d_loss_rf = -torch.mean(real_validity) + torch.mean(fake_validity)什么意思

这段代码是计算对抗生成网络（GAN）中的判别器损失函数（discriminator loss），其中real_validity和fake_validity分别表示真实样本和生成样本在经过判别器后的输出值。具体地，该损失函数计算了真实样本被判别为真实样本的概率（即real_validity），以及生成样本被判别为真实样本的概率（即fake_validity），然后取这两个概率的负平均值作为判别器的损失值。

相关问题

代码不完整从return epoch_g_loss,往下继续

以下是 `main` 函数中从 `return epoch_g_loss, ...` 开始的部分：

    ...
    return epoch_g_loss, epoch_d_loss, epoch_iou, epoch_psnr, epoch_ssim

def validate_correction_model(generator, discriminator, dataloader, device, lambda_gp):
    generator.eval()
    discriminator.eval()
    running_g_loss = 0.0
    running_d_loss = 0.0
    running_iou = 0.0
    running_psnr = 0.0
    running_ssim = 0.0
    with torch.no_grad():
        for inputs, targets in tqdm(dataloader, desc="Validation"):
            inputs = inputs.to(device)
            targets = targets.to(device)
            corrected_images = generator(inputs)
            real_validity = discriminator(targets)
            fake_validity = discriminator(corrected_images)
            d_loss = -torch.mean(real_validity) + torch.mean(fake_validity)
            g_adv_loss = -torch.mean(fake_validity)
            g_loss = g_adv_loss
            running_g_loss += g_loss.item()
            running_d_loss += d_loss.item()
            iou = compute_iou(corrected_images, targets)
            psnr = compute_psnr(corrected_images, targets)
            ssim = compute_ssim(corrected_images, targets)
            running_iou += iou
            running_psnr += psnr
            running_ssim += ssim
    epoch_g_loss = running_g_loss / len(dataloader)
    epoch_d_loss = running_d_loss / len(dataloader)
    epoch_iou = running_iou / len(dataloader)
    epoch_psnr = running_psnr / len(dataloader)
    epoch_ssim = running_ssim / len(dataloader)
    return epoch_g_loss, epoch_d_loss, epoch_iou, epoch_psnr, epoch_ssim

def visualize_results(generator, dataloader, device, num_images=10, save_path='./results'):
    generator.eval()
    inputs, targets = next(iter(dataloader))
    inputs = inputs.to(device)
    targets = targets.to(device)
    with torch.no_grad():
        corrected_images = generator(inputs)
    inputs = inputs.cpu().numpy()
    targets = targets.cpu().numpy()
    corrected_images = corrected_images.cpu().numpy()
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    plt.figure(figsize=(20, 10))
    for i in range(num_images):
        plt.subplot(3, num_images, i + 1)
        plt.imshow(targets[i].transpose(1, 2, 0))
        plt.title("Original")
        plt.axis('off')
        plt.subplot(3, num_images, i + 1 + num_images)
        plt.imshow(inputs[i].transpose(1, 2, 0))
        plt.title("Simulated Colorblind")
        plt.axis('off')
        plt.subplot(3, num_images, i + 1 + 2 * num_images)
        plt.imshow(corrected_images[i].transpose(1, 2, 0))
        plt.title("Corrected")
        plt.axis('off')
    plt.tight_layout()
    plt.savefig(f'{save_path}_visualization.png')
    plt.show()

