"original code: https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/cgan/cgan.py" import argparse import os import numpy as np import math import torchvision.transforms as transforms from torchvision.utils import save_image from torch.utils.data import DataLoader from torchvision import datasets from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torch parser = argparse.ArgumentParser() parser.add_argument("--n_epochs", type=int, default=10, help="number of epochs of training") 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("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation") parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space") parser.add_argument("--n_classes", type=int, default=10, help="number of classes for dataset") parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension") parser.add_argument("--channels", type=int, default=1, help="number of image channels") parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling") opt = parser.parse_args(args=[]) print(opt) img_shape = (opt.channels, opt.img_size, opt.img_size) cuda = True if torch.cuda.is_available() else False class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes) 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(opt.latent_dim + opt.n_classes, 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, noise, labels): # Concatenate label embedding and image to produce input gen_input = torch.cat((self.label_emb(labels), noise), -1) img = self.model(gen_input) img = img.view(img.size(0), *img_shape) return img class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.label_embedding = nn.Embedding(opt.n_classes, opt.n_classes) self.model = nn.Sequential( nn.Linear(opt.n_classes + int(np.prod(img_shape)), 512), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 512), nn.Dropout(0.4), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 512), nn.Dropout(0.4), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 1), ) def forward(self, img, labels): # Concatenate label embedding and image to produce input d_in = torch.cat((img.view(img.size(0), -1), self.label_embedding(labels)), -1) validity = self.model(d_in) return validity # Loss functions adversarial_loss = torch.nn.MSELoss() # Initialize generator and discriminator generator = Generator() discriminator = Discriminator() if cuda: generator.cuda() discriminator.cuda() adversarial_loss.cuda() # Configure data loader os.makedirs("../../data/mnist", exist_ok=True) dataloader = torch.utils.data.DataLoader( datasets.MNIST( "../../data/mnist", train=True, download=True, transform=transforms.Compose( [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] ), ), batch_size=opt.batch_size, shuffle=True, ) # Optimizers optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor os.makedirs("images", exist_ok=True) def sample_image(n_row, batches_done): """Saves a grid of generated digits ranging from 0 to n_classes""" # Sample noise z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim)))) # Get labels ranging from 0 to n_classes for n rows labels = np.array([num for _ in range(n_row) for num in range(n_row)]) labels = Variable(LongTensor(labels)) gen_imgs = generator(z, labels) save_image(gen_imgs.data, "images/%d.png" % batches_done, nrow=n_row, normalize=True) # ---------- # Training # ---------- for epoch in range(opt.n_epochs): for i, (imgs, labels) in enumerate(dataloader): batch_size = imgs.shape[0] # Adversarial ground truths valid = Variable(FloatTensor(batch_size, 1).fill_(1.0), requires_grad=False) fake = Variable(FloatTensor(batch_size, 1).fill_(0.0), requires_grad=False) # Configure input real_imgs = Variable(imgs.type(FloatTensor)) labels = Variable(labels.type(LongTensor)) # ----------------- # Train Generator # ----------------- optimizer_G.zero_grad() # Sample noise and labels as generator input z = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim)))) gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size))) # Generate a batch of images gen_imgs = generator(z, gen_labels) # Loss measures generator's ability to fool the discriminator validity = discriminator(gen_imgs, gen_labels) g_loss = adversarial_loss(validity, valid) g_loss.backward() optimizer_G.step() # --------------------- # Train Discriminator # --------------------- optimizer_D.zero_grad() # Loss for real images validity_real = discriminator(real_imgs, labels) d_real_loss = adversarial_loss(validity_real, valid) # Loss for fake images validity_fake = discriminator(gen_imgs.detach(), gen_labels) d_fake_loss = adversarial_loss(validity_fake, fake) # Total discriminator loss d_loss = (d_real_loss + d_fake_loss) / 2 d_loss.backward() optimizer_D.step() print( "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()) ) batches_done = epoch * len(dataloader) + i if batches_done % opt.sample_interval == 0: sample_image(n_row=10, batches_done=batches_done)