前言
本项目使用 DCGAN 模型，在自建数据集上进行实验。
本项目使用的数据集是人脸嘴巴区域——微笑表情的数据集
数据集文件夹结构如下，图片供 4357 张
├─mouth │ └─smile ├─1smile.jpg ├─2smile.jpg ├─3smile.jpg └─....
同时，创建一个 out 文件夹来保存训练的中间结果，主要就是看 DCGAN 是如何从一张噪声照片生成我们期待的图片
import os import time if os.path.exists("out"): print("移除现有 out 文件夹！") os.system("rm -r ./out") time.sleep(1) print("创建 out 文件夹！") os.mkdir("./out")
移除现有 out 文件夹！
创建 out 文件夹！
下方链接为该数据集压缩包，需要者自取：数据集
运行下面代码，对数据集进行解压。
由于图片数量多，解压需要一定时间
!unzip mouth.zip -d ./mouth print("解压完毕！")
导入所需包
from __future__ import print_function #%matplotlib inline import argparse import os import random import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils as vutils import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from IPython.display import HTML os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
基本参数配置
# 设置一个随机种子，方便进行可重复性实验 manualSeed = 999 print("Random Seed: ", manualSeed) random.seed(manualSeed) torch.manual_seed(manualSeed) # 数据集所在路径 dataroot = "mouth/" # 数据加载的进程数 workers = 0 # Batch size 大小 batch_size = 64 # Spatial size of training images. All images will be resized to this # size using a transformer. # 图片大小 image_size = 64 # 图片的通道数 nc = 3 # Size of z latent vector (i.e. size of generator input) nz = 100 # Size of feature maps in generator ngf = 64 # Size of feature maps in discriminator ndf = 64 # Number of training epochs num_epochs = 10 # Learning rate for optimizers lr = 0.0003 # Beta1 hyperparam for Adam optimizers beta1 = 0.5 # Number of GPUs available. Use 0 for CPU mode. ngpu = 1 # Decide which device we want to run on device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
导入数据集
# We can use an image folder dataset the way we have it setup. # Create the dataset dataset = dset.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)
简单看一下我们的原始数据集长啥样
# 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[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0))) # plt.show()
<matplotlib.image.AxesImage at 0x7f67b59d9cf8>
定义生成器与判别器
# 权重初始化函数，为生成器和判别器模型初始化 def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm') != -1: nn.init.normal_(m.weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0) # Generator Code 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) class Discriminator(nn.Module): def __init__(self, ngpu): super(Discriminator, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( # input is (nc) x 64 x 64 nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf) x 32 x 32 nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*2) x 16 x 16 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*4) x 8 x 8 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (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)
初始化生成器和判别器
# 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))) # Apply the weights_init function to randomly initialize all weights # to mean=0, stdev=0.2. netG.apply(weights_init) # Print the model print(netG) # 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))) # Apply the weights_init function to randomly initialize all weights # to mean=0, stdev=0.2. netD.apply(weights_init) # Print the model print(netD)
定义损失函数
# Initialize BCELoss function criterion = nn.BCELoss()
开始训练
# Create batch of latent vectors that we will use to visualize # the progression of the generator fixed_noise = torch.randn(64, nz, 1, 1, device=device) # Establish convention for real and fake labels during training real_label = 1.0 fake_label = 0.0 # Setup Adam optimizers for both G and D optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999)) optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999)) # Training Loop # Lists to keep track of progress img_list = [] G_losses = [] D_losses = [] iters = 0 print("Starting Training Loop...") # For each epoch for epoch in range(num_epochs): import time start = time.time() # 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))) ########################### ## Train with all-real batch netD.zero_grad() # Format batch real_cpu = data[0].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 all-real batch errD_real = criterion(output, label) # Calculate gradients for D in backward pass errD_real.backward() D_x = output.mean().item() ## Train with all-fake batch # Generate batch of latent vectors noise = torch.randn(b_size, nz, 1, 1, device=device) # Generate fake image batch with G fake = netG(noise) label.fill_(fake_label) # Classify all fake batch with D output = netD(fake.detach()).view(-1) # Calculate D's loss on the all-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))) ########################### netG.zero_grad() label.fill_(real_label) # fake labels are real for generator cost # Since we just updated D, perform another forward pass of all-fake batch through D output = netD(fake).view(-1) # Calculate G's loss based on this output errG = criterion(output, label) # Calculate gradients for G errG.backward() D_G_z2 = output.mean().item() # Update G optimizerG.step() # Output training stats if i % 50 == 0: print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f' % (epoch, num_epochs, i, len(dataloader), errD.item(), errG.item(), D_x, D_G_z1, D_G_z2)) # Save Losses for plotting later G_losses.append(errG.item()) D_losses.append(errD.item()) # Check how the generator is doing by saving G's output on fixed_noise if (iters % 20 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)): with torch.no_grad(): fake = netG(fixed_noise).detach().cpu() img_list.append(vutils.make_grid(fake, padding=2, normalize=True)) i = vutils.make_grid(fake, padding=2, normalize=True) fig = plt.figure(figsize=(8, 8)) plt.imshow(np.transpose(i, (1, 2, 0))) plt.axis('off') # 关闭坐标轴 plt.savefig("out/%d_%d.png" % (epoch, iters)) plt.close(fig) iters += 1 print('time:', time.time() - start)
绘制损失曲线
plt.figure(figsize=(10,5)) plt.title("Generator and Discriminator Loss During Training") plt.plot(G_losses,label="G") plt.plot(D_losses,label="D") plt.xlabel("iterations") plt.ylabel("Loss") plt.legend() plt.show()
真假对比
# Grab a batch of real images from the dataloader # real_batch = next(iter(dataloader)) # Plot the real images plt.figure(figsize=(15,15)) plt.subplot(1,2,1) plt.axis("off") plt.title("Real Images") plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0))) # Plot the fake images from the last epoch plt.subplot(1,2,2) plt.axis("off") plt.title("Fake Images") plt.imshow(np.transpose(img_list[-1],(1,2,0))) plt.show()
