diff --git a/bob/bio/base/test/test_transformers.py b/bob/bio/base/test/test_transformers.py
index e78049d736482e56f0afdd3e000b35853b6810d4..0bd43dcab8ab5e655b346ba893a3b845553bffa3 100644
--- a/bob/bio/base/test/test_transformers.py
+++ b/bob/bio/base/test/test_transformers.py
@@ -16,10 +16,12 @@ import tempfile
import os
import bob.io.base
from bob.bio.base.wrappers import (
- wrap_preprocessor,
- wrap_extractor,
- wrap_algorithm,
- wrap_bob_legacy,
+ wrap_checkpoint_preprocessor,
+ wrap_checkpoint_extractor,
+ wrap_checkpoint_algorithm,
+ wrap_sample_preprocessor,
+ wrap_sample_extractor,
+ wrap_sample_algorithm,
)
from sklearn.pipeline import make_pipeline
@@ -30,7 +32,7 @@ class FakePreprocesor(Preprocessor):
class FakeExtractor(Extractor):
- def __call__(self, data, metadata=None):
+ def __call__(self, data):
return data.flatten()
@@ -56,7 +58,7 @@ class FakeAlgorithm(Algorithm):
self.split_training_features_by_client = True
self.model = None
- def project(self, data, metadata=None):
+ def project(self, data):
return data + self.model
def train_projector(self, training_features, projector_file):
@@ -259,25 +261,30 @@ def test_algorithm():
def test_wrap_bob_pipeline():
- def run_pipeline(with_dask):
+ def run_pipeline(with_dask, with_checkpoint):
with tempfile.TemporaryDirectory() as dir_name:
+ if with_checkpoint:
+ pipeline = make_pipeline(
+ wrap_checkpoint_preprocessor(FakePreprocesor(), dir_name,),
+ wrap_checkpoint_extractor(FakeExtractor(), dir_name,),
+ wrap_checkpoint_algorithm(FakeAlgorithm(), dir_name),
+ )
+ else:
+ pipeline = make_pipeline(
+ wrap_sample_preprocessor(FakePreprocesor()),
+ wrap_sample_extractor(FakeExtractor(), dir_name,),
+ wrap_sample_algorithm(FakeAlgorithm(), dir_name),
+ )
- pipeline = make_pipeline(
- wrap_bob_legacy(
- FakePreprocesor(),
- dir_name,
- transform_extra_arguments=(("annotations", "annotations"),),
- ),
- wrap_bob_legacy(FakeExtractor(), dir_name,),
- wrap_bob_legacy(FakeAlgorithm(), dir_name),
- )
oracle = [7.0, 7.0, 7.0, 7.0]
training_samples = generate_samples(n_subjects=2, n_samples_per_subject=2)
test_samples = generate_samples(n_subjects=1, n_samples_per_subject=1)
if with_dask:
pipeline = mario.wrap(["dask"], pipeline)
transformed_samples = (
- pipeline.fit(training_samples).transform(test_samples).compute(scheduler="single-threaded")
+ pipeline.fit(training_samples)
+ .transform(test_samples)
+ .compute(scheduler="single-threaded")
)
else:
transformed_samples = pipeline.fit(training_samples).transform(
@@ -285,5 +292,7 @@ def test_wrap_bob_pipeline():
)
assert assert_sample(transformed_samples, oracle)
- run_pipeline(False)
- run_pipeline(True)
+ run_pipeline(False, False)
+ run_pipeline(False, True)
+ run_pipeline(True, False)
+ run_pipeline(True, True)
diff --git a/bob/bio/base/test/test_vanilla_biometrics.py b/bob/bio/base/test/test_vanilla_biometrics.py
index ff7d7c4da706b589a3873764b8a2db512a6ad9b0..ff525ab9e06c428852da76ab889af0b3a3cbff14 100644
--- a/bob/bio/base/test/test_vanilla_biometrics.py
+++ b/bob/bio/base/test/test_vanilla_biometrics.py
@@ -8,7 +8,11 @@ import numpy as np
import tempfile
from sklearn.pipeline import make_pipeline
from bob.bio.base.wrappers import wrap_bob_legacy
-from bob.bio.base.test.test_transformers import FakePreprocesor, FakeExtractor, FakeAlgorithm
+from bob.bio.base.test.test_transformers import (
+ FakePreprocesor,
+ FakeExtractor,
+ FakeAlgorithm,
+)
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
VanillaBiometricsPipeline,
@@ -16,7 +20,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
dask_vanilla_biometrics,
FourColumnsScoreWriter,
CSVScoreWriter,
- BioAlgorithmLegacy
+ BioAlgorithmLegacy,
