diff --git a/bob/bio/gmm/algorithm/GMM.py b/bob/bio/gmm/algorithm/GMM.py
index fc9400071f8fd337e9192cd9452c15a663631fbd..d412abbcf5ff62ecc266e5224a296296462a5fab 100644
--- a/bob/bio/gmm/algorithm/GMM.py
+++ b/bob/bio/gmm/algorithm/GMM.py
@@ -98,7 +98,7 @@ class GMM (Algorithm):
#######################################################
################ UBM training #########################
- def _train_projector_using_array(self, array):
+ def train_ubm(self, array):
logger.debug(" .... Training with %d feature vectors", array.shape[0])
@@ -112,7 +112,7 @@ class GMM (Algorithm):
# Trains using the KMeansTrainer
logger.info(" -> Training K-Means")
- bob.learn.em.train(self.kmeans_trainer, kmeans, array, self.kmeans_training_iterations, self.training_threshold, bob.core.random.mt19937(self.init_seed))
+ bob.learn.em.train(self.kmeans_trainer, kmeans, array, self.kmeans_training_iterations, self.training_threshold, self.rng)
variances, weights = kmeans.get_variances_and_weights_for_each_cluster(array)
means = kmeans.means
@@ -125,10 +125,10 @@ class GMM (Algorithm):
# Trains the GMM
logger.info(" -> Training GMM")
- bob.learn.em.train(self.ubm_trainer, self.ubm, array, self.gmm_training_iterations, self.training_threshold, bob.core.random.mt19937(self.init_seed))
+ bob.learn.em.train(self.ubm_trainer, self.ubm, array, self.gmm_training_iterations, self.training_threshold, self.rng)
- def _save_projector(self, projector_file):
+ def save_ubm(self, projector_file):
"""Save projector to file"""
# Saves the UBM to file
logger.debug(" .... Saving model to file '%s'", projector_file)
@@ -144,9 +144,9 @@ class GMM (Algorithm):
# Loads the data into an array
array = numpy.vstack(train_features)
- self._train_projector_using_array(array)
+ self.train_ubm(array)
- self._save_projector(projector_file)
+ self.save_ubm(projector_file)
#######################################################
@@ -169,7 +169,7 @@ class GMM (Algorithm):
self.rng = bob.core.random.mt19937(self.init_seed)
- def _project_using_array(self, array):
+ def project_ubm(self, array):
logger.debug(" .... Projecting %d feature vectors" % array.shape[0])
# Accumulates statistics
gmm_stats = bob.learn.em.GMMStats(self.ubm.shape[0], self.ubm.shape[1])
@@ -182,7 +182,7 @@ class GMM (Algorithm):
def project(self, feature):
"""Computes GMM statistics against a UBM, given an input 2D numpy.ndarray of feature vectors"""
self._check_feature(feature)
- return self._project_using_array(feature)
+ return self.project_ubm(feature)
def read_gmm_stats(self, gmm_stats_file):
@@ -193,7 +193,7 @@ class GMM (Algorithm):
"""Read the type of features that we require, namely GMM_Stats"""
return self.read_gmm_stats(feature_file)
- def _enroll_using_array(self, array):
+ def enroll_gmm(self, array):
logger.debug(" .... Enrolling with %d feature vectors", array.shape[0])
gmm = bob.learn.em.GMMMachine(self.ubm)
@@ -206,7 +206,7 @@ class GMM (Algorithm):
[self._check_feature(feature) for feature in feature_arrays]
array = numpy.vstack(feature_arrays)
# Use the array to train a GMM and return it
- return self._enroll_using_array(array)
+ return self.enroll_gmm(array)
######################################################
diff --git a/bob/bio/gmm/algorithm/ISV.py b/bob/bio/gmm/algorithm/ISV.py
index bcd28def06d9499f24d95d39f19c2bbcf7e5ccc3..e0cf0ead399614f39b76211e92bbaf2f0c1bf0d8 100644
--- a/bob/bio/gmm/algorithm/ISV.py
+++ b/bob/bio/gmm/algorithm/ISV.py
@@ -56,15 +56,15 @@ class ISV (GMM):
self.subspace_dimension_of_u = subspace_dimension_of_u
self.isv_training_iterations = isv_training_iterations
self.isv_enroll_iterations = isv_enroll_iterations
- self.trainer = bob.learn.em.ISVTrainer(self.relevance_factor)
+ self.isv_trainer = bob.learn.em.ISVTrainer(self.relevance_factor)
- def _train_isv(self, data):
+ def train_isv(self, data):
"""Train the ISV model given a dataset"""
logger.info(" -> Training ISV enroller")
self.isvbase = bob.learn.em.ISVBase(self.ubm, self.subspace_dimension_of_u)
# train ISV model
- bob.learn.em.train(self.trainer, self.isvbase, data, self.isv_training_iterations, rng=self.rng)
+ bob.learn.em.train(self.isv_trainer, self.isvbase, data, self.isv_training_iterations, rng=self.rng)
def train_projector(self, train_features, projector_file):
@@ -72,21 +72,16 @@ class ISV (GMM):
[self._check_feature(feature) for client in train_features for feature in client]
data1 = numpy.vstack([feature for client in train_features for feature in client])
