import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision.utils import save_image
from torchvision import transforms
from PIL import Image
import osclass SelfAttention(nn.Module):def __init__(self, in_dim):super(SelfAttention, self).__init__()self.query = nn.utils.spectral_norm(nn.Conv2d(in_dim, in_dim // 8, kernel_size=1))self.key = nn.utils.spectral_norm(nn.Conv2d(in_dim, in_dim // 8, kernel_size=1))self.value = nn.utils.spectral_norm(nn.Conv2d(in_dim, in_dim, kernel_size=1))self.gamma = nn.Parameter(torch.zeros(1))def forward(self, x):batch_size, C, width, height = x.size()proj_query = self.query(x).view(batch_size, -1, width * height).permute(0, 2, 1)proj_key = self.key(x).view(batch_size, -1, width * height)energy = torch.bmm(proj_query, proj_key)attention = F.softmax(energy, dim=-1)proj_value = self.value(x).view(batch_size, -1, width * height)out = torch.bmm(proj_value, attention.permute(0, 2, 1))out = out.view(batch_size, C, width, height)out = self.gamma * out + xreturn outclass Generator(nn.Module):def __init__(self, noise_dim, label_dim):super(Generator, self).__init__()self.label_dim = label_dimself.fc = nn.Sequential(nn.Linear(noise_dim + label_dim, 1024 * 4 * 4),nn.BatchNorm1d(1024 * 4 * 4),nn.ReLU(True))self.deconv_layers = nn.Sequential(nn.utils.spectral_norm(nn.ConvTranspose2d(1024, 512, 4, 2, 1)), # 4x4 -> 8x8nn.BatchNorm2d(512),nn.ReLU(True),SelfAttention(512),nn.utils.spectral_norm(nn.ConvTranspose2d(512, 256, 4, 2, 1)), # 8x8 -> 16x16nn.BatchNorm2d(256),nn.ReLU(True),nn.utils.spectral_norm(nn.ConvTranspose2d(256, 128, 4, 2, 1)), # 16x16 -> 32x32nn.BatchNorm2d(128),nn.ReLU(True),nn.utils.spectral_norm(nn.ConvTranspose2d(128, 64, 4, 2, 1)), # 32x32 -> 64x64nn.BatchNorm2d(64),nn.ReLU(True),SelfAttention(64),nn.utils.spectral_norm(nn.ConvTranspose2d(64, 3, 4, 2, 1)), # 64x64 -> 128x128nn.Tanh())def forward(self, noise, labels):x = torch.cat((noise, labels), dim=1)x = self.fc(x).view(-1, 1024, 4, 4)x = self.deconv_layers(x)return xclass Discriminator(nn.Module):def __init__(self, input_channels, label_dim):super(Discriminator, self).__init__()self.label_dim = label_dimself.conv1 = nn.Sequential(nn.utils.spectral_norm(nn.Conv2d(input_channels + label_dim, 64, 4, 2, 1)),nn.LeakyReLU(0.2, inplace=True))self.conv2 = nn.Sequential(nn.utils.spectral_norm(nn.Conv2d(64, 128, 4, 2, 1)),nn.LeakyReLU(0.2, inplace=True))self.conv3 = nn.Sequential(nn.utils.spectral_norm(nn.Conv2d(128, 256, 4, 2, 1)),nn.LeakyReLU(0.2, inplace=True))self.self_attn = SelfAttention(256)self.conv4 = nn.Sequential(nn.utils.spectral_norm(nn.Conv2d(256, 512, 4, 2, 1)),nn.LeakyReLU(0.2, inplace=True))self.fc = nn.utils.spectral_norm(nn.Linear(512 * 8 * 8, 1))def forward(self, x, labels):batch_size = x.size(0)img_size = x.size(2)labels = labels.view(batch_size, self.label_dim, 1, 1)labels = labels.expand(batch_size, self.label_dim, img_size, img_size)x = torch.cat([x, labels], dim=1)x = self.conv1(x)x = self.conv2(x)x = self.conv3(x)x = self.self_attn(x)x = self.conv4(x)x = x.view(batch_size, -1)x = self.fc(x)return xclass TrafficSignDataset(Dataset):def __init__(self, root_dir, labels_file, transform=None):self.root_dir = root_dirself.transform = transformself.image_paths = []self.labels = []with open(labels_file, 'r') as f:lines = f.readlines()for line in lines:img_name, label = line.strip().split()img_path = os.path.join(root_dir, img_name)self.image_paths.append(img_path)self.labels.append(int(label))def __len__(self):return len(self.image_paths)def __getitem__(self, idx):img_path = self.image_paths[idx]image = Image.open(img_path).convert('RGB')label = self.labels[idx]if self.transform:image = self.transform(image)return image, label# 设置超参数
noise_dim = 100 # 噪声维度
label_dim = 58 # 标签维度
batch_size =8 # 批大小