)
import bob.pipelines as mario
@@ -24,6 +28,7 @@ import uuid
import shutil
import itertools
+
class DummyDatabase:
def __init__(self, delayed=False, n_references=10, n_probes=10, dim=10, one_d=True):
self.delayed = delayed
@@ -36,13 +41,23 @@ class DummyDatabase:
def _create_random_1dsamples(self, n_samples, offset, dim):
return [
- Sample(np.random.rand(dim), key=str(uuid.uuid4()), annotations=1, subject=str(i))
+ Sample(
+ np.random.rand(dim),
+ key=str(uuid.uuid4()),
+ annotations=1,
+ subject=str(i),
+ )
for i in range(offset, offset + n_samples)
]
def _create_random_2dsamples(self, n_samples, offset, dim):
return [
- Sample(np.random.rand(dim, dim), key=str(uuid.uuid4()), annotations=1, subject=str(i))
+ Sample(
+ np.random.rand(dim, dim),
+ key=str(uuid.uuid4()),
+ annotations=1,
+ subject=str(i),
+ )
for i in range(offset, offset + n_samples)
]
@@ -74,7 +89,7 @@ class DummyDatabase:
return sample_set
def background_model_samples(self):
- samples = [sset.samples for sset in self._create_random_sample_set()]
+ samples = [sset.samples for sset in self._create_random_sample_set()]
return list(itertools.chain(*samples))
def references(self):
@@ -101,11 +116,12 @@ def _make_transformer(dir_name):
dir_name,
transform_extra_arguments=(("annotations", "annotations"),),
),
- wrap_bob_legacy(FakeExtractor(), dir_name,)
+ wrap_bob_legacy(FakeExtractor(), dir_name,),
)
return pipeline
+
def _make_transformer_with_algorithm(dir_name):
pipeline = make_pipeline(
wrap_bob_legacy(
@@ -114,7 +130,7 @@ def _make_transformer_with_algorithm(dir_name):
transform_extra_arguments=(("annotations", "annotations"),),
),
wrap_bob_legacy(FakeExtractor(), dir_name),
- wrap_bob_legacy(FakeAlgorithm(), dir_name)
+ wrap_bob_legacy(FakeAlgorithm(), dir_name),
)
return pipeline
@@ -197,7 +213,9 @@ def test_checkpoint_bioalg_as_transformer():
if isinstance(score_writer, CSVScoreWriter):
base_path = os.path.join(dir_name, "concatenated_scores")
score_writer.concatenate_write_scores(scores, base_path)
- assert len(open(os.path.join(base_path, "chunk_0.csv")).readlines()) == 101
+ assert (
+ len(open(os.path.join(base_path, "chunk_0.csv")).readlines()) == 101
+ )
else:
filename = os.path.join(dir_name, "concatenated_scores.txt")
score_writer.concatenate_write_scores(scores, filename)
@@ -205,24 +223,24 @@ def test_checkpoint_bioalg_as_transformer():
run_pipeline(False)
run_pipeline(False) # Checking if the checkpointng works
- shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
+ shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
os.makedirs(dir_name, exist_ok=True)
# Dask
run_pipeline(True)
run_pipeline(True) # Checking if the checkpointng works
- shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
+ shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
os.makedirs(dir_name, exist_ok=True)
# CSVWriter
run_pipeline(False, CSVScoreWriter())
- run_pipeline(False, CSVScoreWriter()) # Checking if the checkpointng works
- shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
+ run_pipeline(False, CSVScoreWriter()) # Checking if the checkpointng works
+ shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
os.makedirs(dir_name, exist_ok=True)
# CSVWriter + Dask
run_pipeline(True, CSVScoreWriter())
- run_pipeline(True, CSVScoreWriter()) # Checking if the checkpointng works
+ run_pipeline(True, CSVScoreWriter()) # Checking if the checkpointng works
def test_checkpoint_bioalg_as_bioalg():
@@ -231,12 +249,15 @@ def test_checkpoint_bioalg_as_bioalg():
def run_pipeline(with_dask, score_writer=FourColumnsScoreWriter()):
database = DummyDatabase()
-
+
transformer = _make_transformer_with_algorithm(dir_name)
