Create LO-WGAN-gp.py

implementations/LO-WGAN-gp/lo-wgan-gp.py

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+import argparse
+import os
+import numpy as np
+import math
+import sys
+
+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.autograd as autograd
+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=0.00005, help="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("--latent_method", type=str, default="ngd", help="The latent optimization method")
+parser.add_argument("--img_size", type=int, default=28, help="size of each image dimension")
+parser.add_argument("--channels", type=int, default=1, help="number of image channels")
+parser.add_argument("--n_critic", type=int, default=5, help="number of training steps for discriminator per iter")
+parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen image samples")
+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__()
+
+        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, 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, z):
+        img = self.model(z)
+        img = img.view(img.shape[0], *img_shape)
+        return img
+
+
+class Discriminator(nn.Module):
+    def __init__(self):
+        super(Discriminator, self).__init__()
+
+        self.model = nn.Sequential(
+            nn.Linear(int(np.prod(img_shape)), 512),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(512, 256),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Linear(256, 1),
+        )
+
+    def forward(self, img):
+        img_flat = img.view(img.shape[0], -1)
+        validity = self.model(img_flat)
+        return validity
+
+
+# Loss weight for gradient penalty
+lambda_gp = 10
+
+# Initialize generator and discriminator
+generator = Generator()
+discriminator = Discriminator()
+
+if cuda:
+    generator.cuda()
+    discriminator.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))
+
+Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
+
+#Latent optimization
+
+def latent_opt(Gen, Dis, z, method , batch_size, alpha= 0.9, beta= 0.1):
+    method = method.lower()
+
+    #Using gradient descent
+    if method == "gd":
+
+        fake = Gen(z)
+        f_z = Dis(dake.view(batch_size, opt.channels, opt.img_size, opt.img_size))
+
+        d_fz = torch.autograd.grad(outputs=f_z,
+                                    inputs= z,
+                                    grad_outputs=torch.ones_like(f_z),
+                                    retain_graph=True,
+                                    create_graph= True
+                                       )[0]
+
+        delta_z = torch.ones_like(d_fz)
+        delta_z = alpha * d_fz
+
+        with torch.no_grad():
+            z_prime = torch.clamp(z + delta_z, min=-1, max=1)
+
+        return z_prime
+    #Using natural gradient descent
+    elif method == "ngd":
+        fake = Gen(z)
+        f_z = Dis(fake.view(batch_size, opt.channels, opt.img_size, opt.img_size))
+
+        d_fz = torch.autograd.grad(outputs=f_z,
+                                    inputs= z,
+                                    grad_outputs=torch.ones_like(f_z),
+                                    retain_graph=True,
+                                    create_graph= True
+                                       )[0]
+
+        delta_z = torch.ones_like(d_fz)
+        delta_z = (alpha * d_fz) / (beta +  torch.norm(delta_z, p=2, dim=0))
+        with torch.no_grad():
+            z_prime = torch.clamp(z + delta_z, min=-1, max=1)
+
+        return z_prime
+
+def compute_gradient_penalty(D, real_samples, fake_samples):
+    """Calculates the gradient penalty loss for WGAN GP"""
+    # Random weight term for interpolation between real and fake samples
+    alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))
+    # Get random interpolation between real and fake samples
+    interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
+    d_interpolates = D(interpolates)
+    fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)
+    # Get gradient w.r.t. interpolates
+    gradients = autograd.grad(
+        outputs=d_interpolates,
+        inputs=interpolates,
+        grad_outputs=fake,
+        create_graph=True,
+        retain_graph=True,
+        only_inputs=True,
+    )[0]
+    gradients = gradients.view(gradients.size(0), -1)
+    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
+    return gradient_penalty
+
+
+
+# ----------
+#  Training
+# ----------
+
+batches_done = 0
+for epoch in range(opt.n_epochs):
+
+    for i, (imgs, _) in enumerate(dataloader):
+
+        # Configure input
+        real_imgs = Variable(imgs.type(Tensor))
+
+        # ---------------------
+        #  latent optimization step
+        # ---------------------
+
+        optimizer_G.zero_grad(), optimizer_D.zero_grad()
+
+        # Sample optimized noise as an input for the generator
+        z = Variable(Tensor(np.random.uniform(-1, 1, (imgs.shape[0], opt.latent_dim))), requires_grad=True)
+        z_prime = latent_opt(generator, discriminator, opt.latent_method, z, imgs.shape[0])
+
+        # ---------------------
+        #  Train Discriminator
+        # ---------------------
+
+        optimizer_D.zero_grad()
+
+
+        # Generate a batch of images
+        fake_imgs = generator(z_prime)
+
+        # Real images
+        real_validity = discriminator(real_imgs)
+        # Fake images
+        fake_validity = discriminator(fake_imgs)
+        # Gradient penalty
+        gradient_penalty = compute_gradient_penalty(discriminator, real_imgs.data, fake_imgs.data)
+        # Adversarial loss
+        d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + lambda_gp * gradient_penalty
+
+        d_loss.backward()
+        optimizer_D.step()
+
+        optimizer_G.zero_grad()
+
+        # Train the generator every n_critic steps
+        if i % opt.n_critic == 0:
+
+
+            # -----------------
+            #  Train Generator
+            # -----------------
+
+            # Generate a batch of images
+            fake_imgs = generator(z_prime)
+            # Loss measures generator's ability to fool the discriminator
+            # Train on fake images
+            fake_validity = discriminator(fake_imgs)
+            g_loss = -torch.mean(fake_validity)
+
+            g_loss.backward()
+            optimizer_G.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())
+            )
+
+            if batches_done % opt.sample_interval == 0:
+                save_image(fake_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)
+
+            batches_done += opt.n_critic