diff --git a/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py b/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
index f1dda5351c6093f307ec6f98f0f80ef7e9f87879..e27c27aabcd9d4f40cf87feeace3a497b094bab9 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
+++ b/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 = [
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
index fa64f218519688ae19487c843cebb03a73ec11b2..a1beb9ce0595a371a3fb8aa55e8e30f78b6429c7 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
+++ b/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
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/mixins.py b/bob/bio/base/pipelines/vanilla_biometrics/mixins.py
index af26b8363cb40d78a8c22e47fac97712d0d94339..d21bc9c8f1bed30560639db35a0cdac8b525413d 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/mixins.py
+++ b/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
diff --git a/bob/bio/base/script/vanilla_biometrics.py b/bob/bio/base/script/vanilla_biometrics.py
index 2b3e25674a06dcb4f9a8bf507fbe948eb6cb4256..14b910352c26ad1713b4e6329caa1c7ac62446e6 100644
--- a/bob/bio/base/script/vanilla_biometrics.py
+++ b/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):