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DCGANIan J. Goodfellow首次提出了GAN之后，生成反抗只是神经网络还不是深度卷积神经网络，所以有人提出一种基于深度神经网络的生成反抗网络，这个就是DCGAN。相比之前的GAN，DCGAN在生成者与判别者网络上的革新如下：1.使用步长卷积与反卷积替代池化实现上下采样2.在生成者与判别者网络使用BN层3.删除全链接层4.在生成者网络使用ReLU作为激活函数，最后一层使用tanh5.在判别者网络使用LeakyReLU作为激活函数生成者网络如下：使用celebA人脸数据集，20W张人脸数据，完成DCGAN的训练，最终生存生成者模型。下面是DCGAN的代码实现与，训练与基于生成者实现人脸数据的复制。DCGAN代码实现与训练01生成者生成者卷积神经网络的代码实现如下：class Generator(nn.Module): def __init__(self, ngpu): super(Generator, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( # input is Z, going into a convolution nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf * 8), nn.ReLU(True), # state size. (ngf*8) x 4 x 4 nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), # state size. (ngf*4) x 8 x 8 nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), # state size. (ngf*2) x 16 x 16 nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), # state size. (ngf) x 32 x 32 nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False), nn.Tanh() # state size. (nc) x 64 x 64 ) def forward(self, input): return self.main(input)02判别者判别者卷积神经网络代码实现如下： 1class Discriminator(nn.Module): 2 def __init__(self, ngpu): 3 super(Discriminator, self).__init__() 4 self.ngpu = ngpu 5 self.main = nn.Sequential( 6 # input is (nc) x 64 x 64 7 nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), 8 nn.LeakyReLU(0.2, inplace=True), 9 # state size. (ndf) x 32 x 3210 nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),11 nn.BatchNorm2d(ndf * 2),12 nn.LeakyReLU(0.2, inplace=True),13 # state size. (ndf*2) x 16 x 1614 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),15 nn.BatchNorm2d(ndf * 4),16 nn.LeakyReLU(0.2, inplace=True),17 # state size. (ndf*4) x 8 x 818 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),19 nn.BatchNorm2d(ndf * 8),20 nn.LeakyReLU(0.2, inplace=True),21 # state size. (ndf*8) x 4 x 422 nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),23 nn.Sigmoid()24 )2526 def forward(self, input):27 return self.main(input)03模型训练初始化与模型训练 1# Create the Discriminator 2netD = Discriminator(ngpu).to(device) 3 4# Handle multi-gpu if desired 5if (device.type == 'cuda') and (ngpu > 1): 6 netD = nn.DataParallel(netD, list(range(ngpu))) 7 8# Apply the weights_init function to randomly initialize all weights 9# to mean=0, stdev=0.2. 10netD.apply(weights_init) 11 12# Print the model 13print(netD) 14 15 16# Initialize BCELoss function 17criterion = nn.BCELoss() 18 19# Create batch of latent vectors that we will use to visualize 20# the progression of the generator 21fixed_noise = torch.randn(64, nz, 1, 1, device=device) 22 23# Establish convention for real and fake labels during training 24real_label = 1. 25fake_label = 0. 26 27# Setup Adam optimizers for both G and D 28optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999)) 29optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999)) 30 31 32# Training Loop 33 34# Lists to keep track of progress 35img_list = [] 36G_losses = [] 37D_losses = [] 38iters = 0 39 40if __name__ == "__main__": 41 print("Starting Training Loop...") 42 # For each epoch 43 for epoch in range(num_epochs): 44 # For each batch in the dataloader 45 for i, data in enumerate(dataloader, 0): 46 47 ############################ 48 # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) 49 ########################### 50 ## Train with all-real batch 51 netD.zero_grad() 52 # Format batch 53 real_cpu = data[0].to(device) 54 b_size = real_cpu.size(0) 55 label = torch.full((b_size,), real_label, dtype=torch.float, device=device) 56 # Forward pass real batch through D 57 output = netD(real_cpu).view(-1) 58 # Calculate loss on all-real batch 59 errD_real = criterion(output, label) 60 # Calculate gradients for D in backward pass 61 errD_real.backward() 62 D_x = output.mean().item() 63 64 ## Train with all-fake batch 65 # Generate batch of latent vectors 66 noise = torch.randn(b_size, nz, 1, 1, device=device) 67 # Generate fake image batch with G 68 fake = netG(noise) 69 label.fill_(fake_label) 70 # Classify all fake batch with D 71 output = netD(fake.detach()).view(-1) 72 # Calculate D's loss on the all-fake batch 73 errD_fake = criterion(output, label) 74 # Calculate the gradients for this batch 75 errD_fake.backward() 76 D_G_z1 = output.mean().item() 77 # Add the gradients from the all-real and all-fake batches 78 errD = errD_real + errD_fake 79 # Update D 80 optimizerD.step() 81 82 ############################ 83 # (2) Update G network: maximize log(D(G(z))) 84 ########################### 85 netG.zero_grad() 86 label.fill_(real_label) # fake labels are real for generator cost 87 # Since we just updated D, perform another forward pass of all-fake batch through D 88 output = netD(fake).view(-1) 89 # Calculate G's loss based on this output 90 errG = criterion(output, label) 91 # Calculate gradients for G 92 errG.backward() 93 D_G_z2 = output.mean().item() 94 # Update G 95 optimizerG.step() 96 97 # Output training stats 98 if i % 50 == 0: 99 print('[%d/%d][%d/%d]tLoss_D: %.4ftLoss_G: %.4ftD(x): %.4ftD(G(z)): %.4f / %.4f'100 % (epoch, num_epochs, i, len(dataloader),101 errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))102103 # Save Losses for plotting later104 G_losses.append(errG.item())105 D_losses.append(errD.item())106107 # Check how the generator is doing by saving G's output on fixed_noise108 if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):109 with torch.no_grad():110 fake = netG(fixed_noise).detach().cpu()111 iters += 1112113 # save model114 netG.eval()115 torch.save(netG, 'generate_model.pt')基于celebA人脸数据集训练，我的机械是GTX1050Ti，所以只训练了3个epoch就over了，主要是为了省电跟环保@_@！然后生存了生成者模型。

模型使用基于DCGAN的生成者模型，直接加载，生成100个随机数作为输入样本，生成的人脸图像如下：从上图可以看出已经有点效果了，感兴趣可以继续训练，演示代码如下： 1import torch 2import cv2 as cv 3import numpy as np 4from dcgan_model import Generator 5from torchvision.utils import save_image 6 7 8def dcgan_generate_face_demo(): 9 netG = torch.load("./generate_model.pt")10 netG.cuda()11 for i in range(4):12 noise = torch.randn(64, 100, 1, 1, device="cuda")13 # Generate fake image batch with G14 generated = netG(noise)15 print(generated.size())16 save_image(generated.view(generated.size(0), 3, 64, 64), 'D:/sample_%d'%i + '.png')171819if __name__ == "__main__":20 dcgan_generate_face_demo()转自 OpenCV学堂。

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