文章目录

1.实现效果
2.环境配置
- 2.1Python
- 2.2Pytorch、CUDA
- 2.3Python IDE
3.具体实现
- 3.1数据预处理（data.py）
- - （1）导入包
  - （2）定义数据类
- 3.2模型Generator，Discriminator，权重初始化（model.py）
- - （1）导入包
  - （2）Generator
  - （3）Discriminator
  - （4）权重初始化
- 3.3网络训练（net.py）
- - （1）导入包
  - （2）创建类
- 3.4 主函数（main.py）
- - （1）导入文件
  - （2）定义超参数
  - （3）实例化
  - （4）进行训练
4.训练过程
- 4.1 Generator和Discriminator的Loss损失曲线图
- 4.2 D(x)和D(G(z))曲线图
- 4.3最终生成结果图
5.完整代码
6.引用参考
7.问题反馈

1.实现效果

使用DCGAN训练faces数据集，最终实现生成二次元动漫头像。
最后虽然生成了动漫头像，但是一些细节还是和真实的图像差别较大，比如说眼睛大小，眼睛颜色等。
之后我会将MINIST数据集、Oxford17数据集、以及faces数据集在训练过程中不同轮次的输出结果做一个总结。
生成二次元动漫头像的程序依然是沿用data.py、model.py、net.py、main.py但具体的编程的细节呢有所改变。
之前MINIST以及Oxford17数据集的程序
这里：
【Pytorch】DCGAN实战（一）：基于MINIST数据集的手写数字生成
【Pytorch】DCGAN实战（二）：基于Oxord17的鲜花图像生成

2.环境配置

2.1Python

Python版本为3.7

2.2Pytorch、CUDA

在这里不详细介绍了，网上有很多的安装教程，小伙伴们自行查找吧！

2.3Python IDE

Pycharm

3.具体实现

整体分为4个文件：data.py、model.py、net.py、main.py

3.1数据预处理（data.py）

（1）导入包

from torch.utils.data import DataLoader
from torchvision import utils, datasets, transforms

（2）定义数据类

class ReadData():def __init__(self,data_path,image_size=64):self.root=data_pathself.image_size=image_sizeself.dataset=self.getdataset()def getdataset(self):#3.datasetdataset = datasets.ImageFolder(root=self.root,transform=transforms.Compose([transforms.Resize(self.image_size),transforms.CenterCrop(self.image_size),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),]))print(f'Total Size of Dataset: {len(dataset)}')return datasetdef getdataloader(self,batch_size=128):dataloader = DataLoader(self.dataset,batch_size=batch_size,shuffle=True,num_workers=0)return dataloader

3.2模型Generator，Discriminator，权重初始化（model.py）

（1）导入包

import torch.nn as nn

（2）Generator

class Generator(nn.Module):def __init__(self, nz,ngf,nc):super(Generator, self).__init__()self.nz = nzself.ngf = ngfself.nc=ncself.main = nn.Sequential(# input is Z, going into a convolutionnn.ConvTranspose2d(self.nz, self.ngf * 8, 4, 1, 0, bias=False),nn.BatchNorm2d(self.ngf * 8),nn.ReLU(True),# state size. (ngf*8) x 4 x 4nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ngf * 4),nn.ReLU(True),# state size. (ngf*4) x 8 x 8nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ngf * 2),nn.ReLU(True),# state size. (ngf*2) x 16 x 16nn.ConvTranspose2d(self.ngf * 2, self.ngf, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ngf),nn.ReLU(True),# state size. (ngf) x 32 x 32nn.ConvTranspose2d(self.ngf, self.nc, 4, 2, 1, bias=False),nn.Tanh()# state size. (nc) x 64 x 64)def forward(self, input):return self.main(input)

（3）Discriminator

class Discriminator(nn.Module):def __init__(self, ndf,nc):super(Discriminator, self).__init__()self.ndf=ndfself.nc=ncself.main = nn.Sequential(# input is (nc) x 64 x 64nn.Conv2d(self.nc, self.ndf, 4, 2, 1, bias=False),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf) x 32 x 32nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ndf * 2),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*2) x 16 x 16nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ndf * 4),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*4) x 8 x 8nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),nn.BatchNorm2d(self.ndf * 8),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*8) x 4 x 4nn.Conv2d(self.ndf * 8, 1, 4, 1, 0, bias=False),# state size. (1) x 1 x 1nn.Sigmoid())def forward(self, input):return self.main(input)