projector_file = transformer[2].estimator.estimator.projector_file
biometric_algorithm = BioAlgorithmLegacy(
- FakeAlgorithm(), base_dir=dir_name, score_writer=score_writer, projector_file=projector_file
+ FakeAlgorithm(),
+ base_dir=dir_name,
+ score_writer=score_writer,
+ projector_file=projector_file,
)
vanilla_biometrics_pipeline = VanillaBiometricsPipeline(
@@ -265,11 +286,11 @@ def test_checkpoint_bioalg_as_bioalg():
run_pipeline(False)
run_pipeline(False) # Checking if the checkpointng works
- shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
+ shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
os.makedirs(dir_name, exist_ok=True)
# Dask
run_pipeline(True)
run_pipeline(True) # Checking if the checkpointng works
- shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
+ shutil.rmtree(dir_name) # Deleting the cache so it runs again from scratch
os.makedirs(dir_name, exist_ok=True)
diff --git a/bob/bio/base/wrappers.py b/bob/bio/base/wrappers.py
index dc2ef7d82176b28fd98c60f02a44e80adb4a3d6a..b37cb730d56674d045abdaa5a7402b89b6b7c108 100644
--- a/bob/bio/base/wrappers.py
+++ b/bob/bio/base/wrappers.py
@@ -11,6 +11,7 @@ from bob.bio.base.extractor import Extractor
from bob.bio.base.algorithm import Algorithm
import bob.pipelines as mario
import os
+from bob.bio.base.utils import is_argument_available
def wrap_bob_legacy(
@@ -18,7 +19,7 @@ def wrap_bob_legacy(
dir_name,
fit_extra_arguments=(("y", "subject"),),
transform_extra_arguments=None,
- dask_it=False
+ dask_it=False,
):
"""
Wraps either :any:`bob.bio.base.preprocessor.Preprocessor`, :any:`bob.bio.base.extractor.Extractor`
@@ -47,33 +48,20 @@ def wrap_bob_legacy(
"""
if isinstance(bob_object, Preprocessor):
- preprocessor_transformer = PreprocessorTransformer(bob_object)
- transformer = wrap_preprocessor(
- preprocessor_transformer,
- features_dir=os.path.join(dir_name, "preprocessor"),
- transform_extra_arguments=transform_extra_arguments,
+ transformer = wrap_checkpoint_preprocessor(
+ bob_object, features_dir=os.path.join(dir_name, "preprocessor"),
)
elif isinstance(bob_object, Extractor):
- extractor_transformer = ExtractorTransformer(bob_object)
- path = os.path.join(dir_name, "extractor")
- transformer = wrap_extractor(
- extractor_transformer,
- features_dir=path,
- model_path=os.path.join(path, "extractor.pkl"),
- transform_extra_arguments=transform_extra_arguments,
- fit_extra_arguments=fit_extra_arguments,
+ transformer = wrap_checkpoint_extractor(
+ bob_object,
+ features_dir=os.path.join(dir_name, "extractor"),
+ model_path=dir_name,
)
elif isinstance(bob_object, Algorithm):
- path = os.path.join(dir_name, "algorithm")
- algorithm_transformer = AlgorithmTransformer(
- bob_object, projector_file=os.path.join(path, "Projector.hdf5")
- )
- transformer = wrap_algorithm(
- algorithm_transformer,
- features_dir=path,
- model_path=os.path.join(path, "algorithm.pkl"),
- transform_extra_arguments=transform_extra_arguments,
- fit_extra_arguments=fit_extra_arguments,
+ transformer = wrap_checkpoint_algorithm(
+ bob_object,
+ features_dir=os.path.join(dir_name, "algorithm"),
+ model_path=dir_name,
)
else:
raise ValueError(
@@ -86,132 +74,351 @@ def wrap_bob_legacy(
return transformer
-def wrap_preprocessor(
- preprocessor_transformer, features_dir=None, transform_extra_arguments=None,
+def wrap_sample_preprocessor(
+ preprocessor,
+ transform_extra_arguments=(("annotations", "annotations"),),
+ **kwargs
+):
+ """
+ Wraps :any:`bob.bio.base.preprocessor.Preprocessor` with
+ :any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
+
+ .. warning::
+ This wrapper doesn't checkpoint data
+
+ Parameters
+ ----------
+
