Fbopt matchdiva

domainlab/algos/trainers/train_fbopt_b.py

@@ -166,7 +166,10 @@ def tr_epoch(self, epoch, flag_info=False):
             logger = Logger.get_logger()
             logger.info(f"current multiplier: {self.model.dict_multiplier}")
 
-        flag = super().tr_epoch(epoch, self.flag_setpoint_updated)
+        if self._decoratee is not None:
+            flag = self._decoratee.tr_epoch(epoch, self.flag_setpoint_updated)
+        else:
+            flag = super().tr_epoch(epoch, self.flag_setpoint_updated)
         # is it good to update setpoint after we know the new value of each loss?
         self.flag_setpoint_updated = self.hyper_scheduler.update_setpoint(
             self.epo_reg_loss_tr, self.epo_task_loss_tr

domainlab/algos/trainers/train_matchdg.py

@@ -42,7 +42,7 @@ def init_business(
         self.tuple_tensor_ref_domain2each_y = None
         self.tuple_tensor_refdomain2each = None
 
-    def tr_epoch(self, epoch):
+    def tr_epoch(self, epoch, flag_info=False):
         """
         # data in one batch comes from two sources: one part from loader,
         # the other part from match tensor

run_fbopt_match_diva.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=""   
+# although garbage collector has been explicitly called, sometimes there is still CUDA out of memory error
+# so it is better not to use GPU to do the pytest to ensure every time there is no CUDA out of memory error occuring
+# pytest -s tests/test_fbopt.py
+python main_out.py --te_d=caltech --task=mini_vlcs --bs=8 --model=diva --trainer=fbopt_matchdg --nname=alexnet --nname_dom=alexnet --gamma_d=3  --gamma_y=3 --epos=200 --es=100