From 7a36caa3d41335b5a5b514c05ba61b4e1cd72511 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:25:31 +0800 Subject: [PATCH 01/21] Initial commit --- README.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..d3eac25 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# NST + From 3ff98ee9d30be0e20319e517865383502393cbe6 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:30:15 +0800 Subject: [PATCH 02/21] Update README.md --- README.md | 73 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 72 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d3eac25..630ede6 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,73 @@ -# NST +# 🎨 AI 画风迁移器 (StyleTransferAI) +一个新颖、轻量级的Python项目，旨在通过神经风格迁移算法，实现快速有趣的艺术风格转换效果。 + +## 🚀 快速开始 + +1. **克隆项目** + ```bash + git clone https://github.com/yourname/style_transfer_ai.git + cd style_transfer_ai + ``` + +2. **创建虚拟环境并安装依赖** + ```bash + python -m venv venv + source venv/bin/activate # Linux/Mac + # venv\Scripts\activate # Windows + pip install -r requirements.txt + ``` + +3. **准备图像** + 在 `data/raw/` 目录下放置你的内容图 (`content.jpg`) 和风格图 (`style.jpg`)。 + +4. **运行程序** + ```bash + python scripts/run_style_transfer.py + ``` + +5. **查看结果** + 结果将保存在 `data/processed/result_时间戳.jpg`。 + +## 💡 项目特点 + +- **高质量风格迁移**: 基于VGG16深度学习模型，生成高质量艺术效果。 +- **速度快**: CPU优化，无需GPU即可快速运行。 +- **可视化监控**: 实时显示训练损失，生成详细损失曲线图。 +- **高度可定制**: 修改 `configs/default.yaml` 或核心代码即可调整风格迁移效果。 +- **多模式训练**: 提供快速、扩展、高级三种训练模式。 + +## 📂 项目结构 + +``` +style_transfer_ai/ +│ +├── configs/ # 配置文件目录 +│ └── default.yaml # 默认超参数配置（学习率、迭代次数、权重等） +│ +├── data/ # 数据目录 +│ ├── raw/ # 原始输入图像 +│ │ ├── content.jpg # 内容图像（用户自定义） +│ │ └── style.jpg # 风格图像（用户自定义） +│ └── processed/ # 输出结果目录（自动生成） +│ └── result_20251202_2227.jpg # 示例输出（带时间戳） +│ +├── models/ # 模型相关代码 +│ └── vgg16_extractor.py # VGG16特征提取器封装 +│ +├── scripts/ # 可执行脚本 +│ └── run_style_transfer.py # 主程序入口 +│ +├── src/ # 核心源代码 +│ ├── style_transfer.py # 风格迁移核心算法实现 +│ ├── loss.py # 内容损失、风格损失计算 +│ ├── utils.py # 图像预处理、后处理、可视化工具 +│ └── trainer.py # 训练循环与优化器管理 +│ +├── outputs/ # 日志与可视化输出 +│ └── loss_curve_*.png # 损失变化曲线图（按运行时间命名） +│ +├── requirements.txt # Python依赖列表 +├── README.md # 项目说明文档（即本文件） +└── .gitignore # Git忽略规则 +``` \ No newline at end of file From 3badce0659051058a22953fa4d40ddd012c9c912 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:31:18 +0800 Subject: [PATCH 03/21] Update README.md --- README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index 630ede6..9061de9 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,7 @@ ## 🚀 快速开始 -1. **克隆项目** - ```bash - git clone https://github.com/yourname/style_transfer_ai.git - cd style_transfer_ai - ``` +1.**项目介绍** 2. **创建虚拟环境并安装依赖** ```bash From 00f0e079b62a74ac5437ce5476dd7ce87f755f7c Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:36:08 +0800 Subject: [PATCH 04/21] ADD file via upload --- main | 171 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 main diff --git a/main b/main new file mode 100644 index 0000000..50558e5 --- /dev/null +++ b/main @@ -0,0 +1,171 @@ +""" +图像合成器模块 - 基础版本和传统方法。 +""" + +import numpy as np +from typing import Tuple, Dict, Any +from scipy.optimize import minimize +import warnings +from abc import ABC, abstractmethod + +warnings.filterwarnings("ignore", category=UserWarning) + + +class BaseImageSynthesizer(ABC): + """图像合成器基类""" + + @abstractmethod + def set_content(self, image: np.ndarray): + """设置内容图像""" + pass + + @abstractmethod + def set_style_features(self, features: Dict[str, Any]): + """设置风格特征""" + pass + + @abstractmethod + def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray: + """合成图像""" + pass + + +class ImageSynthesizer(BaseImageSynthesizer): + """ + 传统图像合成器，负责生成最终的风格化图像。 + """ + + def __init__(self, config: Dict[str, Any]): + """ + 初始化合成器。 + + Args: + config: 配置字典。 + """ + self.config = config + self.content_image: np.ndarray = None + self.style_features: Dict[str, Any] = {} + self.synthesized_image: np.ndarray = None + + def set_content(self, image: np.ndarray): + """ + 设置内容图像。 + + Args: + image: 内容图像，形状为 (H, W, C)。 + """ + self.content_image = image.astype(np.float32) / 255.0 + + def set_style_features(self, features: Dict[str, Any]): + """ + 设置风格特征。 + + Args: + features: 由 StyleFeatureExtractor 提取的特征字典。 + """ + self.style_features = features + + def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray: + """ + 合成风格化图像。 + + Args: + initial_image: 初始图像，如果为None，则使用内容图像。 + max_iter: 最大迭代次数。 + + Returns: + 合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。 + """ + if initial_image is None: + initial_image = self.content_image.copy() + + # 将图像展平为一维向量以便于优化 + h, w, c = self.content_image.shape + x0 = initial_image.ravel() + + # 定义损失函数 + def loss_function(x): + # 重塑回图像形状 + img = x.reshape((h, w, c)) + + # 1. 内容损失：保持与原图相似 + content_loss = np.mean((img - self.content_image) ** 2) + + # 2. 风格损失：匹配风格特征 + style_loss = self._calculate_style_loss(img) + + # 总损失 + total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss + + return total_loss + + # 执行优化 + result = minimize(loss_function, x0, method='L-BFGS-B', + options={'maxiter': max_iter, 'disp': False}) + + # 重塑结果并转换回[0, 255] + synthesized = result.x.reshape((h, w, c)) + synthesized = np.clip(synthesized, 0, 1) * 255 + self.synthesized_image = synthesized.astype(np.uint8) + + return self.synthesized_image + + def _calculate_style_loss(self, image: np.ndarray) -> float: + """ + 计算当前图像与目标风格特征之间的损失。 + + Args: + image: 当前合成的图像。 + + Returns: + 风格损失值。 + """ + loss = 0.0 + + # 1. 颜色直方图损失 + hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1)) + hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1)) + hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1)) + current_hist = np.concatenate([hist_r, hist_g, hist_b]) + target_hist = self.style_features['color_histogram'] + # 使用KL散度作为距离度量 + epsilon = 1e-10 + kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon))) + loss += kl_div + + # 2. 纹理强度损失 + diff_h = np.abs(np.diff(image, axis=1)).mean() + diff_v = np.abs(np.diff(image, axis=0)).mean() + current_texture = np.array([diff_h, diff_v]) + target_texture = self.style_features['texture_strength'] + texture_loss = np.mean((current_texture - target_texture) ** 2) + loss += texture_loss + + # 3. 平均颜色损失 + current_mean = image.mean(axis=(0, 1)) + target_mean = self.style_features['mean_color'] + mean_loss = np.mean((current_mean - target_mean) ** 2) + loss += mean_loss + + return loss + + +# 工厂函数 +def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer: + """ + 创建图像合成器工厂函数。 + + Args: + synthesizer_type: 合成器类型 ('traditional' 或 'neural') + config: 配置字典 + + Returns: + 图像合成器实例 + """ + if synthesizer_type == 'traditional': + return ImageSynthesizer(config) + elif synthesizer_type == 'neural': + from .neural_image_synthesizer import NeuralImageSynthesizer + return NeuralImageSynthesizer(config) + else: + raise ValueError(f"不支持的合成器类型: {synthesizer_type}") \ No newline at end of file From c69d17d7733b5913e600a5dac1ee628f6f1a0711 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:37:54 +0800 Subject: [PATCH 05/21] ADD file via upload --- run_style_transfer.py | 263 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 263 insertions(+) create mode 100644 run_style_transfer.py diff --git a/run_style_transfer.py b/run_style_transfer.py new file mode 100644 index 0000000..80f5be2 --- /dev/null +++ b/run_style_transfer.py @@ -0,0 +1,263 @@ +# scripts/extended_training.py +import warnings + +warnings.filterwarnings("ignore", category=UserWarning, module="torchvision") + +import torch +import torch.nn as nn +import torch.optim as optim +from torchvision import models, transforms +from PIL import Image +from pathlib import Path +import yaml +import time +import numpy as np + + +print("=" * 60) + + +def main(): + # 加载配置 + config_path = Path(__file__).parent.parent / "configs" / "default.yaml" + with open(config_path, 'r', encoding='utf-8') as f: + config = yaml.safe_load(f) + + # ========== 关键修复：大幅提高风格影响 ========== + config['model']['image_size'] = 256 + config['model']['content_weight'] = 1e3 # 降低内容权重 + config['model']['style_weight'] = 1e7 # 大幅提高风格权重 + config['model']['learning_rate'] = 0.003 + config['training']['num_steps'] = 400 + config['multi_scale']['enabled'] = False + + print("📋 长时间训练参数:") + print(f" - 图像尺寸: 256x256") + print(f" - 内容权重: 1e3 (风格权重的1/10000)") + print(f" - 风格权重: 1e7 (内容权重的10000倍)") + print(f" - 权重比例: 1:10000 (强调风格)") + print(f" - 学习率: 0.003") + print(f" - 训练步数: 400 (约5分钟)") + + # 设备设置 + device = torch.device("cpu") + torch.set_num_threads(2) + + # 加载图像 + project_root = Path(__file__).parent.parent + content_path = project_root / config['paths']['content_image'] + style_path = project_root / config['paths']['style_image'] + output_dir = project_root / config['paths']['output_dir'] + + print(f"\n📷 加载图像...") + content_img = Image.open(content_path).convert('RGB') + style_img = Image.open(style_path).convert('RGB') + + image_size = 256 + content_img = content_img.resize((image_size, image_size), Image.Resampling.LANCZOS) + style_img = style_img.resize((image_size, image_size), Image.Resampling.LANCZOS) + + print(f" 调整到: {image_size}x{image_size}") + + # 预处理（移除标准化） + preprocess = transforms.Compose([ + transforms.ToTensor(), + # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 注释掉 + ]) + + content_tensor = preprocess(content_img).unsqueeze(0).to(device) + style_tensor = preprocess(style_img).unsqueeze(0).to(device) + target_tensor = content_tensor.clone().requires_grad_(True) + + # 加载VGG16模型 + print("\n🔧 加载VGG16模型...") + start_time = time.time() + vgg = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features.to(device).eval() + + for param in vgg.parameters(): + param.requires_grad_(False) + + print(f"✅ 模型加载完成 ({time.time() - start_time:.2f}秒)") + + # 定义层索引 + content_layers = ["21"] # conv4_2 + style_layers = ["0", "5", "10", "19", "28"] # conv1_1, conv2_1, conv3_1, conv4_1, conv5_1 + + # 特征提取函数 + def get_features_fixed(x, layers): + features = {} + x_tmp = x + layers_int = set([int(l) for l in layers]) + + for idx, (name, layer) in enumerate(vgg._modules.items()): + x_tmp = layer(x_tmp) + if idx in layers_int: + features[str(idx)] = x_tmp + + return features + + # 提取特征 + print("\n📊 提取特征...") + start_time = time.time() + + # 标准化输入以匹配VGG的预训练权重 + def normalize_for_vgg(tensor): + mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device) + std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device) + return (tensor - mean) / std + + content_features = get_features_fixed(normalize_for_vgg(content_tensor), content_layers) + style_features = get_features_fixed(normalize_for_vgg(style_tensor), style_layers) + + # 修改Gram矩阵计算（关键修复） + def gram_matrix_fixed(tensor): + b, c, h, w = tensor.size() + + # 展平特征 + tensor_flat = tensor.view(b, c, -1) # [b, c, h*w] + + # 计算Gram矩阵 + gram = torch.bmm(tensor_flat, tensor_flat.transpose(1, 2)) # [b, c, c] + + # 关键修改：只除以h*w，而不是c*h*w（原始论文做法） + gram = gram / (h * w) + + return gram + + # 预计算风格Gram矩阵 + style_grams = {} + for layer, feat in style_features.items(): + style_grams[layer] = gram_matrix_fixed(feat) + print(f" 层 {layer} Gram矩阵形状: {style_grams[layer].shape}") + + print(f"✅ 特征提取完成 ({time.time() - start_time:.2f}秒)") + + # 优化器设置 + learning_rate = 0.003 # 使用更高的学习率 + optimizer = optim.Adam([target_tensor], lr=learning_rate) + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5) + + # 训练参数 + content_weight = 1e3 + style_weight = 1e7 # 大幅提高 + num_steps = 400 + log_interval = 40 + + print(f"\n🎨 开始长时间训练 (约5分钟)...") + print(f" 优化器: Adam (初始lr={learning_rate})") + print(f" 内容权重: {content_weight}") + print(f" 风格权重: {style_weight} (非常高，强调风格转移)") + print(f" 权重比例: 1:{int(style_weight / content_weight)}") + + start_time = time.time() + + # 记录损失历史 + loss_history = [] + content_loss_history = [] + style_loss_history = [] + + for step in range(1, num_steps + 1): + optimizer.zero_grad() + + # 提取目标特征（需要标准化） + target_normalized = normalize_for_vgg(target_tensor) + target_features = get_features_fixed(target_normalized, content_layers + style_layers) + + # 计算内容损失 + content_loss = torch.tensor(0.0, device=device) + for layer in content_layers: + if layer in target_features and layer in content_features: + diff = target_features[layer] - content_features[layer] + content_loss += torch.mean(diff ** 2) + + # 计算风格损失 + style_loss = torch.tensor(0.0, device=device) + for layer in style_layers: + if layer in target_features and layer in style_grams: + target_gram = gram_matrix_fixed(target_features[layer]) + style_gram = style_grams[layer] + diff = target_gram - style_gram + style_loss += torch.mean(diff ** 2) + + # 总损失 + total_loss = content_weight * content_loss + style_weight * style_loss + + # 反向传播 + total_loss.backward() + + # 梯度裁剪（提高限制） + torch.nn.utils.clip_grad_norm_([target_tensor], max_norm=10.0) + + optimizer.step() + + # 更新学习率 + if step % 100 == 0: + scheduler.step() + current_lr = optimizer.param_groups[0]['lr'] + print(f"📉 第 {step} 步: 学习率降低到 {current_lr:.6f}") + + # 限制像素值 + with torch.no_grad(): + target_tensor.data.clamp_(0, 1) + + # 记录损失 + loss_history.append(total_loss.item()) + content_loss_history.append(content_loss.item()) + style_loss_history.append(style_loss.item()) + + # 定期输出 + if step % log_interval == 0 or step == num_steps: + elapsed = time.time() - start_time + steps_per_second = step / elapsed if elapsed > 0 else 0 + remaining_steps = num_steps - step + estimated_remaining = remaining_steps / steps_per_second if steps_per_second > 0 else 0 + + # 计算风格损失贡献 + style_contribution = style_weight * style_loss.item() + content_contribution = content_weight * content_loss.item() + style_ratio = style_contribution / (style_contribution + content_contribution) * 100 + + print(f"⏳ 第 {step:3d}/{num_steps} | " + f"总损失: {total_loss.item():8.2f} | " + f"内容: {content_loss.item():6.4f} | " + f"风格: {style_loss.item():6.4f} | " + f"风格贡献: {style_ratio:.1f}% | " + f"用时: {elapsed:.1f}s | " + f"剩余: {estimated_remaining:.1f}s") + + total_time = time.time() - start_time + + # 分析损失变化 + print(f"\n📈 损失分析:") + print(f" 总损失变化: {loss_history[0]:.2f} → {loss_history[-1]:.2f}") + print(f" 内容损失变化: {content_loss_history[0]:.4f} → {content_loss_history[-1]:.4f}") + print(f" 风格损失变化: {style_loss_history[0]:.4f} → {style_loss_history[-1]:.4f}") + + # 保存结果 + postprocess = transforms.Compose([ + # transforms.Normalize(mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225], + # std=[1/0.229, 1/0.224, 1/0.225]), # 如果之前没标准化，这里也不需要 + transforms.ToPILImage() + ]) + + output_dir.mkdir(parents=True, exist_ok=True) + with torch.no_grad(): + result_img = postprocess(target_tensor.squeeze(0).cpu()) + + timestamp = time.strftime("%H%M%S") + output_path = output_dir / f"extended_result_{timestamp}.jpg" + result_img.save(output_path, quality=95, optimize=True) + + print(f"\n🎉 训练完成!") + print(f" 总用时: {total_time:.1f}秒") + print(f" 结果保存至: {output_path}") + + # 尝试显示结果 + try: + result_img.show(title="风格迁移结果") + except: + print(" ℹ️ 请手动打开图像查看") + + +if __name__ == "__main__": + main() \ No newline at end of file From 258c7111fd3ddb1125bb90dc1af7d5780f730a8a Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:38:20 +0800 Subject: [PATCH 06/21] ADD file via upload --- requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..fd393b8 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy>=1.24.0 +Pillow>=9.0.0 +scipy>=1.10.0 +PyYAML>=6.0.0 \ No newline at end of file From 25241cf44dc93bdef2e386c45da10a003d701f86 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:38:42 +0800 Subject: [PATCH 07/21] ADD file via upload --- default.yaml | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 default.yaml diff --git a/default.yaml b/default.yaml new file mode 100644 index 0000000..8c24c2e --- /dev/null +++ b/default.yaml @@ -0,0 +1,36 @@ +# configs/stable_config.yaml +# 稳定训练配置 + +# 模型配置 +model: + model_type: "vgg16" + image_size: 192 + content_layers: ["19"] # conv4_2 + style_layers: ["0", "5", "10", "17", "24"] # 全部5个层 + content_weight: 1e4 # 增加内容权重 + style_weight: 5e4 # 降低风格权重 + learning_rate: 0.005 # 低学习率稳定训练 + +# 路径配置 +paths: + content_image: "data/raw/content.jpg" + style_image: "data/raw/tem.jpg" + output_dir: "data/processed" + +# 训练配置 +training: + num_steps: 150 # 减少步数 + log_interval: 15 + tv_weight: 1e-5 + +# 多尺度配置（先禁用，稳定后再启用） +multi_scale: + enabled: false + scales: [192] + scale_weights: [1.0] + +# 性能配置 +performance: + use_gpu: false + batch_size: 1 + precision: "float32" \ No newline at end of file From 3837c10388b03241961b860296493bd7e416a967 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:39:41 +0800 Subject: [PATCH 08/21] ADD file via upload --- feature_extractor.py | 103 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 feature_extractor.py diff --git a/feature_extractor.py b/feature_extractor.py new file mode 100644 index 0000000..32d85b6 --- /dev/null +++ b/feature_extractor.py @@ -0,0 +1,103 @@ +""" +风格特征提取器模块 - 基础版本和传统方法。 +""" + +import numpy as np +from typing import Tuple, Dict, Any +from abc import ABC, abstractmethod + + +class BaseFeatureExtractor(ABC): + """特征提取器基类""" + + @abstractmethod + def extract(self, image: np.ndarray) -> Dict[str, Any]: + """提取特征抽象方法""" + pass + + @abstractmethod + def get_features(self) -> Dict[str, Any]: + """获取特征抽象方法""" + pass + + +class StyleFeatureExtractor(BaseFeatureExtractor): + """ + 传统风格特征提取器。 + 通过计算图像的直方图和局部纹理特征来定义"风格"。 + """ + + def __init__(self, config: Dict[str, Any]): + """ + 初始化提取器。 + + Args: + config: 配置字典，包含特征提取参数。 + """ + self.config = config + self.style_features: Dict[str, Any] = {} + + def extract(self, image: np.ndarray) -> Dict[str, Any]: + """ + 从图像中提取风格特征。 + + Args: + image: 输入图像，形状为 (H, W, C)，值域 [0, 255]。 + + Returns: + 一个包含各种风格特征的字典。 + """ + # 确保图像在[0, 1]范围 + image_normalized = image.astype(np.float32) / 255.0 + + features = {} + + # 1. 颜色直方图 (RGB通道) + hist_r, _ = np.histogram(image_normalized[:, :, 0].ravel(), bins=32, range=(0, 1)) + hist_g, _ = np.histogram(image_normalized[:, :, 1].ravel(), bins=32, range=(0, 1)) + hist_b, _ = np.histogram(image_normalized[:, :, 2].ravel(), bins=32, range=(0, 1)) + features['color_histogram'] = np.concatenate([hist_r, hist_g, hist_b]) + + # 2. 局部纹理特征 + diff_h = np.abs(np.diff(image_normalized, axis=1)).mean() + diff_v = np.abs(np.diff(image_normalized, axis=0)).mean() + features['texture_strength'] = np.array([diff_h, diff_v]) + + # 3. 平均颜色 + features['mean_color'] = image_normalized.mean(axis=(0, 1)) + + # 4. 颜色标准差 + features['color_std'] = image_normalized.std(axis=(0, 1)) + + self.style_features = features + return features + + def get_features(self) -> Dict[str, Any]: + """ + 获取最近一次提取的风格特征。 + + Returns: + 风格特征字典。 + """ + return self.style_features.copy() + + +# 工厂函数，便于创建不同类型的提取器 +def create_feature_extractor(extractor_type: str, config: Dict[str, Any]) -> BaseFeatureExtractor: + """ + 创建特征提取器工厂函数。 + + Args: + extractor_type: 提取器类型 ('traditional' 或 'neural') + config: 配置字典 + + Returns: + 特征提取器实例 + """ + if extractor_type == 'traditional': + return StyleFeatureExtractor(config) + elif extractor_type == 'neural': + from .neural_feature_extractor import NeuralStyleFeatureExtractor + return NeuralStyleFeatureExtractor(config) + else: + raise ValueError(f"不支持的提取器类型: {extractor_type}") \ No newline at end of file From 57208129b4500853b93714dd56d177000e80d5e5 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:45:01 +0800 Subject: [PATCH 09/21] Delete 'main' --- main | 171 ----------------------------------------------------------- 1 file changed, 171 deletions(-) delete mode 100644 main diff --git a/main b/main deleted file mode 100644 index 50558e5..0000000 --- a/main +++ /dev/null @@ -1,171 +0,0 @@ -""" -图像合成器模块 - 基础版本和传统方法。 -""" - -import numpy as np -from typing import Tuple, Dict, Any -from scipy.optimize import minimize -import warnings -from abc import ABC, abstractmethod - -warnings.filterwarnings("ignore", category=UserWarning) - - -class BaseImageSynthesizer(ABC): - """图像合成器基类""" - - @abstractmethod - def set_content(self, image: np.ndarray): - """设置内容图像""" - pass - - @abstractmethod - def set_style_features(self, features: Dict[str, Any]): - """设置风格特征""" - pass - - @abstractmethod - def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray: - """合成图像""" - pass - - -class ImageSynthesizer(BaseImageSynthesizer): - """ - 传统图像合成器，负责生成最终的风格化图像。 - """ - - def __init__(self, config: Dict[str, Any]): - """ - 初始化合成器。 - - Args: - config: 配置字典。 - """ - self.config = config - self.content_image: np.ndarray = None - self.style_features: Dict[str, Any] = {} - self.synthesized_image: np.ndarray = None - - def set_content(self, image: np.ndarray): - """ - 设置内容图像。 - - Args: - image: 内容图像，形状为 (H, W, C)。 - """ - self.content_image = image.astype(np.float32) / 255.0 - - def set_style_features(self, features: Dict[str, Any]): - """ - 设置风格特征。 - - Args: - features: 由 StyleFeatureExtractor 提取的特征字典。 - """ - self.style_features = features - - def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray: - """ - 合成风格化图像。 - - Args: - initial_image: 初始图像，如果为None，则使用内容图像。 - max_iter: 最大迭代次数。 - - Returns: - 合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。 - """ - if initial_image is None: - initial_image = self.content_image.copy() - - # 将图像展平为一维向量以便于优化 - h, w, c = self.content_image.shape - x0 = initial_image.ravel() - - # 定义损失函数 - def loss_function(x): - # 重塑回图像形状 - img = x.reshape((h, w, c)) - - # 1. 内容损失：保持与原图相似 - content_loss = np.mean((img - self.content_image) ** 2) - - # 2. 风格损失：匹配风格特征 - style_loss = self._calculate_style_loss(img) - - # 总损失 - total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss - - return total_loss - - # 执行优化 - result = minimize(loss_function, x0, method='L-BFGS-B', - options={'maxiter': max_iter, 'disp': False}) - - # 重塑结果并转换回[0, 255] - synthesized = result.x.reshape((h, w, c)) - synthesized = np.clip(synthesized, 0, 1) * 255 - self.synthesized_image = synthesized.astype(np.uint8) - - return self.synthesized_image - - def _calculate_style_loss(self, image: np.ndarray) -> float: - """ - 计算当前图像与目标风格特征之间的损失。 - - Args: - image: 当前合成的图像。 - - Returns: - 风格损失值。 - """ - loss = 0.0 - - # 1. 颜色直方图损失 - hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1)) - hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1)) - hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1)) - current_hist = np.concatenate([hist_r, hist_g, hist_b]) - target_hist = self.style_features['color_histogram'] - # 使用KL散度作为距离度量 - epsilon = 1e-10 - kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon))) - loss += kl_div - - # 2. 纹理强度损失 - diff_h = np.abs(np.diff(image, axis=1)).mean() - diff_v = np.abs(np.diff(image, axis=0)).mean() - current_texture = np.array([diff_h, diff_v]) - target_texture = self.style_features['texture_strength'] - texture_loss = np.mean((current_texture - target_texture) ** 2) - loss += texture_loss - - # 3. 平均颜色损失 - current_mean = image.mean(axis=(0, 1)) - target_mean = self.style_features['mean_color'] - mean_loss = np.mean((current_mean - target_mean) ** 2) - loss += mean_loss - - return loss - - -# 工厂函数 -def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer: - """ - 创建图像合成器工厂函数。 - - Args: - synthesizer_type: 合成器类型 ('traditional' 或 'neural') - config: 配置字典 - - Returns: - 图像合成器实例 - """ - if synthesizer_type == 'traditional': - return ImageSynthesizer(config) - elif synthesizer_type == 'neural': - from .neural_image_synthesizer import NeuralImageSynthesizer - return NeuralImageSynthesizer(config) - else: - raise ValueError(f"不支持的合成器类型: {synthesizer_type}") \ No newline at end of file From af1c5cd019fa3d7a26c61c1fbbf3891aae5dfec8 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:59:22 +0800 Subject: [PATCH 10/21] Delete 'feature_extractor.py' --- feature_extractor.py | 103 ------------------------------------------- 1 file changed, 103 deletions(-) delete mode 100644 feature_extractor.py diff --git a/feature_extractor.py b/feature_extractor.py deleted file mode 100644 index 32d85b6..0000000 --- a/feature_extractor.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -风格特征提取器模块 - 基础版本和传统方法。 -""" - -import numpy as np -from typing import Tuple, Dict, Any -from abc import ABC, abstractmethod - - -class BaseFeatureExtractor(ABC): - """特征提取器基类""" - - @abstractmethod - def extract(self, image: np.ndarray) -> Dict[str, Any]: - """提取特征抽象方法""" - pass - - @abstractmethod - def get_features(self) -> Dict[str, Any]: - """获取特征抽象方法""" - pass - - -class StyleFeatureExtractor(BaseFeatureExtractor): - """ - 传统风格特征提取器。 - 通过计算图像的直方图和局部纹理特征来定义"风格"。 - """ - - def __init__(self, config: Dict[str, Any]): - """ - 初始化提取器。 - - Args: - config: 配置字典，包含特征提取参数。 - """ - self.config = config - self.style_features: Dict[str, Any] = {} - - def extract(self, image: np.ndarray) -> Dict[str, Any]: - """ - 从图像中提取风格特征。 - - Args: - image: 输入图像，形状为 (H, W, C)，值域 [0, 255]。 - - Returns: - 一个包含各种风格特征的字典。 - """ - # 确保图像在[0, 1]范围 - image_normalized = image.astype(np.float32) / 255.0 - - features = {} - - # 1. 颜色直方图 (RGB通道) - hist_r, _ = np.histogram(image_normalized[:, :, 0].ravel(), bins=32, range=(0, 1)) - hist_g, _ = np.histogram(image_normalized[:, :, 1].ravel(), bins=32, range=(0, 1)) - hist_b, _ = np.histogram(image_normalized[:, :, 2].ravel(), bins=32, range=(0, 1)) - features['color_histogram'] = np.concatenate([hist_r, hist_g, hist_b]) - - # 2. 局部纹理特征 - diff_h = np.abs(np.diff(image_normalized, axis=1)).mean() - diff_v = np.abs(np.diff(image_normalized, axis=0)).mean() - features['texture_strength'] = np.array([diff_h, diff_v]) - - # 3. 平均颜色 - features['mean_color'] = image_normalized.mean(axis=(0, 1)) - - # 4. 颜色标准差 - features['color_std'] = image_normalized.std(axis=(0, 1)) - - self.style_features = features - return features - - def get_features(self) -> Dict[str, Any]: - """ - 获取最近一次提取的风格特征。 - - Returns: - 风格特征字典。 - """ - return self.style_features.copy() - - -# 工厂函数，便于创建不同类型的提取器 -def create_feature_extractor(extractor_type: str, config: Dict[str, Any]) -> BaseFeatureExtractor: - """ - 创建特征提取器工厂函数。 - - Args: - extractor_type: 提取器类型 ('traditional' 或 'neural') - config: 配置字典 - - Returns: - 特征提取器实例 - """ - if extractor_type == 'traditional': - return StyleFeatureExtractor(config) - elif extractor_type == 'neural': - from .neural_feature_extractor import NeuralStyleFeatureExtractor - return NeuralStyleFeatureExtractor(config) - else: - raise ValueError(f"不支持的提取器类型: {extractor_type}") \ No newline at end of file From ec2ca99c9b76c9183207045851e2b0e37da18eaa Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 16:59:29 +0800 Subject: [PATCH 11/21] Delete 'default.yaml' --- default.yaml | 36 ------------------------------------ 1 file changed, 36 deletions(-) delete mode 100644 default.yaml diff --git a/default.yaml b/default.yaml deleted file mode 100644 index 8c24c2e..0000000 --- a/default.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# configs/stable_config.yaml -# 稳定训练配置 - -# 模型配置 -model: - model_type: "vgg16" - image_size: 192 - content_layers: ["19"] # conv4_2 - style_layers: ["0", "5", "10", "17", "24"] # 全部5个层 - content_weight: 1e4 # 增加内容权重 - style_weight: 5e4 # 降低风格权重 - learning_rate: 0.005 # 低学习率稳定训练 - -# 路径配置 -paths: - content_image: "data/raw/content.jpg" - style_image: "data/raw/tem.jpg" - output_dir: "data/processed" - -# 训练配置 -training: - num_steps: 150 # 减少步数 - log_interval: 15 - tv_weight: 1e-5 - -# 多尺度配置（先禁用，稳定后再启用） -multi_scale: - enabled: false - scales: [192] - scale_weights: [1.0] - -# 性能配置 -performance: - use_gpu: false - batch_size: 1 - precision: "float32" \ No newline at end of file From ea953576e134fc1e2193227b46c21f250abfddab Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:04:12 +0800 Subject: [PATCH 12/21] ADD file via upload --- extended_result_174121.jpg | Bin 0 -> 35499 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 extended_result_174121.jpg diff --git a/extended_result_174121.jpg b/extended_result_174121.