Create cgan.py

cgan.py

@@ -0,0 +1,206 @@
+"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)