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7673cc9279
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33b72751ab |
8
Pytorch/Project/DCGAN1/.idea/DCGAN.iml
generated
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8
Pytorch/Project/DCGAN1/.idea/DCGAN.iml
generated
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@@ -0,0 +1,8 @@
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266
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7
Pytorch/Project/DCGAN1/.idea/misc.xml
generated
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<?xml version="1.0" encoding="UTF-8"?>
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<component name="Black">
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8
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generated
Normal file
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<?xml version="1.0" encoding="UTF-8"?>
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53
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53
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generated
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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245
Pytorch/Project/DCGAN1/main.py
Normal file
245
Pytorch/Project/DCGAN1/main.py
Normal file
@@ -0,0 +1,245 @@
|
||||
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)
|
||||
176
Pytorch/Project/DCGAN1/test.py
Normal file
176
Pytorch/Project/DCGAN1/test.py
Normal file
@@ -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
|
||||
Reference in New Issue
Block a user