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4f086d226c25bfee93a6396094343c89d6b7556b GIT binary patch literal 35499 zcmb4qWl$VE)b3K;VS%=|%VNddDYCoh;##yg#ogWAiu>YFikG6rTPUzN6nA$i^7Z}h z{c-=^lgXK6l04^Rl9|kryv)C>0SF-SV0i!%5&(eouK-?_0J4BrNdM7){MCPig8Uyx zML|JEK|@7D``?O=iGhZWiH?SbfsKKQ^&kCf;b3Fo{MY#pG60e!kMvsJ@GDLG<6Ix7A0tWFX~;kE<2Q&T31C9n zQs0p4X2XwVGmR+f2HX$Iz8oW^1%z`x;NYR}9Jb;$ z8kUX06b1x2r+8B1emnalVR2w^%2<}NsE8%4BCj}sOcq%^$zJVz--_+PHGMT_DJs4z zLooGL-#5g`#d$~6^Fn~huqes##v$;_R*Gc;-P1+*2f?JPH<`-Rnff(SKwE5mbzm}S z!{){JQ!hp~Z4AW`W`~HLiZa&SOm6LZ4xdW+Oy2t%o0YF_8d+t3mC4v?LE&Qt(Gpmp zQ5hk56QA-`kPbL^MGYL|m8bs>8MA2=Yy5&#A;^#~f;`#lU}|bp_(zPT+wT*-(vd=d z>QH`c9gse)c{%pYZOjpz$}#t-cNXn+O%0%oA*HdT1p}StM8sAp=+RQ}^1e%Z{LBXS zyowD+iti=io0 z4tOG63As3xwxURJXLR}~#i-VHu2Kk2zYCn7WT>_pfyw7}*m>iqZB%*Trx&}Q>ag|ks?Oq-FA5HD3h6#0r;g$VX=W!1<~1jQ zjjuMZNa&)T)HhREi&*YLe@W1iQmfTrDSejlO-nQ~{%lAU%0w{njT^OZxDM`|?)o#Y z&Rp6s?&8*d&Sa+PV8S6?o8gz`-nCD&w0$3LB^yL7O@erR=))fdQ~E#X47rPs?fc>1 z%X!^_M~$`Z!Th|+W~^4)1c!O7z534lw4j6I{F=Y3PVhg;TBi4{;_V-h7rVidw5?{e zn;Wh40|pi(#7JRYZSzDYX}c@vQFi4URXXejHW#oqw{^yMUSQCBIP{Qlig!7G=}6-J z`N*9i`#@w`9=oV8qHBp?=;(ce!iw5=p&9ACSMmI>g+s~RIkoQfav*X(?Ik$OgZA~6 z->TZ?=}AQe#X*udiSME*0_M9v!Wmm12jLUbpkNj^knxxcf^a4TPtfn?`mH4Aqn-9O zkDT!zvs~D#Y*l$!UepA56r8mNR@ZP=jBEyg}4|bxkgB&y`E5CGzi3$HE^nJFj6btmF1B4F3#K zQ>Qxpjwpysjx6#ocr55?r5|kL6L|lSRy831E}CaMCj<5~kOUpWiMfI@8eqZZx8~Ma z8?*H3QqK*6m8PXq1$!j%JXMlWl)MYQSPSU(k&AZMEcu~v%Ja+74d`Ku@^Lg za!(mWZtpSiCYKWkeF3yk1R1xoKtj2#DD-)OxXM;S%Wji>A2ZRKi|Wi)&pT3lzV?+X zFh8bEWtaVmEha*2B6NO6*&9xcT8JD1VQE`68~``CTZLOSiU)FRnektqdzP*ypWi#k z^45)47wvitWNkZRWjY`0=qMZ_{VZM+pF!r1rP*2X#z%!%>0@?9>VO$3KLBINTM5nK zp<#bT8j?@qI~BAgN9*ZZUvChNl=wJA*HWUEkjSpCo$33M&!z(0=rp0&-aU?o0vor+RKy;M1Te~%RR zY4xX10VkWnLcA;OxTPaYTxa65`A1SLm~;>OPa7JP6tPv;zy#mi*nOJL13I~cS=qV@ zsx*`biUO5K>{m&OS9geIE5nj8b=1ThN`cFw32TVn!<)kHX-G?kVTOfA)p<%ZU!*#% zDSkgkI16C|9x4e1RlU0~iUmAEymquJWT2FrM?7H(;#ObL^zd6#W~L(s<3PgT!KrO7 z=MRpuuAG2n60}z-Ag-+h%E3X2heB05VMZ(spnNtIKJtC$H)U%v#1&*EI%!=mF@xc| z@c3OWeG-o(RPG;Rjw&uM54s$Cz>d1uQ7VFD9rc$1rXewdpSQotX{Iyik6YZ*nHMIe zbaX`iPT;1`SfRK4ZBR6F+HE9v{Q}t4TN{BPCi_|>{Cq8E#Oey=hZ;8AAxX=I^gZv_ zy$TJYS`|rjI64$JEg{VW-y40ls5kHlul^YGxexkREGVOb=6DKE?y+d%f_S@6)?=A; zl=vC{?VGYKzEFg+tT{A4(g@|DfJE;(fP$81N)sK!>bK2Uni_EzvhgF0gIMRj$aJus zxxM49&a;X0Am{<9T)o4*JZwW9AqDyHb{uKm?YOZ`!UTDD-eXgKhc#Ll#C(!a$h(*K z0-)_riH2k^)AwIfVd=iPx(miw?%HpaHBmPNd5TkhLSEX*YkRWGRHkB&<~eq24{c|q zKJIPiMs-^yvD;_Sg*kclJ}sNs++t;7qJ#7=(9m`xp4vYK)0UJ$MvMyx-msJs$$()B$B;-M|T{77iDLBbn+bHpI4 z7RV*{8vKe_kRa=9!<@un?Fnn_>XkiHUu(XnBqy;70x|qxc;*-6!c6pyLyYUJ}Yh`^?Z+3dH&ZONd`BH>S-4pV00w5zVt*dub|*ls#+xxl#v+4 zk?;5Yn#G@`>@K{!IW{x9?Qd!qY(pVav!H7D3qU4R&BiIIj0XLKYFsO}zY$IATb|^^ z7l5YU1jR{vT{&A4GKqfbw7FL$!Pr(hPm=ih{_5NX!}j-^W;>-PnO!?|8!UTYakQtZ z{)7#Anb{PCT3m1l!7>95ZX-#2@PJ>*^p2)QGl|1hToLoK_)P2O>izO`wC*O1P@Bvh zDTtkYNw|hbU&!*+oPWD|!mm{CfP+fsW85l*Gnw3qWdhnp`Jp}?}4v5;8t7eMXj$ts#E&RV9_Qo(RD zPbuf$lQJnHU`SaJlF(6Llj!51;1Gd`9nG|xOUl6r=ij#mZKB)nE1oUR8fVzrGxtY~FF zo>a;M%6DhteY+|-?uAy3cqDBsZaE08=h{_tpoEN|9Ept_CrU83Dr9Ag(|3S8K zj7zyKja!N^C?ap!f&@Gmf;Gk{>1SLRC~o`TUJmEPnDwGi$z6T&sP)@u8eL+Dhvx66vWBadw1wv~h;IByT67XX@3ZMY-$Ld*g0L)MEV2;O23l^hYhiq%uS+i17{7M%y17;@=?F*K-^^ zr0C7CLwM{s)p0FR#?h=*bNHuOISD}NPbKVJ_7DWy;XMf5|GF~S7 z`Vj$NwrDo&vN`z8JX|vp1oxL)3-VU{qchn0ftqSz#rSaS?vFNQ?AVWnnq~ z>)koC>Zgio#~x>Fcgf*Qeq$*JKSJK3!Z?e>~56w^3CpPI;@I3oCe1j&9<`^U` zCgRo;xC0E?&Xy01(G(qo6e&T|51uPTnqO8YLwd2}k2$j{|E&LPX)HriPm#dPN=mYFIg0idZYc{}ud z)py8dO1@02(hp>n{spg?sglw7Eq}f%vZ~`aud*y8>|8C~&aYi9m2!@iY1?sR#^bki z-0JkEpnoo51TVsB55E?jOE5;0)Shk4V?0kPD7G%6*(P@}%`$R*y0Y#UD*Z`kCh0%( z>hH=kg-;7-rN9dSG#maZk5bqrTLegyeobQGTjJV|yc@iOjw9MJe`Cb!pKU(OAr?WN z)l?XyevFA?61-aa#5)j(SXz{Oouk>x_O*YkHBVxUq~(#JSma9@0N z*KF%RGjK>N&5eI(Ic{6G)la6i6)gGp7V@4a`ggZA?FI&%&eGkV)$)t#UOoRzBoJIT zXso_?^#);}al%W|L=>FML?04|_FhJrb@4i@H~&R-s87OZk`#E2&&K>OIkmBxK;Ck^UtmloA#L z;6Wg7fo&IZQ)p7JlsRL5=MXkz&dTUAn4@ogxuC!~9Ixn`ej;gi>}YBg&&Fk(sqbG- zoelzAv(n%fyggm&oxj!aShh02RQf=Ajfq|>>X2sMIy5HXd;V^0FvZ|;-<4i!o2Q*5 z+O4WjFWtzZFt;%hIZEC&9JR6`J$@K#P@3VK{e+p-F~Hd0Gqx-V@F`yW_B{GT!TP3@ z1Gk;ib{pK5%%EkhQ3~ZW5kZVf=?-PRwfS<{d$ADx^MfcqsSo#?e

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zc8w+4iyvsOw9eGjwn`Z8R@i0R9c63Nqop44>Zyn*K>!RTrid{tNcBZk-tCdV4WCY}M|NoCyy^NaI|9KaHBESG=cU8Ro4|JiH3SGE8E literal 0 HcmV?d00001 From 066f039a45e28cb35e1f0e3c8e1d8c8ecb5b4878 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:05:58 +0800 Subject: [PATCH 14/21] ADD file via upload --- feature_extractor.py | 103 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 feature_extractor.py diff --git a/feature_extractor.py b/feature_extractor.py new file mode 100644 index 0000000..32d85b6 --- /dev/null +++ b/feature_extractor.py @@ -0,0 +1,103 @@ +""" +风格特征提取器模块 - 基础版本和传统方法。 +""" + +import numpy as np +from typing import Tuple, Dict, Any +from abc import ABC, abstractmethod + + +class BaseFeatureExtractor(ABC): + """特征提取器基类""" + + @abstractmethod + def extract(self, image: np.ndarray) -> Dict[str, Any]: + """提取特征抽象方法""" + pass + + @abstractmethod + def get_features(self) -> Dict[str, Any]: + """获取特征抽象方法""" + pass + + +class StyleFeatureExtractor(BaseFeatureExtractor): + """ + 传统风格特征提取器。 + 通过计算图像的直方图和局部纹理特征来定义"风格"。 + """ + + def __init__(self, config: Dict[str, Any]): + """ + 初始化提取器。 + + Args: + config: 配置字典，包含特征提取参数。 + """ + self.config = config + self.style_features: Dict[str, Any] = {} + + def extract(self, image: np.ndarray) -> Dict[str, Any]: + """ + 从图像中提取风格特征。 + + Args: + image: 输入图像，形状为 (H, W, C)，值域 [0, 255]。 + + Returns: + 一个包含各种风格特征的字典。 + """ + # 确保图像在[0, 1]范围 + image_normalized = image.astype(np.float32) / 255.0 + + features = {} + + # 1. 颜色直方图 (RGB通道) + hist_r, _ = np.histogram(image_normalized[:, :, 0].ravel(), bins=32, range=(0, 1)) + hist_g, _ = np.histogram(image_normalized[:, :, 1].ravel(), bins=32, range=(0, 1)) + hist_b, _ = np.histogram(image_normalized[:, :, 2].ravel(), bins=32, range=(0, 1)) + features['color_histogram'] = np.concatenate([hist_r, hist_g, hist_b]) + + # 2. 局部纹理特征 + diff_h = np.abs(np.diff(image_normalized, axis=1)).mean() + diff_v = np.abs(np.diff(image_normalized, axis=0)).mean() + features['texture_strength'] = np.array([diff_h, diff_v]) + + # 3. 平均颜色 + features['mean_color'] = image_normalized.mean(axis=(0, 1)) + + # 4. 颜色标准差 + features['color_std'] = image_normalized.std(axis=(0, 1)) + + self.style_features = features + return features + + def get_features(self) -> Dict[str, Any]: + """ + 获取最近一次提取的风格特征。 + + Returns: + 风格特征字典。 + """ + return self.style_features.copy() + + +# 工厂函数，便于创建不同类型的提取器 +def create_feature_extractor(extractor_type: str, config: Dict[str, Any]) -> BaseFeatureExtractor: + """ + 创建特征提取器工厂函数。 + + Args: + extractor_type: 提取器类型 ('traditional' 或 'neural') + config: 配置字典 + + Returns: + 特征提取器实例 + """ + if extractor_type == 'traditional': + return StyleFeatureExtractor(config) + elif extractor_type == 'neural': + from .neural_feature_extractor import NeuralStyleFeatureExtractor + return NeuralStyleFeatureExtractor(config) + else: + raise ValueError(f"不支持的提取器类型: {extractor_type}") \ No newline at end of file From d4b8d18af9b02d5b786148d46c5a024d78b60b8a Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:06:10 +0800 Subject: [PATCH 15/21] ADD file via upload --- image_synthesizer.py | 171 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 image_synthesizer.py diff --git a/image_synthesizer.py b/image_synthesizer.py new file mode 100644 index 0000000..50558e5 --- /dev/null +++ b/image_synthesizer.py @@ -0,0 +1,171 @@ +""" +图像合成器模块 - 基础版本和传统方法。 +""" + +import numpy as np +from typing import Tuple, Dict, Any +from scipy.optimize import minimize +import warnings +from abc import ABC, abstractmethod + +warnings.filterwarnings("ignore", category=UserWarning) + + +class BaseImageSynthesizer(ABC): + """图像合成器基类""" + + @abstractmethod + def set_content(self, image: np.ndarray): + """设置内容图像""" + pass + + @abstractmethod + def set_style_features(self, features: Dict[str, Any]): + """设置风格特征""" + pass + + @abstractmethod + def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray: + """合成图像""" + pass + + +class ImageSynthesizer(BaseImageSynthesizer): + """ + 传统图像合成器，负责生成最终的风格化图像。 + """ + + def __init__(self, config: Dict[str, Any]): + """ + 初始化合成器。 + + Args: + config: 配置字典。 + """ + self.config = config + self.content_image: np.ndarray = None + self.style_features: Dict[str, Any] = {} + self.synthesized_image: np.ndarray = None + + def set_content(self, image: np.ndarray): + """ + 设置内容图像。 + + Args: + image: 内容图像，形状为 (H, W, C)。 + """ + self.content_image = image.astype(np.float32) / 255.0 + + def set_style_features(self, features: Dict[str, Any]): + """ + 设置风格特征。 + + Args: + features: 由 StyleFeatureExtractor 提取的特征字典。 + """ + self.style_features = features + + def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray: + """ + 合成风格化图像。 + + Args: + initial_image: 初始图像，如果为None，则使用内容图像。 + max_iter: 最大迭代次数。 + + Returns: + 合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。 + """ + if initial_image is None: + initial_image = self.content_image.copy() + + # 将图像展平为一维向量以便于优化 + h, w, c = self.content_image.shape + x0 = initial_image.ravel() + + # 定义损失函数 + def loss_function(x): + # 重塑回图像形状 + img = x.reshape((h, w, c)) + + # 1. 内容损失：保持与原图相似 + content_loss = np.mean((img - self.content_image) ** 2) + + # 2. 风格损失：匹配风格特征 + style_loss = self._calculate_style_loss(img) + + # 总损失 + total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss + + return total_loss + + # 执行优化 + result = minimize(loss_function, x0, method='L-BFGS-B', + options={'maxiter': max_iter, 'disp': False}) + + # 重塑结果并转换回[0, 255] + synthesized = result.x.reshape((h, w, c)) + synthesized = np.clip(synthesized, 0, 1) * 255 + self.synthesized_image = synthesized.astype(np.uint8) + + return self.synthesized_image + + def _calculate_style_loss(self, image: np.ndarray) -> float: + """ + 计算当前图像与目标风格特征之间的损失。 + + Args: + image: 当前合成的图像。 + + Returns: + 风格损失值。 + """ + loss = 0.0 + + # 1. 颜色直方图损失 + hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1)) + hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1)) + hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1)) + current_hist = np.concatenate([hist_r, hist_g, hist_b]) + target_hist = self.style_features['color_histogram'] + # 使用KL散度作为距离度量 + epsilon = 1e-10 + kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon))) + loss += kl_div + + # 2. 纹理强度损失 + diff_h = np.abs(np.diff(image, axis=1)).mean() + diff_v = np.abs(np.diff(image, axis=0)).mean() + current_texture = np.array([diff_h, diff_v]) + target_texture = self.style_features['texture_strength'] + texture_loss = np.mean((current_texture - target_texture) ** 2) + loss += texture_loss + + # 3. 平均颜色损失 + current_mean = image.mean(axis=(0, 1)) + target_mean = self.style_features['mean_color'] + mean_loss = np.mean((current_mean - target_mean) ** 2) + loss += mean_loss + + return loss + + +# 工厂函数 +def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer: + """ + 创建图像合成器工厂函数。 + + Args: + synthesizer_type: 合成器类型 ('traditional' 或 'neural') + config: 配置字典 + + Returns: + 图像合成器实例 + """ + if synthesizer_type == 'traditional': + return ImageSynthesizer(config) + elif synthesizer_type == 'neural': + from .neural_image_synthesizer import NeuralImageSynthesizer + return NeuralImageSynthesizer(config) + else: + raise ValueError(f"不支持的合成器类型: {synthesizer_type}") \ No newline at end of file From 6c595120508944cc399853d6848ff138c38f7292 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:43:38 +0800 Subject: [PATCH 16/21] Delete 'extended_result_133911.jpg' --- extended_result_133911.jpg | Bin 41775 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 extended_result_133911.jpg diff --git a/extended_result_133911.jpg b/extended_result_133911.jpg deleted file mode 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------------------------------------------- 1 file changed, 103 deletions(-) delete mode 100644 feature_extractor.py diff --git a/feature_extractor.py b/feature_extractor.py deleted file mode 100644 index 32d85b6..0000000 --- a/feature_extractor.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -风格特征提取器模块 - 基础版本和传统方法。 -""" - -import numpy as np -from typing import Tuple, Dict, Any -from abc import ABC, abstractmethod - - -class BaseFeatureExtractor(ABC): - """特征提取器基类""" - - @abstractmethod - def extract(self, image: np.ndarray) -> Dict[str, Any]: - """提取特征抽象方法""" - pass - - @abstractmethod - def get_features(self) -> Dict[str, Any]: - """获取特征抽象方法""" - pass - - -class StyleFeatureExtractor(BaseFeatureExtractor): - """ - 传统风格特征提取器。 - 通过计算图像的直方图和局部纹理特征来定义"风格"。 - """ - - def __init__(self, config: Dict[str, Any]): - """ - 初始化提取器。 - - Args: - config: 配置字典，包含特征提取参数。 - """ - self.config = config - self.style_features: Dict[str, Any] = {} - - def extract(self, image: np.ndarray) -> Dict[str, Any]: - """ - 从图像中提取风格特征。 - - Args: - image: 输入图像，形状为 (H, W, C)，值域 [0, 255]。 - - Returns: - 一个包含各种风格特征的字典。 - """ - # 确保图像在[0, 1]范围 - image_normalized = image.astype(np.float32) / 255.0 - - features = {} - - # 1. 颜色直方图 (RGB通道) - hist_r, _ = np.histogram(image_normalized[:, :, 0].ravel(), bins=32, range=(0, 1)) - hist_g, _ = np.histogram(image_normalized[:, :, 1].ravel(), bins=32, range=(0, 1)) - hist_b, _ = np.histogram(image_normalized[:, :, 2].ravel(), bins=32, range=(0, 1)) - features['color_histogram'] = np.concatenate([hist_r, hist_g, hist_b]) - - # 2. 局部纹理特征 - diff_h = np.abs(np.diff(image_normalized, axis=1)).mean() - diff_v = np.abs(np.diff(image_normalized, axis=0)).mean() - features['texture_strength'] = np.array([diff_h, diff_v]) - - # 3. 