393 lines
13 KiB
Python
393 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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基于原始IC版图数据训练扩散模型，生成相似图像的完整实现。
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使用DDPM (Denoising Diffusion Probabilistic Models)
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针对单通道灰度IC版图图像进行优化。
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"""
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import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image
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from pathlib import Path
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms
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import logging
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# 尝试导入tqdm，如果没有则使用简单的进度显示
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try:
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from tqdm import tqdm
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except ImportError:
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def tqdm(iterable, **kwargs):
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return iterable
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class ICDiffusionDataset(Dataset):
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"""IC版图扩散模型训练数据集"""
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def __init__(self, image_dir, image_size=256, augment=True):
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self.image_dir = Path(image_dir)
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self.image_size = image_size
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# 获取所有PNG图像
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self.image_paths = []
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for ext in ['*.png', '*.jpg', '*.jpeg']:
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self.image_paths.extend(list(self.image_dir.glob(ext)))
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# 数据变换
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self.transform = transforms.Compose([
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transforms.Resize((image_size, image_size)),
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transforms.ToTensor(), # 转换到[0,1]范围
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])
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# 数据增强
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self.augment = augment
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if augment:
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self.aug_transform = transforms.Compose([
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transforms.RandomHorizontalFlip(p=0.5),
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transforms.RandomVerticalFlip(p=0.5),
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transforms.RandomRotation(90, fill=0),
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])
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def __len__(self):
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return len(self.image_paths)
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def __getitem__(self, idx):
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img_path = self.image_paths[idx]
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image = Image.open(img_path).convert('L') # 确保是灰度图
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# 基础变换
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image = self.transform(image)
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# 数据增强
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if self.augment and np.random.random() > 0.5:
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image = self.aug_transform(image)
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return image
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class UNet(nn.Module):
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"""简化的U-Net架构用于扩散模型"""
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def __init__(self, in_channels=1, out_channels=1, time_dim=256):
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super().__init__()
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# 时间嵌入
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self.time_mlp = nn.Sequential(
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nn.Linear(1, time_dim),
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nn.SiLU(),
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nn.Linear(time_dim, time_dim)
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)
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# 编码器
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self.encoder = nn.ModuleList([
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nn.Conv2d(in_channels, 64, 3, padding=1),
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nn.Conv2d(64, 128, 3, stride=2, padding=1),
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nn.Conv2d(128, 256, 3, stride=2, padding=1),
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nn.Conv2d(256, 512, 3, stride=2, padding=1),
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])
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# 中间层
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self.middle = nn.Sequential(
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nn.Conv2d(512, 512, 3, padding=1),
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nn.SiLU(),
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nn.Conv2d(512, 512, 3, padding=1)
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)
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# 解码器
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self.decoder = nn.ModuleList([
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nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1),
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nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
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nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
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])
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# 输出层
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self.output = nn.Conv2d(64, out_channels, 3, padding=1)
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# 时间融合层
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self.time_fusion = nn.ModuleList([
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nn.Linear(time_dim, 64),
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nn.Linear(time_dim, 128),
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nn.Linear(time_dim, 256),
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nn.Linear(time_dim, 512),
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])
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# 归一化层
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self.norms = nn.ModuleList([
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nn.GroupNorm(8, 64),
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nn.GroupNorm(8, 128),
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nn.GroupNorm(8, 256),
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nn.GroupNorm(8, 512),
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])
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def forward(self, x, t):
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# 时间嵌入
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t_emb = self.time_mlp(t.float().unsqueeze(-1)) # [B, time_dim]
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# 编码器路径
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skips = []
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for i, (conv, norm, fusion) in enumerate(zip(self.encoder, self.norms, self.time_fusion)):
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x = conv(x)
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x = norm(x)
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# 融合时间信息
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t_feat = fusion(t_emb).unsqueeze(-1).unsqueeze(-1)
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x = x + t_feat
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x = F.silu(x)
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skips.append(x)
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if i < len(self.encoder) - 1:
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x = F.silu(x)
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# 中间层
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x = self.middle(x)
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x = F.silu(x)
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# 解码器路径
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for i, (deconv, skip) in enumerate(zip(self.decoder, reversed(skips[:-1]))):
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x = deconv(x)
