57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
|
import argparse
|
||
|
|
import json
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from openpyxl.styles.builtins import output
|
||
|
|
|
||
|
|
from FC_ML_Data.FC_ML_Data_Process.Data_Process_Normalization import Normalizer
|
||
|
|
from FC_ML_NN_Model.Poly_Model import PolyModel
|
||
|
|
from FC_ML_Tool.Serialization import parse_json_file
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser(description='代理模型训练参数输入')
|
||
|
|
parser.add_argument('--param', default='D:\liyong\project\TVS_ML\FC_ML_Baseline\FC_ML_Baseline_Test\Train\param.json',
|
||
|
|
help='配置参数文件绝对路径')
|
||
|
|
args = parser.parse_args()
|
||
|
|
params = parse_json_file(args.param)
|
||
|
|
source_dir = params["path"] + "/"
|
||
|
|
model_file = source_dir + params["modelFile"]
|
||
|
|
inputs = []
|
||
|
|
names = []
|
||
|
|
#获取输入特征
|
||
|
|
for input_value in params["input"]:
|
||
|
|
inputs.append(input_value["value"])
|
||
|
|
names.append(input_value["name"])
|
||
|
|
#记载模型进行预测
|
||
|
|
input_size = params["modelParams"]["inputSize"]
|
||
|
|
output_size = params["modelParams"]["outputSize"]
|
||
|
|
model_path = params["path"] + "/" + params["modelFile"]
|
||
|
|
device = torch.device('cpu')
|
||
|
|
model = PolyModel(input_size,output_size).to(device)
|
||
|
|
model.load_state_dict(torch.load(model_file))
|
||
|
|
model.eval()
|
||
|
|
|
||
|
|
#加载数据处理器
|
||
|
|
normalization_type = params["modelParams"]["normalizerType"]
|
||
|
|
normalization_max = params["modelParams"]["normalizerMax"]
|
||
|
|
normalization_min = params["modelParams"]["normalizerMin"]
|
||
|
|
normalizer = Normalizer(method=normalization_type)
|
||
|
|
normalizer.load_params(normalization_type,normalization_min,normalization_max)
|
||
|
|
input_data = normalizer.transform(torch.tensor(inputs))
|
||
|
|
|
||
|
|
#执行模型预测
|
||
|
|
with torch.no_grad():
|
||
|
|
output_data = model(input_data)
|
||
|
|
print(f"Prediction result: {output_data.item():.4f}")
|
||
|
|
output_data_ori = normalizer.inverse_transform(output_data)
|
||
|
|
print(f"Prediction real result: {output_data_ori.item():.4f}")
|
||
|
|
#输出预测结果到文件中
|
||
|
|
output_datas = output_data_ori.tolist()
|
||
|
|
json_str = {}
|
||
|
|
if len(output_datas) == len(names):
|
||
|
|
for i in range(len(names)):
|
||
|
|
json_str[names[i]] = output_datas[i]
|
||
|
|
|
||
|
|
with open(source_dir + "forecast.json", ) as f:
|
||
|
|
f.write(json.dumps(json_str, indent=None, ensure_ascii=False))
|