Created optimization that allows you to do fast scoring if such option is...

Created optimization that allows you to do fast scoring if such option is available in the BioAlgorithm AND the database allows you to do it. I still think that the Vanilla Pipeline should be in a class. This will come in a next MR

bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py

@@ -12,10 +12,6 @@ class BioAlgorithm(metaclass=ABCMeta):
     Parameters
     ----------
 
-        allow_score_multiple_references: bool
-          If true will call `self.score_multiple_biometric_references`, at scoring time, to compute scores in one shot with multiple probes.
-          This optiization is useful when all probes needs to be compared with all biometric references AND
-          your scoring function allows this broadcast computation.
 
     """
 
@@ -65,7 +61,7 @@ class BioAlgorithm(metaclass=ABCMeta):
         """
         pass
 
-    def score_samples(self, probe_features, biometric_references):
+    def score_samples(self, probe_features, biometric_references, allow_scoring_with_all_biometric_references=False):
         """Scores a new sample against multiple (potential) references
 
         Parameters
@@ -80,6 +76,12 @@ class BioAlgorithm(metaclass=ABCMeta):
                 scoring the input probes, must have an ``id`` attribute that
                 will be used to cross-reference which probes need to be scored.
 
+            allow_scoring_with_all_biometric_references: bool
+                If true will call `self.score_multiple_biometric_references`, at scoring time, to compute scores in one shot with multiple probes.
+                This optiization is useful when all probes needs to be compared with all biometric references AND
+                your scoring function allows this broadcast computation.
+
+
         Returns
         -------
 
@@ -92,10 +94,10 @@ class BioAlgorithm(metaclass=ABCMeta):
 
         retval = []
         for p in probe_features:
-            retval.append(self._score_sample_set(p, biometric_references))
+            retval.append(self._score_sample_set(p, biometric_references, allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references))
         return retval
 
-    def _score_sample_set(self, sampleset, biometric_references):
+    def _score_sample_set(self, sampleset, biometric_references, allow_scoring_with_all_biometric_references):
         """Given a sampleset for probing, compute the scores and retures a sample set with the scores
         """
 
@@ -117,7 +119,7 @@ class BioAlgorithm(metaclass=ABCMeta):
             # Creating one sample per comparison
             subprobe_scores = []
 
-            if self.allow_score_multiple_references:
+            if allow_scoring_with_all_biometric_references:
                 # Multiple scoring
                 if self.stacked_biometric_references is None:
                     self.stacked_biometric_references = [

bob/bio/base/pipelines/vanilla_biometrics/legacy.py

@@ -202,7 +202,12 @@ def _get_pickable_method(method):
 
 
 class Preprocessor(CheckpointMixin, SampleMixin, _Preprocessor):
-    def __init__(self, callable, transform_extra_arguments=(("annotations", "annotations"),), **kwargs):
+    def __init__(
+        self,
+        callable,
+        transform_extra_arguments=(("annotations", "annotations"),),
+        **kwargs,
+    ):
         instance = callable()
         super().__init__(
             callable=callable,
@@ -240,7 +245,10 @@ class _Extractor(_NonPickableWrapper, TransformerMixin, BaseEstimator):
         return self
 
     def _more_tags(self):
-        return {"requires_fit": self.instance.requires_training}
+        return {
+            "requires_fit": self.instance.requires_training,
+            "stateless": not self.instance.requires_training,
+        }
 
 
 class Extractor(CheckpointMixin, SampleMixin, _Extractor):
@@ -381,7 +389,12 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
         # Enroll
         return self.enroll(sampleset)
 
-    def _score_sample_set(self, sampleset, biometric_references):
+    def _score_sample_set(
+        self,
+        sampleset,
+        biometric_references,
+        allow_scoring_with_all_biometric_references=False,
+    ):
         """Given a sampleset for probing, compute the scores and retures a sample set with the scores
         """
 
@@ -400,7 +413,7 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
             # Creating one sample per comparison
             subprobe_scores = []
 
-            if self.allow_score_multiple_references:
+            if allow_scoring_with_all_biometric_references:
                 if self.stacked_biometric_references is None:
                     self.stacked_biometric_references = [
                         ref.data for ref in biometric_references

bob/bio/base/pipelines/vanilla_biometrics/mixins.py

@@ -65,12 +65,21 @@ class BioAlgCheckpointMixin(CheckpointMixin):
 
         return delayed_enrolled_sample
 
-    def _score_sample_set(self, sampleset, biometric_references):
+    def _score_sample_set(
+        self,
+        sampleset,
+        biometric_references,
+        allow_scoring_with_all_biometric_references=False
+    ):
         """Given a sampleset for probing, compute the scores and retures a sample set with the scores
         """
 
         # Computing score
-        scored_sample_set = super()._score_sample_set(sampleset, biometric_references)
+        scored_sample_set = super()._score_sample_set(
+            sampleset,
+            biometric_references,
+            allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+        )
         for s in scored_sample_set:
             # Checkpointing score
             path = os.path.join(self.score_dir, str(s.path) + ".txt")
@@ -89,7 +98,12 @@ class BioAlgDaskMixin:
         )
         return biometric_references
 
-    def score_samples(self, probe_features, biometric_references):
+    def score_samples(
+        self,
+        probe_features,
+        biometric_references,
+        allow_scoring_with_all_biometric_references=False,
+    ):
 
         # TODO: Here, we are sending all computed biometric references to all
         # probes.  It would be more efficient if only the models related to each
@@ -100,5 +114,9 @@ class BioAlgDaskMixin:
 
         all_references = dask.delayed(list)(biometric_references)
 
-        scores = probe_features.map_partitions(super().score_samples, all_references)
+        scores = probe_features.map_partitions(
+            super().score_samples,
+            all_references,
+            allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+        )
         return scores

bob/bio/base/script/vanilla_biometrics.py

@@ -168,12 +168,19 @@ def vanilla_biometrics(
 
             logger.info(f"Running vanilla biometrics for group {group}")
 
+            allow_scoring_with_all_biometric_references = (
+                database.allow_scoring_with_all_biometric_references
+                if hasattr(database, "allow_scoring_with_all_biometric_references")
+                else False
+            )
+
             result = biometric_pipeline(
                 database.background_model_samples(),
                 biometric_references,
                 database.probes(group=group),
                 transformer,
                 algorithm,
+                allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references
             )
 
             if isinstance(result, dask.bag.core.Bag):