72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
import argparse
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import json
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import torch
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import sys
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import os
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# 获取当前脚本（Data_Load.py）所在的目录
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current_script_dir = os.path.dirname(__file__) # 结果：/home/app/model/ModelTrainingPython/FC_ML_Baseline/FC_ML_Baseline_Data_Handler
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# 从当前目录回退 2 级，得到项目根目录 ModelTrainingPython
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root_path = os.path.abspath(os.path.join(current_script_dir, "..", ".."))
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# 将根目录添加到 Python 搜索路径
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sys.path.append(root_path)
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from FC_ML_Data.FC_ML_Data_Process.Data_Process_Normalization import Normalizer
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from FC_ML_NN_Model.Poly_Model import PolyModel
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from FC_ML_Tool.Serialization import parse_json_file
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='代理模型训练参数输入')
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parser.add_argument('--param', default='D:\liyong\project\ModelTrainingPython\FC_ML_Baseline\FC_ML_Baseline_Test\pred\param.json',
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help='配置参数文件绝对路径')
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args = parser.parse_args()
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params = parse_json_file(args.param)
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print(params)
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source_dir = params["path"] + "/"
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model_file = source_dir + params["modelFile"]
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inputs = []
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names = params["output"]["names"]
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#获取输入特征
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for input_value in params["input"]:
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inputs.append(input_value["value"])
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# names.append(input_value["name"])
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#记载模型进行预测
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input_size = params["modelParams"]["inputSize"]
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output_size = params["modelParams"]["outputSize"]
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model_path = params["path"] + "/" + params["modelFile"]
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device = torch.device('cpu')
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model = PolyModel(input_size,output_size).to(device)
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model.load_state_dict(torch.load(model_file))
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model.eval()
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#加载数据处理器
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normalization_type = params["modelParams"]["normalizerType"]
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normalization_max = params["modelParams"]["normalizerMax"]
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normalization_min = params["modelParams"]["normalizerMin"]
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normalizer = Normalizer(method=normalization_type)
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normalizer.load_params(normalization_type,normalization_min[0:input_size],normalization_max[0:input_size])
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input_data = normalizer.transform(torch.tensor(inputs))
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#执行模型预测
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with torch.no_grad():
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output_data = model(input_data)
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# print(f"Prediction result: {output_data.item().tolist():.4f}")
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normalizer.load_params(normalization_type, normalization_min[-output_size:], normalization_max[-output_size:])
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output_data_ori = normalizer.inverse_transform(output_data)
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# print(f"Prediction real result: {output_data_ori.item().tolist():.4f}")
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#输出预测结果到文件中
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output_datas = output_data_ori.tolist()
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json_str = {}
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if len(output_datas) == len(names):
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for i in range(len(names)):
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json_str[names[i]] = output_datas[i]
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with open(source_dir + "forecast.json","w") as f:
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f.write(json.dumps(json_str, indent=None, ensure_ascii=False))
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