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x = x + skip # 跳跃连接
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x = F.silu(x)
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# 输出
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x = self.output(x)
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return x
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class NoiseScheduler:
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"""噪声调度器"""
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def __init__(self, num_timesteps=1000, beta_start=1e-4, beta_end=0.02):
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self.num_timesteps = num_timesteps
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# beta调度
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self.betas = torch.linspace(beta_start, beta_end, num_timesteps)
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# 预计算
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, axis=0)
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self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
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self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - self.alphas_cumprod)
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def add_noise(self, x_0, t):
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"""向干净图像添加噪声"""
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noise = torch.randn_like(x_0)
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sqrt_alphas_cumprod_t = self.sqrt_alphas_cumprod[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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sqrt_one_minus_alphas_cumprod_t = self.sqrt_one_minus_alphas_cumprod[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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return sqrt_alphas_cumprod_t * x_0 + sqrt_one_minus_alphas_cumprod_t * noise, noise
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def sample_timestep(self, batch_size):
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"""采样时间步"""
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return torch.randint(0, self.num_timesteps, (batch_size,))
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def step(self, model, x_t, t):
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"""单步去噪"""
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# 预测噪声
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predicted_noise = model(x_t, t)
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# 计算系数
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alpha_t = self.alphas[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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sqrt_alpha_t = torch.sqrt(alpha_t)
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beta_t = self.betas[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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sqrt_one_minus_alpha_cumprod_t = self.sqrt_one_minus_alphas_cumprod[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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# 计算均值
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model_mean = (1.0 / sqrt_alpha_t) * (x_t - (beta_t / sqrt_one_minus_alpha_cumprod_t) * predicted_noise)
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if t.min() == 0:
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return model_mean
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else:
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noise = torch.randn_like(x_t)
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return model_mean + torch.sqrt(beta_t) * noise
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class DiffusionTrainer:
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"""扩散模型训练器"""
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def __init__(self, model, scheduler, device='cuda'):
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self.model = model.to(device)
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self.scheduler = scheduler
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self.device = device
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self.loss_fn = nn.MSELoss()
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def train_step(self, optimizer, dataloader):
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"""单步训练"""
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self.model.train()
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total_loss = 0
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for batch in dataloader:
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batch = batch.to(self.device)
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# 采样时间步
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t = self.scheduler.sample_timestep(batch.shape[0]).to(self.device)
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# 添加噪声
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noisy_batch, noise = self.scheduler.add_noise(batch, t)
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# 预测噪声
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predicted_noise = self.model(noisy_batch, t)
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# 计算损失
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loss = self.loss_fn(predicted_noise, noise)
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# 反向传播
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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return total_loss / len(dataloader)
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def generate(self, num_samples, image_size=256, save_dir=None):
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"""生成图像"""
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self.model.eval()
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with torch.no_grad():
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# 从纯噪声开始
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x = torch.randn(num_samples, 1, image_size, image_size).to(self.device)
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# 逐步去噪
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for t in reversed(range(self.scheduler.num_timesteps)):
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t_batch = torch.full((num_samples,), t, device=self.device)
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x = self.scheduler.step(self.model, x, t_batch)
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# 限制到[0,1]范围
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x = torch.clamp(x, 0.0, 1.0)
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# 保存图像
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if save_dir:
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save_dir = Path(save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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for i in range(num_samples):
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img_tensor = x[i].cpu()
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img_array = (img_tensor.squeeze().numpy() * 255).astype(np.uint8)
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img = Image.fromarray(img_array, mode='L')
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img.save(save_dir / f"generated_{i:06d}.png")
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return x.cpu()
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def train_diffusion_model(args):
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"""训练扩散模型的主函数"""
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# 设置日志
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# 设备检查
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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logger.info(f"使用设备: {device}")
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# 创建数据集和数据加载器
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dataset = ICDiffusionDataset(args.data_dir, args.image_size, args.augment)
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dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
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logger.info(f"数据集大小: {len(dataset)}")
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# 创建模型和调度器
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model = UNet(in_channels=1, out_channels=1)
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scheduler = NoiseScheduler(num_timesteps=args.timesteps)
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trainer = DiffusionTrainer(model, scheduler, device)
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# 优化器
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optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
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# 训练循环
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logger.info(f"开始训练 {args.epochs} 个epoch...")
