新增训练过程生成曲线功能

FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/training.log

@@ -270,3 +270,9 @@ Epoch 260 | Train Loss: 0.0623 | Test Loss: 0.1268 | Loss Factor: 0.49:1
 Epoch 270 | Train Loss: 0.0623 | Test Loss: 0.1269 | Loss Factor: 0.49:1
 Epoch 280 | Train Loss: 0.0622 | Test Loss: 0.1271 | Loss Factor: 0.49:1
 Epoch 290 | Train Loss: 0.0622 | Test Loss: 0.1271 | Loss Factor: 0.49:1
+Epoch 0 | Train Loss: 0.2575 | Test Loss: 0.2492 | Loss Factor: 1.03:1
+Epoch 10 | Train Loss: 0.1152 | Test Loss: 0.1740 | Loss Factor: 0.66:1
+Epoch 20 | Train Loss: 0.0915 | Test Loss: 0.1562 | Loss Factor: 0.59:1
+Epoch 0 | Train Loss: 0.3483 | Test Loss: 0.1410 | Loss Factor: 2.47:1
+Epoch 10 | Train Loss: 0.1358 | Test Loss: 0.1807 | Loss Factor: 0.75:1
+Epoch 20 | Train Loss: 0.1086 | Test Loss: 0.1600 | Loss Factor: 0.68:1

FC_ML_Baseline/FC_ML_Baseline_Test/Train/param.json

@@ -14,7 +14,7 @@
     "studyPercent": 0.001,
     "stepCounts": 3,
     "roundPrint": 10,
-    "round": 300,
+    "round": 30,
     "preDisposeData": true,
     "disposeMethod": "minmax",
     "dataNoOrder": true

FC_ML_Baseline/FC_ML_Baseline_Train/Train_Proxy_Model.py

@@ -38,6 +38,8 @@ import argparse
 import sys
 import os
 
+from FC_ML_Model.Model_Train_Curve_Data import TrainCurveData
+
 # 获取当前脚本（Data_Load.py）所在的目录
 current_script_dir = os.path.dirname(__file__)  # 结果：/home/app/model/ModelTrainingPython/FC_ML_Baseline/FC_ML_Baseline_Data_Handler
 
@@ -63,6 +65,8 @@ 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\ModelTrainingPython\FC_ML_Baseline\FC_ML_Baseline_Test\Train\param.json',
                         help='配置参数文件绝对路径')
+    parser.add_argument('--export', default='curve.json',
+                        help='导出JSON文件名')
     args = parser.parse_args()
     params = parse_json_file(args.param)
     print(params)
@@ -123,6 +127,16 @@ if __name__ == '__main__':
     # 在训练循环前初始化损失记录列表
     train_losses = []
     test_losses = []
+    train_curve_data = TrainCurveData()
+    train_curve_data.source_curve_data["legend"].append("Train Loss")
+    train_curve_data.source_curve_data["legend"].append("Test Loss")
+    serie_test = {}
+    serie_test["name"] = "Test Loss"
+    serie_test["data"] = []
+    serie_train = {}
+    serie_train["name"] = "Train Loss"
+    serie_train["data"] = []
+
     for epoch in range(round_count):
         #模型启用训练模式
         epoch_train_loss = 0
@@ -154,10 +168,18 @@ if __name__ == '__main__':
         test_losses.append(avg_test_loss)
         #每100次迭代输出一次损失数值
         if epoch % round_print == 0:
+            train_curve_data.source_curve_data["xAxis"].append(epoch)
+            serie_test["data"].append(avg_test_loss)
+            serie_train["data"].append(avg_train_loss)
+            train_curve_data.source_curve_data["series"].clear()
+            train_curve_data.source_curve_data["series"].append(serie_test)
+            train_curve_data.source_curve_data["series"].append(serie_train)
             print(
                 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} | Loss Factor: {avg_train_loss / avg_test_loss:.2f}:1\n")  # 自动换行追加
+            with open(params["path"] + "/" + args.export, 'w') as f:
+                f.write(train_curve_data.to_json())  # 写
     #导出训练后的模型
     export_model(model,source_dir,"model",export_format,torch.randn(1, input_Size))
 

FC_ML_Model/Model_Train_Curve_Data.py

@@ -0,0 +1,25 @@
+import json
+
+class TrainCurveData:
+    def __init__(self):
+        """
+        初始化车辆数据类
+        参数与之前保持一致
+        """
+        self.source_curve_data = {}
+        self.source_curve_data["legend"] = []
+        self.source_curve_data["xAxis"] = []
+        self.source_curve_data["series"] = []
+
+    def to_dict(self):
+        """将类实例转换为字典格式"""
+        return {
+            'source_curve_data': self.source_curve_data
+        }
+
+    def to_json(self, indent=None):
+        """将类实例序列化为JSON字符串"""
+        return json.dumps(self.to_dict(), indent=indent, ensure_ascii=False)
+
+    # 保留原有获取数据的方法
+    def get_curve_data(self): return self.source_curve_data