Python和PyTorch库实现基于生成对抗网络（GAN）将小纹理合成大纹理的详细步骤及代码示例

以下是使用Python和PyTorch库实现基于生成对抗网络（GAN）将小纹理合成大纹理的详细步骤及代码示例。

思路概述

我们将使用生成对抗网络（GAN）来完成小纹理到大纹理的合成任务。GAN由生成器（Generator）和判别器（Discriminator）组成。生成器的目标是生成逼真的大纹理图像，而判别器的任务是区分生成的图像和真实的大纹理图像。通过两者的对抗训练，最终生成器能够学习到如何合成高质量的大纹理图像。

代码实现

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os# 定义数据集类
class TextureDataset(Dataset):def __init__(self, root_dir, transform=None):self.root_dir = root_dirself.transform = transformself.image_files = [os.path.join(root_dir, f) for f in os.listdir(root_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]def __len__(self):return len(self.image_files)def __getitem__(self, idx):image_path = self.image_files[idx]image = Image.open(image_path).convert('RGB')if self.transform:image = self.transform(image)return image# 定义生成器
class Generator(nn.Module):def __init__(self, z_dim, img_channels):super(Generator, self).__init__()self.gen = nn.Sequential(self._block(z_dim, 1024, 4, 1, 0),self._block(1024, 512, 4, 2, 1),self._block(512, 256, 4, 2, 1),self._block(256, 128, 4, 2, 1),nn.ConvTranspose2d(128, img_channels, kernel_size=4, stride=2, padding=1),nn.Tanh())def _block(self, in_channels, out_channels, kernel_size, stride, padding):return nn.Sequential(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),nn.BatchNorm2d(out_channels),nn.ReLU(True))def forward(self, x):return self.gen(x)# 定义判别器
class Discriminator(nn.Module):def __init__(self, img_channels):super(Discriminator, self).__init__()self.disc = nn.Sequential(nn.Conv2d(img_channels, 64, kernel_size=4, stride=2, padding=1),nn.LeakyReLU(0.2),self._block(64, 128, 4, 2, 1),self._block(128, 256, 4, 2, 1),self._block(256, 512, 4, 2, 1),nn.Conv2d(512, 1, kernel_size=4, stride=2, padding=0),nn.Sigmoid())def _block(self, in_channels, out_channels, kernel_size, stride, padding):return nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),nn.BatchNorm2d(out_channels),nn.LeakyReLU(0.2))def forward(self, x):return self.disc(x)# 超参数设置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
lr = 0.0002
batch_size = 32
image_size = 64
z_dim = 100
img_channels = 3
num_epochs = 50# 数据预处理
transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])# 加载数据集
dataset = TextureDataset(root_dir='path/to/your/texture/images', transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)# 初始化生成器和判别器
gen = Generator(z_dim, img_channels).to(device)
disc = Discriminator(img_channels).to(device)# 定义优化器和损失函数
opt_gen = optim.Adam(gen.parameters(), lr=lr, betas=(0.5, 0.999))
opt_disc = optim.Adam(disc.parameters(), lr=lr, betas=(0.5, 0.999))
criterion = nn.BCELoss()# 训练循环
for epoch in range(num_epochs):for i, real_images in enumerate(dataloader):real_images = real_images.to(device)### 训练判别器opt_disc.zero_grad()noise = torch.randn(batch_size, z_dim, 1, 1).to(device)fake_images = gen(noise)disc_real = disc(real_images).reshape(-1)lossD_real = criterion(disc_real, torch.ones_like(disc_real))disc_fake = disc(fake_images.detach()).reshape(-1)lossD_fake = criterion(disc_fake, torch.zeros_like(disc_fake))lossD = (lossD_real + lossD_fake) / 2lossD.backward()opt_disc.step()### 训练生成器opt_gen.zero_grad()output = disc(fake_images).reshape(-1)lossG = criterion(output, torch.ones_like(output))lossG.backward()opt_gen.step()print(f"Epoch [{epoch+1}/{num_epochs}] Loss D: {lossD.item():.4f}, Loss G: {lossG.item():.4f}")# 生成大纹理图像
num_samples = 1
noise = torch.randn(num_samples, z_dim, 1, 1).to(device)
generated_images = gen(noise)
generated_images = (generated_images + 1) / 2  # 反归一化
generated_images = generated_images.cpu().detach().permute(0, 2, 3, 1).numpy()# 显示生成的图像
plt.imshow(generated_images[0])
plt.axis('off')
plt.show()