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for epoch in range(args.epochs):
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loss = trainer.train_step(optimizer, dataloader)
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logger.info(f"Epoch {epoch+1}/{args.epochs}, Loss: {loss:.6f}")
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# 定期保存模型
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if (epoch + 1) % args.save_interval == 0:
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checkpoint = {
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'epoch': epoch,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'loss': loss,
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}
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checkpoint_path = Path(args.output_dir) / f"diffusion_epoch_{epoch+1}.pth"
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checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
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torch.save(checkpoint, checkpoint_path)
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logger.info(f"保存检查点: {checkpoint_path}")
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# 生成样本
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logger.info("生成示例图像...")
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trainer.generate(
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num_samples=args.num_samples,
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image_size=args.image_size,
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save_dir=os.path.join(args.output_dir, 'samples')
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)
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# 保存最终模型
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final_checkpoint = {
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'epoch': args.epochs,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'loss': loss,
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}
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final_path = Path(args.output_dir) / "diffusion_final.pth"
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torch.save(final_checkpoint, final_path)
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logger.info(f"训练完成，最终模型保存在: {final_path}")
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def generate_with_trained_model(args):
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"""使用训练好的模型生成图像"""
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 加载模型
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model = UNet(in_channels=1, out_channels=1)
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checkpoint = torch.load(args.checkpoint, map_location=device)
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model.load_state_dict(checkpoint['model_state_dict'])
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model.to(device)
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# 创建调度器和训练器
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scheduler = NoiseScheduler(num_timesteps=args.timesteps)
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trainer = DiffusionTrainer(model, scheduler, device)
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# 生成图像
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trainer.generate(
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num_samples=args.num_samples,
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image_size=args.image_size,
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save_dir=args.output_dir
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)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="IC版图扩散模型训练和生成")
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subparsers = parser.add_subparsers(dest='command', help='命令')
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# 训练命令
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train_parser = subparsers.add_parser('train', help='训练扩散模型')
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train_parser.add_argument('--data_dir', type=str, required=True, help='训练数据目录')
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train_parser.add_argument('--output_dir', type=str, required=True, help='输出目录')
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train_parser.add_argument('--image_size', type=int, default=256, help='图像尺寸')
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train_parser.add_argument('--batch_size', type=int, default=8, help='批次大小')
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train_parser.add_argument('--epochs', type=int, default=100, help='训练轮数')
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train_parser.add_argument('--lr', type=float, default=1e-4, help='学习率')
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train_parser.add_argument('--timesteps', type=int, default=1000, help='扩散时间步数')
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train_parser.add_argument('--num_samples', type=int, default=50, help='生成的样本数量')
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train_parser.add_argument('--save_interval', type=int, default=10, help='保存间隔')
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train_parser.add_argument('--augment', action='store_true', help='启用数据增强')
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# 生成命令
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gen_parser = subparsers.add_parser('generate', help='使用训练好的模型生成图像')
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gen_parser.add_argument('--checkpoint', type=str, required=True, help='模型检查点路径')
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gen_parser.add_argument('--output_dir', type=str, required=True, help='输出目录')
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gen_parser.add_argument('--num_samples', type=int, default=200, help='生成样本数量')
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gen_parser.add_argument('--image_size', type=int, default=256, help='图像尺寸')
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gen_parser.add_argument('--timesteps', type=int, default=1000, help='扩散时间步数')
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args = parser.parse_args()
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if args.command == 'train':
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train_diffusion_model(args)
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elif args.command == 'generate':
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generate_with_trained_model(args)
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else:
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parser.print_help() |