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]}]")