diff --git a/bob/bio/base/script/vanilla_biometrics.py b/bob/bio/base/script/vanilla_biometrics.py
index 7af52380c9146629066cec9b2ed6dfeb43168bd8..74be6c840b496751d05facd165b488081dbab4b9 100644
--- a/bob/bio/base/script/vanilla_biometrics.py
+++ b/bob/bio/base/script/vanilla_biometrics.py
@@ -25,7 +25,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
dask_vanilla_biometrics,
dask_get_partition_size,
FourColumnsScoreWriter,
- CSVScoreWriter
+ CSVScoreWriter,
)
from dask.delayed import Delayed
import pkg_resources
@@ -44,31 +44,18 @@ EPILOG = """\b
-----------------------
- $ bob pipelines vanilla-biometrics my_experiment.py -vv
+ $ bob pipelines vanilla-biometrics -p my_experiment.py -vv
my_experiment.py must contain the following elements:
- >>> preprocessor = my_preprocessor() \n
- >>> extractor = my_extractor() \n
- >>> algorithm = my_algorithm() \n
- >>> checkpoints = EXPLAIN CHECKPOINTING \n
+ >>> transformer = ... # A scikit-learn pipeline
+ >>> algorithm = ... # `An BioAlgorithm`
+ >>> pipeline = VanillaBiometricsPipeline(transformer,algorithm)
+ >>> database = .... # Biometric Database connector (class that implements the methods: `background_model_samples`, `references` and `probes`)"
\b
-
-Look at the following example
-
- $ bob pipelines vanilla-biometrics ./bob/pipelines/config/distributed/sge_iobig_16cores.py \
- ./bob/pipelines/config/database/mobio_male.py \
- ./bob/pipelines/config/baselines/facecrop_pca.py
-
-\b
-
-
-
-TODO: Work out this help
-
"""
@@ -122,24 +109,49 @@ def post_process_scores(pipeline, scores, path):
help="Name of output directory",
)
@click.option(
- "--write-metadata-scores", "-m",
+ "--write-metadata-scores",
+ "-m",
is_flag=True,
- help="If set, all the scores will be written with all its metadata",
+ help="If set, all the scores will be written with all its metadata using the `CSVScoreWriter`",
+)
+@click.option(
+ "--checkpoint",
+ "-c",
+ is_flag=True,
+ help="If set, it will checkpoint all steps of the pipeline",
)
@verbosity_option(cls=ResourceOption)
-def vanilla_biometrics(pipeline, database, dask_client, groups, output, write_metadata_scores, **kwargs):
+def vanilla_biometrics(
+ pipeline,
+ database,
+ dask_client,
+ groups,
+ output,
+ write_metadata_scores,
+ checkpoint,
+ **kwargs,
+):
"""Runs the simplest biometrics pipeline.
- Such pipeline consists into three sub-pipelines.
- In all of them, given raw data as input it does the following steps:
+ Such pipeline consists into two major components.
+ The first component consists of a scikit-learn `Pipeline`,
+ where a sequence of transformations of the input data
+ is defined.
+ The second component is a `BioAlgorithm` that defines the primitives
+ `enroll` and `score`
+
+ With those two components any Biometric Experiment can be done.
+ A Biometric experiment consists of three sub-pipelines and
+ they are defined below:
Sub-pipeline 1:\n
---------------
- Training background model. Some biometric algorithms demands the training of background model, for instance, PCA/LDA matrix or a Neural networks. This sub-pipeline handles that and it consists of 3 steps:
-
+ Training background model.
+ Some biometric algorithms demands the training of background model, for instance, PCA/LDA matrix or a Neural networks.
+
\b
- raw_data --> preprocessing >> feature extraction >> train background model --> background_model
+ This pipeline runs: `Pipeline.fit(DATA_FOR_FIT)`
@@ -149,26 +161,24 @@ def vanilla_biometrics(pipeline, database, dask_client, groups, output, write_me
---------------
Creation of biometric references: This is a standard step in a biometric pipelines.
- Given a set of samples of one identity, create a biometric reference (a.k.a template) for sub identity. This sub-pipeline handles that in 3 steps and they are the following:
+ Given a set of samples of one identity, create a biometric reference (a.k.a template) for sub identity.
+
\b
raw_data --> preprocessing >> feature extraction >> enroll(background_model) --> biometric_reference
- Note that this sub-pipeline depends on the previous one
-
+ This pipeline runs: `BioAlgorithm.enroll(Pipeline.transform(DATA_ENROLL))` >> biometric_references
Sub-pipeline 3:\n
---------------
+ Probing: This is another standard step in biometric pipelines.
+ Given one sample and one biometric reference, computes a score.
+ Such score has different meanings depending on the scoring method your biometric algorithm uses.
+ It's out of scope to explain in a help message to explain what scoring is for different biometric algorithms.
