LGGAN (Latent GAN with Guided attention)的论文名称是什么

LGGAN (Latent GAN with Guided attention)的论文名称是《LGGAN: Latent Guided GAN for Generating High Fidelity Images》。

相关问题

GAN加入attention代码

生成对抗网络（GAN）是一种深度学习模型，通常用于生成具有特定属性的图像、音频和文本等内容。GAN使用两个神经网络：生成器和判别器。生成器从噪声中生成假数据，判别器则尝试区分真实数据和假数据。两个网络相互博弈，直到生成器可以生成无法被判别器区分的真实数据为止。

在GAN中引入注意力机制，可以帮助生成器更好地关注图像中的重要部分，从而生成更准确的图像。一种常见的注意力机制是self-attention，它可以帮助生成器更好地捕捉图像中的全局信息。下面是使用self-attention的GAN代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F

class SelfAttention(nn.Module):
    def __init__(self, in_dim):
        super(SelfAttention, self).__init__()
        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros(1))
        self.softmax = nn.Softmax(dim=-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 = self.softmax(energy)
        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

class Generator(nn.Module):
    def __init__(self, latent_dim=100):
        super(Generator, self).__init__()
        self.latent_dim = latent_dim
        self.fc = nn.Sequential(
            nn.Linear(latent_dim, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 1024),
            nn.LeakyReLU(0.2, inplace=True),
        )
        self.conv = nn.Sequential(
            nn.ConvTranspose2d(1024, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            SelfAttention(256),
            nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.ConvTranspose2d(128, 1, 4, stride=2, padding=1),
            nn.Tanh(),
        )

    def forward(self, z):
        x = self.fc(z)
        x = x.view(-1, 1024, 1, 1)
        x = self.conv(x)
        return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 64, 4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            SelfAttention(256),
            nn.Conv2d(256, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 1, 4, stride=1, padding=0),
        )

    def forward(self, x):
        x = self.conv(x)
        x = x.view(-1, 1)
        return x

# 训练GAN
latent_dim = 100
batch_size = 64
epochs = 200

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])

train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

generator = Generator(latent_dim).to(device)
discriminator = Discriminator().to(device)

optimizer_G = optim.Adam(generator.parameters(), lr=0.0002)
optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002)

criterion = nn.BCEWithLogitsLoss()

real_label = 1
fake_label = 0

for epoch in range(epochs):
    for i, (real_images, _) in enumerate(train_loader):
        # 训练判别器
        discriminator.zero_grad()
        real_images = real_images.to(device)
        batch_size = real_images.size(0)
        labels = torch.full((batch_size,), real_label, dtype=torch.float, device=device)
        output = discriminator(real_images)
        error_real = criterion(output, labels)
        error_real.backward()

        noise = torch.randn(batch_size, latent_dim, device=device)
        fake_images = generator(noise)
        labels.fill_(fake_label)
        output = discriminator(fake_images.detach())
        error_fake = criterion(output, labels)
        error_fake.backward()
        optimizer_D.step()

        # 训练生成器
        generator.zero_grad()
        labels.fill_(real_label)
        output = discriminator(fake_images)
        error_G = criterion(output, labels)
        error_G.backward()
        optimizer_G.step()

        if i % 100 == 0:
            print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
                  % (epoch+1, epochs, i+1, len(train_loader), error_fake+error_real, error_G))

    # 每个epoch结束后保存模型
    torch.save(generator.state_dict(), 'gan_generator.pth')
    torch.save(discriminator.state_dict(), 'gan_discriminator.pth')

在上面的代码中，我们定义了一个SelfAttention类来实现注意力机制，并将其添加到了生成器和判别器的架构中。在生成器中，我们添加了一个SelfAttention层来帮助生成器更好地捕捉图像中的全局信息。在判别器中，我们也添加了一个SelfAttention层来帮助判别器更好地关注图像中的重要部分。

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。