def plot_and_save_metrics(train_metrics, val_metrics, epoch, path='./metrics_plots'):
    if not os.path.exists(path):
        os.makedirs(path)
    epochs = np.arange(1, epoch + 1)
    train_g_losses, train_d_losses, train_ious, train_psnrs, train_ssims = zip(*train_metrics)
    val_g_losses, val_d_losses, val_ious, val_psnrs, val_ssims = zip(*val_metrics)
    plt.figure()
    plt.plot(epochs, train_g_losses, label='Training Generator Loss')
    plt.plot(epochs, val_g_losses, label='Validation Generator Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.title('Generator Loss over Epochs')
    plt.legend()
    plt.savefig(f'{path}/generator_loss.png')
    plt.close()
    plt.figure()
    plt.plot(epochs, train_d_losses, label='Training Discriminator Loss')
    plt.plot(epochs, val_d_losses, label='Validation Discriminator Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.title('Discriminator Loss over Epochs')
    plt.legend()
    plt.savefig(f'{path}/discriminator_loss.png')
    plt.close()
    plt.figure()
    plt.plot(epochs, train_ious, label='Training IoU')
    plt.plot(epochs, val_ious, label='Validation IoU')
    plt.xlabel('Epoch')
    plt.ylabel('IoU')
    plt.title('IoU over Epochs')
    plt.legend()
    plt.savefig(f'{path}/iou.png')
    plt.close()
    plt.figure()
    plt.plot(epochs, train_psnrs, label='Training PSNR')
    plt.plot(epochs, val_psnrs, label='Validation PSNR')
    plt.xlabel('Epoch')
    plt.ylabel('PSNR')
    plt.title('PSNR over Epochs')
    plt.legend()
    plt.savefig(f'{path}/psnr.png')
    plt.close()
    plt.figure()
    plt.plot(epochs, train_ssims, label='Training SSIM')
    plt.plot(epochs, val_ssims, label='Validation SSIM')
    plt.xlabel('Epoch')
    plt.ylabel('SSIM')
    plt.title('SSIM over Epochs')
    plt.legend()
    plt.savefig(f'{path}/ssim.png')
    plt.close()

def main(args):
    transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
    ])
    train_dataset = ColorblindDataset(args.dataset_dir, mode='train', transform=transform)
    val_dataset = ColorblindDataset(args.dataset_dir, mode='val', transform=transform)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=4, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=4, shuffle=False)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    generator = Generator().to(device)
    discriminator = Discriminator().to(device)
    if args.generator_model_weight_path:
        print(f"Loading generator weights from {args.generator_model_weight_path}")
        generator.load_state_dict(torch.load(args.generator_model_weight_path))
    if args.discriminator_model_weight_path:
        print(f"Loading discriminator weights from {args.discriminator_model_weight_path}")
        discriminator.load_state_dict(torch.load(args.discriminator_model_weight_path))
    optimizer_G = optim.Adam(generator.parameters(), lr=args.generator_learning_rate, betas=(0.5, 0.999))
    optimizer_D = optim.Adam(discriminator.parameters(), lr=args.discriminator_learning_rate, betas=(0.5, 0.999))
    lambda_gp = args.lambda_gp
    lambda_pixel = args.lambda_pixel
    n_critic = args.n_critic
    train_metrics = []
    val_metrics = []
    for epoch in range(1, args.num_epochs + 1):
        train_g_loss, train_d_loss, train_iou, train_psnr, train_ssim = train_correction_model(
            generator, discriminator, train_loader, optimizer_G, optimizer_D, device, lambda_gp, lambda_pixel, n_critic)
        val_g_loss, val_d_loss, val_iou, val_psnr, val_ssim = validate_correction_model(
            generator, discriminator, val_loader, device, lambda_gp)
        train_metrics.append((train_g_loss, train_d_loss, train_iou, train_psnr, train_ssim))
        val_metrics.append((val_g_loss, val_d_loss, val_iou, val_psnr, val_ssim))
        print(f'Epoch {epoch}, Generator Training Loss: {train_g_loss:.4f}, Discriminator Training Loss: {train_d_loss:.4f}, '
              f'IoU: {train_iou:.4f}, PSNR: {train_psnr:.4f}, SSIM: {train_ssim:.4f}')
        print(f'Epoch {epoch}, Generator Validation Loss: {val_g_loss:.4f}, Discriminator Validation Loss: {val_d_loss:.4f}, '
              f'IoU: {val_iou:.4f}, PSNR: {val_psnr:.4f}, SSIM: {val_ssim:.4f}')
        plot_and_save_metrics(train_metrics, val_metrics, epoch)
        torch.save(generator.state_dict(), args.generator_model_save_path)
        torch.save(discriminator.state_dict(), args.discriminator_model_save_path)
        visualize_results(generator, train_loader, device, save_path='./results/train')
        visualize_results(generator, val_loader, device, save_path='./results/val')