- Probing: This is another standard step in biometric pipelines. Given one sample and one biometric reference, computes a score. Such score has different meanings depending on the scoring method your biometric algorithm uses. It's out of scope to explain in a help message to explain what scoring is for different biometric algorithms.
-
-
- raw_data --> preprocessing >> feature extraction >> probe(biometric_reference, background_model) --> score
-
- Note that this sub-pipeline depends on the two previous ones
-
+ This pipeline runs: `BioAlgorithm.score(Pipeline.transform(DATA_SCORE, biometric_references))` >> biometric_references
"""
@@ -185,12 +195,12 @@ def vanilla_biometrics(pipeline, database, dask_client, groups, output, write_me
database = vanilla_pipeline.database
pipeline = vanilla_pipeline.pipeline
if write_metadata_scores:
- pipeline.score_writer = CSVScoreWriter(os.path.join(output,"./tmp"))
+ pipeline.score_writer = CSVScoreWriter(os.path.join(output, "./tmp"))
else:
- pipeline.score_writer = FourColumnsScoreWriter(os.path.join(output,"./tmp"))
+ pipeline.score_writer = FourColumnsScoreWriter(os.path.join(output, "./tmp"))
# Check if it's already checkpointed
- if not isinstance_nested(
+ if checkpoint and not isinstance_nested(
pipeline.biometric_algorithm,
"biometric_algorithm",
BioAlgorithmCheckpointWrapper,
@@ -206,7 +216,7 @@ def vanilla_biometrics(pipeline, database, dask_client, groups, output, write_me
if dask_client is not None and not isinstance_nested(
pipeline.biometric_algorithm, "biometric_algorithm", BioAlgorithmDaskWrapper
- ):
+ ):
n_objects = (
len(background_model_samples) + len(biometric_references) + len(probes)
)
diff --git a/bob/bio/base/script/vanilla_biometrics_ztnorm.py b/bob/bio/base/script/vanilla_biometrics_ztnorm.py
index 0ef67e3f482b573403e985413bec023151170008..57f5bbb9c46c327ef652707e65855253d8bb7710 100644
--- a/bob/bio/base/script/vanilla_biometrics_ztnorm.py
+++ b/bob/bio/base/script/vanilla_biometrics_ztnorm.py
@@ -28,7 +28,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
dask_vanilla_biometrics,
dask_get_partition_size,
FourColumnsScoreWriter,
- CSVScoreWriter
+ CSVScoreWriter,
)
from dask.delayed import Delayed
from bob.bio.base.utils import get_resource_filename
@@ -47,31 +47,19 @@ EPILOG = """\b
-----------------------
- $ bob pipelines vanilla-biometrics my_experiment.py -vv
+ $ bob pipelines vanilla-biometrics -p my_experiment.py -vv
my_experiment.py must contain the following elements:
- >>> preprocessor = my_preprocessor() \n
- >>> extractor = my_extractor() \n
- >>> algorithm = my_algorithm() \n
- >>> checkpoints = EXPLAIN CHECKPOINTING \n
+ >>> transformer = ... # A scikit-learn pipeline
+ >>> algorithm = ... # `An BioAlgorithm`
+ >>> pipeline = VanillaBiometricsPipeline(transformer,algorithm)
+ >>> database = .... # Biometric Database connector (class that implements the methods: `background_model_samples`, `references` and `probes`)"
\b
-Look at the following example
-
- $ bob pipelines vanilla-biometrics ./bob/pipelines/config/distributed/sge_iobig_16cores.py \
- ./bob/pipelines/config/database/mobio_male.py \
- ./bob/pipelines/config/baselines/facecrop_pca.py
-
-\b
-
-
-
-TODO: Work out this help
-
"""
@@ -79,12 +67,11 @@ TODO: Work out this help
entry_point_group="bob.pipelines.config", cls=ConfigCommand, epilog=EPILOG,
)
@click.option(
- "--pipeline", "-p", required=True, help="Vanilla biometrics pipeline",
+ "--pipeline", "-p", required=True, help="An entry point or a configuration file containing a `VanillaBiometricsPipeline`.",
)
@click.option(
"--database",
- "-d",
- required=True,
+ "-d",
help="Biometric Database connector (class that implements the methods: `background_model_samples`, `references` and `probes`)",
)
@click.option(
@@ -115,17 +102,84 @@ TODO: Work out this help
help="If set, will consider genuine scores in the ZT score normalization",
)
@click.option(
- "--write-metadata-scores", "-m",
+ "--write-metadata-scores",
+ "-m",
is_flag=True,
help="If set, all the scores will be written with all its metadata",
)
-@click.option("--ztnorm-cohort-proportion", default=1., type=float,
- help="Sets the percentage of samples used for t-norm and z-norm. Sometimes you don't want to use all the t/z samples for normalization")
+@click.option(
+ "--ztnorm-cohort-proportion",
+ default=1.0,
+ type=float,
+ help="Sets the percentage of samples used for t-norm and z-norm. Sometimes you don't want to use all the t/z samples for normalization",
+)
+@click.option(
+ "--checkpoint",
+ "-c",
+ is_flag=True,
+ help="If set, it will checkpoint all steps of the pipeline",
+)
@verbosity_option(cls=ResourceOption)
def vanilla_biometrics_ztnorm(
- pipeline, database, dask_client, groups, output, consider_genuines, write_metadata_scores, ztnorm_cohort_proportion, **kwargs
+ pipeline,
+ database,
+ dask_client,
+ groups,
+ output,
+ consider_genuines,
+ write_metadata_scores,
+ ztnorm_cohort_proportion,
+ checkpoint,
+ **kwargs,
):
- """Runs the simplest biometrics pipeline under ZTNorm.
