221 lines
6.9 KiB
Python
221 lines
6.9 KiB
Python
"""
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To run this template just do:
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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 os
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from argparse import ArgumentParser, Namespace
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from collections import OrderedDict
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import numpy as np
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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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import torchvision
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader
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from torchvision.datasets import MNIST
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from pytorch_lightning.core import LightningModule
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from pytorch_lightning.trainer import Trainer
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class Generator(nn.Module):
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def __init__(self, latent_dim, img_shape):
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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(np.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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img = img.view(img.size(0), *self.img_shape)
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return img
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class Discriminator(nn.Module):
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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(np.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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nn.Sigmoid(),
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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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validity = self.model(img_flat)
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return validity
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class GAN(LightningModule):
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def __init__(self,
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latent_dim: int = 100,
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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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batch_size: int = 64, **kwargs):
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super().__init__()
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self.latent_dim = latent_dim
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self.lr = lr
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self.b1 = b1
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self.b2 = b2
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self.batch_size = batch_size
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# networks
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mnist_shape = (1, 28, 28)
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self.generator = Generator(latent_dim=self.latent_dim, img_shape=mnist_shape)
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self.discriminator = Discriminator(img_shape=mnist_shape)
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self.validation_z = torch.randn(8, self.latent_dim)
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self.example_input_array = torch.zeros(2, hparams.latent_dim)
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def forward(self, z):
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return self.generator(z)
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def adversarial_loss(self, y_hat, y):
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return F.binary_cross_entropy(y_hat, y)
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def training_step(self, batch, batch_idx, optimizer_idx):
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imgs, _ = batch
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# sample noise
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z = torch.randn(imgs.shape[0], self.latent_dim)
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z = z.type_as(imgs)
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# train generator
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if optimizer_idx == 0:
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# generate images
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self.generated_imgs = self(z)
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# log sampled images
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sample_imgs = self.generated_imgs[:6]
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grid = torchvision.utils.make_grid(sample_imgs)
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self.logger.experiment.add_image('generated_images', grid, 0)
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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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# adversarial loss is binary cross-entropy
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g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
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tqdm_dict = {'g_loss': g_loss}
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output = OrderedDict({
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'loss': g_loss,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict
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})
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return output
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# train discriminator
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if optimizer_idx == 1:
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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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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(
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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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tqdm_dict = {'d_loss': d_loss}
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output = OrderedDict({
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'loss': d_loss,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict
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})
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return output
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def configure_optimizers(self):
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lr = self.lr
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b1 = self.b1
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b2 = self.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 train_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5])])
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dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
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return DataLoader(dataset, batch_size=self.batch_size)
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def on_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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self.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(**vars(args))
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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# If use distubuted 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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trainer = Trainer()
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# ------------------------
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# 3 START TRAINING
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# ------------------------
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trainer.fit(model)
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if __name__ == '__main__':
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parser = ArgumentParser()
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parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
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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,
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help="adam: decay of first order momentum of gradient")
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parser.add_argument("--b2", type=float, default=0.999,
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help="adam: decay of first order momentum of gradient")
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parser.add_argument("--latent_dim", type=int, default=100,
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help="dimensionality of the latent space")
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hparams = parser.parse_args()
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main(hparams)
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