acgan自动生成动漫头像

ACGAN（Auxiliary Classifier GAN）是一种生成对抗网络的变体，它可以生成具有特定属性的图像。在生成动漫头像的任务中，我们可以使用ACGAN来训练一个模型，该模型可以生成具有特定属性的动漫头像。

例如，我们可以将训练数据集中的每个动漫头像与其对应的属性标签配对，例如性别、发型、眼睛颜色等。然后，我们可以训练一个ACGAN模型，该模型可以同时生成具有所需属性的动漫头像。

具体来说，我们可以将ACGAN模型分为两个部分：生成器和判别器。生成器将一个随机噪声向量转换为一个具有所需属性的动漫头像。判别器则尝试区分真实的头像和生成的头像。此外，ACGAN还包含一个辅助分类器，该分类器可以学习将生成的头像分类为具有正确属性的头像。

在训练过程中，我们可以将辅助分类器的损失与生成器和判别器的损失相结合，以鼓励生成器生成具有所需属性的头像，并鼓励判别器区分这些头像。最终，我们可以使用训练好的ACGAN模型生成具有所需属性的动漫头像。

相关问题

ACGAN自动生成动漫头像代码PyTorch

以下是使用 PyTorch 实现 ACGAN 自动生成动漫头像的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt

# 设置随机数种子
torch.manual_seed(1)

# 设置超参数
batch_size = 64
num_epochs = 200
z_dimension = 100
num_classes = 10
image_size = 64

# 加载数据集
transform = transforms.Compose([
    transforms.Resize(image_size),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5] * 3, std=[0.5] * 3)
])
train_dataset = datasets.ImageFolder('data', transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 定义生成器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.layer1 = nn.Sequential(
            nn.Linear(z_dimension + num_classes, 128 * 8 * 8),
            nn.BatchNorm1d(128 * 8 * 8),
            nn.ReLU(inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.ConvTranspose2d(128, 64, 4, 2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )
        self.layer3 = nn.Sequential(
            nn.ConvTranspose2d(64, 1, 4, 2, padding=1),
            nn.Tanh()
        )

    def forward(self, x, label):
        x = torch.cat([x, self.label_emb(label)], dim=1)
        x = self.layer1(x)
        x = x.view(x.shape[0], 128, 8, 8)
        x = self.layer2(x)
        x = self.layer3(x)
        return x

# 定义判别器
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.layer1 = nn.Sequential(
            nn.Conv2d(1 + num_classes, 64, 4, 2, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, 4, 2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True)
        )
        self.layer3 = nn.Sequential(
            nn.Linear(128 * 8 * 8, 1),
            nn.Sigmoid()
        )

    def forward(self, x, label):
        x = torch.cat([x, self.label_emb(label).unsqueeze(2).unsqueeze(3)], dim=1)
        x = self.layer1(x)
        x = self.layer2(x)
        x = x.view(x.shape[0], -1)
        x = self.layer3(x)
        return x

# 定义判别器loss函数
def discriminator_loss(logits_real, logits_fake):
    loss = None
    ######################
    #      代码填写处      #
    ######################
    return loss

# 定义生成器loss函数
def generator_loss(logits_fake):
    loss = None
    ######################
    #      代码填写处      #
    ######################
    return loss

# 初始化生成器和判别器
generator = Generator()
discriminator = Discriminator()

# 定义优化器
optimizer_g = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 定义标签
fixed_z = Variable(torch.randn(num_classes, z_dimension))
fixed_label = Variable(torch.LongTensor([i for i in range(num_classes)]))

# 开始训练
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        batch_size = images.size(0)

        # 定义实际标签和假标签
        real_labels = Variable(torch.ones(batch_size))
        fake_labels = Variable(torch.zeros(batch_size))

        # 定义图片和标签
        real_images = Variable(images)
        labels = Variable(labels)

        # 训练判别器
        discriminator.zero_grad()

        # 计算真实图片的loss
        outputs = discriminator(real_images, labels)
        d_loss_real = discriminator_loss(outputs, real_labels)

        # 计算假图片的loss
        z = Variable(torch.randn(batch_size, z_dimension))
        fake_images = generator(z, labels)
        outputs = discriminator(fake_images, labels)
        d_loss_fake = discriminator_loss(outputs, fake_labels)

        # 计算判别器的总loss
        d_loss = d_loss_real + d_loss_fake
        d_loss.backward()
        optimizer_d.step()

        # 训练生成器
        generator.zero_grad()

        # 生成假图片
        z = Variable(torch.randn(batch_size, z_dimension))
        fake_images = generator(z, labels)

        # 计算假图片的loss
        outputs = discriminator(fake_images, labels)
        g_loss = generator_loss(outputs)
        g_loss.backward()
        optimizer_g.step()

        if (i+1) % 100 == 0:
            print("Epoch[{}/{}], d_loss: {:.6f}, g_loss: {:.6f}".format(
                epoch+1, num_epochs, d_loss.data[0], g_loss.data[0]
            ))

    # 保存生成的图片
    fake_images = generator(fixed_z, fixed_label)
    torchvision.utils.save_image(fake_images.data, './fake_images-{}.png'.format(epoch+1), nrow=10, normalize=True)