+ preprocessor: :any:`bob.bio.base.preprocessor.Preprocessor`
+ Instance of :any:`bob.bio.base.transformers.PreprocessorTransformer` to be wrapped
+
+ transform_extra_arguments: [tuple]
+ Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
+
+ """
+
+ transformer = PreprocessorTransformer(preprocessor)
+ return mario.wrap(
+ ["sample"],
+ transformer,
+ transform_extra_arguments=transform_extra_arguments,
+ )
+
+
+def wrap_checkpoint_preprocessor(
+ preprocessor,
+ features_dir=None,
+ transform_extra_arguments=(("annotations", "annotations"),),
+ load_func=None,
+ save_func=None,
+ extension=".hdf5",
):
"""
- Wraps :any:`bob.bio.base.transformers.PreprocessorTransformer` with
+ Wraps :any:`bob.bio.base.preprocessor.Preprocessor` with
:any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
Parameters
----------
- preprocessor_transformer: :any:`bob.bio.base.transformers.PreprocessorTransformer`
+ preprocessor: :any:`bob.bio.base.preprocessor.Preprocessor`
Instance of :any:`bob.bio.base.transformers.PreprocessorTransformer` to be wrapped
features_dir: str
- Features directory to be checkpointed
+ Features directory to be checkpointed (see :any:bob.pipelines.CheckpointWrapper`).
+
+ extension : str, optional
+ Extension o preprocessed files (see :any:bob.pipelines.CheckpointWrapper`).
+
+ load_func : None, optional
+ Function that loads data to be preprocessed.
+ The default is :any:`bob.bio.base.preprocessor.Preprocessor.read_data`
+
+ save_func : None, optional
+ Function that saves preprocessed data.
+ The default is :any:`bob.bio.base.preprocessor.Preprocessor.write_data`
transform_extra_arguments: [tuple]
Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
"""
- if not isinstance(preprocessor_transformer, PreprocessorTransformer):
- raise ValueError(
- f"Expected an instance of PreprocessorTransformer, not {preprocessor_transformer}"
- )
-
+ transformer = PreprocessorTransformer(preprocessor)
return mario.wrap(
["sample", "checkpoint"],
- preprocessor_transformer,
- load_func=preprocessor_transformer.callable.read_data,
- save_func=preprocessor_transformer.callable.write_data,
+ transformer,
+ load_func=load_func or preprocessor.read_data,
+ save_func=save_func or preprocessor.write_data,
features_dir=features_dir,
transform_extra_arguments=transform_extra_arguments,
+ extension=extension,
)
-def wrap_extractor(
- extractor_transformer,
+def _prepare_extractor_sample_args(
+ extractor, transform_extra_arguments, fit_extra_arguments
+):
+ if transform_extra_arguments is None and is_argument_available(
+ "metadata", extractor.__call__
+ ):
+ transform_extra_arguments = (("metadata", "metadata"),)
+
+ if (
+ fit_extra_arguments is None
+ and extractor.requires_training
+ and extractor.split_training_data_by_client
+ ):
+ fit_extra_arguments = (("y", "subject"),)
+
+ return transform_extra_arguments, fit_extra_arguments
+
+
+def wrap_sample_extractor(
+ extractor,
fit_extra_arguments=None,
transform_extra_arguments=None,
+ model_path=None,
+ **kwargs,
+):
+ """
+ Wraps :any:`bob.bio.base.extractor.Extractor` with
+ :any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
+
+ Parameters
+ ----------
+
+ extractor: :any:`bob.bio.base.extractor.Preprocessor`
+ Instance of :any:`bob.bio.base.transformers.ExtractorTransformer` to be wrapped
+
+ transform_extra_arguments: [tuple], optional
+ Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
+
+ model_path: str
+ Path to `extractor_file` in :any:`bob.bio.base.extractor.Extractor`
+
+ """
+
+ extractor_file = (
+ os.path.join(model_path, "Extractor.hdf5") if model_path is not None else None
+ )
+
+ transformer = ExtractorTransformer(extractor, model_path=extractor_file)
