From 33b72751ab11c6794bfbe204fdb56ca47013f48c Mon Sep 17 00:00:00 2001
From: e2hang <2099307493@qq.com>
Date: Tue, 28 Oct 2025 22:27:20 +0800
Subject: [PATCH] DCGAN
---
Pytorch/Project/DCGAN/.idea/DCGAN.iml | 8 +
.../inspectionProfiles/Project_Default.xml | 266 ++++++++++++++++++
.../inspectionProfiles/profiles_settings.xml | 6 +
Pytorch/Project/DCGAN/.idea/misc.xml | 7 +
Pytorch/Project/DCGAN/.idea/modules.xml | 8 +
Pytorch/Project/DCGAN/.idea/workspace.xml | 53 ++++
Pytorch/Project/DCGAN/main.py | 245 ++++++++++++++++
Pytorch/Project/DCGAN/test.py | 176 ++++++++++++
8 files changed, 769 insertions(+)
create mode 100644 Pytorch/Project/DCGAN/.idea/DCGAN.iml
create mode 100644 Pytorch/Project/DCGAN/.idea/inspectionProfiles/Project_Default.xml
create mode 100644 Pytorch/Project/DCGAN/.idea/inspectionProfiles/profiles_settings.xml
create mode 100644 Pytorch/Project/DCGAN/.idea/misc.xml
create mode 100644 Pytorch/Project/DCGAN/.idea/modules.xml
create mode 100644 Pytorch/Project/DCGAN/.idea/workspace.xml
create mode 100644 Pytorch/Project/DCGAN/main.py
create mode 100644 Pytorch/Project/DCGAN/test.py
diff --git a/Pytorch/Project/DCGAN/.idea/DCGAN.iml b/Pytorch/Project/DCGAN/.idea/DCGAN.iml
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diff --git a/Pytorch/Project/DCGAN/.idea/inspectionProfiles/Project_Default.xml b/Pytorch/Project/DCGAN/.idea/inspectionProfiles/Project_Default.xml
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diff --git a/Pytorch/Project/DCGAN/.idea/inspectionProfiles/profiles_settings.xml b/Pytorch/Project/DCGAN/.idea/inspectionProfiles/profiles_settings.xml
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diff --git a/Pytorch/Project/DCGAN/.idea/misc.xml b/Pytorch/Project/DCGAN/.idea/misc.xml
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diff --git a/Pytorch/Project/DCGAN/.idea/modules.xml b/Pytorch/Project/DCGAN/.idea/modules.xml
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diff --git a/Pytorch/Project/DCGAN/.idea/workspace.xml b/Pytorch/Project/DCGAN/.idea/workspace.xml
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diff --git a/Pytorch/Project/DCGAN/main.py b/Pytorch/Project/DCGAN/main.py
new file mode 100644
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+++ b/Pytorch/Project/DCGAN/main.py
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+import os
+import torch
+import torch.nn as nn
+from torchvision import transforms, utils
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
+import torch.multiprocessing
+from PIL import Image # 自定义数据集需要 import PIL
+import matplotlib.pyplot as plt
+
+
+# 判别器
+class Detector(nn.Module):
+ def __init__(self):
+ super(Detector, self).__init__()
+ self.model = nn.Sequential(
+ # 3 x 64 x 64 -> 64 x 32 x 32
+ nn.Conv2d(3, 64, 4, 2, 1, bias=False),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 64 x 32 x 32 -> 128 x 16 x 16
+ nn.Conv2d(64, 128, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(128),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 128 x 16 x 16 -> 256 x 8 x 8
+ nn.Conv2d(128, 256, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 256 x 8 x 8 -> 512 x 4 x 4
+ nn.Conv2d(256, 512, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(512),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 512 x 4 x 4 -> 1024 x 2 x 2
+ nn.Conv2d(512, 1024, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(1024),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 1024 x 2 x 2 -> 1 x 1 x 1
+ nn.Conv2d(1024, 1, 2, 1, 0, bias=False),
+ nn.Sigmoid()
+ )
+
+ def forward(self, x):
+ return self.model(x)
+
+
+# 生成器
+class Generator(nn.Module):
+ def __init__(self, z_dim=100):
+ super(Generator, self).__init__()
+ self.model = nn.Sequential(
+ # z -> 1024 x 4 x 4
+ nn.ConvTranspose2d(z_dim, 1024, 4, 1, 0, bias=False),
+ nn.BatchNorm2d(1024),
+ nn.ReLU(True),
+
+ # 1024 x 4 x 4 -> 512 x 8 x 8
+ nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(512),
+ nn.ReLU(True),
+
+ # 512 x 8 x 8 -> 256 x 16 x 16
+ nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.ReLU(True),
+
+ # 256 x 16 x 16 -> 128 x 32 x 32
+ nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(128),
+ nn.ReLU(True),
+
+ # 128 x 32 x 32 -> 3 x 64 x 64
+ nn.ConvTranspose2d(128, 3, 4, 2, 1, bias=False),
+ nn.Tanh()
+ )
+
+ def forward(self, z):
+ return self.model(z)
+
+
+# 数据集加载（使用自定义路径 ./data/images）
+class FlatImageDataset(Dataset):
+ def __init__(self, root_dir, transform=None):
+ self.root_dir = root_dir
+ self.transform = transform
+ if not os.path.exists(root_dir):
+ raise FileNotFoundError(f"Dataset directory '{root_dir}' not found. Please create it and add images.")
