code style

README.md

@@ -45,7 +45,7 @@ conda activate domainlab_py39
 conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
 conda install torchmetrics==0.10.3
 git checkout fbopt
-pip install -r requirements_notorch.txt 
+pip install -r requirements_notorch.txt
 conda install tensorboard
 ```
 
@@ -60,7 +60,7 @@ https://github.com/marrlab/DomainLab/blob/fbopt/data/script/download_pacs.py
 step 2:
 make a symbolic link following the example script in https://github.com/marrlab/DomainLab/blob/master/sh_pacs.sh
 
-where `mkdir -p data/pacs` is executed under the repository directory, 
+where `mkdir -p data/pacs` is executed under the repository directory,
 
 `ln -s /dir/to/yourdata/pacs/raw  ./data/pacs/PACS`
 will create a symbolic link under the repository directory

domainlab/algos/builder_diva.py

@@ -2,10 +2,10 @@
 Builder pattern to build different component for experiment with DIVA
 """
 from domainlab.algos.a_algo_builder import NodeAlgoBuilder
-from domainlab.algos.msels.c_msel_val import MSelValPerf
-from domainlab.algos.msels.c_msel_val_top_k import MSelValPerfTopK
 from domainlab.algos.msels.c_msel_oracle import MSelOracleVisitor
 from domainlab.algos.msels.c_msel_setpoint_delay import MSelSetpointDelay
+from domainlab.algos.msels.c_msel_val import MSelValPerf
+from domainlab.algos.msels.c_msel_val_top_k import MSelValPerfTopK
 from domainlab.algos.observers.b_obvisitor import ObVisitor
 from domainlab.algos.observers.c_obvisitor_cleanup import ObVisitorCleanUp
 from domainlab.algos.observers.c_obvisitor_gen import ObVisitorGen
@@ -37,19 +37,23 @@ def init_business(self, exp):
         request = RequestVAEBuilderCHW(task.isize.c, task.isize.h, task.isize.w, args)
         node = VAEChainNodeGetter(request)()
         task.get_list_domains_tr_te(args.tr_d, args.te_d)
-        model = mk_diva(str_diva_multiplier_type=args.str_diva_multiplier_type)(node,
-                          zd_dim=args.zd_dim,
-                          zy_dim=args.zy_dim,
-                          zx_dim=args.zx_dim,
-                          list_str_y=task.list_str_y,
-                          list_d_tr=task.list_domain_tr,
-                          gamma_d=args.gamma_d,
-                          gamma_y=args.gamma_y,
-                          beta_x=args.beta_x,
-                          beta_y=args.beta_y,
-                          beta_d=args.beta_d)
+        model = mk_diva(str_diva_multiplier_type=args.str_diva_multiplier_type)(
+            node,
+            zd_dim=args.zd_dim,
+            zy_dim=args.zy_dim,
+            zx_dim=args.zx_dim,
+            list_str_y=task.list_str_y,
+            list_d_tr=task.list_domain_tr,
+            gamma_d=args.gamma_d,
+            gamma_y=args.gamma_y,
+            beta_x=args.beta_x,
+            beta_y=args.beta_y,
+            beta_d=args.beta_d,
+        )
         device = get_device(args)
-        model_sel = MSelSetpointDelay(MSelOracleVisitor(MSelValPerfTopK(max_es=args.es)))
+        model_sel = MSelSetpointDelay(
+            MSelOracleVisitor(MSelValPerfTopK(max_es=args.es))
+        )
         if not args.gen:
             observer = ObVisitor(model_sel)
         else:

domainlab/algos/builder_fbopt_dial.py

@@ -9,6 +9,7 @@ class NodeAlgoBuilderFbOptDial(NodeAlgoBuilderDIVA):
     """
     builder for feedback optimization for dial
     """
+
     def init_business(self, exp):
         """
         return trainer, model, observer

domainlab/algos/builder_jigen1.py

@@ -2,10 +2,10 @@
 builder for JiGen
 """
 from domainlab.algos.a_algo_builder import NodeAlgoBuilder
-from domainlab.algos.msels.c_msel_val import MSelValPerf
-from domainlab.algos.msels.c_msel_val_top_k import MSelValPerfTopK
 from domainlab.algos.msels.c_msel_oracle import MSelOracleVisitor
 from domainlab.algos.msels.c_msel_setpoint_delay import MSelSetpointDelay
+from domainlab.algos.msels.c_msel_val import MSelValPerf
+from domainlab.algos.msels.c_msel_val_top_k import MSelValPerfTopK
 from domainlab.algos.observers.b_obvisitor import ObVisitor
 from domainlab.algos.observers.c_obvisitor_cleanup import ObVisitorCleanUp
 from domainlab.algos.trainers.hyper_scheduler import HyperSchedulerWarmupExponential

domainlab/algos/msels/a_model_sel.py

@@ -92,7 +92,7 @@ def best_te_metric(self):
         if self.msel is not None:
             return self.msel.best_te_metric
         return -1
-        
+
     @property
     def sel_model_te_acc(self):
         """
