config.py

# TODO: Please set global configuration parameters here before running the code

PROXY = ""
# for getting osm address
SERVING_IP = ""
# local server ip
SERVER_IP = ""
# local deployed model key
LOCAL_MODEL_KEY = ""
# MongoDB address
MONGODB_URI = ""
###########
MAP_CACHE_PATH="examples/cache/"
ROUTING_PATH="resource/routing_linux_amd64"
RESOURCE_PATH = "resource/"
# Set the region for experiment
REGION_EXP = "Beijing"
# Set the minimum road length (in meters) to consider
MIN_ROAD_LENGTH = 100
# Map name mapping for different cities
MAP_DICT={
    "Beijing":"map_beijing_fsq_20250105",
    # "Beijing":"map_beijing_20240808",
    "Shanghai":"map_shanghai_20240806",
    "Mumbai":"map_mumbai_20240806",
    "Tokyo":"map_tokyo_20240807",
    "London":"map_london_20240807",
    "Paris":"map_paris_20240808",
    "Moscow":"map_moscow_20240807",
    "NewYork":"map_newyork_20240808",
    "SanFrancisco":"map_san_francisco_20240807",
    "SaoPaulo":"map_san_paulo_20240808",
    "Nairobi":"map_nairobi_20240807",
    "CapeTown":"map_cape_town_20240808",
    "Sydney":"map_sydney_20240807"
}


REGION_BOUNDARY = {

    "SanFrancisco": [(-122.5099, 37.8076), (-122.5099, 37.6153), (-122.3630, 37.6153), (-122.3630, 37.8076)],
    "NewYork": [(-74.0186, 40.7751), (-74.0186, 40.6551), (-73.8068, 40.6551), (-73.8068, 40.7751)],
    "Beijing": [(116.1536, 40.0891), (116.1536, 39.7442), (116.6082, 39.7442), (116.6082, 40.0891)],
    "Shanghai": [(121.1215, 31.4193), (121.1215, 30.7300), (121.9730, 30.7300), (121.9730, 31.4193)],
    "Mumbai": [(72.7576, 19.2729), (72.7576, 18.9797), (72.9836, 18.9797), (72.9836, 19.2729)],    
    "Tokyo": [(139.6005, 35.8712), (139.6005, 35.5859), (139.9713, 35.5859), (139.9713, 35.8712)],
    "London": [(-0.3159, 51.6146), (-0.3159, 51.3598), (0.1675, 51.3598), (0.1675, 51.6146)],
    "Paris": [(2.249, 48.9038), (2.249, 48.8115), (2.4239, 48.8115), (2.4239, 48.9038)],
    "Moscow": [(37.4016, 55.8792), (37.4016, 55.6319), (37.8067, 55.6319), (37.8067, 55.8792)],
    "SaoPaulo": [(-46.8251, -23.4242), (-46.8251, -23.7765), (-46.4365, -23.7765), (-46.4365, -23.4242)],
    "Nairobi": [(36.6868, -1.1906), (36.6868, -1.3381), (36.9456, -1.3381), (36.9456, -1.1906)],
    "CapeTown": [(18.3472, -33.8179), (18.3472, -34.0674), (18.6974, -34.0674), (18.6974, -33.8179)],
    "Sydney": [(150.8382, -33.6450), (150.8382, -34.0447), (151.2982, -34.0447), (151.2982, -33.6450)]    
}

OSM_ADDRESS = False
DATA_VERSION = "eval"

EVAL_DATA = True
LANDMARK_DATA = False
DETAIL_INTEREST = False
VISION_DATA = False


#### 以下配置用于生成评估数据，evaluate文件夹下
EVAL_TASK_MAPPING_v1 = {
    "city_image": {
        "poi2coor": "poi2coor.csv",
        "road_length": "eval_road_length.csv", 
        "road_od": "eval_road_od.csv",
        "road_link": "eval_road_link.csv",
        "aoi2addr": "aoi2addr.csv",
        "landmark_path": "eval_landmark_path.csv",  
        "boundary_road": "eval_boundary_road.csv", 
        "road_aoi": "eval_road_aoi.csv",
        "aoi_near": "aoi_near.csv"
    },
    "urban_semantics": {
        "landmark_env": "eval_landmark_env.csv",  
        "aoi2type": "aoi2type.csv", 
        "type2aoi": "type2aoi.csv",  
    },
    "spatial_reasoning_route": {
        "aoi2aoi_dir_routine":"aoi2aoi_dir_routine.csv",
        "aoi2rd_dis_routine":"aoi2rd_dis_routine.csv",
        "aoi2rd_dir_routine":"aoi2rd_dir_routine.csv",
        "aoi2aoi_dis_routine":"aoi2aoi_dis_routine.csv",
    },
    "spatial_reasoning_noroute": {
        "aoi2rd_dis_noroutine":"aoi2rd_dis_noroutine.csv",
        "aoi2rd_dir_noroutine":"aoi2rd_dir_noroutine.csv",
        "aoi2aoi_dis_noroutine":"aoi2aoi_dis_noroutine.csv",
        "aoi2aoi_dir_noroutine":"aoi2aoi_dir_noroutine.csv",
    }
}