平均颜色 - features['mean_color'] = image_normalized.mean(axis=(0, 1)) - - # 4. 颜色标准差 - features['color_std'] = image_normalized.std(axis=(0, 1)) - - self.style_features = features - return features - - def get_features(self) -> Dict[str, Any]: - """ - 获取最近一次提取的风格特征。 - - Returns: - 风格特征字典。 - """ - return self.style_features.copy() - - -# 工厂函数，便于创建不同类型的提取器 -def create_feature_extractor(extractor_type: str, config: Dict[str, Any]) -> BaseFeatureExtractor: - """ - 创建特征提取器工厂函数。 - - Args: - extractor_type: 提取器类型 ('traditional' 或 'neural') - config: 配置字典 - - Returns: - 特征提取器实例 - """ - if extractor_type == 'traditional': - return StyleFeatureExtractor(config) - elif extractor_type == 'neural': - from .neural_feature_extractor import NeuralStyleFeatureExtractor - return NeuralStyleFeatureExtractor(config) - else: - raise ValueError(f"不支持的提取器类型: {extractor_type}") \ No newline at end of file From ca034d7cc74d80e86b6618e24eeeffcbe8b842e4 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:43:54 +0800 Subject: [PATCH 19/21] Delete 'image_synthesizer.py' --- image_synthesizer.py | 171 ------------------------------------------- 1 file changed, 171 deletions(-) delete mode 100644 image_synthesizer.py diff --git a/image_synthesizer.py b/image_synthesizer.py deleted file mode 100644 index 50558e5..0000000 --- a/image_synthesizer.py +++ /dev/null @@ -1,171 +0,0 @@ -""" -图像合成器模块 - 基础版本和传统方法。 -""" - -import numpy as np -from typing import Tuple, Dict, Any -from scipy.optimize import minimize -import warnings -from abc import ABC, abstractmethod - -warnings.filterwarnings("ignore", category=UserWarning) - - -class BaseImageSynthesizer(ABC): - """图像合成器基类""" - - @abstractmethod - def set_content(self, image: np.ndarray): - """设置内容图像""" - pass - - @abstractmethod - def set_style_features(self, features: Dict[str, Any]): - """设置风格特征""" - pass - - @abstractmethod - def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray: - """合成图像""" - pass - - -class ImageSynthesizer(BaseImageSynthesizer): - """ - 传统图像合成器，负责生成最终的风格化图像。 - """ - - def __init__(self, config: Dict[str, Any]): - """ - 初始化合成器。 - - Args: - config: 配置字典。 - """ - self.config = config - self.content_image: np.ndarray = None - self.style_features: Dict[str, Any] = {} - self.synthesized_image: np.ndarray = None - - def set_content(self, image: np.ndarray): - """ - 设置内容图像。 - - Args: - image: 内容图像，形状为 (H, W, C)。 - """ - self.content_image = image.astype(np.float32) / 255.0 - - def set_style_features(self, features: Dict[str, Any]): - """ - 设置风格特征。 - - Args: - features: 由 StyleFeatureExtractor 提取的特征字典。 - """ - self.style_features = features - - def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray: - """ - 合成风格化图像。 - - Args: - initial_image: 初始图像，如果为None，则使用内容图像。 - max_iter: 最大迭代次数。 - - Returns: - 合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。 - """ - if initial_image is None: - initial_image = self.content_image.copy() - - # 将图像展平为一维向量以便于优化 - h, w, c = self.content_image.shape - x0 = initial_image.ravel() - - # 定义损失函数 - def loss_function(x): - # 重塑回图像形状 - img = x.reshape((h, w, c)) - - # 1. 内容损失：保持与原图相似 - content_loss = np.mean((img - self.content_image) ** 2) - - # 2. 风格损失：匹配风格特征 - style_loss = self._calculate_style_loss(img) - - # 总损失 - total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss - - return total_loss - - # 执行优化 - result = minimize(loss_function, x0, method='L-BFGS-B', - options={'maxiter': max_iter, 'disp': False}) - - # 重塑结果并转换回[0, 255] - synthesized = result.x.reshape((h, w, c)) - synthesized = np.clip(synthesized, 0, 1) * 255 - self.synthesized_image = synthesized.astype(np.uint8) - - return self.synthesized_image - - def _calculate_style_loss(self, image: np.ndarray) -> float: - """ - 计算当前图像与目标风格特征之间的损失。 - - Args: - image: 当前合成的图像。 - - Returns: - 风格损失值。 - """ - loss = 0.0 - - # 1. 颜色直方图损失 - hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1)) - hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1)) - hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1)) - current_hist = np.concatenate([hist_r, hist_g, hist_b]) - target_hist = self.style_features['color_histogram'] - # 使用KL散度作为距离度量 - epsilon = 1e-10 - kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon))) - loss += kl_div - - # 2. 纹理强度损失 - diff_h = np.abs(np.diff(image, axis=1)).mean() - diff_v = np.abs(np.diff(image, axis=0)).mean() - current_texture = np.array([diff_h, diff_v]) - target_texture = self.style_features['texture_strength'] - texture_loss = np.mean((current_texture - target_texture) ** 2) - loss += texture_loss - - # 3. 平均颜色损失 - current_mean = image.mean(axis=(0, 1)) - target_mean = self.style_features['mean_color'] - mean_loss = np.mean((current_mean - target_mean) ** 2) - loss += mean_loss - - return loss - - -# 工厂函数 -def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer: - """ - 创建图像合成器工厂函数。 - - Args: - synthesizer_type: 合成器类型 ('traditional' 或 'neural') - config: 配置字典 - - Returns: - 图像合成器实例 - """ - if synthesizer_type == 'traditional': - return ImageSynthesizer(config) - elif synthesizer_type == 'neural': - from .neural_image_synthesizer import NeuralImageSynthesizer - return NeuralImageSynthesizer(config) - else: - raise ValueError(f"不支持的合成器类型: {synthesizer_type}") \ No newline at end of file From 9e5fad485721ba861aa34c2aaf670ac664616675 Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:43:59 +0800 Subject: [PATCH 20/21] Delete 'requirements.txt' --- requirements.txt | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index fd393b8..0000000 --- a/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -numpy>=1.24.0 -Pillow>=9.0.0 -scipy>=1.10.0 -PyYAML>=6.0.0 \ No newline at end of file From 626e5f869ec0cab1cc44a7f603dcaedb22fd4cce Mon Sep 17 00:00:00 2001 From: p82b7rtam <1761133400@qq.com> Date: Mon, 29 Dec 2025 17:44:03 +0800 Subject: [PATCH 21/21] Delete 'run_style_transfer.py' --- run_style_transfer.py | 263 ------------------------------------------ 1 file changed, 263 deletions(-) delete mode 100644 run_style_transfer.py diff --git a/run_style_transfer.py b/run_style_transfer.py deleted file mode 100644 index 80f5be2..0000000 --- a/run_style_transfer.py +++ /dev/null @@ -1,263 +0,0 @@ -# scripts/extended_training.py -import warnings - -warnings.filterwarnings("ignore", category=UserWarning, module="torchvision") - -import torch -import torch.nn as nn -import torch.optim as optim -from torchvision import models, transforms -from PIL import Image -from pathlib import Path -import yaml -import time -import numpy as np - - -print("=" * 60) - - -def main(): - # 加载配置 - config_path = Path(__file__).parent.parent / "configs" / "default.yaml" - with open(config_path, 'r', encoding='utf-8') as f: - config = yaml.safe_load(f) - - # ========== 关键修复：大幅提高风格影响 ========== - config['model']['image_size'] = 256 - config['model']['content_weight'] = 1e3 # 降低内容权重 - config['model']['style_weight'] = 1e7 # 大幅提高风格权重 - config['model']['learning_rate'] = 0.003 - config['training']['num_steps'] = 400 - config['multi_scale']['enabled'] = False - - print("📋 长时间训练参数:") - print(f" - 图像尺寸: 256x256") - print(f" - 内容权重: 1e3 (风格权重的1/10000)") - print(f" - 风格权重: 1e7 (内容权重的10000倍)") - print(f" - 权重比例: 1:10000 (强调风格)") - print(f" - 学习率: 0.003") - print(f" - 训练步数: 400 (约5分钟)") - - # 设备设置 - device = torch.device("cpu") - torch.set_num_threads(2) - - # 加载图像 - project_root = Path(__file__).parent.parent - content_path = project_root / config['paths']['content_image'] - style_path = project_root / config['paths']['style_image'] - output_dir = project_root / config['paths']['output_dir'] - - print(f"\n📷 加载图像...") - content_img = Image.open(content_path).convert('RGB') - style_img = Image.open(style_path).convert('RGB') - - image_size = 256 - content_img = content_img.resize((image_size, image_size), Image.Resampling.LANCZOS) - style_img = style_img.resize((image_size, image_size), Image.Resampling.LANCZOS) - - print(f" 调整到: {image_size}x{image_size}") - - # 预处理（移除标准化） - preprocess = transforms.Compose([ - transforms.ToTensor(), - # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 注释掉 - ]) - - content_tensor = preprocess(content_img).unsqueeze(0).to(device) - style_tensor = preprocess(style_img).unsqueeze(0).to(device) - target_tensor = content_tensor.clone().requires_grad_(True) - - # 加载VGG16模型 - print("\n🔧 加载VGG16模型...") - start_time = time.time() - vgg = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features.to(device).eval() - - for param in vgg.parameters(): - param.requires_grad_(False) - - print(f"✅ 模型加载完成 ({time.time() - start_time:.2f}秒)") - - # 定义层索引 - content_layers = ["21"] # conv4_2 - style_layers = ["0", "5", "10", "19", "28"] # conv1_1, conv2_1, conv3_1, conv4_1, conv5_1 - - # 特征提取函数 - def get_features_fixed(x, layers): - features = {} - x_tmp = x - layers_int = set([int(l) for l in layers]) - - for idx, (name, layer) in enumerate(vgg._modules.items()): - x_tmp = layer(x_tmp) - if idx in layers_int: - features[str(idx)] = x_tmp - - return features - - # 提取特征 - print("\n📊 提取特征...") - start_time = time.time() - - # 标准化输入以匹配VGG的预训练权重 - def normalize_for_vgg(tensor): - mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device) - std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device) - return (tensor - mean) / std - - content_features = get_features_fixed(normalize_for_vgg(content_tensor), content_layers) - style_features = get_features_fixed(normalize_for_vgg(style_tensor), style_layers) - - # 修改Gram矩阵计算（关键修复） - def gram_matrix_fixed(tensor): - b, c, h, w = tensor.size() - - # 展平特征 - tensor_flat = tensor.view(b, c, -1) # [b, c, h*w] - - # 计算Gram矩阵 - gram = torch.bmm(tensor_flat, tensor_flat.transpose(1, 2)) # [b, c, c] - - # 关键修改：只除以h*w，而不是c*h*w（原始论文做法） - gram = gram / (h * w) - - return gram - - # 预计算风格Gram矩阵 - style_grams = {} - for layer, feat in style_features.items(): - style_grams[layer] = gram_matrix_fixed(feat) - print(f" 层 {layer} Gram矩阵形状: {style_grams[layer].shape}") - - print(f"✅ 特征提取完成 ({time.time() - start_time:.2f}秒)") - - # 优化器设置 - learning_rate = 0.003 # 使用更高的学习率 - optimizer = optim.Adam([target_tensor], lr=learning_rate) - scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5) - - # 训练参数 - content_weight = 1e3 - style_weight = 1e7 # 大幅提高 - num_steps = 400 - log_interval = 40 - - print(f"\n🎨 开始长时间训练 (约5分钟)...") - print(f" 优化器: Adam (初始lr={learning_rate})") - print(f" 内容权重: {content_weight}") - print(f" 风格权重: {style_weight} (非常高，强调风格转移)") - print(f" 权重比例: 1:{int(style_weight / content_weight)}") - - start_time = time.time() - - # 记录损失历史 - loss_history = [] - content_loss_history = [] - style_loss_history = [] - - for step in range(1, num_steps + 1): - optimizer.zero_grad() - - # 提取目标特征（需要标准化） - target_normalized = normalize_for_vgg(target_tensor) - target_features = get_features_fixed(target_normalized, content_layers + style_layers) - - # 计算内容损失 - content_loss = torch.tensor(0.0, device=device) - for layer in content_layers: - if layer in target_features and layer in content_features: - diff = target_features[layer] - content_features[layer] - content_loss += torch.mean(diff ** 2) - - # 计算风格损失 - style_loss = torch.tensor(0.0, device=device) - for layer in style_layers: - if layer in target_features and layer in style_grams: - target_gram = gram_matrix_fixed(target_features[layer]) - style_gram = style_grams[layer] - diff = target_gram - style_gram - style_loss += torch.mean(diff ** 2) - - # 总损失 - total_loss = content_weight * content_loss + style_weight * style_loss - - # 反向传播 - total_loss.backward() - - # 梯度裁剪（提高限制） - torch.nn.utils.clip_grad_norm_([target_tensor], max_norm=10.0) - - optimizer.step() - - # 更新学习率 - if step % 100 == 0: - scheduler.step() - current_lr = optimizer.param_groups[0]['lr'] - print(f"📉 第 {step} 步: 学习率降低到 {current_lr:.6f}") - - # 限制像素值 - with torch.no_grad(): - target_tensor.data.clamp_(0, 1) - - # 记录损失 - loss_history.append(total_loss.item()) - content_loss_history.append(content_loss.item()) - style_loss_history.append(style_loss.item()) - - # 定期输出 - if step % log_interval == 0 or step == num_steps: - elapsed = time.time() - start_time - steps_per_second = step / elapsed if elapsed > 0 else 0 - remaining_steps = num_steps - step - estimated_remaining = remaining_steps / steps_per_second if steps_per_second > 0 else 0 - - # 计算风格损失贡献 - style_contribution = style_weight * style_loss.item() - content_contribution = content_weight * content_loss.item() - style_ratio = style_contribution / (style_contribution + content_contribution) * 100 - - print(f"⏳ 第 {step:3d}/{num_steps} | " - f"总损失: {total_loss.item():8.2f} | " - f"内容: {content_loss.item():6.4f} | " - f"风格: {style_loss.item():6.4f} | " - f"风格贡献: {style_ratio:.1f}% | " - f"用时: {elapsed:.1f}s | " - f"剩余: {estimated_remaining:.1f}s") - - total_time = time.time() - start_time - - # 分析损失变化 - print(f"\n📈 损失分析:") - print(f" 总损失变化: {loss_history[0]:.2f} → {loss_history[-1]:.2f}") - print(f" 内容损失变化: {content_loss_history[0]:.4f} → {content_loss_history[-1]:.4f}") - print(f" 风格损失变化: {style_loss_history[0]:.4f} → {style_loss_history[-1]:.4f}") - - # 保存结果 - postprocess = transforms.Compose([ - # transforms.Normalize(mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225], - # std=[1/0.229, 1/0.224, 1/0.225]), # 如果之前没标准化，这里也不需要 - transforms.ToPILImage() - ]) - - output_dir.mkdir(parents=True, exist_ok=True) - with torch.no_grad(): - result_img = postprocess(target_tensor.squeeze(0).cpu()) - - timestamp = time.strftime("%H%M%S") - output_path = output_dir / f"extended_result_{timestamp}.jpg" - result_img.save(output_path, quality=95, optimize=True) - - print(f"\n🎉 训练完成!") - print(f" 总用时: {total_time:.1f}秒") - print(f" 结果保存至: {output_path}") - - # 尝试显示结果 - try: - result_img.show(title="风格迁移结果") - except: - print(" ℹ️ 请手动打开图像查看") - - -if __name__ == "__main__": - main() \ No newline at end of file