+
+ transform_extra_arguments, fit_extra_arguments = _prepare_extractor_sample_args(
+ extractor, transform_extra_arguments, fit_extra_arguments
+ )
+
+ return mario.wrap(
+ ["sample"],
+ transformer,
+ transform_extra_arguments=transform_extra_arguments,
+ fit_extra_arguments=fit_extra_arguments,
+ **kwargs,
+ )
+
+
+def wrap_checkpoint_extractor(
+ extractor,
features_dir=None,
+ fit_extra_arguments=None,
+ transform_extra_arguments=None,
+ load_func=None,
+ save_func=None,
+ extension=".hdf5",
model_path=None,
+ **kwargs,
):
"""
- Wraps :any:`bob.bio.base.transformers.ExtractorTransformer` with
+ Wraps :any:`bob.bio.base.extractor.Extractor` with
:any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
Parameters
----------
- extractor_transformer: :any:`bob.bio.base.transformers.ExtractorTransformer`
+ extractor: :any:`bob.bio.base.extractor.Preprocessor`
Instance of :any:`bob.bio.base.transformers.ExtractorTransformer` to be wrapped
features_dir: str
- Features directory to be checkpointed
+ Features directory to be checkpointed (see :any:bob.pipelines.CheckpointWrapper`).
- model_path: str
- Path to checkpoint the model
+ extension : str, optional
+ Extension o preprocessed files (see :any:bob.pipelines.CheckpointWrapper`).
+
+ load_func : None, optional
+ Function that loads data to be preprocessed.
+ The default is :any:`bob.bio.base.extractor.Extractor.read_feature`
+
+ save_func : None, optional
+ Function that saves preprocessed data.
+ The default is :any:`bob.bio.base.extractor.Extractor.write_feature`
fit_extra_arguments: [tuple]
Same behavior as in Check :any:`bob.pipelines.wrappers.fit_extra_arguments`
- transform_extra_arguments: [tuple]
+ transform_extra_arguments: [tuple], optional
Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
+ model_path: str
+ See :any:`TransformerExtractor`.
+
"""
- if not isinstance(extractor_transformer, ExtractorTransformer):
- raise ValueError(
- f"Expected an instance of ExtractorTransformer, not {extractor_transformer}"
- )
+ extractor_file = (
+ os.path.join(model_path, "Extractor.hdf5") if model_path is not None else None
+ )
+
+ model_file = (
+ os.path.join(model_path, "Extractor.pkl") if model_path is not None else None
+ )
+ transformer = ExtractorTransformer(extractor, model_path=extractor_file)
+
+ transform_extra_arguments, fit_extra_arguments = _prepare_extractor_sample_args(
+ extractor, transform_extra_arguments, fit_extra_arguments
+ )
return mario.wrap(
["sample", "checkpoint"],
- extractor_transformer,
- load_func=extractor_transformer.callable.read_feature,
- save_func=extractor_transformer.callable.write_feature,
- model_path=model_path,
+ transformer,
+ load_func=load_func or extractor.read_feature,
+ save_func=save_func or extractor.write_feature,
+ model_path=model_file,
features_dir=features_dir,
transform_extra_arguments=transform_extra_arguments,
fit_extra_arguments=fit_extra_arguments,
+ **kwargs,
)
-def wrap_algorithm(
- algorithm_transformer,
+def _prepare_algorithm_sample_args(
+ algorithm, transform_extra_arguments, fit_extra_arguments
+):
+
+ if transform_extra_arguments is None and is_argument_available(
+ "metadata", algorithm.project
+ ):
+ transform_extra_arguments = (("metadata", "metadata"),)
+
+ if (
+ fit_extra_arguments is None
+ and algorithm.requires_projector_training
+ and algorithm.split_training_features_by_client
+ ):
+ fit_extra_arguments = (("y", "subject"),)
+
+ return transform_extra_arguments, fit_extra_arguments
+
+
+def wrap_sample_algorithm(
+ algorithm,
+ model_path=None,
fit_extra_arguments=None,
transform_extra_arguments=None,
- features_dir=None,
+ **kwargs,
+):
+ """