+ self.image_files = [f for f in os.listdir(root_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp'))]
+ if len(self.image_files) == 0:
+ raise ValueError(f"No valid image files found in '{root_dir}'. Supported: .png, .jpg, .jpeg, .bmp")
+ self.image_paths = [os.path.join(root_dir, f) for f in self.image_files]
+
+ 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') # 确保转为 RGB（3 通道）
+ if self.transform:
+ image = self.transform(image)
+ return image, 0 # 返回图像和虚拟标签（GAN 不使用）
+
+
+# 使用自定义数据集
+transform = transforms.Compose([
+ transforms.Resize(64),
+ transforms.CenterCrop(64),
+ transforms.ToTensor(),
+ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+# 加载自定义数据集
+dataset = FlatImageDataset(root_dir='./data/images', transform=transform)
+print(f"Loaded {len(dataset)} images from ./data/images") # 调试：打印数据集大小
+dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=2) # 减小 num_workers 避免 Windows 问题
+
+
+# 参数
+z_dim = 100
+num_epochs = 100 # 6.2w
+lr_d = 0.001
+lr_g = 0.002
+n_g_steps = 2 # 标准 DCGAN是1步G
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# 模型与优化器
+d = Detector().to(device)
+g = Generator(z_dim=z_dim).to(device)
+criterion = nn.BCELoss()
+optimizer_d = torch.optim.Adam(d.parameters(), lr=lr_d, betas=(0.5, 0.999))
+optimizer_g = torch.optim.Adam(g.parameters(), lr=lr_g, betas=(0.5, 0.999))
+
+# 固定噪声用于观察训练过程
+z_fixed = torch.randn(64, z_dim, 1, 1, device=device)
+
+# 创建保存目录
+os.makedirs("results", exist_ok=True)
+
+
+# 根据保存的 G dict 生成图片的函数
+def generate_from_g_dict(model_path, z_dim=100, num_images=64, output_path='generated.png'):
+ """
+ 从保存的生成器 state_dict 文件加载模型，并生成图片保存。
+ """
+ if not os.path.exists(model_path):
+ print(f"Model path '{model_path}' not found. Skipping generation.")
