在网格搜索中加入了可视化，但是训练脚本好像给改错了。

faiss_vector_db/build_faiss_index.py

@@ -358,6 +358,64 @@ class FAISSIndexBuilder:
         return index
 
 
+def extract_embeddings_only(model_path: str, 
+                            train_dir: str, 
+                            output_dir: str,
+                            embedding_dim: int = 512,
+                            batch_size: int = 16) -> Tuple[np.ndarray, List[int]]:
+    """
+    仅提取特征向量并保存为JSON（用于可视化）
+    不构建完整的FAISS索引，节省时间
+    
+    Args:
+        model_path: 模型路径
+        train_dir: 训练数据目录
+        output_dir: 输出目录
+        embedding_dim: 特征向量维度
+        batch_size: 批处理大小
+    
+    Returns:
+        (embeddings, labels): 特征向量数组和标签列表
+    """
+    print("=" * 60)
+    print("开始提取特征向量用于可视化")
+    print("=" * 60)
+    
+    builder = FAISSIndexBuilder(model_path, embedding_dim)
+    
+    # 扫描数据
+    image_paths, class_names, labels = builder.scan_training_data(train_dir)
+    builder.image_paths = image_paths
+    builder.labels = labels
+    
+    # 提取特征
+    embeddings = builder.extract_features_batch(image_paths, batch_size)
+    builder.embeddings = embeddings
+    
+    # 仅保存 embeddings.json 和 labels.json（可视化需要的）
+    os.makedirs(output_dir, exist_ok=True)
+    
+    embeddings_json_path = os.path.join(output_dir, 'embeddings.json')
+    labels_json_path = os.path.join(output_dir, 'labels.json')
+    
+    print(f"保存特征向量到: {embeddings_json_path}")
+    with open(embeddings_json_path, "w", encoding="utf-8") as f:
+        json.dump(embeddings.tolist(), f, ensure_ascii=False)
+    
+    print(f"保存标签到: {labels_json_path}")
+    with open(labels_json_path, "w", encoding="utf-8") as f:
+        json.dump(labels, f, ensure_ascii=False)
+    
+    print("=" * 60)
+    print(f"✓ 特征向量提取完成")
+    print(f"  输出目录: {output_dir}")
+    print(f"  样本数: {len(embeddings)}")
+    print(f"  特征维度: {embedding_dim}")
+    print("=" * 60)
+    
+    return embeddings, labels
+
+
 class FAISSSearcher:
     """FAISS相似度检索器"""
     

faiss_vector_db/visualize_embeddings.py

@@ -160,6 +160,84 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
     plt.show()
 
 
+def visualize_embeddings_from_files(embeddings_path: str,
+                                    labels_path: str,
+                                    output_dir: str,
+                                    method: str = "pca",
+                                    max_points: Optional[int] = None,
+                                    seed: int = 42,
+                                    **kwargs) -> str:
+    """
+    从文件加载并可视化embeddings（用于训练脚本调用）
+    
+    Args:
+        embeddings_path: embeddings.json路径
+        labels_path: labels.json路径
+        output_dir: 输出目录
+        method: 降维方法 (pca/tsne/umap)
+        max_points: 抽样上限
+        seed: 随机种子
+        **kwargs: 其他降维参数
+    
+    Returns:
+        输出的PNG图片路径
+    """
+    print("=" * 60)
+    print(f"开始可视化 embeddings")
+    print(f"降维方法: {method}")
+    print("=" * 60)
+    
+    # 加载数据
+    print(f"加载数据: {embeddings_path}")
+    X = load_embeddings_json(embeddings_path)
+    y = load_labels_json(labels_path)
+    
+    # 数据对齐
+    if X.shape[0] != y.shape[0]:
+        n = min(X.shape[0], y.shape[0])
+        print(f"警告: 数据不一致，截断到 {n}")
+        X, y = X[:n], y[:n]
+    
+    # 抽样
+    Xs, ys, _ = subsample(X, y, max_points, seed=seed)
+    if Xs.shape[0] < X.shape[0]:
+        print(f"已抽样: {Xs.shape[0]}/{X.shape[0]}")
+    
+    # 降维参数
+    tsne_perplexity = kwargs.get('tsne_perplexity', 30)
+    umap_n_neighbors = kwargs.get('umap_n_neighbors', 15)
+    umap_min_dist = kwargs.get('umap_min_dist', 0.1)
+    
+    # 降维
+    print(f"执行降维: {method}")
+    Z = reduce_dim(Xs, method, seed, tsne_perplexity, umap_n_neighbors, umap_min_dist)
+    
+    # 诊断
+    print("\n==== 诊断信息 ====")
+    diag_info = diagnostics(Xs, ys, reduced2d=Z, method=method)
+    print(diag_info)
+    
+    # 保存诊断信息
+    diag_path = os.path.join(output_dir, f"embedding_{method}_diagnostics.txt")
+    with open(diag_path, 'w', encoding='utf-8') as f:
+        f.write(diag_info)
+    print(f"✓ 诊断信息已保存: {diag_path}")
+    
+    # 绘图
+    out_png = os.path.join(output_dir, f"embedding_{method}_2d.png")
+    title = f"Embedding {method.upper()} 2D (N={Xs.shape[0]})"
+    
+    # 为了在训练脚本中调用时不弹出窗口，我们需要关闭交互模式
+    plt.ioff()  # 关闭交互模式
+    plot_2d(Z, ys, title, out_path=out_png)
+    plt.close('all')  # 关闭所有图形
+    
+    print(f"✓ 可视化图已保存: {out_png}")
+    print("=" * 60)
+    
+    return out_png
+
+
 def main():
     parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断")
     # parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")

