fake_imgs = generator(torch.randn(batch_size, 3, 1, 1,device=device))把以上代码输入改成带遮罩的图片

如果要生成带遮罩的图片，可以考虑使用掩模（mask）技术，即将需要生成的部位使用一个掩模矩阵遮住，这样生成的图片只会在掩模的范围内产生效果，掩模范围之外的区域则会保留原始图像的像素值。

具体来说，可以将掩模矩阵与随机噪声向量相乘，得到带有遮罩的随机噪声向量，然后将该向量输入生成器中，即可生成带有遮罩的图片。

代码示例如下：

# 创建一个 3×1×1 的掩模矩阵，其中掩模范围为（0，0）到（0.5，1）
mask = torch.zeros(batch_size, 3, 1, 1, device=device)
mask[:, :, :int(0.5*img_size[1]), :] = 1

# 生成带遮罩的随机噪声向量
masked_noise = torch.randn(batch_size, 3, img_size[1], img_size[2], device=device) * mask

# 使用带遮罩的随机噪声向量生成图片
fake_imgs = generator(masked_noise)

相关问题

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。

基于pytorch的LSTM-GAN读取11*11灰度图像文件夹并保存模型，后加载模型，生成的每个不同图像保存 代码实现

好的，以下是代码实现：

1. 读取11*11灰度图像文件夹

import os
from PIL import Image
import numpy as np

def load_data(data_dir):
    data = []
    for filename in os.listdir(data_dir):
        if filename.endswith('.jpg'):
            img = Image.open(os.path.join(data_dir, filename)).convert('L')
            img = img.resize((11, 11), Image.ANTIALIAS)
            img_data = np.asarray(img, dtype=np.uint8)
            img_data = img_data / 255.0  # 归一化到[0,1]之间
            data.append(img_data)
    return np.array(data)

2. 定义LSTM-GAN模型

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(Generator, self).__init__()
        self.hidden_size = hidden_size
        self.output_size = output_size

        self.lstm = nn.LSTMCell(output_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x, hidden):
        h, c = self.lstm(x, hidden)
        x = self.fc(h)
        return x, (h, c)
    
class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Discriminator, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

        self.lstm = nn.LSTMCell(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, 1)

    def forward(self, x, hidden):
        h, c = self.lstm(x, hidden)
        x = self.fc(h)
        return x, (h, c)

3. 训练LSTM-GAN模型并保存

import torch.optim as optim

# 加载数据
data_dir = './data'
data = load_data(data_dir)

# 定义超参数
input_size = 1
hidden_size = 32
output_size = 121
batch_size = 64
num_epochs = 100
lr = 0.001

# 定义模型
generator = Generator(hidden_size, output_size)
discriminator = Discriminator(input_size, hidden_size)

# 定义优化器和损失函数
g_optimizer = optim.Adam(generator.parameters(), lr=lr)
d_optimizer = optim.Adam(discriminator.parameters(), lr=lr)
criterion = nn.BCEWithLogitsLoss()

# 训练模型
for epoch in range(num_epochs):
    for i in range(0, data.shape[0] - batch_size, batch_size):
        # 生成fake样本
        z = torch.randn(batch_size, hidden_size)
        fake_imgs = []
        hidden = (torch.zeros(batch_size, hidden_size), torch.zeros(batch_size, hidden_size))
        for j in range(output_size):
            x, hidden = generator(z, hidden)
            fake_imgs.append(x)
        fake_imgs = torch.stack(fake_imgs, dim=1)

        # 训练判别器
        real_imgs = torch.from_numpy(data[i:i+batch_size]).float().unsqueeze(1)
        real_labels = torch.ones(batch_size, 1)
        fake_labels = torch.zeros(batch_size, 1)
        d_optimizer.zero_grad()
        real_logits, _ = discriminator(real_imgs, hidden)
        d_loss_real = criterion(real_logits, real_labels)
        fake_logits, _ = discriminator(fake_imgs.detach(), hidden)
        d_loss_fake = criterion(fake_logits, fake_labels)
        d_loss = d_loss_real + d_loss_fake
        d_loss.backward()
        d_optimizer.step()

        # 训练生成器
        g_optimizer.zero_grad()
        z = torch.randn(batch_size, hidden_size)
        fake_imgs = []
        hidden = (torch.zeros(batch_size, hidden_size), torch.zeros(batch_size, hidden_size))
        for j in range(output_size):
            x, hidden = generator(z, hidden)
            fake_imgs.append(x)
        fake_imgs = torch.stack(fake_imgs, dim=1)
        fake_logits, _ = discriminator(fake_imgs, hidden)
        g_loss = criterion(fake_logits, real_labels)
        g_loss.backward()
        g_optimizer.step()

    print('Epoch [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}'.format(epoch+1, num_epochs, d_loss.item(), g_loss.item()))

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

4. 加载模型并生成不同图像

import matplotlib.pyplot as plt

# 加载模型
generator = Generator(hidden_size, output_size)
generator.load_state_dict(torch.load('generator.pth'))

# 生成不同图像
n_samples = 10
z = torch.randn(n_samples, hidden_size)
samples = []
hidden = (torch.zeros(n_samples, hidden_size), torch.zeros(n_samples, hidden_size))
for i in range(output_size):
    x, hidden = generator(z, hidden)
    samples.append(x.detach().numpy())
samples = np.stack(samples, axis=1)

# 显示图像
fig, axes = plt.subplots(nrows=n_samples, ncols=1, figsize=(10, 10))
for i, ax in enumerate(axes):
    ax.imshow(samples[i].reshape(11, 11), cmap='gray')
plt.show()

希望能够帮助到你！