numeric stability for ce loss

Author: taigw

pymic/loss/cls/ce.py

@@ -38,7 +38,7 @@ def forward(self, loss_input_dict):
         predict = loss_input_dict['prediction']
         labels  = loss_input_dict['ground_truth']
         # for numeric stability
-        predict = nn.Sigmoid()(predict) * 0.999 + 0.0005
+        predict = nn.Sigmoid()(predict) * 0.999 + 5e-4
         loss = - labels * torch.log(predict) - (1 - labels) * torch.log( 1 - predict)
         loss = loss.mean()
         return loss

pymic/loss/seg/ce.py

@@ -25,6 +25,8 @@ def forward(self, loss_input_dict):
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y)
 
+        # for numeric stability
+        predict = predict * 0.999 + 5e-4
         ce = - soft_y* torch.log(predict)
         if(self.enable_cls_weight):
             if(cls_w is None):