GAN的原理分析与实例

为了便于理解，可以先玩一玩这个网站：GAN Lab: Play with Generative Adversarial Networks in Your Browser!

GAN的本质：枯叶蝶和鸟。生成器的目标：让枯叶蝶进化，变得像枯叶，不被鸟准确识别。判别器的目标：准确判别是枯叶还是鸟

伪代码：

案例：

原始数据：

案例结果：

案例完整代码：

# import os
import torch
import torch.nn as nn
import torchvision as tv
from torch.autograd import Variable
import tqdm
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False

# dir = '... your path/faces/'
dir = './data/train_data'
# path = []
#
# for fileName in os.listdir(dir):
#     path.append(fileName)       # len(path)=51223


noiseSize = 100     # 噪声维度
n_generator_feature = 64        # 生成器feature map数
n_discriminator_feature = 64        # 判别器feature map数
batch_size = 50
d_every = 1     # 每一个batch训练一次discriminator
g_every = 5     # 每五个batch训练一次generator


class NetGenerator(nn.Module):
    def __init__(self):
        super(NetGenerator,self).__init__()
        self.main = nn.Sequential(      # 神经网络模块将按照在传入构造器的顺序依次被添加到计算图中执行
            nn.ConvTranspose2d(noiseSize, n_generator_feature * 8, kernel_size=4, stride=1, padding=0, bias=False),
            #转置卷积层：输入特征映射的尺寸会放大，通道数可能会减小，普通卷积层：输入特征映射的尺寸会缩小，但通道数可能会增加
            nn.BatchNorm2d(n_generator_feature * 8),
            nn.ReLU(True),       # (n_generator_feature * 8) × 4 × 4        (1-1)*1+1*(4-1)+0+1 = 4
            nn.ConvTranspose2d(n_generator_feature * 8, n_generator_feature * 4, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_generator_feature * 4),
            nn.ReLU(True),      # (n_generator_feature * 4) × 8 × 8     (4-1)*2-2*1+1*(4-1)+0+1 = 8
            nn.ConvTranspose2d(n_generator_feature * 4, n_generator_feature * 2, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_generator_feature * 2),
            nn.ReLU(True),  # (n_generator_feature * 2) × 16 × 16
            nn.ConvTranspose2d(n_generator_feature * 2, n_generator_feature, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_generator_feature),
            nn.ReLU(True),      # (n_generator_feature) × 32 × 32
            nn.ConvTranspose2d(n_generator_feature, 3, kernel_size=5, stride=3, padding=1, bias=False),
            nn.Tanh()       # 3 * 96 * 96
        )

    def forward(self, input):
        return self.main(input)


class NetDiscriminator(nn.Module):
    def __init__(self):
        super(NetDiscriminator,self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(3, n_discriminator_feature, kernel_size=5, stride=3, padding=1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),        # n_discriminator_feature * 32 * 32
            nn.Conv2d(n_discriminator_feature, n_discriminator_feature * 2, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_discriminator_feature * 2),
            nn.LeakyReLU(0.2, inplace=True),         # (n_discriminator_feature*2) * 16 * 16
            nn.Conv2d(n_discriminator_feature * 2, n_discriminator_feature * 4, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_discriminator_feature * 4),
            nn.LeakyReLU(0.2, inplace=True),  # (n_discriminator_feature*4) * 8 * 8
            nn.Conv2d(n_discriminator_feature * 4, n_discriminator_feature * 8, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(n_discriminator_feature * 8),
            nn.LeakyReLU(0.2, inplace=True),  # (n_discriminator_feature*8) * 4 * 4
            nn.Conv2d(n_discriminator_feature * 8, 1, kernel_size=4, stride=1, padding=0, bias=False),
            nn.Sigmoid()        # 输出一个概率
        )

    def forward(self, input):
        return self.main(input).view(-1)


def train():
    for i, (image,_) in tqdm.tqdm(enumerate(dataloader)):       # type((image,_)) = <class 'list'>, len((image,_)) = 2 * 256 * 3 * 96 * 96
        real_image = Variable(image)
        #real_image = real_image.cuda()