EVAL_TASK_MAPPING_v2 = {
    "city_image": {
        "aoi_poi": "aoi_poi.csv", 
        "poi_aoi": "poi_aoi.csv",  
        "poi2coor": "poi2coor.csv",
        "poi2addr": "poi2addr.csv",
        "road_length": "road_length.csv", 
        "road_od": "road_od.csv",
        "road_link": "road_link.csv", 
        "road_arrived_pois": "road_arrived_pois.csv",
        "aoi2addr": "aoi2addr.csv",
        "landmark_path": "landmark_path.csv",
        "boundary_road": "boundary_road.csv", 
        "aoi_boundary_poi": "aoi_boundary_poi.csv",
        "districts_poi_type": "districts_poi_type.csv",
    },
    "urban_semantics": {
        "landmark_env": "landmark_env.csv",  
        "aoi_group": "aoi_group.csv", 
        "poi2type": "poi2type.csv",
        "type2poi": "type2poi.csv",
        "aoi2type": "aoi2type.csv", 
        "type2aoi": "type2aoi.csv",  
    },
    "spatial_reasoning_route": {
    "aoi2aoi_dir_routine": "aoi2aoi_dir_routine.csv",
    "poi2poi_dir_routine": "poi2poi_dir_routine.csv",
    "poi2poi_dis_routine": "poi2poi_dis_routine.csv",
    "poi2aoi_dis_routine": "poi2aoi_dis_routine.csv",
    "poi2aoi_dir_routine": "poi2aoi_dir_routine.csv",
    "poi2rd_dis_routine": "poi2rd_dis_routine.csv",
    "poi2rd_dir_routine": "poi2rd_dir_routine.csv",
    "aoi2rd_dis_routine": "aoi2rd_dis_routine.csv",
    "aoi2rd_dir_routine": "aoi2rd_dir_routine.csv",
    "aoi2aoi_dis_routine": "aoi2aoi_dis_routine.csv",
    },
    "spatial_reasoning_noroute": {
    "aoi2aoi_dir_noroutine": "aoi2aoi_dir_noroutine.csv",
    "poi2poi_dir_noroutine": "poi2poi_dir_noroutine.csv",
    "poi2poi_dis_noroutine": "poi2poi_dis_noroutine.csv",
    "poi2aoi_dis_noroutine": "poi2aoi_dis_noroutine.csv",
    "poi2aoi_dir_noroutine": "poi2aoi_dir_noroutine.csv",
    "poi2rd_dis_noroutine": "poi2rd_dis_noroutine.csv",
    "poi2rd_dir_noroutine": "poi2rd_dir_noroutine.csv",
    "aoi2rd_dis_noroutine": "aoi2rd_dis_noroutine.csv",
    "aoi2rd_dir_noroutine": "aoi2rd_dir_noroutine.csv",
    "aoi2aoi_dis_noroutine": "aoi2aoi_dis_noroutine.csv",
    }
}
DIS2CORNER = 50
STEP = 12
REASON_QUES_NUM = 500
TRAIN_DATA_PATH = "simulate/examples/"
LLM_MODELS = [
    "Qwen2-7B", "Qwen2-72B", "Intern2.5-7B", "Intern2.5-20B", 
    "Mistral-7B", "Mixtral-8x22B", "LLama3-8B", "LLama3-70B", "Gemma2-9B", "Gemma2-27B", 
    "DeepSeek-67B", "DeepSeekV2", "GPT3.5-Turbo", "GPT4-Turbo"]

INFER_SERVER = {
    "OpenAI": ["GPT3.5-Turbo", "GPT4-Turbo", "GPT4omini"],
    "DeepInfra": ["Mistral-7B", "Mixtral-8x22B", "LLama3-8B", "LLama3-70B", "Gemma2-9B", "Gemma2-27B", "LLama-3.2-90B", "LLama-3.2-11B"],
    "Siliconflow": ["Qwen2-7B", "Qwen2-72B", "Intern2.5-7B", "Intern2.5-20B", "DeepSeekV2", "Qwen2-VL-72B"],
    "DeepBricks": ["gpt-4o-mini"]
}
LLM_MODEL_MAPPING = {
    "Qwen2-7B":"Qwen/Qwen2-7B-Instruct",
    "Qwen2-72B":"Qwen/Qwen2-72B-Instruct",
    "Intern2.5-7B":"internlm/internlm2_5-7b-chat",
    "Intern2.5-20B":"internlm/internlm2_5-20b-chat",
    "Mistral-7B":"mistralai/Mistral-7B-Instruct-v0.2", 
    "Mixtral-8x22B":"mistralai/Mixtral-8x22B-Instruct-v0.1",
    "LLama3-8B":"meta-llama/Meta-Llama-3-8B-Instruct",
    "LLama3-70B":"meta-llama/Meta-Llama-3-70B-Instruct",
    "Gemma2-9B":"google/gemma-2-9b-it",
    "Gemma2-27B":"google/gemma-2-27b-it",
    "DeepSeekV2":"deepseek-ai/DeepSeek-V2-Chat",
    "GPT3.5-Turbo":"gpt-3.5-turbo-0125",
    "GPT4-Turbo":"gpt-4-turbo-2024-04-09",
    "GPT4omini":"gpt-4o-mini-2024-07-18",
    # "GPT4o":"gpt-4o"
}