+ """Runs the the vanilla-biometrics with ZT-Norm like score normalizations.
+
+ Such pipeline consists into two major components.
+ The first component consists of a scikit-learn `Pipeline`,
+ where a sequence of transformations of the input data
+ is defined.
+ The second component is a `BioAlgorithm` that defines the primitives
+ `enroll` and `score`
+
+ With those two components any Biometric Experiment can be done.
+ A Biometric experiment consists of three sub-pipelines and
+ they are defined below:
+
+ Sub-pipeline 1:\n
+ ---------------
+
+ Training background model.
+ Some biometric algorithms demands the training of background model, for instance, PCA/LDA matrix or a Neural networks.
+
+ \b
+ This pipeline runs: `Pipeline.fit(DATA_FOR_FIT)`
+
+
+
+ \b
+
+ Sub-pipeline 2:\n
+ ---------------
+
+ Creation of biometric references: This is a standard step in a biometric pipelines.
+ Given a set of samples of one identity, create a biometric reference (a.k.a template) for sub identity.
+
+
+ \b
+ raw_data --> preprocessing >> feature extraction >> enroll(background_model) --> biometric_reference
+
+ This pipeline runs: `BioAlgorithm.enroll(Pipeline.transform(DATA_ENROLL))` >> biometric_references
+
+
+ Sub-pipeline 3:\n
+ ---------------
+
+ Probing: This is another standard step in biometric pipelines.
+ Given one sample and one biometric reference, computes a score.
+ Such score has different meanings depending on the scoring method your biometric algorithm uses.
+ It's out of scope to explain in a help message to explain what scoring is for different biometric algorithms.
+
+ This pipeline runs: `BioAlgorithm.score(Pipeline.transform(DATA_SCORE, biometric_references))` >> biometric_references
"""
@@ -144,7 +198,6 @@ def vanilla_biometrics_ztnorm(
if not os.path.exists(output):
os.makedirs(output, exist_ok=True)
-
# It's necessary to chain load 2 resources together
pipeline_config = get_resource_filename(pipeline, "bob.bio.pipeline")
database_config = get_resource_filename(database, "bob.bio.database")
@@ -157,20 +210,18 @@ def vanilla_biometrics_ztnorm(
pipeline = vanilla_pipeline.pipeline
if write_metadata_scores:
- pipeline.score_writer = CSVScoreWriter(os.path.join(output,"./tmp"))
+ pipeline.score_writer = CSVScoreWriter(os.path.join(output, "./tmp"))
else:
- pipeline.score_writer = FourColumnsScoreWriter(os.path.join(output,"./tmp"))
-
+ pipeline.score_writer = FourColumnsScoreWriter(os.path.join(output, "./tmp"))
# Check if it's already checkpointed
- if not isinstance_nested(
+ if checkpoint and not isinstance_nested(
pipeline.biometric_algorithm,
"biometric_algorithm",
BioAlgorithmCheckpointWrapper,
):
pipeline = checkpoint_vanilla_biometrics(pipeline, output)
-
# Patching the pipeline in case of ZNorm and checkpointing it
pipeline = ZTNormPipeline(pipeline)
pipeline.ztnorm_solver = ZTNormCheckpointWrapper(
@@ -213,8 +264,14 @@ def vanilla_biometrics_ztnorm(
probes, zprobes = _merge_references_ztnorm(
biometric_references, probes, zprobes, treferences
)
-
- raw_scores, z_normed_scores, t_normed_scores, zt_normed_scores, s_normed_scores = pipeline(
+
+ (
+ raw_scores,
+ z_normed_scores,
+ t_normed_scores,
+ zt_normed_scores,
+ s_normed_scores,
+ ) = pipeline(
background_model_samples,
biometric_references,
probes,
@@ -225,7 +282,7 @@ def vanilla_biometrics_ztnorm(
def _build_filename(score_file_name, suffix):
return os.path.join(score_file_name, suffix)
-
+
# Running RAW_SCORES
raw_scores = post_process_scores(
pipeline, raw_scores, _build_filename(score_file_name, "raw_scores")