diff --git a/bob/bio/base/config/extractor/linearize.py b/bob/bio/base/config/extractor/linearize.py
index ddc02165757ec3ed8a66d43d0d59d8f3ce6edaf5..7d452d6e3363c553ecd9f069982e0269b3f0166c 100644
--- a/bob/bio/base/config/extractor/linearize.py
+++ b/bob/bio/base/config/extractor/linearize.py
@@ -3,4 +3,4 @@
import bob.bio.base
# Linearization of the data to a vector, no data type specified
-extractor = bob.bio.base.extractor.Linearize
+extractor = bob.bio.base.extractor.Linearize()
diff --git a/bob/bio/base/mixins/__init__.py b/bob/bio/base/mixins/__init__.py
index 62acd81c13d30ee5eb3adb9526d466727af2bfda..729af155f8f3f72cd1e3805f4a6efe40e2787dd7 100644
--- a/bob/bio/base/mixins/__init__.py
+++ b/bob/bio/base/mixins/__init__.py
@@ -1,2 +1,2 @@
from .linearize import Linearize, SampleLinearize, CheckpointSampleLinearize
-#from .pca import CheckpointSamplePCA, SamplePCA
+from .pca import CheckpointSamplePCA, SamplePCA
diff --git a/bob/bio/base/mixins/legacy.py b/bob/bio/base/mixins/legacy.py
index 616bdf43d683cb69a6d4ad85407c091b627b86e9..a1c523dea8b7e175de10abb61e6d1dbc4f199d79 100644
--- a/bob/bio/base/mixins/legacy.py
+++ b/bob/bio/base/mixins/legacy.py
@@ -83,12 +83,23 @@ class LegacyProcessorMixin(TransformerMixin):
def transform(self, X):
- X = check_array(X, allow_nd=True)
-
# Instantiates and do the "real" transform
if self.instance is None:
self.instance = self.callable()
- return [self.instance(x) for x in X]
+
+ if isinstance(X[0], dict):
+ # Handling annotations if it's the case
+ retval = []
+ for x in X:
+ data = x["data"]
+ annotations = x["annotations"]
+
+ retval.append(self.instance(data, annotations=annotations))
+ return retval
+
+ else:
+ X = check_array(X, allow_nd=True)
+ return [self.instance(x) for x in X]
from bob.pipelines.mixins import CheckpointMixin, SampleMixin
diff --git a/bob/bio/base/mixins/pca.py b/bob/bio/base/mixins/pca.py
index 83e7bf47baef37ad611c8b53f3e88fa0f8ce6400..1412188f66aa8b111d39b741093486cf5bf780c8 100644
--- a/bob/bio/base/mixins/pca.py
+++ b/bob/bio/base/mixins/pca.py
@@ -7,7 +7,7 @@
TODO: This should be deployed in bob.pipelines
"""
-from bob.pipelines.processor import CheckpointMixin, SampleMixin
+from bob.pipelines.mixins import CheckpointMixin, SampleMixin
from sklearn.base import TransformerMixin
from sklearn.decomposition import PCA
import numpy
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/database.py b/bob/bio/base/pipelines/vanilla_biometrics/database.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py b/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
index fe346a6aa4e0cf20649cbebb24e1367174ffd4d5..9b32ae55f66ca498b1d0cbf88692b842fc9e6406 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
@@ -12,6 +12,9 @@ import dask.bag
import dask.delayed
from bob.pipelines.sample import samplesets_to_samples
+import logging
+logger = logging.getLogger(__name__)
+
def biometric_pipeline(
background_model_samples,
@@ -20,16 +23,21 @@ def biometric_pipeline(
extractor,
biometric_algorithm,
):
+ logger.info(f" >> Vanilla Biometrics: Training background model with pipeline {extractor}")
## Training background model (fit will return even if samples is ``None``,
## in which case we suppose the algorithm is not trainable in any way)
extractor = train_background_model(background_model_samples, extractor)
+ logger.info(f" >> Creating biometric references with the biometric algorithm {biometric_algorithm}")
+
## Create biometric samples
biometric_references = create_biometric_reference(
biometric_reference_samples, extractor, biometric_algorithm
)
+ logger.info(f" >> Computing scores with the biometric algorithm {biometric_algorithm}")
+
## Scores all probes
return compute_scores(
probe_samples, biometric_references, extractor, biometric_algorithm
@@ -38,7 +46,6 @@ def biometric_pipeline(
def train_background_model(background_model_samples, extractor):
- # TODO: Maybe here is supervised
X, y = samplesets_to_samples(background_model_samples)
extractor = extractor.fit(X, y=y)
diff --git a/bob/bio/base/script/vanilla_biometrics.py b/bob/bio/base/script/vanilla_biometrics.py
index f8e17b31f5ec48d7e7ae3c480dcc8d8787d84b9f..b58204d2bf9fcbe4a51386fb993ee7352dbb6dd1 100644
--- a/bob/bio/base/script/vanilla_biometrics.py
+++ b/bob/bio/base/script/vanilla_biometrics.py
@@ -171,6 +171,8 @@ def vanilla_biometrics(
with open(os.path.join(output,f"scores-{g}"), "w") as f:
biometric_references = database.references(group=g)
+
+ logger.info(f"Running vanilla biometrics for group {g}")
result = biometric_pipeline(
database.background_model_samples(),
