From 12e231dc6c064fb42e142df6bc14380154e8ea0e Mon Sep 17 00:00:00 2001 From: zhangpu <1250681871@qq.com> Date: Tue, 11 Nov 2025 09:02:45 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=94=B9=E9=85=8D=E7=BD=AE=E6=96=87?= =?UTF-8?q?=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- classifier/embedding_food_classifier_app.py | 8 ++++---- faiss_vector_db/build_faiss_index.py | 12 ++++++------ faiss_vector_db/visualize_embeddings.py | 8 ++++---- toAndroid/toAndroidEmbedding.py | 8 ++++---- train/train_cosface_embedding.py | 4 ++-- 5 files changed, 20 insertions(+), 20 deletions(-) diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py index 056ab88..4f31438 100644 --- a/classifier/embedding_food_classifier_app.py +++ b/classifier/embedding_food_classifier_app.py @@ -66,15 +66,15 @@ class EmbeddingFoodClassifierApp: # model_path = "../model/embedding_20251011_133653/best_embedding_model.pth" # model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth") # model_path = os.path.join(BASE_DIR, "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth") - model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth") - # model_path = os.path.join(BASE_DIR, "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth") + # model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth") + model_path = os.path.join(BASE_DIR, "../model/DishClassification/cosface_20251110_144822/best_cosface_model.pth") # FAISS索引目录 # index_dir = "../faiss_vector_db/faiss_index" # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index") # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/ProcessedIngredientRecognition/faiss_index") - index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index") - # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index") + # index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index") + index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index") if os.path.exists(model_path) and os.path.exists(index_dir): # 1. 加载embedding模型 diff --git a/faiss_vector_db/build_faiss_index.py b/faiss_vector_db/build_faiss_index.py index 676fc45..0f2dded 100644 --- a/faiss_vector_db/build_faiss_index.py +++ b/faiss_vector_db/build_faiss_index.py @@ -497,16 +497,16 @@ def main(): # 配置参数 # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth" # MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/best_embedding_model.pth" - MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth" + # MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth" # MODEL_PATH = "../model/DishClassification/cosface_20251105_200551/best_embedding_model.pth" - # MODEL_PATH = "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth" + MODEL_PATH = "../model/DishClassification/cosface_20251110_144822/best_cosface_model.pth" # TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train" - TRAIN_DIR = "../dataset/WholeIngredientRecognition/train" - # TRAIN_DIR = "../dataset/DishClassification/train" + # TRAIN_DIR = "../dataset/WholeIngredientRecognition/train" + TRAIN_DIR = "../dataset/DishClassification/train" # OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index" - OUTPUT_DIR = "WholeIngredientRecognition/faiss_index" - # OUTPUT_DIR = "DishClassification/faiss_index" + # OUTPUT_DIR = "WholeIngredientRecognition/faiss_index" + OUTPUT_DIR = "DishClassification/faiss_index" BATCH_SIZE = 16 INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw' EMBEDDING_DIM = 512 diff --git a/faiss_vector_db/visualize_embeddings.py b/faiss_vector_db/visualize_embeddings.py index ae6eb9a..aacfecc 100644 --- a/faiss_vector_db/visualize_embeddings.py +++ b/faiss_vector_db/visualize_embeddings.py @@ -162,11 +162,11 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] = 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") - parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径") + parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径") + # parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径") # parser.add_argument("--embeddings", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径") - # parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径") - parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径") + parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径") + # parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径") # parser.add_argument("--labels", type=str, default=os.path.join("ProcessedIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径") parser.add_argument("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法") parser.add_argument("--seed", type=int, default=42) diff --git a/toAndroid/toAndroidEmbedding.py b/toAndroid/toAndroidEmbedding.py index 91f1adf..66959bc 100644 --- a/toAndroid/toAndroidEmbedding.py +++ b/toAndroid/toAndroidEmbedding.py @@ -17,8 +17,8 @@ def main(): # 1. 加载训练好的embedding模型权重 # base_model = create_resnet50_embedding(embedding_dim=512, pretrained=True) base_model = create_mobile_resnet50_embedding(embedding_dim=512, pretrained=True) - # model_path = "../model/DishClassification/cosface_20251105_200551/best_cosface_model.pth" - model_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth" + model_path = "../model/DishClassification/cosface_20251110_144822/best_cosface_model.pth" + # model_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth" # model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth" if not os.path.exists(model_path): @@ -92,8 +92,8 @@ def main(): traced_model = torch.jit.trace(mobile_wrapper, single_input) # 保存模型 - # output_path = "../model/DishClassification/cosface_20251105_200551/best_embedding_model_mobile.pt" - output_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt" + output_path = "../model/DishClassification/cosface_20251110_144822/best_embedding_model_mobile.pt" + # output_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt" # output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt" traced_model.save(output_path) print(f"✓ TorchScript模型保存成功: {output_path}") diff --git a/train/train_cosface_embedding.py b/train/train_cosface_embedding.py index 6ab6426..ad8a0f2 100644 --- a/train/train_cosface_embedding.py +++ b/train/train_cosface_embedding.py @@ -347,8 +347,8 @@ def main(task_key: str = 'dish', s: float = 64.0, m: float = 0.35, num_epochs: i if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() - # parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='dish') - parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='whole_ingredient') + parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='dish') + # parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='whole_ingredient') parser.add_argument('--s', type=float, default=64.0) parser.add_argument('--m', type=float, default=0.35) parser.add_argument('--epochs', type=int, default=60)