cyclegan代码

引用提到了一些参考资料，其中知乎量子位的分享和GitHub上的代码可以帮助理解和实现CycleGAN。引用中的链接也提供了对CycleGAN的原理解释。而引用则提到了CycleGAN中Generator网络部分的结构和一些细节，包括降采样、残差连接、上采样以及使用的Normalization方法。综合这些资料，可以帮助你更好地理解和实现CycleGAN的代码。

相关问题

cycle GAN代码

这里是一个简单的CycleGAN代码示例，使用PyTorch实现。CycleGAN是一种无监督的图像转换模型，可以将一类图像转换为另一类图像。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image

class CycleGANDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.img_names = os.listdir(root_dir)
        self.transform = transform

    def __len__(self):
        return len(self.img_names)

    def __getitem__(self, idx):
        img_name = self.img_names[idx]
        img_path = os.path.join(self.root_dir, img_name)
        image = Image.open(img_path).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),
            nn.Tanh()
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)

    def forward(self, x):
        x = nn.functional.leaky_relu(self.conv1(x), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn2(self.conv2(x)), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn3(self.conv3(x)), 0.2, inplace=True)
        x = nn.functional.leaky_relu(self.bn4(self.conv4(x)), 0.2, inplace=True)
        x = torch.sigmoid(self.conv5(x))
        return x

class CycleGAN():
    def __init__(self, device, lr=0.0002, lambda_cycle=10):
        self.device = device
        self.generator_A = Generator().to(device)
        self.generator_B = Generator().to(device)
        self.discriminator_A = Discriminator().to(device)
        self.discriminator_B = Discriminator().to(device)
        self.optimizer_G = optim.Adam(list(self.generator_A.parameters()) + list(self.generator_B.parameters()), lr=lr, betas=(0.5, 0.999))
        self.optimizer_D = optim.Adam(list(self.discriminator_A.parameters()) + list(self.discriminator_B.parameters()), lr=lr, betas=(0.5, 0.999))
        self.criterion_cycle = nn.L1Loss()
        self.criterion_adv = nn.BCELoss()
        self.lambda_cycle = lambda_cycle

    def train(self, dataloader_A, dataloader_B, num_epochs):
        self.generator_A.train()
        self.generator_B.train()
        self.discriminator_A.train()
        self.discriminator_B.train()
        for epoch in range(num_epochs):
            for i, (real_A, real_B) in enumerate(zip(dataloader_A, dataloader_B)):
                # Move data to device
                real_A, real_B = real_A.to(self.device), real_B.to(self.device)

                # Train generators
                self.optimizer_G.zero_grad()

                # Adversarial loss
                fake_B = self.generator_A(real_A)
                pred_fake_B = self.discriminator_B(fake_B)
                loss_adv_B = self.criterion_adv(pred_fake_B, torch.ones_like(pred_fake_B))
                fake_A = self.generator_B(real_B)
                pred_fake_A = self.discriminator_A(fake_A)
                loss_adv_A = self.criterion_adv(pred_fake_A, torch.ones_like(pred_fake_A))

                # Cycle consistency loss
                cycle_A = self.generator_B(fake_B)
                loss_cycle_A = self.criterion_cycle(cycle_A, real_A)
                cycle_B = self.generator_A(fake_A)
                loss_cycle_B = self.criterion_cycle(cycle_B, real_B)

                # Total generator loss
                loss_G = loss_adv_A + loss_adv_B + self.lambda_cycle * (loss_cycle_A + loss_cycle_B)
                loss_G.backward()
                self.optimizer_G.step()

                # Train discriminators
                self.optimizer_D.zero_grad()

                # Real loss
                pred_real_A = self.discriminator_A(real_A)
                loss_real_A = self.criterion_adv(pred_real_A, torch.ones_like(pred_real_A))
                pred_real_B = self.discriminator_B(real_B)
                loss_real_B = self.criterion_adv(pred_real_B, torch.ones_like(pred_real_B))

                # Fake loss
                pred_fake_A = self.discriminator_A(fake_A.detach())
                loss_fake_A = self.criterion_adv(pred_fake_A, torch.zeros_like(pred_fake_A))
                pred_fake_B = self.discriminator_B(fake_B.detach())
                loss_fake_B = self.criterion_adv(pred_fake_B, torch.zeros_like(pred_fake_B))

