
# cache for generated images
|
|
self.generated_imgs = None
|
|
self.last_imgs = None
|
|
|
|
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
|
|
self.last_imgs = imgs
|
|
|
|
# train generator
|
|
if optimizer_idx == 0:
|
|
# sample noise
|
|
z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
|
|
z = z.type_as(imgs)
|
|
|
|
# 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.generated_imgs), valid)
|
|
tqdm_dict = {'g_loss': g_loss}
|
|
output = OrderedDict({
|
|
'loss': g_loss,
|
|
'progress_bar': tqdm_dict,
|
|
'log': tqdm_dict
|
|
})
|
|
return output
|
|
|
|
# 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(fake)
|
|
|
|
fake_loss = self.adversarial_loss(
|
|
self.discriminator(self.generated_imgs.detach()), fake)
|
|
|
|
# discriminator loss is the average of these
|
|
d_loss = (real_loss + fake_loss) / 2
|
|
tqdm_dict = {'d_loss': d_loss}
|
|
output = OrderedDict({
|
|
'loss': d_loss,
|
|
'progress_bar': tqdm_dict,
|
|
'log': tqdm_dict
|
|
})
|
|
return output
|
|
|
|
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 train_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5])])
|
|
dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
|
|
return DataLoader(dataset, batch_size=self.hparams.batch_size)
|
|
|
|
def on_epoch_end(self):
|
|
z = torch.randn(8, self.hparams.latent_dim)
|
|
z = z.type_as(self.last_imgs)
|
|
|
|
# log sampled images
|
|
sample_imgs = self(z)
|
|
grid = torchvision.utils.make_grid(sample_imgs)
|
|
self.logger.experiment.add_image(f'generated_images', grid, self.current_epoch)
|
|
|
|
|
|
def main(hparams):
|
|
# ------------------------
|
|
# 1 INIT LIGHTNING MODEL
|
|
# ------------------------
|
|
model = GAN(hparams)
|
|
|
|
# ------------------------
|
|
# 2 INIT TRAINER
|
|
# ------------------------
|
|
trainer = Trainer()
|
|
|
|
# ------------------------
|
|
# 3 START TRAINING
|
|
# ------------------------
|
|
trainer.fit(model)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = ArgumentParser()
|
|
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
|
|
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 first order momentum of gradient")
|
|
parser.add_argument("--latent_dim", type=int, default=100,
|
|
help="dimensionality of the latent space")
|
|
|
|
hparams = parser.parse_args()
|
|
|
|
main(hparams)
|