From c0cd0dca2d8dcc9b61945826f8c6c83f3ebdd458 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Sun, 29 Mar 2020 21:47:16 +0200
Subject: [PATCH] Implemented score_for_multiple_biometric_references for
legacy models
---
.../base/config/examples/gabor_mobio-male.py | 4 +-
.../vanilla_biometrics/abstract_classes.py | 8 +--
.../vanilla_biometrics/implemented.py | 22 ++++++--
.../pipelines/vanilla_biometrics/legacy.py | 52 ++++++++++++++-----
4 files changed, 63 insertions(+), 23 deletions(-)
diff --git a/bob/bio/base/config/examples/gabor_mobio-male.py b/bob/bio/base/config/examples/gabor_mobio-male.py
index 000c2b37..a20453ed 100644
--- a/bob/bio/base/config/examples/gabor_mobio-male.py
+++ b/bob/bio/base/config/examples/gabor_mobio-male.py
@@ -55,7 +55,6 @@ extractor = functools.partial(
node_distance=(8, 8),
)
-
transformer = make_pipeline(
Preprocessor(preprocessor, features_dir=os.path.join(base_dir, "face_cropper")),
Extractor(extractor, features_dir=os.path.join(base_dir, "gabor_graph")),
@@ -71,7 +70,8 @@ gabor_jet = functools.partial(
gabor_sigma=math.sqrt(2.0) * math.pi,
)
-algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir)
+algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir, allow_score_multiple_references=True)
+#algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir)
# comment out the code below to disable dask
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py b/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
index e040c0f1..f1dda535 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
@@ -19,7 +19,7 @@ class BioAlgorithm(metaclass=ABCMeta):
"""
- def __init__(self, allow_score_multiple_references=False):
+ def __init__(self, allow_score_multiple_references=False, **kwargs):
self.allow_score_multiple_references = allow_score_multiple_references
self.stacked_biometric_references = None
@@ -126,10 +126,10 @@ class BioAlgorithm(metaclass=ABCMeta):
scores = self.score_multiple_biometric_references(
self.stacked_biometric_references, s
)
-
+
# Wrapping the scores in samples
for ref, score in zip(biometric_references, scores):
- subprobe_scores.append(_write_sample(ref, sampleset, score[0]))
+ subprobe_scores.append(_write_sample(ref, sampleset, score))
else:
for ref in [
@@ -172,7 +172,7 @@ class BioAlgorithm(metaclass=ABCMeta):
@abstractmethod
def score_multiple_biometric_references(self, biometric_references, data):
"""
- It handles the score computation of one probe and multiple biometric references
+ It handles the score computation of one probe against multiple biometric references
This method is called is called if `allow_scoring_multiple_references` is set to true
Parameters
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/implemented.py b/bob/bio/base/pipelines/vanilla_biometrics/implemented.py
index f9bcd6c9..f77e66d3 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/implemented.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/implemented.py
@@ -3,11 +3,14 @@ from sklearn.utils.validation import check_array
import numpy
from .abstract_classes import BioAlgorithm
from .mixins import BioAlgCheckpointMixin
+from scipy.spatial.distance import cdist
class Distance(BioAlgorithm):
- def __init__(self, distance_function=scipy.spatial.distance.euclidean, factor=-1):
-
+ def __init__(
+ self, distance_function=scipy.spatial.distance.euclidean, factor=-1, **kwargs
+ ):
+ super().__init__(**kwargs)
self.distance_function = distance_function
self.factor = factor
@@ -33,7 +36,7 @@ class Distance(BioAlgorithm):
return numpy.mean(enroll_features, axis=0)
- def score(self, model, probe):
+ def score(self, biometric_reference, data):
"""score(model, probe) -> float
Computes the distance of the model to the probe using the distance function specified in the constructor.