train/grid_search_cosface.py

@@ -46,6 +46,10 @@ logger = logging.getLogger(__name__)
         's': [56.0, 60.0, 64.0, 68.0],  # scale参数
         'm': [0.32, 0.35, 0.38, 0.40],  # margin参数
     },
+     'whole_ingredient': {
+        's': [56.0, 60.0, 64.0, 68.0, 72.0],
+        'm': [0.32, 0.35, 0.38, 0.40, 0.45],
+    },
 """
 GRID_PARAMS = {
     'dish': {
@@ -53,8 +57,8 @@ GRID_PARAMS = {
         'm': [0.32, 0.35, 0.38, 0.40,0.45,0.50],  # margin参数
     },
     'whole_ingredient': {
-        's': [56.0, 60.0, 64.0, 68.0, 72.0],
-        'm': [0.32, 0.35, 0.38, 0.40, 0.45],
+        's': [56.0, 60.0 ,64.0, 68.0],
+        'm': [0.32, 0.35, 0.38, 0.40],
     },
     'processed_ingredient': {
         's': [56.0, 60.0, 64.0, 68.0],

train/train_cosface_embedding.py

@@ -411,6 +411,53 @@ def main(task_key: str = 'dish', s: Optional[float] = None, m: Optional[float] =
             logger.info(f'   最佳验证损失: {best_val_loss:.4f}')
             logger.info(f'   最佳验证准确率: {best_val_acc:.2f}%')
             logger.info('=' * 60)
+            
+            # ===== 生成特征向量可视化 =====
+            logger.info('🎨 开始生成特征向量可视化...')
+            try:
+                # 动态导入函数（避免影响其他部分）
+                faiss_db_dir = os.path.join(settings.BASE_DIR, 'faiss_vector_db')
+                if faiss_db_dir not in sys.path:
+                    sys.path.insert(0, faiss_db_dir)
+                
+                from build_faiss_index import extract_embeddings_only
+                from visualize_embeddings import visualize_embeddings_from_files
+                
+                # 创建可视化输出目录
+                vis_output_dir = os.path.join(save_dir, 'embeddings_visualization')
+                
+                # 1. 提取特征向量
+                logger.info('  步骤1/2: 提取训练集特征向量...')
+                extract_embeddings_only(
+                    model_path=best_model_path,
+                    train_dir=cfg.train_dir,
+                    output_dir=vis_output_dir,
+                    embedding_dim=cfg.embedding_dim,
+                    batch_size=cfg.batch_size
+                )
+                
+                # 2. 生成可视化（使用PCA方法，快速）
+                logger.info('  步骤2/2: 生成可视化图...')
+                embeddings_json = os.path.join(vis_output_dir, 'embeddings.json')
+                labels_json = os.path.join(vis_output_dir, 'labels.json')
+                
+                visualize_embeddings_from_files(
+                    embeddings_path=embeddings_json,
+                    labels_path=labels_json,
+                    output_dir=vis_output_dir,
+                    method='pca',  # 使用PCA方法（快速）
+                    max_points=None,  # 训练集全量可视化
+                    seed=42
+                )
+                
+                logger.info(f'✓ 可视化完成，保存至: {vis_output_dir}')
+                
+            except Exception as e:
+                logger.error(f'⚠ 可视化生成失败: {e}')
+                import traceback
+                traceback.print_exc()
+            # ===== 可视化逻辑结束 =====
+            
             break
     else:
         logger.info(f'✓ 完成全部 {num_epochs} 轮训练（未触发早停）')