- GMM._train_projector_using_array(self, data1)
+ self.train_ubm(data1)
# to save some memory, we might want to delete these data
del data1
# project training data
logger.info(" -> Projecting training data")
- data = []
- for client_features in train_features:
- list = []
- for feature in client_features:
- list.append(GMM.project(self, feature))
- data.append(list)
+ data = [[self.project_ubm(feature) for feature in client] for client in train_features]
# train ISV
- self._train_isv(data)
+ self.train_isv(data)
# Save the ISV base AND the UBM into the same file
self.save_projector(projector_file)
@@ -164,7 +159,7 @@ class ISV (GMM):
for feature in enroll_features:
assert isinstance(feature, bob.learn.em.GMMStats)
machine = bob.learn.em.ISVMachine(self.isvbase)
- self.trainer.enroll(machine, enroll_features, self.isv_enroll_iterations)
+ self.isv_trainer.enroll(machine, enroll_features, self.isv_enroll_iterations)
# return the resulting gmm
return machine
diff --git a/bob/bio/gmm/script/verify_gmm.py b/bob/bio/gmm/script/verify_gmm.py
index f04d228a1a14998c787c4362ab9c82b4e0b9f2a9..830f07e24f2c7b5d6becdada367399bcb883c81e 100644
--- a/bob/bio/gmm/script/verify_gmm.py
+++ b/bob/bio/gmm/script/verify_gmm.py
@@ -128,52 +128,6 @@ def add_gmm_jobs(args, job_ids, deps, submitter):
-def add_jobs(args, submitter):
- """Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed."""
-
- assert args.grid is not None
-
- # Here, we use the default bob.bio.base add_jobs function, but intercept it for adding the training
- SKIPS = ['preprocessing', 'extractor_training', 'extraction', 'projector_training', 'projection', 'enroller_training', 'enrollment', 'score_computation', 'concatenation', 'calibration']
- original_skips = {key : args.__dict__["skip_%s" % key] for key in SKIPS}
-
- # first, submit preprocessing and feature extraction; skip all others
- for key in SKIPS[3:]:
- setattr(args, "skip_%s" % key, True)
-
- job_ids = bob.bio.base.script.verify.add_jobs(args, submitter)
-
- for key in SKIPS[3:]:
- setattr(args, "skip_%s" % key, original_skips[key])
-
- # reset skips
- args.skip_preprocessing = original_skips['preprocessing']
- args.skip_extractor_training = original_skips['extractor_training']
- args.skip_extraction = original_skips['extraction']
-
- # if there are any external dependencies, we need to respect them
- deps = args.external_dependencies[:]
- # also, we depend on all previous steps
- for n in ['preprocessing', 'extractor-training', 'extraction']:
- if n in job_ids:
- deps.append(job_ids[n])
-
- # now, add our jobs
- job_ids, deps = add_gmm_jobs(args, job_ids, deps, submitter)
-
- # alright, finish the remaining bits
- for key in SKIPS[:4]:
- setattr(args, "skip_%s" % key, True)
-
- args.external_dependencies = deps
- job_ids.update(bob.bio.base.script.verify.add_jobs(args, submitter))
-
- # alright, finish the remaining bits
- for key in SKIPS[:4]:
- setattr(args, "skip_%s" % key, original_skips[key])
-
- return job_ids
-
def execute(args):
"""Run the desired job of the tool chain that is specified on command line.
@@ -267,7 +221,7 @@ def verify(args, command_line_parameters, external_fake_job_id = 0):
else:
# add jobs
submitter = base_tools.GridSubmission(args, command_line_parameters, executable = 'verify_gmm.py', first_fake_job_id = 0) if args.grid else None
- retval = add_jobs(args, submitter)
+ retval = tools.add_jobs(args, submitter, local_job_adder = add_gmm_jobs)
base_tools.write_info(args, command_line_parameters)
if args.grid.is_local() and args.run_local_scheduler:
diff --git a/bob/bio/gmm/script/verify_isv.py b/bob/bio/gmm/script/verify_isv.py
new file mode 100644
index 0000000000000000000000000000000000000000..b01d15a6e377e24b212c81c46bc75ee62e3a3114
--- /dev/null
+++ b/bob/bio/gmm/script/verify_isv.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Manuel Guenther <Manuel.Guenther@idiap.ch>
+from __future__ import print_function
+
+import sys
+import argparse
+
+import logging
+logger = logging.getLogger("bob.bio.gmm")
+
+import bob.bio.base
+from .. import tools, algorithm
+from bob.bio.base import tools as base_tools
+
+
+def parse_arguments(command_line_parameters, exclude_resources_from = []):
+ """This function parses the given options (which by default are the command line options). If exclude_resources_from is specified (as a list), the resources from the given packages are not listed in the help message."""