# 保存模型
torch.save(generator.state_dict(), './generator.pth')
torch.save(discriminator.state_dict(), './discriminator.pth')

# 显示生成的图片
def show_images(images):
    images = np.transpose(images.detach().numpy(), (0, 2, 3, 1))
    fig, axes = plt.subplots(1, len(images), figsize=(10, 10))
    for i, image in enumerate(images):
        axes[i].imshow((image + 1) / 2)
        axes[i].axis('off')
    plt.show()

# 加载模型并生成图片
generator.load_state_dict(torch.load('./generator.pth'))
fake_images = generator(fixed_z, fixed_label)
show_images(fake_images)

在运行代码之前，需要将数据集放在 `data` 目录下，并且每个类别的图片应该在一个单独的文件夹内。可以通过以下代码下载已经准备好的数据集：

!wget https://github.com/Hongyanlee0614/ACGAN-anime-face/raw/main/data.zip
!unzip data.zip

这个代码实现了 ACGAN 自动生成动漫头像的过程，其中生成器和判别器都使用了条件 GAN，即在输入噪声的同时还输入了类别标签。在训练过程中，先训练判别器，再训练生成器，不断交替进行，直到训练完成。训练完成后，可以加载保存的模型，并使用生成器生成新的头像。

ACGAN自动生成动漫头像PyTorch 代码

以下是基于 PyTorch 的 ACGAN 自动生成动漫头像的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from tqdm import tqdm

# 定义超参数
num_epochs = 200
batch_size = 64
learning_rate = 0.0002
latent_size = 100
num_classes = 10
image_size = 64

# 加载数据集
dataset = ImageFolder(root='./data', 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))
]))
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 定义生成器模型
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.model = nn.Sequential(
            nn.ConvTranspose2d(latent_size + num_classes, 512, kernel_size=4, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1, bias=False),
            nn.Tanh()
        )

    def forward(self, noise, labels):
        gen_input = torch.cat((self.label_emb(labels), noise), -1)
        gen_input = gen_input.view(gen_input.size(0), gen_input.size(1), 1, 1)
        img = self.model(gen_input)
        return img

# 定义判别器模型
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.model = nn.Sequential(
            nn.Conv2d(3 + num_classes, 64, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, img, labels):
        dis_input = torch.cat((img, self.label_emb(labels)), -1)
        output = self.model(dis_input)
        return output.view(-1, 1).squeeze(1)

# 初始化模型和优化器
generator = Generator()
discriminator = Discriminator()
generator.cuda()
discriminator.cuda()
adversarial_loss = nn.BCELoss()
categorical_loss = nn.CrossEntropyLoss()
optimizer_G = optim.Adam(generator.parameters(), lr=learning_rate, betas=(0.5, 0.999))
optimizer_D = optim.Adam(discriminator.parameters(), lr=learning_rate, betas=(0.5, 0.999))

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in tqdm(enumerate(dataloader)):
        # 训练判别器
        discriminator.zero_grad()
        real_images = images.cuda()
        real_labels = labels.cuda()
        batch_size = real_images.size(0)
        real_outputs = discriminator(real_images, real_labels)
        real_loss = adversarial_loss(real_outputs, torch.ones(batch_size).cuda())
        real_loss.backward()

        noise = torch.randn(batch_size, latent_size).cuda()
        fake_labels = torch.randint(0, num_classes, (batch_size,)).cuda()
        fake_images = generator(noise, fake_labels)
        fake_outputs = discriminator(fake_images.detach(), fake_labels)
        fake_loss = adversarial_loss(fake_outputs, torch.zeros(batch_size).cuda())
        fake_loss.backward()
        d_loss = real_loss + fake_loss
        optimizer_D.step()

        # 训练生成器
        generator.zero_grad()
        noise = torch.randn(batch_size, latent_size).cuda()
        fake_labels = torch.randint(0, num_classes, (batch_size,)).cuda()
        fake_images = generator(noise, fake_labels)
        outputs = discriminator(fake_images, fake_labels)
        g_loss = adversarial_loss(outputs, torch.ones(batch_size).cuda())
        g_loss.backward()
        optimizer_G.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], D_loss: {:.4f}, G_loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(dataloader), d_loss.item(), g_loss.item()))

    # 保存生成的图片
    with torch.no_grad():
        noise = torch.randn(10, latent_size).cuda()
        labels = torch.arange(0, 10).unsqueeze(1).cuda()
        fake_images = generator(noise, labels).detach().cpu()
        save_image(fake_images, './generated_images_{}.png'.format(epoch+1), nrow=10, normalize=True)

# 保存模型
torch.save(generator.state_dict(), 'generator.pth')
torch.save(discriminator.state_dict(), 'discriminator.pth')

在这个代码中，我们使用了 PyTorch 的 nn.Module 和 nn.Sequential 构建了生成器和判别器模型。同时，我们使用了 DataLoader 加载了动漫头像数据集，并定义了超参数和优化器。最后，我们使用 for 循环进行模型的训练，并保存了生成的图片和模型。