gan face generator
Check out corresponding Medium article: Face Generator - Generating Artificial Faces with Machine Learning. GANs achieve this level of realism by pairing a generator, which learns to produce the target output, with a discriminator, which learns to distinguish true data from the output of the generator. Create some fake images from Generator using Noise # C. train the discriminator on fake data ########################### # Training Discriminator on real data netD.zero_grad() # Format batch real_cpu = data.to(device) b_size = real_cpu.size(0) label = torch.full((b_size,), real_label, device=device) # Forward pass real batch through D output = netD(real_cpu).view(-1) # Calculate loss on real batch errD_real = criterion(output, label) # Calculate gradients for D in backward pass errD_real.backward() D_x = output.mean().item(), ## Create a batch of fake images using generator # Generate noise to send as input to the generator noise = torch.randn(b_size, nz, 1, 1, device=device) # Generate fake image batch with G fake = netG(noise) label.fill_(fake_label), # Classify fake batch with D output = netD(fake.detach()).view(-1) # Calculate D's loss on the fake batch errD_fake = criterion(output, label) # Calculate the gradients for this batch errD_fake.backward() D_G_z1 = output.mean().item() # Add the gradients from the all-real and all-fake batches errD = errD_real + errD_fake # Update D optimizerD.step(), ############################ # (2) Update G network: maximize log(D(G(z))) # Here we: # A. Then it evaluates the new images against the original. Generative Adversarial Networks (GAN) is an architecture introduced by Ian Goodfellow and his colleagues in 2014 for generative modeling, which is using a model to generate new samples that imitate an existing dataset. # Create the generator netG = Generator(ngpu).to(device), # Handle multi-gpu if desired if (device.type == 'cuda') and (ngpu > 1): netG = nn.DataParallel(netG, list(range(ngpu))). Help this AI continue to dream | Contact me. History Step 3: Backpropagate the errors through the generator by computing the loss gathered from discriminator output on fake images as the input and 1’s as the target while keeping the discriminator as untrainable — This ensures that the loss is higher when the generator is not able to fool the discriminator. Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. if ngf= 64 the size is 512 maps of 4x4, # Transpose 2D conv layer 2. nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), # Resulting state size -(ngf*4) x 8 x 8 i.e 8x8 maps, # Transpose 2D conv layer 3. nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), # Resulting state size. Now that we’ve covered the generator architecture, let’s look at the discriminator as a black box. ani.save('animation.gif', writer='imagemagick',fps=5) Image(url='animation.gif'). You can check it yourself like so: if the discriminator gives 0 on the fake image, the loss will be high i.e., BCELoss(0,1). We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. Subscribe to our newsletter for more technical articles. The demo requires Python 3.6 or 3.7 (The version of TensorFlow we specify in requirements.txt is not supported in Python 3.8+). Though this model is not the most perfect anime face generator, using it as a base helps us to understand the basics of generative adversarial networks, which in turn can be used as a stepping stone to more exciting and complex GANs as we move forward. You want, for example, a different face for every random input to your face generator. If nothing happens, download Xcode and try again. Control Style Using New Generator Model 3. Some of the pictures look especially creepy, I think because it's easier to notice when an animal looks wrong, especially around the eyes. and Nvidia. So in this post, we’re going to look at the generative adversarial networks behind AI-generated images, and help you to understand how to create and build your own similar application with PyTorch. Given below is the result of the GAN at different time steps: In this post we covered the basics of GANs for creating fairly believable fake images. You signed in with another tab or window. The first step is to define the models. In my view, GANs will change the way we generate video games and special effects. It’s a little difficult to clear see in the iamges, but their quality improves as the number of steps increases. We’ve reached a stage where it’s becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. to generate the noise to convert into images using our generator architecture, as shown below: nz = 100 noise = torch.randn(64, nz, 1, 1, device=device). If nothing happens, download the GitHub extension for Visual Studio and try again. For more information, see our Privacy Statement. Streamlit Demo: The Controllable GAN Face Generator