@@ -101,9 +101,9 @@ def sel_model_te_acc(self):
         if self.msel is not None:
             return self.msel.sel_model_te_acc
         return -1
-        
+
     @property
     def oracle_last_setpoint_sel_te_acc(self):
         if self.msel is not None:
             return self.msel.oracle_last_setpoint_sel_te_acc
-        return -1
+        return -1

domainlab/algos/msels/c_msel_setpoint_delay.py

@@ -2,6 +2,7 @@
 Multiobjective Model Selection
 """
 import copy
+
 from domainlab.algos.msels.a_model_sel import AMSel
 from domainlab.utils.logger import Logger
 
@@ -11,27 +12,35 @@ class MSelSetpointDelay(AMSel):
     1. Model selection using validation performance
     2. Only update if setpoint has been decreased
     """
+
     def __init__(self, msel):
         super().__init__()
         # NOTE: super() has to come first always otherwise self.msel will be overwritten to be None
         self.msel = msel
         self._oracle_last_setpoint_sel_te_acc = 0.0
-        
+
     @property
     def oracle_last_setpoint_sel_te_acc(self):
         """
         return the last setpoint best acc
         """
         return self._oracle_last_setpoint_sel_te_acc
-    
+
     def update(self, clear_counter=False):
         """
         if the best model should be updated
         """
         logger = Logger.get_logger()
-        logger.info(f"setpoint selected current acc {self._oracle_last_setpoint_sel_te_acc}")
+        logger.info(
+            f"setpoint selected current acc {self._oracle_last_setpoint_sel_te_acc}"
+        )
         if clear_counter:
-            logger.info("setpoint msel te acc updated from {self._oracle_last_setpoint_sel_te_acc} to {self.sel_model_te_acc}")
+            log_message = (
+                f"setpoint msel te acc updated from "
+                f"{self._oracle_last_setpoint_sel_te_acc} to "
+                f"{self.sel_model_te_acc}"
+            )
+            logger.info(log_message)
             self._oracle_last_setpoint_sel_te_acc = self.sel_model_te_acc
         flag = self.msel.update(clear_counter)
         return flag

domainlab/algos/msels/c_msel_tr_loss.py

@@ -27,7 +27,7 @@ def reset(self):
     @property
     def max_es(self):
         return self._max_es
-        
+
     def update(self, clear_counter=False):
         """
         if the best model should be updated

domainlab/algos/msels/c_msel_val.py

@@ -24,7 +24,7 @@ def reset(self):
     @property
     def sel_model_te_acc(self):
         return self._sel_model_te_acc
-      
+
     @property
     def best_val_acc(self):
         """

domainlab/algos/msels/c_msel_val_top_k.py

@@ -10,6 +10,7 @@ class MSelValPerfTopK(MSelValPerf):
     1. Model selection using validation performance
     2. Visitor pattern to trainer
     """
+
     def __init__(self, max_es, top_k=2):
         super().__init__(max_es)  # construct self.tr_obs (observer)
         self.top_k = top_k
@@ -20,32 +21,41 @@ def update(self, clear_counter=False):
         if the best model should be updated
         """
         flag_super = super().update(clear_counter)
-        metric_val_current = \
-            self.tr_obs.metric_val[self.tr_obs.str_metric4msel]
+        metric_val_current = self.tr_obs.metric_val[self.tr_obs.str_metric4msel]
         acc_min = min(self.list_top_k_acc)
         if metric_val_current > acc_min:
             # overwrite
             logger = Logger.get_logger()
-            logger.info(f"top k validation acc: {self.list_top_k_acc} \
-                        overwriting/reset  counter")
+            logger.info(
+                f"top k validation acc: {self.list_top_k_acc} \
+                        overwriting/reset  counter"
+            )
             self.es_c = 0  # restore counter
             ind = self.list_top_k_acc.index(acc_min)
             # avoid having identical values
             if metric_val_current not in self.list_top_k_acc:
                 self.list_top_k_acc[ind] = metric_val_current
-                logger.info(f"top k validation acc updated: \
-                            {self.list_top_k_acc}")
+                logger.info(
+                    f"top k validation acc updated: \
+                            {self.list_top_k_acc}"
+                )
                 # overwrite to ensure consistency
-                # issue #569: initially self.list_top_k_acc will be [xx, 0] and it does not matter since 0 will be overwriten by second epoch validation acc. 
