23333

loss/gan.py

@@ -1,4 +1,5 @@
 import torch.nn as nn
+import torch
 import torch.nn.functional as F
 
 
@@ -10,7 +11,7 @@ class GANLoss(nn.Module):
         self.fake_label_val = fake_label_val
         self.loss_type = loss_type
 
-    def forward(self, prediction, target_is_real: bool, is_discriminator=False):
+    def single_forward(self, prediction, target_is_real: bool, is_discriminator=False):
         """
         gan loss forward
         :param prediction: network prediction
@@ -37,3 +38,20 @@ class GANLoss(nn.Module):
             return loss
         else:
             raise NotImplementedError(f'GAN type {self.loss_type} is not implemented.')
+
+    def forward(self, prediction, target_is_real: bool, is_discriminator=False):
+        if isinstance(prediction, torch.Tensor):
+            # origin
+            return self.single_forward(prediction, target_is_real, is_discriminator)
+        elif isinstance(prediction, list):
+            # for multi scale discriminator, e.g. MultiScaleDiscriminator
+            loss = 0
+            for p in prediction:
+                loss += self.single_forward(p[-1], target_is_real, is_discriminator)
+            return loss
+        elif isinstance(prediction, tuple):
+            # for single discriminator set `need_intermediate_feature` true
+            return self.single_forward(prediction[-1], target_is_real, is_discriminator)
+        else:
+            raise NotImplementedError(f"not support discriminator output: {prediction}")
+