This project highlights Streamlit's new hash_func feature with an app that calls on TensorFlow to generate photorealistic faces, using Nvidia's Progressive Growing of GANs and Shaobo Guan's Transparent Latent-space GAN method for tuning the output face's characteristics. In February 2019, graphics hardware manufacturer NVIDIA released open-source code for their photorealistic face generation software StyleGAN. But at the same time, the police officer also gets better at catching the thief. For more information, check out the tutorial on Towards Data Science. Work fast with our official CLI. # Lists to keep track of progress/Losses img_list =  G_losses =  D_losses =  iters = 0, # Number of training epochs num_epochs = 50 # Batch size during training batch_size = 128, print("Starting Training Loop...") # For each epoch for epoch in range(num_epochs): # For each batch in the dataloader for i, data in enumerate(dataloader, 0): ############################ # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) # Here we: # A. train the discriminator on real data # B. (ndf*8) x 4 x 4 nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False), nn.Sigmoid() ), def forward(self, input): return self.main(input). The diagram below is taken from the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, which explains the DC-GAN generator architecture. To accomplish this, a generative adversarial network (GAN) was trained where one part of it has the goal of creating fake faces, and another part of it has the goal of detecting fake faces. You can also save the animation object as a GIF if you want to send them to some friends. The Generator creates new images while the Discriminator evaluate if they are real or fakeâ¦ # Learning rate for optimizers lr = 0.0002, # Beta1 hyperparam for Adam optimizers beta1 = 0.5, optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999)) optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999)). For color images this is 3 nc = 3 # We can use an image folder dataset the way we have it setup. Later in the article we’ll see how the parameters can be learned by the generator. The concept behind GAN is that it has two networks called Generator Discriminator. We can then instantiate the discriminator exactly as we did the generator: # Create the Discriminator netD = Discriminator(ngpu).to(device), # Handle multi-gpu if desired if (device.type == 'cuda') and (ngpu > 1): netD = nn.DataParallel(netD, list(range(ngpu))). # Final Transpose 2D conv layer 5 to generate final image. (nc) x 64 x 64 ), def forward(self, input): ''' This function takes as input the noise vector''' return self.main(input). We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. In a convolution operation, we try to go from a 4×4 image to a 2×2 image. # Training Discriminator on real data netD.zero_grad() # Format batch real_cpu = data.to(device) b_size = real_cpu.size(0) label = torch.full((b_size,), real_label, device=device) # Forward pass real batch through D output = netD(real_cpu).view(-1) # Calculate loss on real batch errD_real = criterion(output, label) # Calculate gradients for D in backward pass errD_real.backward() D_x = output.mean().item() ## Create a batch of fake images using generator # Generate noise to send as input to the generator noise = torch.randn(b_size, nz, 1, 1, device=device) # Generate fake image batch with G fake = netG(noise) label.fill_(fake_label). Generator network loss is a function of discriminator network quality: Loss is high if the generator is not able to fool the discriminator. # Create the dataset dataset = datasets.ImageFolder(root=dataroot, transform=transforms.Compose([ transforms.Resize(image_size), transforms.CenterCrop(image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) # Create the dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=workers) # Decide which device we want to run on device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu") # Plot some training images real_batch = next(iter(dataloader)) plt.figure(figsize=(8,8)) plt.axis("off") plt.title("Training Images") plt.imshow(np.transpose(vutils.make_grid(real_batch.to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0))). The generator is comprised of convolutional-transpose layers, batch norm layers, and ReLU activations. We then reshape the dense vector in the shape of an image of 4×4 with 1024 filters, as shown in the following figure: Note that we don’t have to worry about any weights right now as the network itself will learn those during training. Generator. Perhaps imagine the generator as a robber and the discriminator as a police officer. In this article we create a detection model using YOLOv5, from creating our dataset and annotating it to training and inferencing using their remarkable library.
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