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- 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
-
- 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")
- opt = parser.parse_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
-
-
- 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)
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