diff --git a/bob/bio/base/config/baselines/pca_atnt.py b/bob/bio/base/config/baselines/pca_atnt.py
index 1be261ad4559321999d1f459db2395136dc1c9c6..1aad9f240d131ea49339d1df4299e45770906482 100644
--- a/bob/bio/base/config/baselines/pca_atnt.py
+++ b/bob/bio/base/config/baselines/pca_atnt.py
@@ -24,5 +24,7 @@ extractor = Pipeline(steps=[('0',CheckpointSampleLinearize(features_dir="./examp
('1',CheckpointSamplePCA(features_dir="./example/extractor1", model_path="./example/pca.pkl"))])
#extractor = dask_it(extractor)
-from bob.bio.base.pipelines.vanilla_biometrics.comparator import DistanceComparator
-algorithm = DistanceComparator()
+from bob.bio.base.pipelines.vanilla_biometrics.biometric_algorithm import Distance, BiometricAlgorithmCheckpointMixin
+class CheckpointDistance(BiometricAlgorithmCheckpointMixin, Distance): pass
+algorithm = CheckpointDistance(features_dir="./example/models")
+#algorithm = Distance()
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/comparator.py b/bob/bio/base/pipelines/vanilla_biometrics/biometric_algorithm.py
similarity index 60%
rename from bob/bio/base/pipelines/vanilla_biometrics/comparator.py
rename to bob/bio/base/pipelines/vanilla_biometrics/biometric_algorithm.py
index 5d9a3abea1276d80e9c1c214b1f5bdce52ff29c6..5331631234b198844fbbf5b130ee120a6f3181c3 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/comparator.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/biometric_algorithm.py
@@ -5,9 +5,12 @@
from bob.pipelines.sample import Sample, SampleSet, DelayedSample
import numpy
+import bob.io.base
+import os
+import functools
-class Comparator(object):
+class BiometricAlgorithm(object):
"""Describes a base biometric comparator for the Vanilla Biometrics Pipeline :ref:`_bob.bio.base.struct_bio_rec_sys`_.
biometric model enrollement, via ``enroll()`` and scoring, with
@@ -50,11 +53,18 @@ class Comparator(object):
retval = []
for k in biometric_references:
# compute on-the-fly
- data = [s.data for s in k.samples]
- retval.append(Sample(self.enroll(data), parent=k))
+ retval.append(self._enroll_sample_set(k))
return retval
+ def _enroll_sample_set(self, sampleset):
+
+ # Unpack the sampleset
+ data = [s.data for s in sampleset.samples]
+
+ # Enroll
+ return Sample(self.enroll(data), parent=sampleset)
+
def enroll(self, data, **kwargs):
"""
@@ -106,24 +116,37 @@ class Comparator(object):
retval = []
for p in probes:
- #data = numpy.vstack([s for s in p.samples])
- data = [s.data for s in p.samples]
-
-
- for subprobe_id, (s, parent) in enumerate(zip(data, p.samples)):
- # each sub-probe in the probe needs to be checked
- subprobe_scores = []
- for ref in [r for r in biometric_references if r.key in p.references]:
- subprobe_scores.append(
- Sample(self.score(ref.data, s), parent=ref)
- )
- subprobe = SampleSet(subprobe_scores, parent=p)
- subprobe.subprobe_id = subprobe_id
- retval.append(subprobe)
+ retval.append(self._score_sample_set(p, biometric_references, extractor))
return retval
- def score(self, biometric_reference, data, **kwargs):
+ def _score_sample_set(self, sampleset, biometric_references, extractor):
+ """Given a sampleset for probing, compute the scores and retures a sample set with the scores
+ """
+
+ # Stacking the samples from a sampleset
+ data = [s.data for s in sampleset.samples]
+
+ # Compute scores for each sample inside of the sample set
+ # TODO: In some cases we want to compute 1 score per sampleset (IJB-C)
+ # We should add an agregator function here so we can properlly agregate samples from
+ # a sampleset either after or before scoring.
+ # To be honest, this should be the default behaviour
+ for subprobe_id, (s, parent) in enumerate(zip(data, sampleset.samples)):
+ # Creating one sample per comparison
+ subprobe_scores = []
+ for ref in [r for r in biometric_references if r.key in sampleset.references]:
+ subprobe_scores.append(
+ Sample(self.score(ref.data, s, extractor), parent=ref)
+ )
+ # Creating one sampleset per probe
+ subprobe = SampleSet(subprobe_scores, parent=sampleset)
+ subprobe.subprobe_id = subprobe_id
+
+ return subprobe
+
+
+ def score(self, biometric_reference, data, extractor=None, **kwargs):
"""It handles the score computation for one sample
Parameters
@@ -147,9 +170,65 @@ class Comparator(object):
raise NotImplemented("Please, implement me")
+from bob.pipelines.mixins import CheckpointMixin
+class BiometricAlgorithmCheckpointMixin(CheckpointMixin):
+ """Mixing used to checkpoint Enrolled and Scoring samples.
+
+ Examples
+ --------
+
+ >>> from bob.bio.base.pipelines.vanilla_biometrics.biometric_algorithm import BiometricAlgorithmCheckpointMixin, Distance
+ >>> class DistanceCheckpoint(BiometricAlgorithmCheckpointMixin, Distance) pass:
+ >>> biometric_algorithm = DistanceCheckpoint(features_dir="./")
+ >>> biometric_algorithm.enroll(sample)
+
+ It's possible to use it as with the :py:func:`bob.pipelines.mixins.mix_me_up`
+
+ >>> from bob.pipelines.mixins import mix_me_up
+ >>> biometric_algorithm = mix_me_up([BiometricAlgorithmCheckpointMixin], Distance)(features_dir="./")
+ >>> biometric_algorithm.enroll(sample)
+
+ """
+
+
+ def _enroll_sample_set(self, sampleset):
+ """
+ Enroll a sample set with checkpointing
+ """
+
+
+ path = self.make_path(sampleset)
+ if path is None or not os.path.isfile(path):
+
+ # Enrolling the sample
+ enrolled_sample = super()._enroll_sample_set(sampleset)
+
+ # saving the new sample
+ self.save(enrolled_sample)
+
+ # Dealaying it.
+ # This seems inefficient, but it's crucial with large datasets
+ enrolled_sample = DelayedSample(functools.partial(bob.io.base.load, path), enrolled_sample)
+
+ else:
+ # If sample already there, just load
+ enrolled_sample = self.load(path)
+
+ return enrolled_sample
+
+
+ #def _score_sample_set(self, sampleset, biometric_references, extractor):
+ # """Given a sampleset for probing, compute the scores and retures a sample set with the scores
+ # """
+
+ # scored_sample =
+
+ # return subprobe
+
+
import scipy.spatial.distance
from sklearn.utils.validation import check_array
-class DistanceComparator(Comparator):
+class Distance(BiometricAlgorithm):
def __init__(self,distance_function = scipy.spatial.distance.euclidean,factor=-1):