diff --git a/bob/bio/base/test/test_processor.py b/bob/bio/base/test/test_processor.py
deleted file mode 100644
index fc06fcf88667731438379b7ca69c085a3091ba1f..0000000000000000000000000000000000000000
--- a/bob/bio/base/test/test_processor.py
+++ /dev/null
@@ -1,68 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from bob.pipelines.sample import Sample, SampleSet, DelayedSample
-import os
-import numpy
-import tempfile
-from sklearn.utils.validation import check_is_fitted
-
-from bob.bio.base.processor import Linearize, SampleLinearize, CheckpointSampleLinearize
-def test_linearize_processor():
-
- ## Test the transformer only
- transformer = Linearize()
- X = numpy.zeros(shape=(10,10))
- X_tr = transformer.transform(X)
- assert X_tr.shape == (100,)
-
-
- ## Test wrapped in to a Sample
- sample = Sample(X, key="1")
- transformer = SampleLinearize()
- X_tr = transformer.transform([sample])
- assert X_tr[0].data.shape == (100,)
-
- ## Test checkpoint
- with tempfile.TemporaryDirectory() as d:
- transformer = CheckpointSampleLinearize(features_dir=d)
- X_tr = transformer.transform([sample])
- assert X_tr[0].data.shape == (100,)
- assert os.path.exists(os.path.join(d, "1.h5"))
-
-
-from bob.bio.base.processor import SamplePCA, CheckpointSamplePCA
-def test_pca_processor():
-
- ## Test wrapped in to a Sample
- X = numpy.random.rand(100,10)
- samples = [Sample(data, key=str(i)) for i, data in enumerate(X)]
-
- # fit
- n_components = 2
- estimator = SamplePCA(n_components=n_components)
- estimator = estimator.fit(samples)
-
- # https://scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html
- assert check_is_fitted(estimator, "n_components_") is None
-
- # transform
- samples_tr = estimator.transform(samples)
- assert samples_tr[0].data.shape == (n_components,)
-
-
- ## Test Checkpoining
- with tempfile.TemporaryDirectory() as d:
- model_path = os.path.join(d, "model.pkl")
- estimator = CheckpointSamplePCA(n_components=n_components, features_dir=d, model_path=model_path)
-
- # fit
- estimator = estimator.fit(samples)
- assert check_is_fitted(estimator, "n_components_") is None
- assert os.path.exists(model_path)
-
- # transform
- samples_tr = estimator.transform(samples)
- assert samples_tr[0].data.shape == (n_components,)
- assert os.path.exists(os.path.join(d, samples_tr[0].key+".h5"))
diff --git a/bob/bio/base/test/test_vanilla_biometrics.py b/bob/bio/base/test/test_vanilla_biometrics.py
index 082622427618c41da91354d016a3ae016d95a121..60e0ac4027c6e7535496db6a4b88b02d284974df 100644
--- a/bob/bio/base/test/test_vanilla_biometrics.py
+++ b/bob/bio/base/test/test_vanilla_biometrics.py
@@ -9,9 +9,6 @@ import tempfile
from sklearn.utils.validation import check_is_fitted
-#from bob.bio.base.processor import Linearize, SampleLinearize, CheckpointSampleLinearize
-
-
class DummyDatabase:
def __init__(self, delayed=False, n_references=10, n_probes=10, dim=10, one_d = True):
@@ -61,7 +58,8 @@ class DummyDatabase:
return probes
-from bob.bio.base.pipelines.vanilla_biometrics.comparator import DistanceComparator
+from bob.bio.base.pipelines.vanilla_biometrics.biometric_algorithm import Distance
+import itertools
def test_distance_comparator():
n_references = 10
@@ -70,40 +68,15 @@ def test_distance_comparator():
database = DummyDatabase(delayed=False, n_references=n_references, n_probes=n_probes, dim=10, one_d = True)
references = database.references()
probes = database.probes()
-
- pass
-
- comparator = DistanceComparator()
- references = comparator.enroll_samples(references)
+
+ comparator = Distance()
+ references = comparator._enroll_samples(references)
assert len(references)== n_references
assert references[0].data.shape == (dim,)
probes = database.probes()
- scores = comparator.score_samples(probes, references)
+ scores = comparator._score_samples(probes, references)
+ scores = list(itertools.chain(*scores))
assert len(scores) == n_probes*n_references
assert len(scores[0].samples)==n_references
-
-
-
- ## Test the transformer only
- #transformer = Linearize()
- #X = numpy.zeros(shape=(10,10))
- #X_tr = transformer.transform(X)
- #assert X_tr.shape == (100,)
-
-
- ## Test wrapped in to a Sample
- #sample = Sample(X, key="1")
- #transformer = SampleLinearize()
- #X_tr = transformer.transform([sample])
- #assert X_tr[0].data.shape == (100,)
-
- ## Test checkpoint
- #with tempfile.TemporaryDirectory() as d:
- #transformer = CheckpointSampleLinearize(features_dir=d)
- #X_tr = transformer.transform([sample])
- #assert X_tr[0].data.shape == (100,)
- #assert os.path.exists(os.path.join(d, "1.h5"))
-
-