（4）权重初始化

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)

3.3网络训练（net.py）

（1）导入包

import torch
import torch.nn as nn
from torchvision import utils, datasets, transforms
import time
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
import os

（2）创建类

class DCGAN():def __init__(self,lr,beta1,nz, batch_size,num_showimage,device, model_save_path,figure_save_path,generator, discriminator, data_loader,):self.real_label=1self.fake_label=0self.nz=nzself.batch_size=batch_sizeself.num_showimage=num_showimageself.device = deviceself.model_save_path=model_save_pathself.figure_save_path=figure_save_pathself.G = generator.to(device)self.D = discriminator.to(device)self.opt_G=torch.optim.Adam(self.G.parameters(), lr=lr, betas=(beta1, 0.999))self.opt_D = torch.optim.Adam(self.D.parameters(), lr=lr, betas=(beta1, 0.999))self.criterion = nn.BCELoss().to(device)self.dataloader=data_loaderself.fixed_noise = torch.randn(self.num_showimage, nz, 1, 1, device=device)self.img_list = []self.G_loss_list = []self.D_loss_list = []self.D_x_list = []self.D_z_list = []def train(self,num_epochs):loss_tep = 10G_loss=0D_loss=0print("Starting Training Loop...")# For each epochfor epoch in range(num_epochs):#**********计时*********************beg_time = time.time()# For each batch in the dataloaderfor i, data in enumerate(self.dataloader):############################# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))###########################x = data[0].to(self.device)b_size = x.size(0)lbx = torch.full((b_size,), self.real_label, dtype=torch.float, device=self.device)D_x = self.D(x).view(-1)LossD_x = self.criterion(D_x, lbx)D_x_item = D_x.mean().item()# print("log(D(x))")z = torch.randn(b_size, self.nz, 1, 1, device=self.device)gz = self.G(z)lbz1 = torch.full((b_size,), self.fake_label, dtype=torch.float, device=self.device)D_gz1 = self.D(gz.detach()).view(-1)LossD_gz1 = self.criterion(D_gz1, lbz1)D_gz1_item = D_gz1.mean().item()# print("log(1 - D(G(z)))")LossD = LossD_x + LossD_gz1# print("log(D(x)) + log(1 - D(G(z)))")self.opt_D.zero_grad()LossD.backward()self.opt_D.step()# print("update LossD")D_loss+=LossD############################# (2) Update G network: maximize log(D(G(z)))###########################lbz2 = torch.full((b_size,), self.real_label, dtype=torch.float, device=self.device) # fake labels are real for generator costD_gz2 = self.D(gz).view(-1)D_gz2_item = D_gz2.mean().item()LossG = self.criterion(D_gz2, lbz2)# print("log(D(G(z)))")self.opt_G.zero_grad()LossG.backward()self.opt_G.step()# print("update LossG")G_loss+=LossGend_time = time.time()# **********计时*********************run_time = round(end_time - beg_time)# print('lalala')print(f'Epoch: [{epoch + 1:0>{len(str(num_epochs))}}/{num_epochs}]',f'Step: [{i + 1:0>{len(str(len(self.dataloader)))}}/{len(self.dataloader)}]',f'Loss-D: {LossD.item():.4f}',f'Loss-G: {LossG.item():.4f}',f'D(x): {D_x_item:.4f}',f'D(G(z)): [{D_gz1_item:.4f}/{D_gz2_item:.4f}]',f'Time: {run_time}s',end='\r\n')# print("lalalal2")# Save Losses for plotting laterself.G_loss_list.append(LossG.item())self.D_loss_list.append(LossD.item())# Save D(X) and D(G(z)) for plotting laterself.D_x_list.append(D_x_item)self.D_z_list.append(D_gz2_item)# # Save the Best Model# if LossG < loss_tep:#     torch.save(self.G.state_dict(), 'model.pt')#     loss_tep = LossGif not os.path.exists(self.model_save_path):os.makedirs(self.model_save_path)torch.save(self.D.state_dict(), self.model_save_path + 'disc_{}.pth'.format(epoch))torch.save(self.G.state_dict(), self.model_save_path + 'gen_{}.pth'.format(epoch))# Check how the generator is doing by saving G's output on fixed_noisewith torch.no_grad():fake = self.G(self.fixed_noise).detach().cpu()self.img_list.append(utils.make_grid(fake * 0.5 + 0.5, nrow=10))print()if not os.path.exists(self.figure_save_path):os.makedirs(self.figure_save_path)plt.figure(1,figsize=(8, 4))plt.title("Generator and Discriminator Loss During Training")plt.plot(self.G_loss_list[::10], label="G")plt.plot(self.D_loss_list[::10], label="D")plt.xlabel("iterations")plt.ylabel("Loss")plt.axhline(y=0, label="0", c="g")  # asymptoteplt.legend()plt.savefig(self.figure_save_path + str(num_epochs) + 'epochs_' + 'loss.jpg', bbox_inches='tight')plt.figure(2,figsize=(8, 4))plt.title("D(x) and D(G(z)) During Training")plt.plot(self.D_x_list[::10], label="D(x)")plt.plot(self.D_z_list[::10], label="D(G(z))")plt.xlabel("iterations")plt.ylabel("Probability")plt.axhline(y=0.5, label="0.5", c="g")  # asymptoteplt.legend()plt.savefig(self.figure_save_path + str(num_epochs) + 'epochs_' + 'D(x)D(G(z)).jpg', bbox_inches='tight')fig = plt.figure(3,figsize=(5, 5))plt.axis("off")ims = [[plt.imshow(item.permute(1, 2, 0), animated=True)] for item in self.img_list]ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)HTML(ani.to_jshtml())# ani.to_html5_video()ani.save(self.figure_save_path + str(num_epochs) + 'epochs_' + 'generation.gif')plt.figure(4,figsize=(8, 4))# Plot the real imagesplt.subplot(1, 2, 1)plt.axis("off")plt.title("Real Images")real = next(iter(self.dataloader))  # real[0]image,real[1]labelplt.imshow(utils.make_grid(real[0][:self.num_showimage] * 0.5 + 0.5, nrow=10).permute(1, 2, 0))# Load the Best Generative Model# self.G.load_state_dict(#     torch.load(self.model_save_path + 'disc_{}.pth'.format(epoch), map_location=torch.device(self.device)))self.G.eval()# Generate the Fake Imageswith torch.no_grad():fake = self.G(self.fixed_noise).cpu()# Plot the fake imagesplt.subplot(1, 2, 2)plt.axis("off")plt.title("Fake Images")fake = utils.make_grid(fake[:self.num_showimage] * 0.5 + 0.5, nrow=10).permute(1, 2, 0)plt.imshow(fake)# Save the comparation resultplt.savefig(self.figure_save_path + str(num_epochs) + 'epochs_' + 'result.jpg', bbox_inches='tight')plt.show()def test(self,epoch):# Size of the Figureplt.figure(figsize=(8, 4))# Plot the real imagesplt.subplot(1, 2, 1)plt.axis("off")plt.title("Real Images")real = next(iter(self.dataloader))#real[0]image,real[1]labelplt.imshow(utils.make_grid(real[0][:self.num_showimage] * 0.5 + 0.5, nrow=10).permute(1, 2, 0))# Load the Best Generative Modelself.G.load_state_dict(torch.load(self.model_save_path + 'disc_{}.pth'.format(epoch), map_location=torch.device(self.device)))self.G.eval()# Generate the Fake Imageswith torch.no_grad():fake = self.G(self.fixed_noise.to(self.device))# Plot the fake imagesplt.subplot(1, 2, 2)plt.axis("off")plt.title("Fake Images")fake = utils.make_grid(fake * 0.5 + 0.5, nrow=10)plt.imshow(fake.permute(1, 2, 0))# Save the comparation resultplt.savefig(self.figure_save_path+'result.jpg', bbox_inches='tight')plt.show()

3.4 主函数（main.py）

（1）导入文件

from data import ReadData
from model import Discriminator, Generator, weights_init
from net import DCGAN
import torch

（2）定义超参数

ngpu=1
ngf=64
ndf=64
nc=3
nz=100
lr=0.003
beta1=0.5
batch_size=100
num_showimage=100data_path="./oxford17_class"
model_save_path="./models/"
figure_save_path="./figures/"device = torch.device('cuda:0' if (torch.cuda.is_available() and ngpu > 0) else 'cpu')

（3）实例化

dataset=ReadData(data_path)
dataloader=dataset.getdataloader(batch_size=batch_size)G = Generator(nz,ngf,nc).apply(weights_init)
print(G)
D = Discriminator(ndf,nc).apply(weights_init)
print(D)dcgan=DCGAN( lr,beta1,nz,batch_size,num_showimage,device, model_save_path,figure_save_path,G, D, dataloader)