ShuffleNetV2-CIFAR10/Classification-noisy/scripts/get_representation.py

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):
                # 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:
                    # 处理元组类型的输出
                    if isinstance(output, tuple):
                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
                        activation[name] = output[0].detach() if len(output) > 0 else None
                    else:
                        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:
                    # 处理元组类型的输出
                    if isinstance(output, tuple):
                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
                        activation[name] = output[0].detach() if len(output) > 0 else None
                    else:
                        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": list(range(50000)),  # 所有数据默认为训练集
                "test": list(range(50000, 60000)),  # 测试集索引从50000到59999
                "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]}]")