                # Total discriminator loss
                loss_D = (loss_real_A + loss_fake_A + loss_real_B + loss_fake_B) * 0.25
                loss_D.backward()
                self.optimizer_D.step()

                # Print loss
                if i % 100 == 0:
                    print('[Epoch %d/%d] [Batch %d/%d] [D loss: %.4f] [G loss: %.4f]' %
                          (epoch+1, num_epochs, i+1, min(len(dataloader_A), len(dataloader_B)), loss_D.item(), loss_G.item()))


# Example usage
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([transforms.Resize(256), transforms.RandomCrop(256), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset_A = CycleGANDataset('path/to/dataset_A', transform=transform)
dataset_B = CycleGANDataset('path/to/dataset_B', transform=transform)
dataloader_A = DataLoader(dataset_A, batch_size=1, shuffle=True)
dataloader_B = DataLoader(dataset_B, batch_size=1, shuffle=True)
cyclegan = CycleGAN(device, lr=0.0002, lambda_cycle=10)
cyclegan.train(dataloader_A, dataloader_B, num_epochs=200)

CycleGAN代码

CycleGAN是一种无监督图像转换算法，可以将一组图像从一个领域转换到另一个领域。以下是使用PyTorch实现CycleGAN的基本代码。

首先，我们需要导入必要的库：

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import transforms
import matplotlib.pyplot as plt

接下来，我们定义一些超参数：

# Hyperparameters
batch_size = 1
epochs = 200
learning_rate = 0.0002
image_size = 256
input_nc = 3  # Number of input channels
output_nc = 3 # Number of output channels

然后，我们定义生成器和判别器的架构：

# Generator architecture
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # Encoder
        self.enc1 = nn.Sequential(nn.Conv2d(input_nc, 64, 4, stride=2, padding=1), nn.LeakyReLU(0.2, True))
        self.enc2 = nn.Sequential(nn.Conv2d(64, 128, 4, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, True))
        self.enc3 = nn.Sequential(nn.Conv2d(128, 256, 4, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, True))
        self.enc4 = nn.Sequential(nn.Conv2d(256, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))
        self.enc5 = nn.Sequential(nn.Conv2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))
        self.enc6 = nn.Sequential(nn.Conv2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))
        self.enc7 = nn.Sequential(nn.Conv2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))
        self.enc8 = nn.Sequential(nn.Conv2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))

        # Decoder
        self.dec1 = nn.Sequential(nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(True))
        self.dec2 = nn.Sequential(nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(True))
        self.dec3 = nn.Sequential(nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(True))
        self.dec4 = nn.Sequential(nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(True))
        self.dec5 = nn.Sequential(nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU(True))
        self.dec6 = nn.Sequential(nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(True))
        self.dec7 = nn.Sequential(nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(True))
        self.dec8 = nn.Sequential(nn.ConvTranspose2d(64, output_nc, 4, stride=2, padding=1), nn.Tanh())

    def forward(self, x):
        # Encoder
        enc1 = self.enc1(x)
        enc2 = self.enc2(enc1)
        enc3 = self.enc3(enc2)
        enc4 = self.enc4(enc3)
        enc5 = self.enc5(enc4)
        enc6 = self.enc6(enc5)
        enc7 = self.enc7(enc6)
        enc8 = self.enc8(enc7)

        # Decoder
        dec1 = self.dec1(enc8)
        dec1 = torch.cat([dec1, enc7], dim=1)
        dec2 = self.dec2(dec1)
        dec2 = torch.cat([dec2, enc6], dim=1)
        dec3 = self.dec3(dec2)
        dec3 = torch.cat([dec3, enc5], dim=1)
        dec4 = self.dec4(dec3)
        dec4 = torch.cat([dec4, enc4], dim=1)
        dec5 = self.dec5(dec4)
        dec5 = torch.cat([dec5, enc3], dim=1)
        dec6 = self.dec6(dec5)
        dec6 = torch.cat([dec6, enc2], dim=1)
        dec7 = self.dec7(dec6)
        dec7 = torch.cat([dec7, enc1], dim=1)
        dec8 = self.dec8(dec7)

        return dec8

# Discriminator architecture
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Sequential(nn.Conv2d(input_nc + output_nc, 64, 4, stride=2, padding=1), nn.LeakyReLU(0.2, True))
        self.conv2 = nn.Sequential(nn.Conv2d(64, 128, 4, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, True))
        self.conv3 = nn.Sequential(nn.Conv2d(128, 256, 4, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, True))
        self.conv4 = nn.Sequential(nn.Conv2d(256, 512, 4, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, True))
        self.conv5 = nn.Sequential(nn.Conv2d(512, 1, 4, padding=1), nn.Sigmoid())

