修复预测脚本和训练脚本的执行bug

FC_ML_Data/FC_ML_Data_Load/Data_Load_Excel.py

@@ -182,7 +182,7 @@ def get_data_from_csv_feature(data_path,skip_rows = 100,sample_rows = 100,normal
         sampled_indices = torch.arange(0, len(data_ori), skip_rows)  # 记录行号
         return label_name,source_data,normalizer.params["min"],normalizer.params["max"],normalizer.params["mean"],sampled_indices,data_sample
 
-def get_train_data_from_csv(data_path,normalization = false,normalization_type = 'minmax'):
+def get_train_data_from_csv(data_path,normalization = True,normalization_type = 'minmax'):
     """读取csv数据文件并生成标准化训练数据
     Args:
         data_path (str): 文件绝对路径
@@ -196,6 +196,8 @@ def get_train_data_from_csv(data_path,normalization = false,normalization_type =
 
     Examples:
         get_data_from_csv_feature("D://test.excel")
+        :param normalization_type:
+        :param normalization:
     """
     # 读取前xx行数据
     df = pd.read_csv(data_path,encoding='gbk')

FC_ML_Data/FC_ML_Data_Output/Data_Output_Pytorch.py

@@ -3,7 +3,7 @@ import torch
 
 def export_model_pt(model,target,name = "model"):
     script_model = torch.jit.script(model)  # 或 torch.jit.trace(model, input)
-    script_model.save(target + name + ".pt")
+    script_model.save(target + name + ".pth")
 #2 通用格式导出
 def export_model_onnx(model,input_tensor,target,name="model"):
     torch.onnx.export(model, input_tensor, target+ name + ".onnx")
@@ -11,12 +11,12 @@ def export_model_onnx(model,input_tensor,target,name="model"):
 def export_model_bin(model,target,name = "weights"):
     torch.save(model.state_dict(), target + name + ".bin")
 
-def export_model(model,target,file_name,name):
+def export_model(model,target,file_name,name,input_tensor):
     if name == 'bin':
         return export_model_bin(model,target,file_name)
     if name == 'onnx':
-        return export_model_onnx(model,target,file_name)
-    if name == 'pt':
-        return export_model_bin(model,target,file_name)
+        return export_model_onnx(model,input_tensor,target,file_name)
+    if name == 'pth':
+        return export_model_pt(model,target,file_name)
     else:
         raise ValueError(f"不支持的导出类型")

FC_ML_Data/FC_ML_Data_Process/Data_Process_Normalization.py

@@ -20,10 +20,10 @@ class Normalizer:
             self.params['max_abs'] = data.abs().max(dim=0)[0]
         return self
 
-    def load_params(self,method = "minmax",min_in = 0,max_in = 0,mean_in =0,std=0,max_abs=0):
+    def load_params(self,method = "minmax",min_in = [],max_in = [],mean_in =[],std=[],max_abs=[]):
         self.method = method
-        self.params['min'] = min_in
-        self.params['max'] = max_in
+        self.params['min'] = torch.tensor(min_in)
+        self.params['max'] = torch.tensor(max_in)
         self.params['mean'] = mean_in
         self.params['std'] = std
         self.params['max_abs'] = max_abs