177 lines
5.5 KiB
Python
177 lines
5.5 KiB
Python
import os
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import torch
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import torch.nn as nn
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from torchvision import transforms, utils
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from torch.utils.data import DataLoader
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from torchvision.datasets import ImageFolder
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from tqdm import tqdm
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import torch.multiprocessing
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# ----------------------------
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# 1. 判别器
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# ----------------------------
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class Detector(nn.Module):
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def __init__(self):
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super(Detector, self).__init__()
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self.model = nn.Sequential(
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# 3 x 64 x 64 -> 64 x 32 x 32
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nn.Conv2d(3, 64, 4, 2, 1, bias=False),
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nn.LeakyReLU(0.2, inplace=True),
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# 64 x 32 x 32 -> 128 x 16 x 16
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nn.Conv2d(64, 128, 4, 2, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.LeakyReLU(0.2, inplace=True),
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# 128 x 16 x 16 -> 256 x 8 x 8
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nn.Conv2d(128, 256, 4, 2, 1, bias=False),
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nn.BatchNorm2d(256),
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nn.LeakyReLU(0.2, inplace=True),
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# 256 x 8 x 8 -> 512 x 4 x 4
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nn.Conv2d(256, 512, 4, 2, 1, bias=False),
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nn.BatchNorm2d(512),
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nn.LeakyReLU(0.2, inplace=True),
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# 512 x 4 x 4 -> 1024 x 2 x 2
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nn.Conv2d(512, 1024, 4, 2, 1, bias=False),
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nn.BatchNorm2d(1024),
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nn.LeakyReLU(0.2, inplace=True),
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# 1024 x 2 x 2 -> 1 x 1 x 1
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nn.Conv2d(1024, 1, 2, 1, 0, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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return self.model(x)
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# ----------------------------
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# 2. 生成器
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# ----------------------------
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class Generator(nn.Module):
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def __init__(self, z_dim=100):
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super(Generator, self).__init__()
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self.model = nn.Sequential(
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# z -> 1024 x 4 x 4
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nn.ConvTranspose2d(z_dim, 1024, 4, 1, 0, bias=False),
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nn.BatchNorm2d(1024),
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nn.ReLU(True),
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# 1024 x 4 x 4 -> 512 x 8 x 8
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nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(True),
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# 512 x 8 x 8 -> 256 x 16 x 16
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nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(True),
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# 256 x 16 x 16 -> 128 x 32 x 32
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nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(True),
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# 128 x 32 x 32 -> 3 x 64 x 64
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nn.ConvTranspose2d(128, 3, 4, 2, 1, bias=False),
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nn.Tanh()
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)
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def forward(self, z):
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return self.model(z)
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# ----------------------------
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# 3. 数据集加载
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# ----------------------------
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transform = transforms.Compose([
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transforms.Resize(64),
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transforms.CenterCrop(64),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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])
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dataset = ImageFolder(root='./data', transform=transform)
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dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4)
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# ----------------------------
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# 4. 参数与设备
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# ----------------------------
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z_dim = 100
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num_epochs = 100
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lr = 0.002
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# ----------------------------
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# 5. 模型与优化器
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# ----------------------------
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d = Detector().to(device)
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g = Generator(z_dim=z_dim).to(device)
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criterion = nn.BCELoss()
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optimizer_d = torch.optim.Adam(d.parameters(), lr=lr, betas=(0.5, 0.999))
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optimizer_g = torch.optim.Adam(g.parameters(), lr=lr, betas=(0.5, 0.999))
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# 固定噪声用于观察训练过程
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z_fixed = torch.randn(64, z_dim, 1, 1, device=device)
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# 创建保存目录
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os.makedirs("results", exist_ok=True)
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# ----------------------------
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# 6. 训练循环
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# ----------------------------
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def train():
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for epoch in range(1, num_epochs + 1):
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loss_d_total, loss_g_total = 0, 0
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for real_images, _ in tqdm(dataloader, desc=f"Epoch {epoch}/{num_epochs}", leave=False):
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real_images = real_images.to(device)
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B = real_images.size(0)
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# 标签平滑（真实 = 0.9, 假 = 0.0）
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real_labels = torch.full((B, 1, 1, 1), 0.9, device=device)
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fake_labels = torch.zeros((B, 1, 1, 1), device=device)
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# 生成假图像
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z = torch.randn(B, z_dim, 1, 1, device=device)
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fake_images = g(z)
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# 判别器训练
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output_real = d(real_images)
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output_fake = d(fake_images.detach())
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loss_real = criterion(output_real, real_labels)
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loss_fake = criterion(output_fake, fake_labels)
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loss_d = loss_real + loss_fake
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optimizer_d.zero_grad()
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loss_d.backward()
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optimizer_d.step()
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# 生成器训练
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output = d(fake_images)
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loss_g = criterion(output, real_labels) # 欺骗D：希望D输出真
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optimizer_g.zero_grad()
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loss_g.backward()
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optimizer_g.step()
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loss_d_total += loss_d.item()
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loss_g_total += loss_g.item()
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# 平均损失
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avg_loss_d = loss_d_total / len(dataloader)
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avg_loss_g = loss_g_total / len(dataloader)
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print(f"Epoch [{epoch}/{num_epochs}] Loss_D: {avg_loss_d:.4f} Loss_G: {avg_loss_g:.4f}")
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# 每5轮保存一次生成图像
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if epoch % 5 == 0:
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with torch.no_grad():
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fake = g(z_fixed).detach().cpu()
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utils.save_image(fake, f"results/epoch_{epoch}.png", normalize=True, nrow=8)
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if __name__ == "__main__":
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torch.multiprocessing.freeze_support() # ✅ Windows 兼容性
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train() # 把训练过程封装成 main() 函数
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