    def forward(self, x):
        x = torch.cat([x[:, :input_nc, :, :], x[:, input_nc:, :, :]], dim=1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)

        return x

接下来，我们定义损失函数和优化器：

# Loss function
criterion = nn.MSELoss()

# Optimizers
G_AB = Generator()
G_BA = Generator()
D_A = Discriminator()
D_B = Discriminator()

G_AB_optimizer = torch.optim.Adam(G_AB.parameters(), lr=learning_rate, betas=(0.5, 0.999))
G_BA_optimizer = torch.optim.Adam(G_BA.parameters(), lr=learning_rate, betas=(0.5, 0.999))
D_A_optimizer = torch.optim.Adam(D_A.parameters(), lr=learning_rate, betas=(0.5, 0.999))
D_B_optimizer = torch.optim.Adam(D_B.parameters(), lr=learning_rate, betas=(0.5, 0.999))

最后，我们定义训练循环：

# Train loop
for epoch in range(epochs):
    for i, (real_A, real_B) in enumerate(dataloader):
        # Set model input
        real_A = real_A.to(device)
        real_B = real_B.to(device)

        # Adversarial ground truths
        valid = torch.ones((real_A.size(0), 1, image_size // 2 ** 4, image_size // 2 ** 4)).to(device)
        fake = torch.zeros((real_A.size(0), 1, image_size // 2 ** 4, image_size // 2 ** 4)).to(device)

        #######################
        # Train generators
        #######################

        G_AB_optimizer.zero_grad()
        G_BA_optimizer.zero_grad()

        # Identity loss
        idt_A = G_BA(real_A)
        loss_idt_A = criterion(idt_A, real_A) * 0.5 * 5.0

        idt_B = G_AB(real_B)
        loss_idt_B = criterion(idt_B, real_B) * 0.5 * 5.0

        # GAN loss
        fake_B = G_AB(real_A)
        loss_GAN_AB = criterion(D_B(torch.cat((real_A, fake_B), 1)), valid)

        fake_A = G_BA(real_B)
        loss_GAN_BA = criterion(D_A(torch.cat((real_B, fake_A), 1)), valid)

        # Cycle loss
        recov_A = G_BA(fake_B)
        loss_cycle_A = criterion(recov_A, real_A) * 10.0

        recov_B = G_AB(fake_A)
        loss_cycle_B = criterion(recov_B, real_B) * 10.0

        # Total loss
        loss_G = loss_GAN_AB + loss_GAN_BA + loss_cycle_A + loss_cycle_B + loss_idt_A + loss_idt_B
        loss_G.backward()
        G_AB_optimizer.step()
        G_BA_optimizer.step()

        #######################
        # Train discriminators
        #######################

        D_A_optimizer.zero_grad()
        D_B_optimizer.zero_grad()

        # Real loss
        loss_real_A = criterion(D_A(torch.cat((real_A, real_B), 1)), valid)
        loss_real_B = criterion(D_B(torch.cat((real_B, real_A), 1)), valid)

        # Fake loss
        loss_fake_A = criterion(D_A(torch.cat((real_A, fake_A.detach()), 1)), fake)
        loss_fake_B = criterion(D_B(torch.cat((real_B, fake_B.detach()), 1)), fake)

        # Total loss
        loss_D_A = (loss_real_A + loss_fake_A) * 0.5
        loss_D_B = (loss_real_B + loss_fake_B) * 0.5
        loss_D = loss_D_A + loss_D_B
        loss_D.backward()
        D_A_optimizer.step()
        D_B_optimizer.step()

        # Print progress
        print(f"[Epoch {epoch}/{epochs}] [Batch {i}/{len(dataloader)}] [D loss: {loss_D.item()}] [G loss: {loss_G.item()}]")

    # Save checkpoints
    torch.save(G_AB.state_dict(), "gen_AB.pth")
    torch.save(G_BA.state_dict(), "gen_BA.pth")
    torch.save(D_A.state_dict(), "disc_A.pth")
    torch.save(D_B.state_dict(), "disc_B.pth")

这是 CycleGAN 的基本实现。当然，您还可以添加其他功能，如样式迁移，条件生成等，以满足您的需求。