ACGAN生成对抗网络训练Pytorch代码 生成指定数字手写数字图片

import argparse import os import numpy as np 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 from models import Generator, Discriminator, weights_init_normal os.makedirs("images", exist_ok=True) parser = argparse.ArgumentParser() parser.add_argument("--n_epochs", type=int, default=200, 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") parser.add_argument("--checkpoint_interval", type=int, default=5000, help="interval between model checkpoints") opt = parser.parse_args() print(opt) cuda = True if torch.cuda.is_available() else False # Loss functions adversarial_loss = torch.nn.BCELoss() auxiliary_loss = torch.nn.CrossEntropyLoss() # Initialize generator and discriminator generator = Generator(opt) discriminator = Discriminator(opt) if cuda: generator.cuda() discriminator.cuda() adversarial_loss.cuda() auxiliary_loss.cuda() # Initialize weights generator.apply(weights_init_normal) discriminator.apply(weights_init_normal) # Configure data loader os.makedirs("data/mnist", exist_ok=True) dataloader = 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 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, pred_label = discriminator(gen_imgs) g_loss = 0.5 * (adversarial_loss(validity, valid) + auxiliary_loss(pred_label, gen_labels)) g_loss.backward() optimizer_G.step() # --------------------- # Train Discriminator # --------------------- optimizer_D.zero_grad() # Loss for real images real_pred, real_aux = discriminator(real_imgs) d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2 # Loss for fake images fake_pred, fake_aux = discriminator(gen_imgs.detach()) d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, gen_labels)) / 2 # Total discriminator loss d_loss = (d_real_loss + d_fake_loss) / 2 # Calculate discriminator accuracy pred = np.concatenate([real_aux.data.cpu().numpy(), fake_aux.data.cpu().numpy()], axis=0) gt = np.concatenate([labels.data.cpu().numpy(), gen_labels.data.cpu().numpy()], axis=0) d_acc = np.mean(np.argmax(pred, axis=1) == gt) d_loss.backward() optimizer_D.step() print( "[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), 100 * d_acc, 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) if batches_done % opt.checkpoint_interval == 0: torch.save(generator.state_dict(), "generator_%d.pth" % batches_done) torch.save(discriminator.state_dict(), "discriminator_%d.pth" % batches_done)