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checked GAN code
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Aladdin Persson
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"""
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Example code of how to code GANs and more specifically DCGAN,
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for more information about DCGANs read: https://arxiv.org/abs/1511.06434
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We then train the DCGAN on the MNIST dataset (toy dataset of handwritten digits)
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and then generate our own. You can apply this more generally on really any dataset
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but MNIST is simple enough to get the overall idea.
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Video explanation: https://youtu.be/5RYETbFFQ7s
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Got any questions leave a comment on youtube :)
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Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
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* 2020-04-20 Initial coding
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"""
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# Imports
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import torch
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import torchvision
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import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
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import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
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import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
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import torchvision.transforms as transforms # Transformations we can perform on our dataset
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from torch.utils.data import (
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DataLoader,
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) # Gives easier dataset managment and creates mini batches
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from torch.utils.tensorboard import SummaryWriter # to print to tensorboard
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from model_utils import (
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Discriminator,
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Generator,
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) # Import our models we've defined (from DCGAN paper)
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# Hyperparameters
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lr = 0.0005
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batch_size = 64
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image_size = 64
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channels_img = 1
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channels_noise = 256
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num_epochs = 10
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# For how many channels Generator and Discriminator should use
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features_d = 16
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features_g = 16
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my_transforms = transforms.Compose(
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[
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transforms.Resize(image_size),
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,)),
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]
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)
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dataset = datasets.MNIST(
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root="dataset/", train=True, transform=my_transforms, download=True
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)
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dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Create discriminator and generator
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netD = Discriminator(channels_img, features_d).to(device)
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netG = Generator(channels_noise, channels_img, features_g).to(device)
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# Setup Optimizer for G and D
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optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(0.5, 0.999))
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optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(0.5, 0.999))
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netG.train()
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netD.train()
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criterion = nn.BCELoss()
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real_label = 1
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fake_label = 0
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fixed_noise = torch.randn(64, channels_noise, 1, 1).to(device)
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writer_real = SummaryWriter(f"runs/GAN_MNIST/test_real")
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writer_fake = SummaryWriter(f"runs/GAN_MNIST/test_fake")
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step = 0
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print("Starting Training...")
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for epoch in range(num_epochs):
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for batch_idx, (data, targets) in enumerate(dataloader):
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data = data.to(device)
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batch_size = data.shape[0]
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### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
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netD.zero_grad()
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label = (torch.ones(batch_size) * 0.9).to(device)
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output = netD(data).reshape(-1)
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lossD_real = criterion(output, label)
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D_x = output.mean().item()
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noise = torch.randn(batch_size, channels_noise, 1, 1).to(device)
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fake = netG(noise)
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label = (torch.ones(batch_size) * 0.1).to(device)
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output = netD(fake.detach()).reshape(-1)
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lossD_fake = criterion(output, label)
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lossD = lossD_real + lossD_fake
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lossD.backward()
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optimizerD.step()
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### Train Generator: max log(D(G(z)))
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netG.zero_grad()
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label = torch.ones(batch_size).to(device)
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output = netD(fake).reshape(-1)
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lossG = criterion(output, label)
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lossG.backward()
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optimizerG.step()
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# Print losses ocassionally and print to tensorboard
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if batch_idx % 100 == 0:
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step += 1
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print(
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f"Epoch [{epoch}/{num_epochs}] Batch {batch_idx}/{len(dataloader)} \
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Loss D: {lossD:.4f}, loss G: {lossG:.4f} D(x): {D_x:.4f}"
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)
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with torch.no_grad():
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fake = netG(fixed_noise)
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img_grid_real = torchvision.utils.make_grid(data[:32], normalize=True)
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img_grid_fake = torchvision.utils.make_grid(fake[:32], normalize=True)
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writer_real.add_image(
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"Mnist Real Images", img_grid_real, global_step=step
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)
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writer_fake.add_image(
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"Mnist Fake Images", img_grid_fake, global_step=step
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)
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@@ -1,4 +0,0 @@
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### Generative Adversarial Network
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DCGAN_mnist.py: main file and training network
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model_utils.py: Generator and discriminator implementation
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@@ -1,76 +0,0 @@
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"""
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Discriminator and Generator implementation from DCGAN paper
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that we import in the main (DCGAN_mnist.py) file.
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"""
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import torch
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import torch.nn as nn
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class Discriminator(nn.Module):
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def __init__(self, channels_img, features_d):
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super(Discriminator, self).__init__()
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self.net = nn.Sequential(
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# N x channels_img x 64 x 64
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nn.Conv2d(channels_img, features_d, kernel_size=4, stride=2, padding=1),
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nn.LeakyReLU(0.2),
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# N x features_d x 32 x 32
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nn.Conv2d(features_d, features_d * 2, kernel_size=4, stride=2, padding=1),
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nn.BatchNorm2d(features_d * 2),
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nn.LeakyReLU(0.2),
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nn.Conv2d(
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features_d * 2, features_d * 4, kernel_size=4, stride=2, padding=1
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),
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nn.BatchNorm2d(features_d * 4),
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nn.LeakyReLU(0.2),
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nn.Conv2d(
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features_d * 4, features_d * 8, kernel_size=4, stride=2, padding=1
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),
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nn.BatchNorm2d(features_d * 8),
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nn.LeakyReLU(0.2),
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# N x features_d*8 x 4 x 4
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nn.Conv2d(features_d * 8, 1, kernel_size=4, stride=2, padding=0),
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# N x 1 x 1 x 1
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nn.Sigmoid(),
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)
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def forward(self, x):
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return self.net(x)
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class Generator(nn.Module):
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def __init__(self, channels_noise, channels_img, features_g):
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super(Generator, self).__init__()
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self.net = nn.Sequential(
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# N x channels_noise x 1 x 1
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nn.ConvTranspose2d(
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channels_noise, features_g * 16, kernel_size=4, stride=1, padding=0
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),
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nn.BatchNorm2d(features_g * 16),
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nn.ReLU(),
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# N x features_g*16 x 4 x 4
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nn.ConvTranspose2d(
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features_g * 16, features_g * 8, kernel_size=4, stride=2, padding=1
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),
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nn.BatchNorm2d(features_g * 8),
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nn.ReLU(),
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nn.ConvTranspose2d(
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features_g * 8, features_g * 4, kernel_size=4, stride=2, padding=1
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),
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nn.BatchNorm2d(features_g * 4),
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nn.ReLU(),
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nn.ConvTranspose2d(
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features_g * 4, features_g * 2, kernel_size=4, stride=2, padding=1
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),
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nn.BatchNorm2d(features_g * 2),
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nn.ReLU(),
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nn.ConvTranspose2d(
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features_g * 2, channels_img, kernel_size=4, stride=2, padding=1
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),
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# N x channels_img x 64 x 64
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nn.Tanh(),
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)
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def forward(self, x):
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return self.net(x)
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