+ return
+ # 加载生成器并恢复权重
+ g_loaded = Generator(z_dim=z_dim).to(device)
+ g_loaded.load_state_dict(torch.load(model_path, map_location=device))
+ g_loaded.eval()
+
+ # 生成假图像
+ with torch.no_grad():
+ z = torch.randn(num_images, z_dim, 1, 1, device=device)
+ fake_images = g_loaded(z).detach().cpu()
+
+ # 保存图片
+ utils.save_image(fake_images, output_path, normalize=True, nrow=8)
+ print(f"Generated images saved to {output_path}")
+
+
+# 训练循环
+def train():
+ for epoch in range(1, num_epochs + 1):
+ loss_d_total, loss_g_total = 0, 0
+ for real_images, _ in tqdm(dataloader, desc=f"Epoch {epoch}/{num_epochs}", leave=False):
+ real_images = real_images.to(device)
+ B = real_images.size(0)
+
+ # 标签平滑（真实 = 0.9, 假 = 0.0）
+ real_labels = torch.full((B, 1, 1, 1), 0.9, device=device)
+ fake_labels = torch.full((B, 1, 1, 1), 0.0, device=device)
+
+ # 生成假图像
+ z = torch.randn(B, z_dim, 1, 1, device=device)
+ fake_images = g(z)
+
+ # 判别器训练
+ output_real = d(real_images)
+ output_fake = d(fake_images.detach())
+
+ loss_real = criterion(output_real, real_labels)
+ loss_fake = criterion(output_fake, fake_labels)
+ loss_d = loss_real + loss_fake
+
+ optimizer_d.zero_grad()
+ loss_d.backward()
+ optimizer_d.step()
+
+ # 生成器训练（标准 BCE）
+ for _ in range(n_g_steps):
+ #每次生成器更新前重新生成假图像
+ z = torch.randn(B, z_dim, 1, 1, device=device)
+ fake_images = g(z)
+
+ output = d(fake_images)
+ loss_g = criterion(output, real_labels) # 欺骗 D：希望 D 输出真
+
+ optimizer_g.zero_grad()
+ loss_g.backward()
+ optimizer_g.step()
+ loss_g_total += loss_g.item()
+
+ loss_d_total += loss_d.item()
+
+ # 平均损失（G 损失已累加 n_g_steps 次）
+ avg_loss_d = loss_d_total / len(dataloader)
+ avg_loss_g = loss_g_total / (len(dataloader) * n_g_steps) # 修复：除以总 G 步数
+ print(f"Epoch [{epoch}/{num_epochs}] Loss_D: {avg_loss_d:.4f} Loss_G: {avg_loss_g:.4f}")
+
+ loss_history = {"D": [], "G": []}
+
+ # 每轮结束时：
+ loss_history["D"].append(avg_loss_d)
+ loss_history["G"].append(avg_loss_g)
+
+ # 最后画图：
+ plt.plot(loss_history["D"], label="Loss_D")
+ plt.plot(loss_history["G"], label="Loss_G")
+ plt.legend()
+ plt.savefig("results/loss_curve.png")
+
+ # 每2轮保存一次生成图像和 G 的 state_dict
+ if epoch % 2 == 0:
+ with torch.no_grad():
+ fake = g(z_fixed).detach().cpu()
+ utils.save_image(fake, f"results/epoch_{epoch}.png", normalize=True, nrow=8)
+
+ # 保存 G 的 state_dict（dict 形式）
+ g_state_dict_path = f"results/g_epoch_{epoch}.pth"
+ torch.save(g.state_dict(), g_state_dict_path)
+ print(f"Generator state_dict saved to {g_state_dict_path}")
+
+
+if __name__ == "__main__":
+ torch.multiprocessing.freeze_support()
+ #train()
+ for i in range(100):
+ text = 'results/generated_after_train' + str(i) + '.png'
+ generate_from_g_dict('results/g_epoch_85.pth', output_path=text)
\ No newline at end of file
diff --git a/Pytorch/Project/DCGAN/test.py b/Pytorch/Project/DCGAN/test.py
new file mode 100644
index 0000000..deeff7f
--- /dev/null
+++ b/Pytorch/Project/DCGAN/test.py
@@ -0,0 +1,176 @@
+import os
+import torch
+import torch.nn as nn
+from torchvision import transforms, utils
+from torch.utils.data import DataLoader
+from torchvision.datasets import ImageFolder
+from tqdm import tqdm
+import torch.multiprocessing
+
+
+# ----------------------------
+# 1. 判别器
+# ----------------------------
+class Detector(nn.Module):
+ def __init__(self):
+ super(Detector, self).__init__()
+ self.model = nn.Sequential(
+ # 3 x 64 x 64 -> 64 x 32 x 32
+ nn.Conv2d(3, 64, 4, 2, 1, bias=False),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 64 x 32 x 32 -> 128 x 16 x 16
+ nn.Conv2d(64, 128, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(128),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 128 x 16 x 16 -> 256 x 8 x 8
+ nn.Conv2d(128, 256, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 256 x 8 x 8 -> 512 x 4 x 4
