修改配置路径
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@@ -61,10 +61,14 @@ class EmbeddingFoodClassifierApp:
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"""加载训练好的embedding模型和FAISS索引"""
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try:
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# 模型路径
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model_path = "../model/embedding_20251010_111257/best_embedding_model.pth"
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# 获取当前脚本所在目录
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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# model_path = "../model/embedding_20251011_133653/best_embedding_model.pth"
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model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth")
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# FAISS索引目录
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index_dir = "../faiss_vector_db/faiss_index"
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# index_dir = "../faiss_vector_db/faiss_index"
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index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index")
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if os.path.exists(model_path) and os.path.exists(index_dir):
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# 1. 加载embedding模型
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@@ -62,10 +62,13 @@ class EmbeddingFoodClassifierApp:
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"""加载训练好的embedding模型和FAISS索引"""
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try:
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# 模型路径
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model_path = "../model/embedding_20251010_111257/best_embedding_model.pth"
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# 获取当前脚本所在目录
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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# model_path = "../model/embedding_20251011_133653/best_embedding_model.pth"
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model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth")
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# FAISS索引目录
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index_dir = "../faiss_vector_db/faiss_index"
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# index_dir = "../faiss_vector_db/faiss_index"
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index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index")
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if os.path.exists(model_path) and os.path.exists(index_dir):
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# 1. 加载embedding模型
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@@ -27,7 +27,7 @@ for class_name in os.listdir(input_root):
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input_dir = os.path.join(input_root, class_name)
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output_dir = os.path.join(output_root, class_name)
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os.makedirs(output_dir, exist_ok=True)
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if class_name == "手撕包菜":
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if class_name == "红烧肉":
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# 读取类别下所有图片路径
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img_files = [f for f in os.listdir(input_dir) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]
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img_paths = [os.path.join(input_dir, f) for f in img_files]
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@@ -477,7 +477,7 @@ class FAISSSearcher:
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def main():
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"""主函数"""
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# 配置参数
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MODEL_PATH = "../model/embedding_20251010_111257/best_embedding_model.pth"
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MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
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TRAIN_DIR = "../dataset/train"
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OUTPUT_DIR = "faiss_index"
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BATCH_SIZE = 16
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