diff --git a/bob/bio/base/config/examples/gabor_mobio-male.py b/bob/bio/base/config/examples/gabor_mobio-male.py
index 8336c510b5cbd927647abd607fb6339f99bcbcc7..6f224e392552efdc0818caac24a497276d7bc420 100644
--- a/bob/bio/base/config/examples/gabor_mobio-male.py
+++ b/bob/bio/base/config/examples/gabor_mobio-male.py
@@ -36,18 +36,15 @@ RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
# FaceCrop
-preprocessor = functools.partial(
- bob.bio.face.preprocessor.INormLBP,
- face_cropper=functools.partial(
- bob.bio.face.preprocessor.FaceCrop,
+preprocessor = bob.bio.face.preprocessor.INormLBP(
+ face_cropper=bob.bio.face.preprocessor.FaceCrop(
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
color_channel="gray",
),
)
-extractor = functools.partial(
- bob.bio.face.extractor.GridGraph,
+extractor = bob.bio.face.extractor.GridGraph(
# Gabor parameters
gabor_sigma=math.sqrt(2.0) * math.pi,
# what kind of information to extract
@@ -63,9 +60,7 @@ transformer = make_pipeline(
## algorithm
-
-gabor_jet = functools.partial(
- bob.bio.face.algorithm.GaborJet,
+gabor_jet = bob.bio.face.algorithm.GaborJet(
gabor_jet_similarity_type="PhaseDiffPlusCanberra",
multiple_feature_scoring="max_jet",
gabor_sigma=math.sqrt(2.0) * math.pi,
@@ -73,10 +68,9 @@ gabor_jet = functools.partial(
algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir, allow_score_multiple_references=True)
#algorithm = AlgorithmAsBioAlg(callable=gabor_jet, features_dir=base_dir)
-
-
from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
#pipeline = VanillaBiometrics(transformer, algorithm)
-pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm), npartitions=48)
+#pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm), npartitions=48)
+pipeline = VanillaBiometrics(transformer, algorithm)
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
index bcf9840bd6e7c8dc7b570cb48756526730b89236..d358dad3e207cddebfac90a74cf21f454a0ecb6c 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
@@ -17,11 +17,11 @@ from .abstract_classes import (
from bob.io.base import HDF5File
from bob.pipelines.mixins import SampleMixin, CheckpointMixin
from bob.pipelines.sample import DelayedSample, SampleSet, Sample
-from bob.pipelines.utils import is_picklable
from sklearn.base import TransformerMixin, BaseEstimator
import logging
import copy
+
logger = logging.getLogger("bob.bio.base")
@@ -165,19 +165,23 @@ class _NonPickableWrapper:
@property
def instance(self):
- if self._instance is None:
+ # Input can be a functools.partial or an object
+ if isinstance(self.callable, functools.partial) and self._instance is None:
self._instance = self.callable()
+ else:
+ self._instance = self.callable
+
return self._instance
def __setstate__(self, d):
# Handling unpicklable objects
- self._instance = None
- # return super().__setstate__(d)
+ self.__dict__ = d
def __getstate__(self):
- # Handling unpicklable objects
- self._instance = None
- # return super().__getstate__()
+ # Handling unpicklable objects
+ if isinstance(self.callable, functools.partial):
+ self._instance = None
+ return self.__dict__
class _Preprocessor(_NonPickableWrapper, TransformerMixin, BaseEstimator):
@@ -192,6 +196,7 @@ class _Preprocessor(_NonPickableWrapper, TransformerMixin, BaseEstimator):
def _get_pickable_method(method):
+ from bob.pipelines.utils import is_picklable
if not is_picklable(method):
logger.warning(
f"The method {method} is not picklable. Returning its unbounded method"
@@ -207,12 +212,18 @@ class Preprocessor(CheckpointMixin, SampleMixin, _Preprocessor):
transform_extra_arguments=(("annotations", "annotations"),),
**kwargs,
):
- instance = callable()
+
+ # Input can be a functools.partial or an object
+ if isinstance(callable, functools.partial):
+ instance = callable()
+ else:
+ instance = callable
+
super().__init__(
callable=callable,
transform_extra_arguments=transform_extra_arguments,
- load_func=_get_pickable_method(instance.read_data),
- save_func=_get_pickable_method(instance.write_data),
+ load_func=instance.read_data,
+ save_func=instance.write_data,
**kwargs,
)
@@ -252,7 +263,11 @@ class _Extractor(_NonPickableWrapper, TransformerMixin, BaseEstimator):
class Extractor(CheckpointMixin, SampleMixin, _Extractor):
def __init__(self, callable, model_path=None, **kwargs):
- instance = callable()
