diff --git a/bob/bio/base/pipelines/vanilla_biometrics/pipelines.py b/bob/bio/base/pipelines/vanilla_biometrics/pipelines.py
index e8f430a05be813d3b3edb091d7dae73fbfa39be5..0a1387ed2fe531b2f5ec76543399e09f26124bd3 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/pipelines.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/pipelines.py
@@ -114,6 +114,7 @@ class VanillaBiometricsPipeline(object):
allow_scoring_with_all_biometric_references,
)
+
if self.score_writer is not None:
return self.write_scores(scores)
@@ -216,8 +217,8 @@ class ZTNormVanillaBiometricsPipeline(object):
background_model_samples,
biometric_reference_samples,
probe_samples,
- zprobe_samples,
- t_biometric_reference_samples,
+ zprobe_samples=None,
+ t_biometric_reference_samples=None,
allow_scoring_with_all_biometric_references=False,
):
@@ -236,23 +237,32 @@ class ZTNormVanillaBiometricsPipeline(object):
# Z NORM
if self.z_norm:
+ if zprobe_samples is None:
+ raise ValueError("No samples for `z_norm` was provided")
+
+
z_normed_scores, z_probe_features = self.compute_znorm_scores(
zprobe_samples,
raw_scores,
biometric_references,
allow_scoring_with_all_biometric_references,
)
- if not self.t_norm:
+ if self.t_norm:
+ if t_biometric_reference_samples is None:
+ raise ValueError("No samples for `t_norm` was provided")
+ else:
+ # In case z_norm=True and t_norm=False
return z_normed_scores
# T NORM
- t_normed_scores, t_biometric_references = self.compute_tnorm_scores(
+ t_normed_scores, t_scores, t_biometric_references = self.compute_tnorm_scores(
t_biometric_reference_samples,
probe_features,
raw_scores,
allow_scoring_with_all_biometric_references,
)
if not self.z_norm:
+ # In case z_norm=False and t_norm=True
return t_normed_scores
@@ -261,7 +271,7 @@ class ZTNormVanillaBiometricsPipeline(object):
z_probe_features,
t_biometric_references,
z_normed_scores,
- t_normed_scores,
+ t_scores,
allow_scoring_with_all_biometric_references,
)
@@ -290,6 +300,21 @@ class ZTNormVanillaBiometricsPipeline(object):
allow_scoring_with_all_biometric_references,
)
+ def _inject_references(self, probe_samples, biometric_references):
+ """
+ Inject references in the current sampleset,
+ so it can run the scores
+ """
+
+ ########## WARNING #######
+ #### I'M MUTATING OBJECTS HERE. THIS CAN GO WRONG
+
+ references = [s.subject for s in biometric_references]
+ for probe in probe_samples:
+ probe.references = references
+ return probe_samples
+
+
def compute_znorm_scores(
self,
zprobe_samples,
@@ -298,11 +323,13 @@ class ZTNormVanillaBiometricsPipeline(object):
allow_scoring_with_all_biometric_references=False,
):
+ zprobe_samples = self._inject_references(zprobe_samples, biometric_references)
+
z_scores, z_probe_features = self.compute_scores(
zprobe_samples, biometric_references
)
- z_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_norm_scores(
+ z_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_znorm_scores(
z_scores, probe_scores, allow_scoring_with_all_biometric_references,
)
@@ -320,6 +347,8 @@ class ZTNormVanillaBiometricsPipeline(object):
t_biometric_reference_samples
)
+ probe_features = self._inject_references(probe_features, t_biometric_references)
+
# Reusing the probe features
t_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.score_samples(
probe_features,
@@ -327,20 +356,21 @@ class ZTNormVanillaBiometricsPipeline(object):
allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
)
- t_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_norm_scores(
+ t_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_tnorm_scores(
t_scores, probe_scores, allow_scoring_with_all_biometric_references,
)
- return t_normed_scores, t_biometric_references
+ return t_normed_scores, t_scores, t_biometric_references
def compute_ztnorm_scores(self,
z_probe_features,
t_biometric_references,
z_normed_scores,
- t_normed_scores,
+ t_scores,
allow_scoring_with_all_biometric_references=False
):
+ z_probe_features = self._inject_references(z_probe_features, t_biometric_references)
# Reusing the zprobe_features and t_biometric_references
zt_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.score_samples(
@@ -350,12 +380,12 @@ class ZTNormVanillaBiometricsPipeline(object):
)
# Z Normalizing the T-normed scores
- z_normed_t_normed = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_norm_scores(
- zt_scores, t_normed_scores, allow_scoring_with_all_biometric_references,
+ z_normed_t_normed = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_znorm_scores(
+ zt_scores, t_scores, allow_scoring_with_all_biometric_references,
)
# (Z Normalizing the T-normed scores) the Z normed scores
- zt_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_norm_scores(
+ zt_normed_scores = self.vanilla_biometrics_pipeline.biometric_algorithm.compute_tnorm_scores(
z_normed_t_normed, z_normed_scores, allow_scoring_with_all_biometric_references,
)
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py b/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py
index 00f5a1f21582719b034d6aa5d37e6b95879e2133..42c9b23b7e24ebede22dd127598fc17439b63a52 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py
@@ -201,7 +201,7 @@ def dask_vanilla_biometrics(vanila_biometrics_pipeline, npartitions=None):
class BioAlgorithmZTNormWrapper(BioAlgorithm):
"""
- Wraps an algorithm with Z-Norm scores
+ Wraps an :any:`BioAlgorithm` with ZT score normalization
"""
def __init__(self, biometric_algorithm, **kwargs):
@@ -220,29 +220,39 @@ class BioAlgorithmZTNormWrapper(BioAlgorithm):
biometric_references, data
)
- def compute_norm_scores(
+ def _norm(self, score, mu, std):
+ return (score - mu) / std
+
+
+ def compute_znorm_scores(
self,
base_norm_scores,
probe_scores,
allow_scoring_with_all_biometric_references=False,
):
"""
- Base normalization function
+ Base Z-normalization function
"""
- def _norm(score, mu, std):
- return (score - mu) / std
-
+ # Dumping all scores
score_floats = np.array([s.data for sset in base_norm_scores for s in sset])
- mu = np.mean(score_floats)
- std = np.std(score_floats)
+
+ # Reshaping in PROBE vs BIOMETRIC_REFERENCES
+ n_probes = len(base_norm_scores)
+ n_references = len(base_norm_scores[0].references)
+ score_floats = score_floats.reshape((n_probes, n_references))
+
+ # AXIS ON THE MODELS
+ big_mu = np.mean(score_floats, axis=0)
+ big_std = np.std(score_floats, axis=0)
# Normalizing
+ # TODO: THIS TENDS TO BE EXTREMLY SLOW
normed_score_samples = []
for probe in probe_scores:
sampleset = SampleSet([], parent=probe)
- for biometric_reference_score in probe:
- score = _norm(biometric_reference_score.data, mu, std)
+ for mu, std, biometric_reference_score in zip(big_mu, big_std, probe):
+ score = self._norm(biometric_reference_score.data, mu, std)
new_sample = Sample(score, parent=biometric_reference_score)
sampleset.samples.append(new_sample)
normed_score_samples.append(sampleset)
@@ -250,22 +260,37 @@ class BioAlgorithmZTNormWrapper(BioAlgorithm):
return normed_score_samples
- def compute_ztnorm_scores(
+ def compute_tnorm_scores(
self,
- z_probe_features,
- t_biometrics_references,
- z_scores,
- t_scores,
+ base_norm_scores,
probe_scores,
- allow_scoring_with_all_biometric_references=False
+ allow_scoring_with_all_biometric_references=False,
):
+ """
+ Base Z-normalization function
+ """
- # TxZ scores
- txz_scores_sset = self.biometric_algorithm.score_samples(
- z_probe_features,
- t_biometrics_references,
- allow_scoring_with_all_biometric_references,
- )
-
+ # Dumping all scores
+ score_floats = np.array([s.data for sset in base_norm_scores for s in sset])