+                # issue #569: initially self.list_top_k_acc will be [xx, 0] and it does not matter since 0 will be overwriten by second epoch validation acc.
                 # actually, after epoch 1, most often, sefl._best_val_acc will be the higher value of self.list_top_k_acc will overwriten by min(self.list_top_k_acc)
-                logger.info(f"top-2 val sel: overwriting best val acc from {self._best_val_acc} to minium of {self.list_top_k_acc} which is {min(self.list_top_k_acc)} to ensure consistency")
+                logger.info(
+                    f"top-2 val sel: overwriting best val acc from {self._best_val_acc} to "
+                    f"minimum of {self.list_top_k_acc} which is {min(self.list_top_k_acc)} "
+                    f"to ensure consistency"
+                )
                 self._best_val_acc = min(self.list_top_k_acc)
             # overwrite test acc, this does not depend on if val top-k acc has been overwritten or not
-            metric_te_current = \
-                self.tr_obs.metric_te[self.tr_obs.str_metric4msel]
+            metric_te_current = self.tr_obs.metric_te[self.tr_obs.str_metric4msel]
             if self._sel_model_te_acc != metric_te_current:
                 # this can only happen if the validation acc has decreased and current val acc is only bigger than min(self.list_top_k_acc} but lower than max(self.list_top_k_acc)
-                logger.info(f"top-2 val sel: overwriting selected model test acc from {self._sel_model_te_acc} to {metric_te_current} to ensure consistency")
+                logger.info(
+                    f"top-2 val sel: overwriting selected model test acc from "
+                    f"{self._sel_model_te_acc} to {metric_te_current} to ensure consistency"
+                )
             self._sel_model_te_acc = metric_te_current
             return True
         return flag_super

domainlab/algos/observers/b_obvisitor.py

@@ -119,8 +119,12 @@ def after_all(self):
         else:
             metric_te.update({"acc_val": -1})
 
-        if hasattr(self, "model_sel") and hasattr(self.model_sel, "oracle_last_setpoint_sel_te_acc"):
-            metric_te.update({"acc_setpoint":self.model_sel.oracle_last_setpoint_sel_te_acc})
+        if hasattr(self, "model_sel") and hasattr(
+            self.model_sel, "oracle_last_setpoint_sel_te_acc"
+        ):
+            metric_te.update(
+                {"acc_setpoint": self.model_sel.oracle_last_setpoint_sel_te_acc}
+            )
         else:
             metric_te.update({"acc_setpoint": -1})
         self.dump_prediction(model_ld, metric_te)

domainlab/algos/observers/c_obvisitor_cleanup.py

@@ -12,7 +12,7 @@ def __init__(self, observer):
 
     def after_all(self):
         self.observer.after_all()
-        self.observer.clean_up()    # FIXME should  be self.clean_up???
+        self.observer.clean_up()  # FIXME should  be self.clean_up???
 
     def accept(self, trainer):
         self.observer.accept(trainer)

domainlab/algos/trainers/a_trainer.py

@@ -89,7 +89,6 @@ def __init__(self, successor_node=None, extend=None):
         self.mu_iter_start = 0
         self.flag_setpoint_updated = False
 
-
     @property
     def model(self):
         """
@@ -226,7 +225,7 @@ def get_model(self):
         if "trainer" not in str(type(self._model)).lower():
             return self._model
         return self._model.get_model()
-      
+
     def as_model(self):
         """
         used for decorator pattern