图像生成——使用DCGAN生成卡通肖像

一、需要注意的几点：

1、生成器的网络和判别器的网络均不含池化层。

2、判别器的最后一层网络输出使用sigmoid激活，生成器的最后一层网络输出使用tanh激活。

3、生成器和判别器的网络结果呈对称形式如：生成器的第一层的卷积核大小，步长，输入通道，输出通道核判别器的最后一层卷积核大小，步长一致，输出通道，输入通道大小一致。

（上图所示的是生成器，判别器的网络刚好对称，从后往前）

4、卷积核使用偶数大小的效果比使用奇数大小的卷积核效果好。

5、使用转置卷积进行上采样。

6、训练是可以每训练两轮生成器训练一次判别器（原因是判别器能力优于生成 器）。

二、代码部分：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader,Dataset
from torchvision.utils import save_image
import numpy as np
import os
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as pltclass Sampling_data(Dataset):def __init__(self,img_path):self.file_names = []self.transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5])])for file in os.listdir(img_path):file_name = os.path.join(img_path,file)self.file_names.append(file_name)def __len__(self):return len(self.file_names)def __getitem__(self, item):file = self.file_names[item]img_array = Image.open(file)xs = self.transform(img_array)return xsclass Dnet(nn.Module):def __init__(self):super(Dnet, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=5,stride=3,padding=1,bias=False),# nn.BatchNorm2d(64)nn.LeakyReLU(0.2, inplace=True))self.conv2 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2,padding=1, bias=False),nn.BatchNorm2d(128),nn.LeakyReLU(0.2, inplace=True))self.conv3 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1, bias=False),nn.BatchNorm2d(256),nn.LeakyReLU(0.2, inplace=True))self.conv4 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1, bias=False),nn.BatchNorm2d(512),nn.LeakyReLU(0.2, inplace=True))self.conv5 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=1, kernel_size=4, stride=1, padding=0, bias=False),nn.Sigmoid())def forward(self, x):y = self.conv1(x)y = self.conv2(y)y = self.conv3(y)y = self.conv4(y)y = self.conv5(y)return yclass Gnet(nn.Module):def __init__(self):super(Gnet, self).__init__()self.conv1 = nn.Sequential(nn.ConvTranspose2d(in_channels=128, out_channels=512, kernel_size=4, stride=1, padding=0, bias=False),nn.BatchNorm2d(512),nn.ReLU(inplace=True))self.conv2 = nn.Sequential(nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1, bias=False),nn.BatchNorm2d(256),nn.ReLU(inplace=True))self.conv3 = nn.Sequential(nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1, bias=False),nn.BatchNorm2d(128),nn.ReLU(inplace=True))self.conv4 = nn.Sequential(nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1, bias=False),nn.BatchNorm2d(64),nn.ReLU(inplace=True))self.conv5 = nn.Sequential(nn.ConvTranspose2d(in_channels=64, out_channels=3, kernel_size=5, stride=3, padding=1, bias=False),nn.Tanh())def forward(self, x):y = self.conv1(x)y = self.conv2(y)y = self.conv3(y)y = self.conv4(y)y = self.conv5(y)return yif __name__ == '__main__':save_params_path = r"params"save_img_path = r"./img"batchsize = 100img_data = r"E:\Learnn\cartoonfaces"            #img_data为卡通人物的路径num_epoch = 500random_num = 128save_real_img_path = os.path.join(save_img_path,"real_img")save_fake_img_path = os.path.join(save_img_path,"fake_img")save_dparam_path = os.path.join(save_params_path, "d_self_net.pth")save_gparam_path = os.path.join(save_params_path, "g_self_net.pth")for path in [save_img_path, save_params_path, save_real_img_path, save_fake_img_path]:if not os.path.exists(path):os.mkdir(path)data_loader = DataLoader(Sampling_data(img_data), batch_size=batchsize, shuffle=True, num_workers=4, drop_last=True)if torch.cuda.is_available():device = torch.device("cuda")else:device = torch.device("cpu")g_net = Gnet().to(device)d_net = Dnet().to(device)g_net.train()d_net.train()if os.path.exists(save_dparam_path and save_gparam_path):  # 两个网络两个参数d_net.load_state_dict(torch.load(save_dparam_path))g_net.load_state_dict(torch.load(save_gparam_path))print("两个参数已经加载成功！！！")else:print("NO Params!!!")loss_fn = nn.BCELoss()d_optimizer = torch.optim.Adam(d_net.parameters(), lr=0.0002, betas=(0.5, 0.999))g_optimizer = torch.optim.Adam(g_net.parameters(), lr=0.0002, betas=(0.5, 0.999))for epoch in range(num_epoch):for i, img in enumerate(data_loader):real_img = img.to(device)real_label = torch.ones(batchsize).view(-1, 1, 1, 1).to(device)fake_label = torch.zeros(batchsize).view(-1, 1, 1, 1).to(device)real_out = d_net(real_img)d_loss_real = loss_fn(real_out, real_label)rand_n = torch.randn(batchsize, random_num, 1, 1).to(device=device)fake_img = g_net(rand_n)fake_out = d_net(fake_img)d_loss_fake = loss_fn(fake_out, fake_label)d_loss = d_loss_real + d_loss_faked_optimizer.zero_grad()d_loss.backward()d_optimizer.step()rand_n1 = torch.randn(batchsize, random_num, 1, 1).to(device=device)fake_img = g_net(rand_n1)output = d_net(fake_img)g_loss = loss_fn(output, real_label)g_optimizer.zero_grad()g_loss.backward()g_optimizer.step()if i%10 == 0:print(real_out.data.mean(),fake_out.data.mean())fake_imgs = (0.5*(fake_img.cpu().data+1)).clamp(0, 1)real_imgs = (0.5 * (real_img.cpu().data + 1)).clamp(0, 1)save_image(fake_imgs, os.path.join(save_fake_img_path,"{}_fake_imgs.jpg".format(epoch+1)),nrow=10,normalize=True,scale_each=True)save_image(real_imgs, os.path.join(save_real_img_path,"{}_real_imgs.jpg".format(epoch+1)),nrow=10,normalize=True,scale_each=True)torch.save(g_net.state_dict(), save_gparam_path)torch.save(d_net.state_dict(), save_dparam_path)

三、效果展示：

1）生成的图像：

2）原始图像：