feat:add get_representation.py

ResNet-CIFAR10/Classification-normal/scripts/get_representation.py

@@ -0,0 +1,272 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import os
+import json
+from tqdm import tqdm
+
+class time_travel_saver:
+    """可视化数据提取器
+    
+    用于保存模型训练过程中的各种数据，包括：
+    1. 模型权重 (.pth)
+    2. 高维特征 (representation/*.npy)
+    3. 预测结果 (prediction/*.npy)
+    4. 标签数据 (label/labels.npy)
+    """
+    
+    def __init__(self, model, dataloader, device, save_dir, model_name, 
+                auto_save_embedding=False, layer_name=None,show = False):
+        """初始化
+        
+        Args:
+            model: 要保存的模型实例
+            dataloader: 数据加载器（必须是顺序加载的）
+            device: 计算设备(cpu or gpu)
+            save_dir: 保存根目录
+            model_name: 模型名称
+        """
+        self.model = model
+        self.dataloader = dataloader
+        self.device = device
+        self.save_dir = save_dir
+        self.model_name = model_name
+        self.auto_save = auto_save_embedding
+        self.layer_name = layer_name
+
+        if show and not layer_name:
+            layer_dimensions = self.show_dimensions()
+            # print(layer_dimensions)
+        
+    def show_dimensions(self):
+        """显示模型中所有层的名称和对应的维度
+        
+        这个函数会输出模型中所有层的名称和它们的输出维度，
+        帮助用户选择合适的层来提取特征。
+        
+        Returns:
+            layer_dimensions: 包含层名称和维度的字典
+        """
+        activation = {}
+        layer_dimensions = {}
+        
+        def get_activation(name):
+            def hook(model, input, output):
+                activation[name] = output.detach()
+            return hook
+        
+        # 注册钩子到所有层
+        handles = []
+        for name, module in self.model.named_modules():
+            if isinstance(module, nn.Module) and not isinstance(module, nn.ModuleList) and not isinstance(module, nn.ModuleDict):
+                handles.append(module.register_forward_hook(get_activation(name)))
+        
+        self.model.eval()
+        with torch.no_grad():
+            # 获取一个batch来分析每层的输出维度
+            inputs, _ = next(iter(self.dataloader))
+            inputs = inputs.to(self.device)
+            _ = self.model(inputs)
+            
+            # 分析所有层的输出维度
+            print("\n模型各层的名称和维度:")
+            print("-" * 50)
+            print(f"{'层名称':<40} {'特征维度':<15} {'输出形状'}")
+            print("-" * 50)
+            
+            for name, feat in activation.items():
+                if feat is None:
+                    continue
+                
+                # 获取特征维度（展平后）
+                feat_dim = feat.view(feat.size(0), -1).size(1)
+                layer_dimensions[name] = feat_dim
+                # 打印层信息
+                shape_str = str(list(feat.shape))
+                print(f"{name:<40} {feat_dim:<15} {shape_str}")
+            
+            print("-" * 50)
+            print("注: 特征维度是将输出张量展平后的维度大小")
+            print("你可以通过修改time_travel_saver的layer_name参数来选择不同的层")
+            print("例如：layer_name='avg_pool'或layer_name='layer4'等")
+        
+        # 移除所有钩子
+        for handle in handles:
+            handle.remove()
+        
+        return layer_dimensions
+
+    def _extract_features_and_predictions(self):
+        """提取特征和预测结果
+        
+        Returns:
+            features: 高维特征 [样本数, 特征维度]
+            predictions: 预测结果 [样本数, 类别数]
+        """
+        features = []
+        predictions = []
+        indices = []
+        activation = {}
+        
+        def get_activation(name):
+            def hook(model, input, output):
+                # 只在需要时保存激活值，避免内存浪费
+                if name not in activation or activation[name] is None:
+                    activation[name] = output.detach()
+            return hook
+        
+        # 根据层的名称或维度来选择层
+
+        # 注册钩子到所有层
+        handles = []
+        for name, module in self.model.named_modules():
+            if isinstance(module, nn.Module) and not isinstance(module, nn.ModuleList) and not isinstance(module, nn.ModuleDict):
+                handles.append(module.register_forward_hook(get_activation(name)))
+        
+        self.model.eval()
+        with torch.no_grad():
+            # 首先获取一个batch来分析每层的输出维度
+            inputs, _ = next(iter(self.dataloader))
+            inputs = inputs.to(self.device)
+            _ = self.model(inputs)
+            
+            # 如果指定了层名，则直接使用该层
+            if self.layer_name is not None:
+                if self.layer_name not in activation:
+                    raise ValueError(f"指定的层 {self.layer_name} 不存在于模型中")
+                
+                feat = activation[self.layer_name]
+                if feat is None:
+                    raise ValueError(f"指定的层 {self.layer_name} 没有输出特征")
+                
+                suitable_layer_name = self.layer_name
+                suitable_dim = feat.view(feat.size(0), -1).size(1)
+                print(f"使用指定的特征层: {suitable_layer_name}, 特征维度: {suitable_dim}")
+            else:
+                # 找到维度在指定范围内的层
+                target_dim_range = (256, 2048)
+                suitable_layer_name = None
+                suitable_dim = None
+                
+                # 分析所有层的输出维度
+                for name, feat in activation.items():
+                    if feat is None:
