model_accuracy/server/tool.py

import sys
import os
import json

# from lemon_master.mutation_executor import lemon_process
from lemon_master.run.mutation_executor import lemon_process


def mock(exp: str, root_dir: str, output_dir: str, mutate_num: int, config_name: str):
    print("running")
    print("exp:", exp)
    print("root_dir:", root_dir)
    print("out_dir:", output_dir)
    print("mutate_num:", mutate_num)
    print("config_name:", config_name)
    data = [
        {
            "model": "lenet5-fashion-mnist",
            "method": "origin0",
            "result": {
                "Losses": 0.3032414233312011,
                "Accuracy": 0.9062593914568424,
                "MemoryInfoList": 1.2329671382904053,
            },
        }
    ]
    return data


result_num = 0
def runtool(
    exp: str, mutate_num: int
):
    global result_num
    config_name = "demo.conf"
    base_dir = os.path.dirname(os.path.abspath(__file__))
    print(base_dir)
    
    result_num = ( result_num + 1 ) % 5
    
    output_dir = os.path.join(base_dir, "output", str(result_num))
    
    if os.path.exists(output_dir):
        print("remove output_dir:", output_dir)
        # TODO: 正式时需要删除 os.system("rm -rf " + output_dir)
    
    print("running")
    print("exp:", exp)
    print("out_dir:", output_dir)
    print("mutate_num:", mutate_num)
    print("config_name:", config_name)

    root_dir = os.path.join(base_dir, "lemon_master")
    
    target_dir = os.path.join(base_dir, "lemon_master", "run")
    print(target_dir)
    sys.path.append(target_dir)
    
    # TODO: 正式时需要注释lemon_process(exp, root_dir, output_dir, mutate_num, config_name)
    
    real_output_dir = os.path.join(output_dir, exp)
    
    mxnet_json = os.path.join(real_output_dir, "mxnet.json")
    with open(
        mxnet_json, "r"
    ) as file1:  # tensorflow.json
        data1 = json.load(file1)
    
    tensorflow_json = os.path.join(real_output_dir, "tensorflow.json")
    with open(tensorflow_json, "r") as file2:
        data2 = json.load(file2)
        
    combined_data = {
        "mxnet": data1,
        "tensorflow": data2
    }
    return combined_data