CGAN and DCGAN的实例代码——以MINST为例

1、加载数据集

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
import matplotlib.pyplot as plt

# 基本参数
z_dim = 100
batch_size = 128
learning_rate = 0.000
total_epochs = 30

# 使用GPU训练，可以在菜单 "代码执行工具" -> "更改运行时类型" 里进行设置
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 加载MNIST数据集
dataloader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=True, download=True,
        transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])
        ), batch_size, shuffle=False, drop_last=True)

2、CGAN

class Discriminator(nn.Module):
	'''全连接判别器，用于1x28x28的MNIST数据,输出是数据和类别'''
	def __init__(self):
		super(Discriminator, self).__init__()
		self.model = nn.Sequential(
			  nn.Linear(28*28+10, 512),
			  nn.LeakyReLU(0.2, inplace=True),
			  nn.Linear(512, 256),
			  nn.LeakyReLU(0.2, inplace=True),
			  nn.Linear(256, 1),
			  nn.Sigmoid()
		)
  
	def forward(self, x, c):
		x = x.view(x.size(0), -1)
		validity = self.model(torch.cat([x, c], -1))
		return validity

class Generator(nn.Module):
	'''全连接生成器，用于1x28x28的MNIST数据，输入是噪声和类别'''
	def __init__(self, z_dim):
		super(Generator, self).__init__()
		self.model = nn.Sequential(
			  nn.Linear(z_dim+10, 128),
			  nn.LeakyReLU(0.2, inplace=True),
			  nn.Linear(128, 256),
			  nn.BatchNorm1d(256, 0.8),
			  nn.LeakyReLU(0.2, inplace=True),
			  nn.Linear(256, 512),
			  nn.BatchNorm1d(512, 0.8),
			  nn.LeakyReLU(0.2, inplace=True),
			  nn.Linear(in_features=512, out_features=28*28),
			  nn.Tanh()
	 	)

	def forward(self, z, c):
		x = self.model(torch.cat([z, c], dim=1))
		x = x.view(-1, 1, 28, 28)
		return x
    
# 初始化构建判别器和生成器
discriminator = Discriminator().to(device)
generator = Generator(z_dim=z_dim).to(device)

# 初始化二值交叉熵损失
bce = torch.nn.BCELoss().to(device)
ones = torch.ones(batch_size).to(device)
zeros = torch.zeros(batch_size).to(device)

# 初始化优化器，使用Adam优化器
g_optimizer = optim.Adam(generator.parameters(), lr=learning_rate)
d_optimizer = optim.Adam(discriminator.parameters(), lr=learning_rate)

# 开始训练，一共训练total_epochs
for epoch in range(total_epochs):

	# torch.nn.Module.train() 指的是模型启用 BatchNormalization 和 Dropout
	# torch.nn.Module.eval() 指的是模型不启用 BatchNormalization 和 Dropout
	# 因此，train()一般在训练时用到， eval() 一般在测试时用到
	generator = generator.train()

	# 训练一个epoch
	for i, data in enumerate(dataloader):

		# 加载真实数据
		real_images, real_labels = data
		real_images = real_images.to(device)
		# 把对应的标签转化成 one-hot 类型
		tmp = torch.FloatTensor(real_labels.size(0), 10).zero_()
		real_labels = tmp.scatter_(dim=1, index=torch.LongTensor(real_labels.view(-1, 1)), value=1)
		real_labels = real_labels.to(device)

		# 生成数据
		# 用正态分布中采样batch_size个随机噪声
		z = torch.randn([batch_size, z_dim]).to(device)
		# 生成 batch_size 个 ont-hot 标签
		c = torch.FloatTensor(batch_size, 10).zero_()
		c = c.scatter_(dim=1, index=torch.LongTensor(np.random.choice(10, batch_size).reshape([batch_size, 1])), value=1)
		c = c.to(device)
		# 生成数据
		fake_images = generator(z,c)

		# 计算判别器损失，并优化判别器
		real_loss = bce(discriminator(real_images, real_labels), ones)
		fake_loss = bce(discriminator(fake_images.detach(), c), zeros)
		d_loss = real_loss + fake_loss

		d_optimizer.zero_grad()
		d_loss.backward()
		d_optimizer.step()

		# 计算生成器损失，并优化生成器
		g_loss = bce(discriminator(fake_images, c), ones)

		g_optimizer.zero_grad()
		g_loss.backward()
		g_optimizer.step()

	# 输出损失
	print("[Epoch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, total_epochs, d_loss.item(), g_loss.item()))
    
#下面我们用随机噪声生成一组图像，看看CGAN的效果：
#用于生成效果图
# 生成100个随机噪声向量
fixed_z = torch.randn([100, z_dim]).to(device)
# 生成100个one_hot向量，每类10个
fixed_c = torch.FloatTensor(100, 10).zero_()
fixed_c = fixed_c.scatter_(dim=1, index=torch.LongTensor(np.array(np.arange(0, 10).tolist()*10).reshape([100, 1])), value=1)
fixed_c = fixed_c.to(device)

generator = generator.eval()
fixed_fake_images = generator(fixed_z, fixed_c)

plt.figure(figsize=(8, 8))
for j in range(10):
    for i in range(10):
        img = fixed_fake_images[j*10+i, 0, :, :].detach().cpu().numpy()
        img = img.reshape([28, 28])
        plt.subplot(10, 10, j*10+i+1)
        plt.imshow(img, 'gray')

得到输出：

3、DCGAN

class D_dcgan(nn.Module):
	'''滑动卷积判别器'''
	def __init__(self):
		super(D_dcgan, self).__init__()
		self.conv = nn.Sequential(
            # 第一个滑动卷积层，不使用BN，LRelu激活函数
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            # 第二个滑动卷积层，包含BN，LRelu激活函数
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.2, inplace=True),
            # 第三个滑动卷积层，包含BN，LRelu激活函数
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            # 第四个滑动卷积层，包含BN，LRelu激活函数
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True)
        )

		# 全连接层+Sigmoid激活函数
		self.linear = nn.Sequential(nn.Linear(in_features=128, out_features=1), nn.Sigmoid())

	def forward(self, x):
		x = self.conv(x)
		x = x.view(x.size(0), -1)
		validity = self.linear(x)
		return validity

class G_dcgan(nn.Module):
	'''反滑动卷积生成器'''

	def __init__(self, z_dim):
		super(G_dcgan, self).__init__()
		self.z_dim = z_dim
		# 第一层：把输入线性变换成256x4x4的矩阵，并在这个基础上做反卷机操作
		self.linear = nn.Linear(self.z_dim, 4*4*256)
		self.model = nn.Sequential(
            # 第二层：bn+relu
            nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=0),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            # 第三层：bn+relu
            nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            # 第四层:不使用BN，使用tanh激活函数
            nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=4, stride=2, padding=2),
            nn.Tanh()
        )

	def forward(self, z):
		# 把随机噪声经过线性变换，resize成256x4x4的大小
		x = self.linear(z)
		x = x.view([x.size(0), 256, 4, 4])
		# 生成图片
		x = self.model(x)
		return x

同理，得到DCGAN生成图：