        if (i + 1) % d_every == 0:  #d_every = 1，每一个batch训练一次discriminator
            optimizer_d.zero_grad()
            output = Discriminator(real_image)      # 尽可能把真图片判为True
            error_d_real = criterion(output, true_labels)
            error_d_real.backward()

            noises.data.copy_(torch.randn(batch_size, noiseSize, 1, 1))
            fake_img = Generator(noises).detach()       # 根据噪声生成假图
            fake_output = Discriminator(fake_img)       # 尽可能把假图片判为False
            error_d_fake = criterion(fake_output, fake_labels)
            error_d_fake.backward()
            optimizer_d.step()

        if (i + 1) % g_every == 0:
            optimizer_g.zero_grad()
            noises.data.copy_(torch.randn(batch_size, noiseSize, 1, 1))
            fake_img = Generator(noises)        # 这里没有detach
            fake_output = Discriminator(fake_img)       # 尽可能让Discriminator把假图片判为True
            error_g = criterion(fake_output, true_labels)
            error_g.backward()
            optimizer_g.step()


def show(num):
    fix_fake_imags = Generator(fix_noises)
    fix_fake_imags = fix_fake_imags.data.cpu()[:64] * 0.5 + 0.5

    # x = torch.rand(64, 3, 96, 96)
    fig = plt.figure(1)

    i = 1
    for image in fix_fake_imags:
        ax = fig.add_subplot(8, 8, eval('%d' % i)) #将Figure划分为8行8列的子图网格，并将当前的子图添加到第i个位置。
        # plt.xticks([]), plt.yticks([])  # 去除坐标轴
        plt.axis('off')
        plt.imshow(image.permute(1, 2, 0)) #permute()函数可以对维度进行重排,Matplotlib期望的图像格式是(H, W, C)，即高度、宽度、通道
        i += 1
    plt.subplots_adjust(left=None,  # the left side of the subplots of the figure
                        right=None,  # the right side of the subplots of the figure
                        bottom=None,  # the bottom of the subplots of the figure
                        top=None,  # the top of the subplots of the figure
                        wspace=0.05,  # the amount of width reserved for blank space between subplots
                        hspace=0.05)  # the amount of height reserved for white space between subplots)
    plt.suptitle('第%d迭代结果' % num, y=0.91, fontsize=15)
    plt.savefig("images/%dcgan.png" % num)


if __name__ == '__main__':
    transform = tv.transforms.Compose([
        tv.transforms.Resize(96),     # 图片尺寸, transforms.Scale transform is deprecated
        tv.transforms.CenterCrop(96),
        tv.transforms.ToTensor(),
        tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))       # 变成[-1,1]的数
    ])

    dataset = tv.datasets.ImageFolder(dir, transform=transform)

    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True)   # module 'torch.utils.data' has no attribute 'DataLoder'

    print('数据加载完毕！')
    Generator = NetGenerator()
    Discriminator = NetDiscriminator()

    optimizer_g = torch.optim.Adam(Generator.parameters(), lr=2e-4, betas=(0.5, 0.999))
    optimizer_d = torch.optim.Adam(Discriminator.parameters(), lr=2e-4, betas=(0.5, 0.999))
    criterion = torch.nn.BCELoss()

    true_labels = Variable(torch.ones(batch_size))     # batch_size
    fake_labels = Variable(torch.zeros(batch_size))
    fix_noises = Variable(torch.randn(batch_size, noiseSize, 1, 1))
    noises = Variable(torch.randn(batch_size, noiseSize, 1, 1))     # 均值为0，方差为1的正态分布

    # if torch.cuda.is_available() == True:
    #     print('Cuda is available!')
    #     Generator.cuda()
    #     Discriminator.cuda()
    #     criterion.cuda()
    #     true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda()
    #     fix_noises, noises = fix_noises.cuda(), noises.cuda()


    #plot_epoch = [1,5,10,50,100,200,500,800,1000,1500,2000,2500,3000]
    plot_epoch = [1,5,10,50,100,200,500,800,1000,1200,1500]

    for i in range(1500):        # 最大迭代次数
        train()
        print('迭代次数：{}'.format(i))
        if i in plot_epoch:
            show(i)

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