+ # Input can be a functools.partial or an object
+ if isinstance(callable, functools.partial):
+ instance = callable()
+ else:
+ instance = callable
transform_extra_arguments = None
self.requires_metadata = False
@@ -268,8 +283,8 @@ class Extractor(CheckpointMixin, SampleMixin, _Extractor):
callable=callable,
transform_extra_arguments=transform_extra_arguments,
fit_extra_arguments=fit_extra_arguments,
- load_func=_get_pickable_method(instance.read_feature),
- save_func=_get_pickable_method(instance.write_feature),
+ load_func=instance.read_feature,
+ save_func=instance.write_feature,
model_path=model_path,
**kwargs,
)
@@ -283,10 +298,31 @@ class Extractor(CheckpointMixin, SampleMixin, _Extractor):
return self
-class _AlgorithmTransformer(_NonPickableWrapper, TransformerMixin, BaseEstimator):
+class _AlgorithmTransformer(TransformerMixin, BaseEstimator):
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.is_projector_loaded = False
+
def transform(self, X):
+ self._load_projector()
return [self.instance.project(feature) for feature in X]
+ def _load_projector(self):
+ """
+ Run :py:meth:`bob.bio.base.algorithm.Algorithm.load_projector` if necessary by
+ :py:class:`bob.bio.base.algorithm.Algorithm`
+ """
+ if self.instance.performs_projection:
+ if self.model_path is None:
+ raise ValueError(
+ "Algorithm " + f"{self.instance} performs_projection. Hence, "
+ "`model_path` needs to passed in `AlgorithmAsTransformer.__init__`"
+ )
+ else:
+ # Loading model
+ self.instance.load_projector(self.model_path)
+
def fit(self, X, y=None):
if not self.instance.requires_projector_training:
return self
@@ -421,9 +457,9 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
"""
if isinstance(ref, DelayedSample):
- new_ref = copy.copy(ref)
-
+ new_ref = copy.copy(ref)
new_ref.load = functools.partial(ref.load.func, self.instance, ref.load.args[1])
+ #new_ref.load = functools.partial(ref.load.func, self.instance, ref.load.args[1])
return new_ref
else:
return ref
@@ -456,19 +492,11 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
if allow_scoring_with_all_biometric_references:
if self.stacked_biometric_references is None:
-
- if self.instance.performs_projection:
- # Hydrating the state of biometric references
- biometric_references = [
- self._restore_state_of_ref(ref)
- for ref in biometric_references
- ]
-
self.stacked_biometric_references = [
ref.data for ref in biometric_references
]
- s = self._restore_state_of_ref(s)
+ #s = self._restore_state_of_ref(s)
scores = self.score_multiple_biometric_references(
self.stacked_biometric_references, s.data
)
@@ -480,7 +508,7 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
for ref in [
r for r in biometric_references if r.key in sampleset.references
]:
- ref = self._restore_state_of_ref(ref)
+
score = self.score(ref.data, s.data)
subprobe_scores.append(_write_sample(ref, sampleset, score))
@@ -518,9 +546,9 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
# Checkpointing
os.makedirs(os.path.dirname(path), exist_ok=True)
self.instance.write_model(model, path)
-
- reader = _get_pickable_method(self.instance.read_model)
- return DelayedSample(functools.partial(reader, path), parent=enroll_features)
+
+ reader = self.instance.read_model
+ return DelayedSample(functools.partial(reader, path), parent=enroll_features)
def score(self, biometric_reference, data, **kwargs):
return self.instance.score(biometric_reference, data)
@@ -532,7 +560,6 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
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
diff --git a/bob/bio/base/script/vanilla_biometrics.py b/bob/bio/base/script/vanilla_biometrics.py
index ea0ad30ec52ce3ea7f6937b890ba2c47f0f97f43..ddcd841d407e51d04e0c7a9d384dcf5e3090b861 100644
--- a/bob/bio/base/script/vanilla_biometrics.py
+++ b/bob/bio/base/script/vanilla_biometrics.py
@@ -77,7 +77,6 @@ TODO: Work out this help
"-l",
required=False,
cls=ResourceOption,
- entry_point_group="bob.pipelines.client", # This should be linked to bob.bio.base
help="Dask client for the execution of the pipeline.",
)
@click.option(
@@ -151,7 +150,7 @@ def vanilla_biometrics(
from bob.pipelines.sample import Sample, DelayedSample
if not os.path.exists(output):
- os.makedirs(output, exist_ok=True)
+ os.makedirs(output, exist_ok=True)
for group in groups: