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FC_ML_Baseline/FC_ML_Baseline_Predict/Model_Pred.py

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+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))