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Correction Model with WGAN-GP")
    parser.add_argument('--dataset_dir', type=str, default='./dataset', help='数据集目录路径')
    parser.add_argument('--batch_size', type=int, default=10, help='训练批次大小')
    parser.add_argument('--generator_learning_rate', type=float, default=0.001, help='优化器学习率')
    parser.add_argument('--discriminator_learning_rate', type=float, default=0.0001, help='优化器学习率')
    parser.add_argument('--lambda_pixel', type=float, default=10.0, help='像素级损失的权重')
    parser.add_argument('--lambda_gp', type=float, default=10.0, help='梯度惩罚系数')
    parser.add_argument('--n_critic', type=int, default=8, help='判别器更新次数')
    parser.add_argument('--num_epochs', type=int, default=100, help='训练的轮数')
    parser.add_argument('--generator_model_save_path', type=str, default='./model_weights/generator.pth', help='保存训练好的生成器模型的路径')
    parser.add_argument('--discriminator_model_save_path', type=str, default='./model_weights/discriminator.pth', help='保存训练好的判别器模型的路径')
    parser.add_argument('--generator_model_weight_path', type=str, default='', help='预训练的生成器模型的路径')
    parser.add_argument('--discriminator_model_weight_path', type=str, default='', help='预训练的判别器模型的路径')
    args = parser.parse_args()
    main(args)

这段代码包括了 `validate_correction_model`、`visualize_results` 和 `plot_and_save_metrics` 函数，以及 `main` 函数的剩余部分。这些函数分别用于验证模型性能、可视化结果和绘制并保存指标图。`main` 函数则负责整体的训练流程控制。

self-attention gan 代码_GAN+异常检测

以下是 Self-Attention GAN 代码和 GAN+异常检测的代码示例：

Self-Attention GAN 代码：

import torch.nn as nn
import torch

class SelfAttention(nn.Module):
    def __init__(self, in_channels):
        super(SelfAttention, self).__init__()

        self.query_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels // 8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels // 8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=1)

        self.gamma = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        m_batchsize, C, width, height = x.size()
        proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1)
        proj_key = self.key_conv(x).view(m_batchsize, -1, width * height)
        energy = torch.bmm(proj_query, proj_key)
        attention = torch.softmax(energy, dim=-1)
        proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)

        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(m_batchsize, C, width, height)

        out = self.gamma * out + x

        return out

GAN+异常检测代码：

import torch.nn as nn
import torch
import numpy as np

class Generator(nn.Module):
    def __init__(self, latent_dim, img_shape):
        super(Generator, self).__init__()

        self.img_shape = img_shape

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *self.img_shape)
        return img

class Discriminator(nn.Module):
    def __init__(self, img_shape):
        super(Discriminator, self).__init__()

        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 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, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)
        return validity

class GAN_Anomaly_Detector(nn.Module):
    def __init__(self, latent_dim, img_shape):
        super(GAN_Anomaly_Detector, self).__init__()

        self.generator = Generator(latent_dim, img_shape)
        self.discriminator = Discriminator(img_shape)

    def forward(self, x):
        z = torch.randn(x.shape[0], LATENT_DIM, device=device)
        gen_imgs = self.generator(z)
        validity_real = self.discriminator(x)
        validity_fake = self.discriminator(gen_imgs)

        return torch.mean(torch.abs(x - gen_imgs)) + valid_loss(validity_real, validity_fake)

def valid_loss(validity_real, validity_fake):
    real_loss = nn.functional.binary_cross_entropy(validity_real, torch.ones_like(validity_real))
    fake_loss = nn.functional.binary_cross_entropy(validity_fake, torch.zeros_like(validity_fake))
    return (real_loss + fake_loss) / 2

这里的 GAN+异常检测是通过计算生成图像与输入图像之间的差异以及判别器的输出来进行异常检测。如果生成的图像与输入图像越接近，则相似度越高，否则就是异常。