+ Wraps :any:`bob.bio.base.algorithm.Algorithm` with
+ :any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
+
+ Parameters
+ ----------
+
+ algorithm_transformer: :any:`bob.bio.base.algorithm.Algorithm`
+ Instance of :any:`bob.bio.base.transformers.AlgorithmTransformer` to be wrapped
+
+ model_path: str
+ Path to `projector_file` in :any:`bob.bio.base.algorithm.Algorithm`
+
+ fit_extra_arguments: [tuple]
+ Same behavior as in Check :any:`bob.pipelines.wrappers.fit_extra_arguments`
+
+ transform_extra_arguments: [tuple]
+ Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
+
+ """
+
+ projector_file = (
+ os.path.join(model_path, "Projector.hdf5") if model_path is not None else None
+ )
+
+ transformer = AlgorithmTransformer(algorithm, projector_file=projector_file)
+
+ transform_extra_arguments, fit_extra_arguments = _prepare_algorithm_sample_args(
+ algorithm, transform_extra_arguments, fit_extra_arguments
+ )
+
+ return mario.wrap(
+ ["sample"],
+ transformer,
+ transform_extra_arguments=transform_extra_arguments,
+ fit_extra_arguments=fit_extra_arguments,
+ )
+
+
+def wrap_checkpoint_algorithm(
+ algorithm,
model_path=None,
+ features_dir=None,
+ extension=".hdf5",
+ fit_extra_arguments=None,
+ transform_extra_arguments=None,
+ load_func=None,
+ save_func=None,
+ **kwargs,
):
"""
- Wraps :any:`bob.bio.base.transformers.AlgorithmTransformer` with
+ Wraps :any:`bob.bio.base.algorithm.Algorithm` with
:any:`bob.pipelines.wrappers.CheckpointWrapper` and :any:`bob.pipelines.wrappers.SampleWrapper`
Parameters
----------
- algorithm_transformer: :any:`bob.bio.base.transformers.AlgorithmTransformer`
+ algorithm_transformer: :any:`bob.bio.base.algorithm.Algorithm`
Instance of :any:`bob.bio.base.transformers.AlgorithmTransformer` to be wrapped
features_dir: str
- Features directory to be checkpointed
+ Features directory to be checkpointed (see :any:bob.pipelines.CheckpointWrapper`).
model_path: str
Path to checkpoint the model
+ extension : str, optional
+ Extension o preprocessed files (see :any:bob.pipelines.CheckpointWrapper`).
+
fit_extra_arguments: [tuple]
Same behavior as in Check :any:`bob.pipelines.wrappers.fit_extra_arguments`
transform_extra_arguments: [tuple]
Same behavior as in Check :any:`bob.pipelines.wrappers.transform_extra_arguments`
+ load_func : None, optional
+ Function that loads data to be preprocessed.
+ The default is :any:`bob.bio.base.extractor.Extractor.read_feature`
+
+ save_func : None, optional
+ Function that saves preprocessed data.
+ The default is :any:`bob.bio.base.extractor.Extractor.write_feature`
+
+
"""
- if not isinstance(algorithm_transformer, AlgorithmTransformer):
- raise ValueError(
- f"Expected an instance of AlgorithmTransformer, not {algorithm_transformer}"
- )
+ projector_file = (
+ os.path.join(model_path, "Projector.hdf5") if model_path is not None else None
+ )
+
+ model_file = (
+ os.path.join(model_path, "Projector.pkl") if model_path is not None else None
+ )
+ transformer = AlgorithmTransformer(algorithm, projector_file=projector_file)
+
+ transform_extra_arguments, fit_extra_arguments = _prepare_algorithm_sample_args(
+ algorithm, transform_extra_arguments, fit_extra_arguments
+ )
return mario.wrap(
["sample", "checkpoint"],
- algorithm_transformer,
- load_func=algorithm_transformer.callable.read_feature,
- save_func=algorithm_transformer.callable.write_feature,
- model_path=model_path,
+ transformer,
+ load_func=load_func or algorithm.read_feature,
+ save_func=save_func or algorithm.write_feature,
+ model_path=model_file,
features_dir=features_dir,
transform_extra_arguments=transform_extra_arguments,
fit_extra_arguments=fit_extra_arguments,