et

codes/trainer/ExtensibleTrainer.py

@@ -123,7 +123,12 @@ class ExtensibleTrainer(BaseModel):
         dnets = []
         all_networks = [g for g in self.netsG.values()] + [d for d in self.netsD.values()]
         for anet in all_networks:
-            if opt['dist']:
+            has_any_trainable_params = False
+            for p in anet.parameters():
+                if not hasattr(p, 'DO_NOT_TRAIN'):
+                    has_any_trainable_params = True
+                    break
+            if has_any_trainable_params and opt['dist']:
                 if opt['dist_backend'] == 'apex':
                     # Use Apex to enable delay_allreduce, which is compatible with gradient checkpointing.
                     from apex.parallel import DistributedDataParallel