@@ -54,9 +57,18 @@ class Distance(BioAlgorithm):
A similarity value between ``model`` and ``probe``
"""
- probe = probe.flatten()
+ data = data.flatten()
# return the negative distance (as a similarity measure)
- return self.factor * self.distance_function(model, probe)
+ return self.factor * self.distance_function(biometric_reference, data)
+
+ def score_multiple_biometric_references(self, biometric_references, data):
+ data = data.flatten()
+ references_stacked = numpy.vstack(biometric_references)
+ scores = self.factor * cdist(
+ references_stacked, data.reshape(1, -1), self.distance_function
+ )
+
+ return list(scores.flatten())
class CheckpointDistance(BioAlgCheckpointMixin, Distance):
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
index 0681fbee..41f6a4d8 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
@@ -173,12 +173,12 @@ class _NonPickableWrapper:
def __setstate__(self, d):
# Handling unpicklable objects
self._instance = None
- #return super().__setstate__(d)
+ # return super().__setstate__(d)
def __getstate__(self):
# Handling unpicklable objects
self._instance = None
- #return super().__getstate__()
+ # return super().__getstate__()
class _Preprocessor(_NonPickableWrapper, TransformerMixin, BaseEstimator):
@@ -345,7 +345,7 @@ class AlgorithmAsTransformer(CheckpointMixin, SampleMixin, _AlgorithmTransformer
return self
-class AlgorithmAsBioAlg(BioAlgorithm, _NonPickableWrapper):
+class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
"""Biometric Algorithm that handles legacy :py:class:`bob.bio.base.algorithm.Algorithm`
@@ -387,19 +387,35 @@ class AlgorithmAsBioAlg(BioAlgorithm, _NonPickableWrapper):
# 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
+
+ def _write_sample(ref, probe, score):
+ data = make_four_colums_score(ref.subject, probe.subject, probe.path, score)
+ return Sample(data, parent=ref)
+
retval = []
for subprobe_id, s in enumerate(sampleset.samples):
# Creating one sample per comparison
subprobe_scores = []
- for ref in [
- r for r in biometric_references if r.key in sampleset.references
- ]:
- score = self.score(ref.data, s.data)
- data = make_four_colums_score(
- ref.subject, sampleset.subject, sampleset.path, score
+ if self.allow_score_multiple_references:
+ if self.stacked_biometric_references is None:
+ self.stacked_biometric_references = [
+ ref.data for ref in biometric_references
+ ]
+ scores = self.score_multiple_biometric_references(
+ self.stacked_biometric_references, s.data
)
- subprobe_scores.append(Sample(data, parent=ref))
+
+ # Wrapping the scores in samples
+ for ref, score in zip(biometric_references, scores):
+ subprobe_scores.append(_write_sample(ref, sampleset, score))
+
+ else:
+ for ref in [
+ r for r in biometric_references if r.key in sampleset.references
+ ]:
+ score = self.score(ref.data, s.data)
+ subprobe_scores.append(_write_sample(ref, sampleset, score))
# Creating one sampleset per probe
subprobe = SampleSet(subprobe_scores, parent=sampleset)
@@ -438,5 +454,17 @@ class AlgorithmAsBioAlg(BioAlgorithm, _NonPickableWrapper):
reader = _get_pickable_method(self.instance.read_model)
return DelayedSample(functools.partial(reader, path), parent=enroll_features)
- def score(self, model, probe, **kwargs):
- return self.instance.score(model, probe)
+ def score(self, biometric_reference, data, **kwargs):
+ return self.instance.score(biometric_reference, data)
+
+ def score_multiple_biometric_references(self, biometric_references, data, **kwargs):
+ """
+ It handles the score computation of one probe against multiple biometric references using legacy
+ `bob.bio.base`
+
+ Basically it wraps :py:meth:`bob.bio.base.algorithm.Algorithm.score_for_multiple_models`.
+
+ """
+
+ scores = self.instance.score_for_multiple_models(biometric_references, data)
+ return scores
--
GitLab