lr = 2e-4
num_epochs = 500
n_critic = 5
lambda_gp = 10
output_dir = r"C:\Users\sun\Desktop\2024102201\out" # 生成图像保存路径if not os.path.exists(output_dir):os.makedirs(output_dir)G = Generator(noise_dim=noise_dim, label_dim=label_dim).to('cuda')
D = Discriminator(input_channels=3, label_dim=label_dim).to('cuda')beta1 = 0.0
beta2 = 0.9
optimizer_G = optim.Adam(G.parameters(), lr=lr, betas=(beta1, beta2))
optimizer_D = optim.Adam(D.parameters(), lr=lr, betas=(beta1, beta2))
scheduler_G = optim.lr_scheduler.StepLR(optimizer_G, step_size=50, gamma=0.5)
scheduler_D = optim.lr_scheduler.StepLR(optimizer_D, step_size=50, gamma=0.5)transform = transforms.Compose([transforms.Resize((128, 128)),transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))
])root_dir = r"C:\Users\sun\Desktop\2024102201\1"
labels_file = r"C:\Users\sun\Desktop\2024102201\1\labels.txt" # 标签文件路径
dataset = TrafficSignDataset(root_dir=root_dir, labels_file=labels_file, transform=transform)
# dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)def discriminator_hinge_loss(real_outputs, fake_outputs):real_loss = torch.mean(F.relu(1.0 - real_outputs))fake_loss = torch.mean(F.relu(1.0 + fake_outputs))return real_loss + fake_lossdef generator_hinge_loss(fake_outputs):return -torch.mean(fake_outputs)def compute_gradient_penalty(D, real_samples, fake_samples, labels):alpha = torch.rand(real_samples.size(0), 1, 1, 1).to(real_samples.device)interpolates = (alpha * real_samples + (1 - alpha) * fake_samples).requires_grad_(True)d_interpolates = D(interpolates, labels)fake = torch.ones(d_interpolates.size()).to(real_samples.device)gradients = torch.autograd.grad(outputs=d_interpolates,inputs=interpolates,grad_outputs=fake,create_graph=True,retain_graph=True,only_inputs=True)[0]gradients = gradients.view(gradients.size(0), -1)gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()return gradient_penaltyfixed_noise = torch.randn(64, noise_dim).to('cuda')
fixed_labels_idx = torch.arange(0, label_dim).repeat(64 // label_dim + 1)[:64].to('cuda')
fixed_labels_one_hot = torch.zeros(64, label_dim).to('cuda')
fixed_labels_one_hot.scatter_(1, fixed_labels_idx.view(-1, 1), 1)for epoch in range(num_epochs):for i, (real_images, real_labels_idx) in enumerate(dataloader):real_images = real_images.to('cuda')real_labels_idx = real_labels_idx.to('cuda')batch_size_current = real_images.size(0)real_labels_one_hot = torch.zeros(batch_size_current, label_dim).to('cuda')real_labels_one_hot.scatter_(1, real_labels_idx.view(-1, 1), 1)optimizer_D.zero_grad()noise = torch.randn(batch_size_current, noise_dim).to('cuda')fake_labels_idx = torch.randint(0, label_dim, (batch_size_current,)).to('cuda')fake_labels_one_hot = torch.zeros(batch_size_current, label_dim).to('cuda')fake_labels_one_hot.scatter_(1, fake_labels_idx.view(-1, 1), 1)fake_images = G(noise, fake_labels_one_hot)real_outputs = D(real_images, real_labels_one_hot)fake_outputs = D(fake_images.detach(), fake_labels_one_hot)d_loss = discriminator_hinge_loss(real_outputs, fake_outputs)gradient_penalty = compute_gradient_penalty(D, real_images, fake_images.detach(), real_labels_one_hot)d_loss += lambda_gp * gradient_penaltyd_loss.backward()optimizer_D.step()if i % n_critic == 0:optimizer_G.zero_grad()fake_outputs = D(fake_images, fake_labels_one_hot)g_loss = generator_hinge_loss(fake_outputs)g_loss.backward()optimizer_G.step()scheduler_G.step()scheduler_D.step()print(f"Epoch [{epoch + 1}/{num_epochs}], D Loss: {d_loss.item():.4f}, G Loss: {g_loss.item():.4f}")with torch.no_grad():fake_images = G(fixed_noise, fixed_labels_one_hot)save_image(fake_images, os.path.join(output_dir, f"epoch_{epoch + 1}.png"), nrow=8, normalize=True)torch.save(G.state_dict(), 'generator.pth')
torch.save(D.state_dict(), 'discriminator.pth')
