输入为1111大小数据集用wgan生成1111灰度图代码

以下是一个简单的 WGAN-GP 的 PyTorch 实现，可以用来训练一个生成 1111 大小的灰度图数据集。请注意，这只是一个示例，需要根据实际情况进行修改和调整。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.utils import save_image
import os

# 定义超参数
batch_size = 64
lr = 0.0001
latent_dim = 100
img_size = 1111
channels = 1
n_epochs = 200
n_critic = 5
clip_value = 0.01
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 定义数据处理
transform = transforms.Compose([
    transforms.Resize(img_size),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = datasets.ImageFolder(root='path/to/dataset', transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 定义生成器和判别器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(1024, img_size * channels),
            nn.Tanh()
        )

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

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(img_size * channels, 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)
        return validity

# 初始化生成器和判别器
generator = Generator().to(device)
discriminator = Discriminator().to(device)

# 定义损失函数和优化器
adversarial_loss = nn.MSELoss()
optimizer_G = optim.RMSprop(generator.parameters(), lr=lr)
optimizer_D = optim.RMSprop(discriminator.parameters(), lr=lr)

# 训练
for epoch in range(n_epochs):
    for i, (imgs, _) in enumerate(dataloader):
        # 训练判别器
        for _ in range(n_critic):
            # 生成随机噪声
            z = torch.randn(imgs.shape[0], latent_dim).to(device)
            # 生成假图片
            gen_imgs = generator(z)
            # 计算损失
            d_loss = -torch.mean(discriminator(imgs.to(device))) + torch.mean(discriminator(gen_imgs.detach()))
            # 反向传播和优化
            optimizer_D.zero_grad()
            d_loss.backward()
            optimizer_D.step()
            # 截断权重
            for p in discriminator.parameters():
                p.data.clamp_(-clip_value, clip_value)

        # 训练生成器
        # 生成随机噪声
        z = torch.randn(imgs.shape[0], latent_dim).to(device)
        # 生成假图片
        gen_imgs = generator(z)
        # 计算损失
        g_loss = -torch.mean(discriminator(gen_imgs))
        # 反向传播和优化
        optimizer_G.zero_grad()
        g_loss.backward()
        optimizer_G.step()

        # 打印训练信息
        if i % 10 == 0:
            print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()))

    # 保存生成器的输出
    if epoch % 10 == 0:
        os.makedirs('output', exist_ok=True)
        save_image(gen_imgs.data[:25], 'output/%d.png' % epoch, nrow=5, normalize=True)

请根据实际情况修改代码中的数据路径、超参数等内容。