add gitigore

.gitignore

@@ -0,0 +1 @@
+test_save.py

test_save.py

@@ -1,114 +0,0 @@
-import os
-import numpy as np
-import torch
-from pathlib import Path
-
-def test_saved_files(content_path, num_epochs=3):
-    """测试保存的文件是否符合要求
-    
-    Args:
-        content_path: 训练过程的根目录
-        num_epochs: 要测试的epoch数量
-    """
-    # 检查目录结构
-    required_dirs = [
-        'model',
-        'dataset/representation',
-        'dataset/prediction',
-        'dataset/label'
-    ]
-    
-    for dir_path in required_dirs:
-        full_path = os.path.join(content_path, dir_path)
-        if not os.path.exists(full_path):
-            print(f"错误: 目录不存在: {full_path}")
-            return False
-    
-    # 检查模型文件
-    print("\n检查模型文件...")
-    for epoch in range(1, num_epochs + 1):
-        model_path = os.path.join(content_path, 'model', f'{epoch}.pth')
-        if not os.path.exists(model_path):
-            print(f"错误: 模型文件不存在: {model_path}")
-            return False
-        try:
-            # 尝试加载模型文件以验证其有效性
-            state_dict = torch.load(model_path, map_location='cpu')
-            print(f"✓ {epoch}.pth 格式正确")
-        except Exception as e:
-            print(f"错误: 无法加载模型文件 {model_path}: {str(e)}")
-            return False
-    
-    # 检查特征向量文件
-    print("\n检查特征向量文件...")
-    prev_samples = None
-    for epoch in range(1, num_epochs + 1):
-        repr_path = os.path.join(content_path, 'dataset', 'representation', f'{epoch}.npy')
-        if not os.path.exists(repr_path):
-            print(f"错误: 特征向量文件不存在: {repr_path}")
-            return False
-        try:
-            features = np.load(repr_path)
-            samples, dim = features.shape
-            if not (512 <= dim <= 1024):
-                print(f"警告: 特征维度 {dim} 不在预期范围[512, 1024]内")
-            if prev_samples is not None and samples != prev_samples:
-                print(f"错误: epoch {epoch} 的样本数量与之前不一致")
-                return False
-            prev_samples = samples
-            print(f"✓ {epoch}.npy 格式正确 [样本数: {samples}, 特征维度: {dim}]")
-        except Exception as e:
-            print(f"错误: 无法加载特征向量文件 {repr_path}: {str(e)}")
-            return False
-    
-    # 检查预测结果文件
-    print("\n检查预测结果文件...")
-    for epoch in range(1, num_epochs + 1):
-        pred_path = os.path.join(content_path, 'dataset', 'prediction', f'{epoch}.npy')
-        if not os.path.exists(pred_path):
-            print(f"错误: 预测结果文件不存在: {pred_path}")
-            return False
-        try:
-            predictions = np.load(pred_path)
-            samples, classes = predictions.shape
-            if samples != prev_samples:
-                print(f"错误: 预测结果的样本数量与特征向量不一致")
-                return False
-            if classes != 10:  # CIFAR-10 有10个类别
-                print(f"警告: 类别数量 {classes} 不等于10")
-            print(f"✓ {epoch}.npy 格式正确 [样本数: {samples}, 类别数: {classes}]")
-        except Exception as e:
-            print(f"错误: 无法加载预测结果文件 {pred_path}: {str(e)}")
-            return False
-    
-    # 检查标签文件
-    print("\n检查标签文件...")
-    label_path = os.path.join(content_path, 'dataset', 'label', 'labels.npy')
-    if not os.path.exists(label_path):
-        print(f"错误: 标签文件不存在: {label_path}")
-        return False
-    try:
-        labels = np.load(label_path)
-        if len(labels.shape) != 1:
-            print(f"错误: 标签文件维度不正确，应为1维数组，实际为{len(labels.shape)}维")
-            return False
-        if labels.shape[0] != prev_samples:
-            print(f"错误: 标签数量与样本数量不一致")
-            return False
-        if not np.all((labels >= 0) & (labels < 10)):  # CIFAR-10 的标签范围是0-9
-            print("错误: 存在超出范围的标签值")
-            return False
-        print(f"✓ labels.npy 格式正确 [样本数: {labels.shape[0]}]")
-    except Exception as e:
-        print(f"错误: 无法加载标签文件 {label_path}: {str(e)}")
-        return False
-    
-    print("\n✓ 所有文件格式检查通过！")
-    return True
-
-if __name__ == "__main__":
-    # 设置要测试的目录路径
-    content_path = "/home/ruofei/RRF/ttvnet/Image/AlexNet/model/0"
-    
-    # 运行测试
-    test_saved_files(content_path, num_epochs=44)