- pass
+ # Reshaping in PROBE vs BIOMETRIC_REFERENCES
+ n_probes = len(base_norm_scores)
+ n_references = len(base_norm_scores[0].references)
+ score_floats = score_floats.reshape((n_probes, n_references))
+
+ # AXIS ON THE PROBES
+ big_mu = np.mean(score_floats, axis=1)
+ big_std = np.std(score_floats, axis=1)
+
+ # Normalizing
+ # TODO: THIS TENDS TO BE EXTREMLY SLOW
+ normed_score_samples = []
+ for mu, std, probe in zip(big_mu, big_std,probe_scores):
+ sampleset = SampleSet([], parent=probe)
+ for biometric_reference_score in probe:
+ score = self._norm(biometric_reference_score.data, mu, std)
+ new_sample = Sample(score, parent=biometric_reference_score)
+ sampleset.samples.append(new_sample)
+ normed_score_samples.append(sampleset)
+
+ return normed_score_samples
diff --git a/bob/bio/base/test/test_vanilla_biometrics_score_norm.py b/bob/bio/base/test/test_vanilla_biometrics_score_norm.py
index eab3ed4ac064b117703df7b947b86c9238f726bb..2ae10893fc426f238b55f59d364e1d072924223d 100644
--- a/bob/bio/base/test/test_vanilla_biometrics_score_norm.py
+++ b/bob/bio/base/test/test_vanilla_biometrics_score_norm.py
@@ -30,6 +30,205 @@ import bob.pipelines as mario
import uuid
import shutil
import itertools
+from scipy.spatial.distance import cdist
+from sklearn.preprocessing import FunctionTransformer
+import copy
+
+def zt_norm_stubs(references, probes, t_references, z_probes):
+ def _norm(scores, norm_base_scores, axis=1):
+ mu = np.mean(norm_base_scores, axis=axis)
+ std = np.std(norm_base_scores, axis=axis)
+
+ if axis == 1:
+ return ((scores.T - mu) / std).T
+ else:
+ return (scores - mu) / std
+
+ n_reference = references.shape[0]
+ n_probes = probes.shape[0]
+ n_t_references = t_references.shape[0]
+ n_z_probes = z_probes.shape[0]
+
+ raw_scores = cdist(references, probes)
+
+ z_scores = cdist(references, z_probes)
+ # Computing the statistics of Z-Probes for each biometric reference
+ # https://arxiv.org/pdf/1709.09868.pdf --> below eq (2) first eq
+ z_normed_scores = _norm(raw_scores, z_scores, axis=1)
+ assert z_normed_scores.shape == (n_reference, n_probes)
+
+ t_scores = cdist(t_references, probes)
+ # Computing the statistics of T-Models for each probe
+ # https://arxiv.org/pdf/1709.09868.pdf --> below eq (2) second eq
+ t_normed_scores = _norm(raw_scores, t_scores, axis=0)
+ assert t_normed_scores.shape == (n_reference, n_probes)
+ assert t_scores.shape == (n_t_references, n_probes)
+
+ ZxT_scores = cdist(t_references, z_probes)
+ assert ZxT_scores.shape == (n_t_references, n_z_probes)
+ # Computing the statistics of T-Models for each z probe
+ # https://arxiv.org/pdf/1709.09868.pdf --> below eq (2) third eq
+ z_t_scores = _norm(t_scores, ZxT_scores, axis=1)
+ assert z_t_scores.shape == (n_t_references, n_probes)
+
+ # FINALLY DOING THE F*****G ZT-NORM
+ zt_normed_scores = _norm(z_normed_scores, z_t_scores, axis=0)
+ assert zt_normed_scores.shape == (n_reference, n_probes)
+
+ return raw_scores, z_normed_scores, t_normed_scores, zt_normed_scores
+
+
+def test_norm_mechanics():
+ def _create_sample_sets(raw_data, offset, references=None):
+ if references is None:
+ return [
+ SampleSet(
+ [Sample(s, subject=str(i + offset), key=str(uuid.uuid4()))],
+ key=str(i + offset),
+ subject=str(i + offset),
+ )
+ for i, s in enumerate(raw_data)
+ ]
+ else:
+ return [
+ SampleSet(
+ [Sample(s, subject=str(i + offset), key=str(uuid.uuid4()))],
+ key=str(i + offset),
+ subject=str(i + offset),
+ references=references,
+ )
+ for i, s in enumerate(raw_data)
+ ]
+
+ def _do_nothing_fn(x):
+ return x
+
+ def _dump_scores_from_samples(scores, shape):
+ # We have to transpose because the tests are BIOMETRIC_REFERENCES vs PROBES
+ # and bob.bio.base is PROBES vs BIOMETRIC_REFERENCES
