修复预测脚本和训练脚本的执行bug

FC_ML_Baseline/FC_ML_Baseline_Train/Train_Proxy_Model.py

@@ -35,26 +35,25 @@
  */
 '''
 import argparse
-from locale import normalize
+from copy import deepcopy
 
 import torch
 from torch.utils.data import TensorDataset, DataLoader
 
-from FC_ML_Data.FC_ML_Data_Load.Data_Load_Excel import get_data_from_csv_feature, get_train_data_from_csv
+from FC_ML_Data.FC_ML_Data_Load.Data_Load_Excel import get_train_data_from_csv
 from FC_ML_Data.FC_ML_Data_Output.Data_Output_Pytorch import export_model
 from FC_ML_Loss_Function.Loss_Function_Selector import LossFunctionSelector
-from FC_ML_Model.Model_Train_Data import TrainData
 from FC_ML_NN_Model.Poly_Model import PolyModel
 from FC_ML_Optim_Function.Optimizer_Selector import OptimizerSelector
 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',
+    parser.add_argument('--param', default='D:\liyong\project\ModelTrainingPython\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)
-    train_data = torch.tensor()
+    print(params)
     # print(params)
     #获取训练参数
     input_Size = params["algorithmParam"]["inputSize"]#输入特征维度
@@ -73,11 +72,15 @@ if __name__ == '__main__':
     dispose_method = params["algorithmParam"]["disposeMethod"]  # 数据预处理方法
     data_no_order = params["algorithmParam"]["dataNoOrder"]  # 训练数据是否乱序处理
     #加载所有训练数据
+    train_data = []
     source_dir = params["path"] + "/"
     for data_file in params["files"]:
         data_file_path = source_dir + data_file
         ori_data,normalize = get_train_data_from_csv(data_file_path,pre_dispose_data,dispose_method)
-        torch.cat((train_data,ori_data),dim=0)#按行拼接
+        if len(train_data) == 0:
+            train_data = deepcopy(ori_data)
+        else:
+            train_data = torch.cat((train_data,ori_data),dim=0)#按行拼接
     #拆分测试集和训练集
     split = int(training_ratio / 100 * len(train_data))
     train_dataset = TensorDataset(train_data[:split,0:input_Size], train_data[:split,input_Size:])
@@ -140,11 +143,11 @@ if __name__ == '__main__':
         #每100次迭代输出一次损失数值
         if epoch % round_print == 0:
             print(
-                f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f} | Test Loss: {avg_test_loss:.4f} | 损失比: {avg_train_loss / avg_test_loss:.2f}:1")
+                f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f} | Test Loss: {avg_test_loss:.4f} | Loss Factor: {avg_train_loss / avg_test_loss:.2f}:1")
             with open(source_dir + "training.log", "a") as f:
-                f.write(f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f} | Test Loss: {avg_test_loss:.4f} | 损失比: {avg_train_loss / avg_test_loss:.2f}:1\n")  # 自动换行追加
+                f.write(f"Epoch {epoch} | Train Loss: {avg_train_loss:.4f} | Test Loss: {avg_test_loss:.4f} | Loss Factor: {avg_train_loss / avg_test_loss:.2f}:1\n")  # 自动换行追加
     #导出训练后的模型
-    export_model(model,source_dir,"model",export_format)
+    export_model(model,source_dir,"model",export_format,torch.randn(1, input_Size))