yixiong/FoodClassifier
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增加一些设置。

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This commit is contained in:
zhangpu
2025-10-17 18:24:04 +08:00
parent 25f8e0a267
commit 5db96600fe
10 changed files with 38 additions and 25 deletions
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.gitignore
+4 -3
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@@ -2,7 +2,8 @@
/.idea/
/model/
/faiss_vector_db/demo_faiss_index/
/faiss_vector_db/faiss_index/
/faiss_vector_db/faiss_index092901/
/faiss_vector_db/faiss_index101001/
/faiss_vector_db/DishClassification/faiss_index/
/faiss_vector_db/DishClassification/faiss_index092901/
/faiss_vector_db/DishClassification/faiss_index101001/
/faiss_vector_db/faiss_index*/
/faiss_vector_db/WholeIngredientRecognition/faiss_index*/
build_faiss_index.py
+1 -1
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@@ -470,7 +470,7 @@ def main():
# 配置参数
MODEL_PATH = "model/embedding_20250930_102826/best_embedding_model.pth"
TRAIN_DIR = "dataset/train"
OUTPUT_DIR = "faiss_vector_db/faiss_index"
OUTPUT_DIR = "faiss_vector_db/DishClassification/faiss_index"
BATCH_SIZE = 16
INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw'
EMBEDDING_DIM = 512
classifier/embedding_food_classifier_app.py
+8 -4
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@@ -64,12 +64,16 @@ class EmbeddingFoodClassifierApp:
# 获取当前脚本所在目录
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# 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/WholeIngredientRecognition/embedding_20251017_145836/best_embedding_model.pth")
# model_path = os.path.join(BASE_DIR, "../model/DishClassification/embedding_20251011_133653/best_embedding_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/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
print("正在加载embedding模型...")
classifier/embedding_food_classifier_search_app.py
+1 -1
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@@ -759,7 +759,7 @@ class EmbeddingFoodClassifierApp:
def save_updated_index(self):
"""保存更新后的索引和元数据"""
try:
index_dir = "../faiss_vector_db/faiss_index"
index_dir = "../faiss_vector_db/DishClassification/faiss_index"
# 保存向量库为 embeddings.json（list[list[float]]）
emb_path = os.path.join(index_dir, 'embeddings.json')
faiss_vector_db/README_FAISS.md
+1 -1
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@@ -111,7 +111,7 @@ builder = FAISSIndexBuilder(
# 构建完整索引
index = builder.build_complete_index(
train_dir="../dataset/train",
output_dir="faiss_index",
output_dir="DishClassification/faiss_index",
batch_size=16,
index_type='flat' # 'flat', 'ivf', 'hnsw'
)
faiss_vector_db/build_faiss_index.py
+13 -7
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@@ -133,10 +133,12 @@ class FAISSIndexBuilder:
print(f"类别 '{class_name}': {len(image_files)} 张图片")
for image_file in image_files:
image_path = os.path.join(class_dir, image_file)
image_paths.append(image_path)
class_names.append(class_name)
labels.append(class_idx)
# 可以不添加那些增强的图片
if image_file.startswith("img"):
image_path = os.path.join(class_dir, image_file)
image_paths.append(image_path)
class_names.append(class_name)
labels.append(class_idx)
print(f"总计扫描到 {len(image_paths)} 张图片")
return image_paths, class_names, labels
@@ -477,9 +479,13 @@ class FAISSSearcher:
def main():
"""主函数"""
# 配置参数
MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
TRAIN_DIR = "../dataset/train"
OUTPUT_DIR = "faiss_index"
# MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
MODEL_PATH = "../model/WholeIngredientRecognition/embedding_20251017_145836/best_embedding_model.pth"
# MODEL_PATH = "../model/DishClassification/embedding_20251011_133653/best_embedding_model.pth"
TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
# TRAIN_DIR = "../dataset/DishClassification/train"
OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
# OUTPUT_DIR = "DishClassification/faiss_index"
BATCH_SIZE = 16
INDEX_TYPE = 'flat' # 'flat', 'ivf', 'hnsw'
EMBEDDING_DIM = 512
faiss_vector_db/run_faiss_demo.py
+2 -2
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@@ -87,7 +87,7 @@ def run_search_demo():
from faiss_vector_db.build_faiss_index import FAISSSearcher
# 配置参数
index_dir = "faiss_index"
index_dir = "DishClassification/faiss_index"
model_path = "../model/embedding_20250917_145342/best_embedding_model.pth"
# 检查索引是否存在
@@ -206,7 +206,7 @@ def main():
print("✓ 文件检查通过")
# 检查是否已有索引
index_exists = os.path.exists("faiss_index/faiss_index.bin")
index_exists = os.path.exists("DishClassification/faiss_index/faiss_index.bin")
if index_exists:
print("✓ 发现已存在的FAISS索引")
faiss_vector_db/visualize_embeddings.py
+4 -2
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@@ -162,8 +162,10 @@ 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( "faiss_index", "embeddings.json"), help="embeddings.json 路径")
parser.add_argument("--labels", type=str, default=os.path.join("faiss_index", "labels.json"), help="labels.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("--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("--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("--max_points", type=int, default=None, help="抽样上限，避免t-SNE/UMAP过慢；None为全量")
toAndroid/toAndroidSearch.py
+2 -2
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@@ -197,8 +197,8 @@ def build_and_export_similarity_head(
def main():
print("开始转换检索头为移动端格式（TorchScript）...")
embeddings_path = "../faiss_vector_db/faiss_index/embeddings.json"
output_path = "../faiss_vector_db/faiss_index/similarity_head.pt"
embeddings_path = "../faiss_vector_db/DishClassification/faiss_index/embeddings.json"
output_path = "../faiss_vector_db/DishClassification/faiss_index/similarity_head.pt"
try:
path, n_items = build_and_export_similarity_head(embeddings_path, output_path)
train/train_embedding.py
+2 -2
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@@ -660,7 +660,7 @@ def main(task_key: str = "dish"):
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=0,
drop_last=True
drop_last=False
)
logger.info(f"训练集大小: {len(train_dataset)}")
@@ -811,4 +811,4 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("task", choices=list(TASKS.keys()), nargs="?", default="dish")
args = parser.parse_args()
main(args.task)
main("whole_ingredient")