add gdl loss to default training

mu_map/training/default.py

@@ -4,6 +4,7 @@ from typing import Dict
 import torch
 
 from mu_map.logging import get_logger
+from mu_map.training.loss import GradientDifferenceLoss
 
 
 class Training:
@@ -39,7 +40,12 @@ class Training:
             self.optimizer, step_size=self.lr_decay_epoch, gamma=self.lr_decay_factor
         )
         # self.loss_func = torch.nn.MSELoss(reduction="mean")
-        self.loss_func = torch.nn.L1Loss(reduction="mean")
+        # self.loss_func = torch.nn.L1Loss(reduction="mean")
+        _loss1 = torch.nn.MSELoss()
+        _loss2 = GradientDifferenceLoss()
+        def _loss_func(outputs, targets):
+            return _loss1(outputs, targets) + _loss2(outputs, targets)
+        self.loss_func = _loss_func
 
     def run(self):
         for epoch in range(1, self.epochs + 1):