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更改配置文件

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zhangpu
2025-11-11 09:02:45 +08:00
parent f204799b3e
commit 12e231dc6c
5 changed files with 20 additions and 20 deletions
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classifier/embedding_food_classifier_app.py
+4 -4
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@@ -66,15 +66,15 @@ class EmbeddingFoodClassifierApp:
# model_path = "../model/embedding_20251011_133653/best_embedding_model.pth" # 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/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/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/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/DishClassification/cosface_20251110_144822/best_cosface_model.pth")
# FAISS索引目录 # FAISS索引目录
# index_dir = "../faiss_vector_db/faiss_index" # 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/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/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/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/DishClassification/faiss_index")
if os.path.exists(model_path) and os.path.exists(index_dir): if os.path.exists(model_path) and os.path.exists(index_dir):
# 1. 加载embedding模型 # 1. 加载embedding模型
faiss_vector_db/build_faiss_index.py
+6 -6
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@@ -497,16 +497,16 @@ def main():
# 配置参数 # 配置参数
# MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth" # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
# MODEL_PATH = "../model/ProcessedIngredientRecognition/embedding_20251103_172012/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_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/ProcessedIngredientRecognition/train"
TRAIN_DIR = "../dataset/WholeIngredientRecognition/train" # TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
# TRAIN_DIR = "../dataset/DishClassification/train" TRAIN_DIR = "../dataset/DishClassification/train"
# OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index" # OUTPUT_DIR = "ProcessedIngredientRecognition/faiss_index"
OUTPUT_DIR = "WholeIngredientRecognition/faiss_index" # OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
# OUTPUT_DIR = "DishClassification/faiss_index" OUTPUT_DIR = "DishClassification/faiss_index"
BATCH_SIZE = 16 BATCH_SIZE = 16
INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw' INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw'
EMBEDDING_DIM = 512 EMBEDDING_DIM = 512
faiss_vector_db/visualize_embeddings.py
+4 -4
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@@ -162,11 +162,11 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
def main(): def main():
parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断") 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("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("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("--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("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("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("--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("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法")
parser.add_argument("--seed", type=int, default=42) parser.add_argument("--seed", type=int, default=42)
toAndroid/toAndroidEmbedding.py
+4 -4
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@@ -17,8 +17,8 @@ def main():
# 1. 加载训练好的embedding模型权重 # 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_resnet50_embedding(embedding_dim=512, pretrained=True)
base_model = create_mobile_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/DishClassification/cosface_20251110_144822/best_cosface_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/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth" # model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
if not os.path.exists(model_path): if not os.path.exists(model_path):
@@ -92,8 +92,8 @@ def main():
traced_model = torch.jit.trace(mobile_wrapper, single_input) traced_model = torch.jit.trace(mobile_wrapper, single_input)
# 保存模型 # 保存模型
# output_path = "../model/DishClassification/cosface_20251105_200551/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/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt"
# output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt" # output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt"
traced_model.save(output_path) traced_model.save(output_path)
print(f"✓ TorchScript模型保存成功: {output_path}") print(f"✓ TorchScript模型保存成功: {output_path}")
train/train_cosface_embedding.py
+2 -2
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@@ -347,8 +347,8 @@ def main(task_key: str = 'dish', s: float = 64.0, m: float = 0.35, num_epochs: i
if __name__ == '__main__': if __name__ == '__main__':
import argparse import argparse
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
# parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='dish') 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='whole_ingredient')
parser.add_argument('--s', type=float, default=64.0) parser.add_argument('--s', type=float, default=64.0)
parser.add_argument('--m', type=float, default=0.35) parser.add_argument('--m', type=float, default=0.35)
parser.add_argument('--epochs', type=int, default=60) parser.add_argument('--epochs', type=int, default=60)