Created checkpoint wrapper

bob/bio/base/pipelines/vanilla_biometrics/__init__.py

@@ -2,7 +2,7 @@ from .pipelines import VanillaBiometricsPipeline
 
 from .biometric_algorithms import Distance
 from .score_writers import FourColumnsScoreWriter, CSVScoreWriter
-from .wrappers import BioAlgorithmCheckpointWrapper, BioAlgorithmDaskWrapper, dask_vanilla_biometrics
+from .wrappers import BioAlgorithmCheckpointWrapper, BioAlgorithmDaskWrapper, dask_vanilla_biometrics, checkpoint_vanilla_biometrics
 
 from .zt_norm import ZTNormPipeline, ZTNormDaskWrapper, ZTNormCheckpointWrapper
 

bob/bio/base/pipelines/vanilla_biometrics/legacy.py

@@ -54,10 +54,33 @@ class DatabaseConnector(Database):
         The name of the protocol to generate samples from.
         To be plugged at :py:method:`bob.db.base.Database.objects`.
 
+    allow_scoring_with_all_biometric_references: bool
+        If True will allow the scoring function to be performed in one shot with multiple probes.
+        This optimization is useful when all probes needs to be compared with all biometric references AND
+        your scoring function allows this broadcast computation.
+
+    annotation_type: str
+        Type of the annotations that the database provide.
+        Allowed types are: `eyes-center` and `bounding-box`
+
+    fixed_positions: dict
+        In case database contains one single annotation for all samples.
+        This is useful for registered databases.
     """
 
-    def __init__(self, database, **kwargs):
+    def __init__(
+        self,
+        database,
+        allow_scoring_with_all_biometric_references=True,
+        annotation_type="eyes-center",
+        fixed_positions=None,
+        ** kwargs,
+    ):
         self.database = database
+        self.allow_scoring_with_all_biometric_references = allow_scoring_with_all_biometric_references
+        self.annotation_type = annotation_type
+        self.fixed_positions=fixed_positions
+
 
     def background_model_samples(self):
         """Returns :py:class:`Sample`'s to train a background model (group

bob/bio/base/pipelines/vanilla_biometrics/wrappers.py

@@ -5,11 +5,12 @@ import dask
 import functools
 from .score_writers import FourColumnsScoreWriter
 from .abstract_classes import BioAlgorithm
-import bob.pipelines as mario
+import bob.pipelines
 import numpy as np
 import h5py
 import cloudpickle
 from .zt_norm import ZTNormPipeline, ZTNormDaskWrapper
+from .legacy import BioAlgorithmLegacy
 
 
 class BioAlgorithmCheckpointWrapper(BioAlgorithm):
@@ -38,7 +39,9 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
 
     """
 
-    def __init__(self, biometric_algorithm, base_dir, group=None, force=False, **kwargs):
+    def __init__(
+        self, biometric_algorithm, base_dir, group=None, force=False, **kwargs
+    ):
         super().__init__(**kwargs)
 
         self.base_dir = base_dir
@@ -47,14 +50,18 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
         self.biometric_algorithm = biometric_algorithm
         self.force = force
         self._biometric_reference_extension = ".hdf5"
-        self._score_extension = ".pkl"        
+        self._score_extension = ".pkl"
 
     def set_score_references_path(self, group):
         if group is None:
-            self.biometric_reference_dir = os.path.join(self.base_dir, "biometric_references")
+            self.biometric_reference_dir = os.path.join(
+                self.base_dir, "biometric_references"
+            )
             self.score_dir = os.path.join(self.base_dir, "scores")
         else:
-            self.biometric_reference_dir = os.path.join(self.base_dir, group, "biometric_references")
+            self.biometric_reference_dir = os.path.join(
+                self.base_dir, group, "biometric_references"
+            )
             self.score_dir = os.path.join(self.base_dir, group, "scores")
 
     def enroll(self, enroll_features):
@@ -113,7 +120,7 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
         def _load(path):
             return cloudpickle.loads(open(path, "rb").read())
 
-            #with h5py.File(path) as hdf5:
+            # with h5py.File(path) as hdf5:
             #    return hdf5_to_sample(hdf5)
 
         def _make_name(sampleset, biometric_references):
@@ -125,7 +132,8 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
             return os.path.join(subject, name + suffix)
 
         path = os.path.join(
-            self.score_dir, _make_name(sampleset, biometric_references) + self._score_extension
+            self.score_dir,
+            _make_name(sampleset, biometric_references) + self._score_extension,
         )
 
         if self.force or not os.path.exists(path):
@@ -221,7 +229,7 @@ def dask_vanilla_biometrics(pipeline, npartitions=None, partition_size=None):
     """
 
     if isinstance(pipeline, ZTNormPipeline):
-        # Dasking the first part of the pipelines        
+        # Dasking the first part of the pipelines
         pipeline.vanilla_biometrics_pipeline = dask_vanilla_biometrics(
             pipeline.vanilla_biometrics_pipeline, npartitions
         )
@@ -231,11 +239,11 @@ def dask_vanilla_biometrics(pipeline, npartitions=None, partition_size=None):
     else:
 
         if partition_size is None:
-            pipeline.transformer = mario.wrap(
+            pipeline.transformer = bob.pipelines.wrap(
                 ["dask"], pipeline.transformer, npartitions=npartitions
             )
         else:
-            pipeline.transformer = mario.wrap(
+            pipeline.transformer = bob.pipelines.wrap(
                 ["dask"], pipeline.transformer, partition_size=partition_size
             )
         pipeline.biometric_algorithm = BioAlgorithmDaskWrapper(
@@ -249,3 +257,38 @@ def dask_vanilla_biometrics(pipeline, npartitions=None, partition_size=None):
         pipeline.write_scores = _write_scores
 
     return pipeline
+
+
+def checkpoint_vanilla_biometrics(pipeline, base_dir):
+    """
+    Given a :any:`VanillaBiometrics`, wraps :any:`VanillaBiometrics.transformer` and
+    :any:`VanillaBiometrics.biometric_algorithm` to be checkpointed
+
+    Parameters
+    ----------
+
+    pipeline: :any:`VanillaBiometrics`
+       Vanilla Biometrics based pipeline to be dasked
+
+    base_dir: str
+       Path to store biometric references and scores
+
+    """
+
+    sk_pipeline = pipeline.transformer
+    for i, name, estimator in sk_pipeline._iter():
+
+        wraped_estimator = bob.pipelines.wrap(
+            ["checkpoint"], estimator, features_dir=os.path.join(base_dir, name)
+        )
+
+        sk_pipeline.steps[i] = (name, wraped_estimator)
+
+    if isinstance(pipeline.biometric_algorithm, BioAlgorithmLegacy):
+        pipeline.biometric_algorithm.base_dir = base_dir
+    else:
+        pipeline.biometric_algorithm = BioAlgorithmCheckpointWrapper(
+            pipeline.biometric_algorithm, base_dir=base_dir
+        )
+
+    return pipeline