+                        continue
+                    feat_dim = feat.view(feat.size(0), -1).size(1)
+                    if target_dim_range[0] <= feat_dim <= target_dim_range[1]:
+                        suitable_layer_name = name
+                        suitable_dim = feat_dim
+                        break
+                
+                if suitable_layer_name is None:
+                    raise ValueError("没有找到合适维度的特征层")
+                    
+                print(f"自动选择的特征层: {suitable_layer_name}, 特征维度: {suitable_dim}")
+            
+            # 保存层信息
+            layer_info = {
+                'layer_id': suitable_layer_name,
+                'dim': suitable_dim
+            }
+            layer_info_path = os.path.join(os.path.dirname(self.save_dir), 'layer_info.json')
+            with open(layer_info_path, 'w') as f:
+                json.dump(layer_info, f)
+            
+            # 清除第一次运行的激活值
+            activation.clear()
+            
+            # 现在处理所有数据
+            for batch_idx, (inputs, _) in enumerate(tqdm(self.dataloader, desc="提取特征和预测结果")):
+                inputs = inputs.to(self.device)
+                outputs = self.model(inputs)  # 获取预测结果
+                
+                # 获取并处理特征
+                feat = activation[suitable_layer_name]
+                flat_features = torch.flatten(feat, start_dim=1)
+                features.append(flat_features.cpu().numpy())
+                predictions.append(outputs.cpu().numpy())
+                
+                # 清除本次的激活值
+                activation.clear()
+        
+        # 移除所有钩子
+        for handle in handles:
+            handle.remove()
+        
+        if len(features) > 0:
+            features = np.vstack(features)
+            predictions = np.vstack(predictions)
+            return features, predictions
+        else:
+            return np.array([]), np.array([])
+    
+    def save_lables_index(self, path):
+        """保存标签数据和索引信息
+        
+        Args:
+            path: 保存路径
+        """
+        os.makedirs(path, exist_ok=True)
+        labels_path = os.path.join(path, 'labels.npy')
+        index_path = os.path.join(path, 'index.json')
+        
+        # 尝试从不同的属性获取标签
+        try:
+            if hasattr(self.dataloader.dataset, 'targets'):
+                # CIFAR10/CIFAR100使用targets属性
+                labels = np.array(self.dataloader.dataset.targets)
+            elif hasattr(self.dataloader.dataset, 'labels'):
+                # 某些数据集使用labels属性
+                labels = np.array(self.dataloader.dataset.labels)
+            else:
+                # 如果上面的方法都不起作用，则从数据加载器中收集标签
+                labels = []
+                for _, batch_labels in self.dataloader:
+                    labels.append(batch_labels.numpy())
+                labels = np.concatenate(labels)
+            
+            # 保存标签数据
+            np.save(labels_path, labels)
+            print(f"标签数据已保存到 {labels_path}")
+            
+            # 创建数据集索引
+            num_samples = len(labels)
+            indices = list(range(num_samples))
+            
+            # 创建索引字典
+            index_dict = {
+                "train": indices,  # 所有数据默认为训练集
+                "test": [],        # 初始为空
+                "validation": []   # 初始为空
+            }
+            
+            # 保存索引到JSON文件
+            with open(index_path, 'w') as f:
+                json.dump(index_dict, f, indent=4)
+            
+            print(f"数据集索引已保存到 {index_path}")
+            
+        except Exception as e:
+            print(f"保存标签和索引时出错: {e}")
+
+    def save_checkpoint_embeddings_predictions(self, model = None):
+        """保存所有数据"""
+        if model is not None:
+            self.model = model
+        # 保存模型权重
+        os.makedirs(self.save_dir, exist_ok=True)
+        model_path = os.path.join(self.save_dir,'model.pth')
+        torch.save(self.model.state_dict(), model_path)
+        
+        if self.auto_save:
+            # 提取并保存特征和预测结果
+            features, predictions = self._extract_features_and_predictions()
+            
+            # 保存特征 
+            np.save(os.path.join(self.save_dir, 'embeddings.npy'), features)
+            # 保存预测结果
+            np.save(os.path.join(self.save_dir, 'predictions.npy'), predictions)
+            print("\n保存了以下数据:")
+            print(f"- 模型权重: {model_path}")
+            print(f"- 特征向量: [样本数: {features.shape[0]}, 特征维度: {features.shape[1]}]")
+            print(f"- 预测结果: [样本数: {predictions.shape[0]}, 类别数: {predictions.shape[1]}]")

ResNet-CIFAR10/Classification-normal/scripts/train.py

@@ -10,15 +10,10 @@ import logging
 import numpy as np
 from tqdm import tqdm
 
-# 将项目根目录添加到Python路径中
-current_dir = Path(__file__).resolve().parent
-project_root = current_dir.parent.parent.parent
-sys.path.append(str(project_root))
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 
 from dataset_utils import get_cifar10_dataloaders
 from model import ResNet18
-from ttv_utils import time_travel_saver
+from get_representation import time_travel_saver
 
 def setup_logger(log_file):
     """配置日志记录器，如果日志文件存在则覆盖

ttv_utils/save_embeddings.py

@@ -6,7 +6,7 @@ import json
 from tqdm import tqdm
 
 class time_travel_saver:
-    """可视化数据保存类
+    """可视化数据提取器
     
     用于保存模型训练过程中的各种数据，包括：
     1. 模型权重 (.pth)