+ return np.array([s.data for sset in scores for s in sset]).reshape(shape).T
+
+ ############
+ # Prepating stubs
+ ############
+ n_references = 2
+ n_probes = 3
+ n_t_references = 4
+ n_z_probes = 5
+
+ references = np.arange(10).reshape(
+ n_references, 5
+ ) # two references (each row different identity)
+ probes = (
+ np.arange(15).reshape(n_probes, 5) * 10
+ ) # three probes (each row different identity matching with references)
+
+ t_references = np.arange(20).reshape(
+ n_t_references, 5
+ ) # four T-REFERENCES (each row different identity)
+ z_probes = (
+ np.arange(25).reshape(n_z_probes, 5) * 10
+ ) # five Z-PROBES (each row different identity matching with t references)
+
+ (
+ raw_scores_ref,
+ z_normed_scores_ref,
+ t_normed_scores_ref,
+ zt_normed_scores_ref,
+ ) = zt_norm_stubs(references, probes, t_references, z_probes)
+
+ ############
+ # Preparing the samples
+ ############
+
+ biometric_reference_sample_sets = _create_sample_sets(references, 0)
+ reference_ids = [r.subject for r in biometric_reference_sample_sets]
+
+ probe_sample_sets = _create_sample_sets(probes, 10, reference_ids)
+
+ t_reference_sample_sets = _create_sample_sets(t_references, 20)
+ t_reference_ids = [r.subject for r in t_reference_sample_sets]
+
+ #z_probe_sample_sets = _create_sample_sets(z_probes, 30, t_reference_ids)
+ z_probe_sample_sets = _create_sample_sets(z_probes, 30, t_reference_ids)
+
+ ############
+ # TESTING REGULAR SCORING
+ #############
+ transformer = FunctionTransformer(func=_do_nothing_fn)
+ biometric_algorithm = Distance(factor=1)
+ vanilla_pipeline = VanillaBiometricsPipeline(
+ transformer, biometric_algorithm, score_writer=None
+ )
+ score_sampes = vanilla_pipeline(
+ [], biometric_reference_sample_sets, probe_sample_sets,
+ allow_scoring_with_all_biometric_references=True
+ )
+
+ raw_scores = _dump_scores_from_samples(score_sampes, shape=(n_probes, n_references))
+
+ assert np.allclose(raw_scores, raw_scores_ref)
+
+
+ ############
+ # TESTING Z-NORM
+ #############
+ z_vanilla_pipeline = ZTNormVanillaBiometricsPipeline(vanilla_pipeline,
+ z_norm=True,
+ t_norm=False,
+ )
+
+ z_normed_score_samples = z_vanilla_pipeline(
+ [],
+ biometric_reference_sample_sets,
+ copy.deepcopy(probe_sample_sets),
+ z_probe_sample_sets,
+ t_reference_sample_sets,
+ )
+
+ z_normed_scores = _dump_scores_from_samples(z_normed_score_samples, shape=(n_probes, n_references))
+ assert np.allclose(z_normed_scores, z_normed_scores_ref)
+
+ ############
+ # TESTING T-NORM
+ #############
+ t_vanilla_pipeline = ZTNormVanillaBiometricsPipeline(vanilla_pipeline,
+ z_norm=False,
+ t_norm=True,
+ )
+
+ t_normed_score_samples = t_vanilla_pipeline(
+ [],
+ biometric_reference_sample_sets,
+ copy.deepcopy(probe_sample_sets),
+ z_probe_sample_sets,
+ t_reference_sample_sets,
+ )
+
+ t_normed_scores = _dump_scores_from_samples(t_normed_score_samples, shape=(n_probes, n_references))
+ assert np.allclose(t_normed_scores, t_normed_scores_ref)
+
+
+ ############
+ # TESTING ZT-NORM
+ #############
+ zt_vanilla_pipeline = ZTNormVanillaBiometricsPipeline(vanilla_pipeline,
+ z_norm=True,
+ t_norm=True,
+ )
+
+ zt_normed_score_samples = zt_vanilla_pipeline(
+ [],
+ biometric_reference_sample_sets,
+ copy.deepcopy(probe_sample_sets),
+ z_probe_sample_sets,
+ t_reference_sample_sets,
+ )
+
+ zt_normed_scores = _dump_scores_from_samples(zt_normed_score_samples, shape=(n_probes, n_references))
+ assert np.allclose(zt_normed_scores, zt_normed_scores_ref)
+
+
def test_znorm_on_memory():
@@ -68,7 +267,7 @@ def test_znorm_on_memory():
assert len(scores) == 10
run_pipeline(False)
- #run_pipeline(False) # Testing checkpoint
+ # run_pipeline(False) # Testing checkpoint
# shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
# os.makedirs(dir_name, exist_ok=True)
# run_pipeline(True)