基于pytorch的输入为11*11灰度图文件夹，用dcgan生成更多11*11来扩充数据集 代码

好的，以下是基于PyTorch的DCGAN生成器的代码，可以根据11x11的灰度图来生成更多的11x11灰度图来扩充数据集：

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

# 定义生成器（Generator）和判别器（Discriminator）
class Generator(nn.Module):
    def __init__(self, nz=100, ngf=64, nc=1):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)


class Discriminator(nn.Module):
    def __init__(self, ndf=64, nc=1):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input).view(-1, 1)

# 定义训练函数
def train(dataloader, generator, discriminator, optimizer_G, optimizer_D, device, epochs, save_interval):
    # 定义损失函数
    criterion = nn.BCELoss()

    # 训练过程
    for epoch in range(epochs):
        for i, data in enumerate(dataloader, 0):
            real_imgs = data[0].to(device)
            batch_size = real_imgs.size(0)
            
            # 训练判别器（Discriminator）
            optimizer_D.zero_grad()
            
            # 准备真实样本
            real_label = torch.full((batch_size,), 1, dtype=torch.float, device=device)
            
            # 计算真实样本的损失
            output = discriminator(real_imgs).view(-1)
            errD_real = criterion(output, real_label)
            
            # 生成假样本
            noise = torch.randn(batch_size, 100, 1, 1, device=device)
            fake_imgs = generator(noise)
            
            # 准备假样本
            fake_label = torch.full((batch_size,), 0, dtype=torch.float, device=device)
            
            # 计算假样本的损失
            output = discriminator(fake_imgs.detach()).view(-1)
            errD_fake = criterion(output, fake_label)
            
            # 计算判别器的总损失
            errD = errD_real + errD_fake
            
            # 反向传播更新判别器
            errD.backward()
            optimizer_D.step()
            
            # 训练生成器（Generator）
            optimizer_G.zero_grad()
            fake_label.fill_(1)
            
            # 重新计算假样本的损失，此处不修改判别器的参数
            output = discriminator(fake_imgs).view(-1)
            errG = criterion(output, fake_label)
            
            # 反向传播更新生成器
            errG.backward()
            optimizer_G.step()
            
            # 输出训练结果
            if i % 50 == 0:
                print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f'
                      % (epoch, epochs, i, len(dataloader), errD.item(), errG.item()))
            
            # 保存生成器的输出
            batches_done = epoch * len(dataloader) + i
            if batches_done % save_interval == 0:
                save_image(fake_imgs.data[:25], 'output/%d.png' % batches_done, nrow=5, normalize=True)

# 定义数据类（Data Class）
class ImageDataset(Dataset):
    def __init__(self, root, transform=None):
        self.transform = transform
        self.image_paths = [os.path.join(root, x) for x in os.listdir(root)]

    def __getitem__(self, index):
        image_path = self.image_paths[index]
        image = Image.open(image_path).convert('L')
        if self.transform is not None:
            image = self.transform(image)
        return image

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

# 设置一些超参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_size = 64
lr = 0.0002
betas = (0.5, 0.999)
epochs = 50
nz = 100
ngf = 64
ndf = 64
nc = 1
save_interval = 500

# 加载数据集
transform = transforms.Compose([
    transforms.Resize(11),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = ImageDataset(root='data_folder_path', transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=2)

# 实例化生成器和判别器，并将它们移到设备上
generator = Generator(nz=nz, ngf=ngf, nc=nc).to(device)
discriminator = Discriminator(ndf=ndf, nc=nc).to(device)

# 定义优化器
optimizer_G = optim.Adam(generator.parameters(), lr=lr, betas=betas)
optimizer_D = optim.Adam(discriminator.parameters(), lr=lr, betas=betas)

# 训练模型
train(dataloader, generator, discriminator, optimizer_G, optimizer_D, device, epochs, save_interval)

其中，'data_folder_path'需要替换为存放11x11灰度图的文件夹的路径。训练完成后，生成的11x11灰度图将保存在output文件夹下。