+ nn.Conv2d(256, 512, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(512),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 512 x 4 x 4 -> 1024 x 2 x 2
+ nn.Conv2d(512, 1024, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(1024),
+ nn.LeakyReLU(0.2, inplace=True),
+
+ # 1024 x 2 x 2 -> 1 x 1 x 1
+ nn.Conv2d(1024, 1, 2, 1, 0, bias=False),
+ nn.Sigmoid()
+ )
+
+ def forward(self, x):
+ return self.model(x)
+
+
+# ----------------------------
+# 2. 生成器
+# ----------------------------
+class Generator(nn.Module):
+ def __init__(self, z_dim=100):
+ super(Generator, self).__init__()
+ self.model = nn.Sequential(
+ # z -> 1024 x 4 x 4
+ nn.ConvTranspose2d(z_dim, 1024, 4, 1, 0, bias=False),
+ nn.BatchNorm2d(1024),
+ nn.ReLU(True),
+
+ # 1024 x 4 x 4 -> 512 x 8 x 8
+ nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(512),
+ nn.ReLU(True),
+
+ # 512 x 8 x 8 -> 256 x 16 x 16
+ nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(256),
+ nn.ReLU(True),
+
+ # 256 x 16 x 16 -> 128 x 32 x 32
+ nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
+ nn.BatchNorm2d(128),
+ nn.ReLU(True),
+
+ # 128 x 32 x 32 -> 3 x 64 x 64
+ nn.ConvTranspose2d(128, 3, 4, 2, 1, bias=False),
+ nn.Tanh()
+ )
+
+ def forward(self, z):
+ return self.model(z)
+
+
+# ----------------------------
+# 3. 数据集加载
+# ----------------------------
+transform = transforms.Compose([
+ transforms.Resize(64),
+ transforms.CenterCrop(64),
+ transforms.ToTensor(),
+ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+dataset = ImageFolder(root='./data', transform=transform)
+dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4)
+
+# ----------------------------
+# 4. 参数与设备
+# ----------------------------
+z_dim = 100
+num_epochs = 100
+lr = 0.002
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ----------------------------
+# 5. 模型与优化器
+# ----------------------------
+d = Detector().to(device)
+g = Generator(z_dim=z_dim).to(device)
+criterion = nn.BCELoss()
+optimizer_d = torch.optim.Adam(d.parameters(), lr=lr, betas=(0.5, 0.999))
+optimizer_g = torch.optim.Adam(g.parameters(), lr=lr, betas=(0.5, 0.999))
+
+# 固定噪声用于观察训练过程
+z_fixed = torch.randn(64, z_dim, 1, 1, device=device)
+
+# 创建保存目录
+os.makedirs("results", exist_ok=True)
+
+# ----------------------------
+# 6. 训练循环
+# ----------------------------
+def train():
+ for epoch in range(1, num_epochs + 1):
+ loss_d_total, loss_g_total = 0, 0
+ for real_images, _ in tqdm(dataloader, desc=f"Epoch {epoch}/{num_epochs}", leave=False):
+ real_images = real_images.to(device)
+ B = real_images.size(0)
+
+ # 标签平滑（真实 = 0.9, 假 = 0.0）
+ real_labels = torch.full((B, 1, 1, 1), 0.9, device=device)
+ fake_labels = torch.zeros((B, 1, 1, 1), device=device)
+
+ # 生成假图像
+ z = torch.randn(B, z_dim, 1, 1, device=device)
+ fake_images = g(z)
+
+ # 判别器训练
+ output_real = d(real_images)
+ output_fake = d(fake_images.detach())
+
+ loss_real = criterion(output_real, real_labels)
+ loss_fake = criterion(output_fake, fake_labels)
+ loss_d = loss_real + loss_fake
+
+ optimizer_d.zero_grad()
+ loss_d.backward()
+ optimizer_d.step()
+
+ # 生成器训练
+ output = d(fake_images)
+ loss_g = criterion(output, real_labels) # 欺骗D：希望D输出真
+ optimizer_g.zero_grad()
+ loss_g.backward()
+ optimizer_g.step()
+
+ loss_d_total += loss_d.item()
+ loss_g_total += loss_g.item()
+
+ # 平均损失
+ avg_loss_d = loss_d_total / len(dataloader)
+ avg_loss_g = loss_g_total / len(dataloader)
+ print(f"Epoch [{epoch}/{num_epochs}] Loss_D: {avg_loss_d:.4f} Loss_G: {avg_loss_g:.4f}")
+
+ # 每5轮保存一次生成图像
+ if epoch % 5 == 0:
+ with torch.no_grad():
+ fake = g(z_fixed).detach().cpu()
+ utils.save_image(fake, f"results/epoch_{epoch}.png", normalize=True, nrow=8)
+
+if __name__ == "__main__":
+ torch.multiprocessing.freeze_support() # ✅ Windows 兼容性
+ train() # 把训练过程封装成 main() 函数