Merge pull request #137 from justusschock/small_fixes

Minor changes

delira/training/base_trainer.py

@@ -107,9 +107,10 @@ def __init__(self,
         start_epoch : int
             epoch to start training at
         metric_keys : dict
-            dict specifying which batch_dict entry to use for which metric as
-            target; default: None, which will result in key "label" for all
-            metrics
+            the batch_dict keys to use for each metric to calculate.
+            Should contain a value for each key in ``metrics``.
+            If no values are given for a key, per default ``pred`` and
+            ``label`` will be used for metric calculation
         convert_batch_to_npy_fn : type, optional
             function converting a batch-tensor to numpy, per default this is
             the identity function
@@ -470,8 +471,8 @@ def reduce_fn(batch):
                     best_val_score, new_val_score, val_score_mode)
 
                 # set best_val_score to new_val_score if is_best
-                best_val_score = int(is_best) * new_val_score + \
-                    (1 - int(is_best)) * best_val_score
+                if is_best:
+                    best_val_score = new_val_score
 
                 if is_best and verbose:
                     logging.info("New Best Value at Epoch %03d : %03.3f" %

delira/training/experiment.py

@@ -48,6 +48,7 @@ def __init__(self,
                  optim_builder=None,
                  checkpoint_freq=1,
                  trainer_cls=BaseNetworkTrainer,
+                 predictor_cls=Predictor,
                  **kwargs):
         """
 
@@ -83,6 +84,8 @@ def __init__(self,
             2 denotes saving every second epoch etc.); default: 1
         trainer_cls : subclass of :class:`BaseNetworkTrainer`
             the trainer class to use for training the model
+        predictor_cls : subclass of :class:`Predictor`
+            the predictor class to use for testing the model
         **kwargs :
             additional keyword arguments
 
@@ -117,6 +120,7 @@ def __init__(self,
         os.makedirs(self.save_path, exist_ok=True)
 
         self.trainer_cls = trainer_cls
+        self.predictor_cls = predictor_cls
 
         if val_score_key is None:
             if params.nested_get("val_metrics", False):
@@ -252,9 +256,10 @@ def _setup_test(self, params, model, convert_batch_to_npy_fn,
             the created predictor
 
         """
-        predictor = Predictor(model=model, key_mapping=self.key_mapping,
-                              convert_batch_to_npy_fn=convert_batch_to_npy_fn,
-                              prepare_batch_fn=prepare_batch_fn, **kwargs)
+        predictor = self.predictor_cls(
+            model=model, key_mapping=self.key_mapping,
+            convert_batch_to_npy_fn=convert_batch_to_npy_fn,
+            prepare_batch_fn=prepare_batch_fn, **kwargs)
         return predictor
 
     def run(self, train_data: BaseDataManager,