+ # set up command line parser
+ parsers = base_tools.command_line_parser(exclude_resources_from = exclude_resources_from)
+
+ # add GMM-related options
+ tools.add_parallel_gmm_options(parsers, sub_module = 'isv')
+
+ # override some parameters
+ parsers['config'].add_argument('-g', '--grid', metavar = 'x', nargs = '+', required=True,
+ help = 'Configuration for the grid setup; required for the parallel execution script.')
+
+ parsers['config'].add_argument('-a', '--algorithm', metavar = 'x', nargs = '+', default = ['gmm'],
+ help = 'Face recognition; only GMM-related algorithms are allowed')
+
+
+ # Add sub-tasks that can be executed by this script
+ parser = parsers['main']
+ parser.add_argument('--sub-task',
+ choices = ('preprocess', 'train-extractor', 'extract', 'normalize-features', 'kmeans-init', 'kmeans-e-step', 'kmeans-m-step', 'gmm-init', 'gmm-e-step', 'gmm-m-step', 'gmm-project', 'isv-train', 'project', 'enroll', 'compute-scores', 'concatenate'),
+ help = argparse.SUPPRESS) #'Executes a subtask (FOR INTERNAL USE ONLY!!!)'
+ parser.add_argument('--iteration', type = int,
+ help = argparse.SUPPRESS) #'Which type of models to generate (Normal or TModels)'
+ parser.add_argument('--model-type', choices = ['N', 'T'],
+ help = argparse.SUPPRESS) #'Which type of models to generate (Normal or TModels)'
+ parser.add_argument('--score-type', choices = ['A', 'B', 'C', 'D', 'Z'],
+ help = argparse.SUPPRESS) #'The type of scores that should be computed'
+ parser.add_argument('--group',
+ help = argparse.SUPPRESS) #'The group for which the current action should be performed'
+
+ # now that we have set up everything, get the command line arguments
+ args = base_tools.initialize(parsers, command_line_parameters,
+ skips = ['preprocessing', 'extractor-training', 'extraction', 'normalization', 'kmeans', 'gmm', 'isv', 'projection', 'enroller-training', 'enrollment', 'score-computation', 'concatenation', 'calibration']
+ )
+
+ args.skip_projector_training = True
+
+ # and add the GMM-related parameters
+ tools.initialize_parallel_gmm(args, sub_module = 'isv')
+
+ # assert that the algorithm is a GMM
+ if args.algorithm.__class__ != algorithm.ISV:
+ raise ValueError("The given algorithm %s is not a (pure) ISV algorithm" % type(args.algorithm))
+
+ return args
+
+from .verify_gmm import add_gmm_jobs
+
+def add_isv_jobs(args, job_ids, deps, submitter):
+ """Adds all GMM-related jobs."""
+
+ # first, add gmm jobs
+ job_ids, deps = add_gmm_jobs(args, job_ids, deps, submitter)
+
+ # now, add two extra steps for ISV
+ if not args.skip_isv:
+ # gmm projection
+ job_ids['gmm-projection'] = submitter.submit(
+ '--sub-task gmm-project',
+ name = 'pro-gmm',
+ number_of_parallel_jobs = args.grid.number_of_projection_jobs,
+ dependencies = deps,
+ **args.grid.projection_queue)
+ deps.append(job_ids['gmm-projection'])
+
+ job_ids['isv-training'] = submitter.submit(
+ '--sub-task isv-train',
+ name = 'train-isv',
+ dependencies = deps,
+ **args.grid.training_queue)
+ deps.append(job_ids['isv-training'])
+
+ return job_ids, deps
+
+
+from .verify_gmm import execute as gmm_execute
+
+
+def execute(args):
+ """Run the desired job of the tool chain that is specified on command line.
+ This job might be executed either in the grid, or locally."""
+
+ # first, let the base script decide if it knows how to execute the job
+ if gmm_execute(args):
+ return True
+
+ # now, check what we can do
+
+ # the file selector object
+ fs = tools.FileSelector.instance()
+
+ if args.sub_task == 'gmm-project':
+ tools.gmm_project(
+ args.algorithm,
+ args.extractor,
+ indices = base_tools.indices(fs.training_list('extracted', 'train_projector'), args.grid.number_of_projection_jobs),
+ force = args.force)
+
+ # train the feature projector
+ elif args.sub_task == 'isv-train':
+ tools.isv_training(
+ args.algorithm,
+ force = args.force)
+
+ else:
+ # Not our keyword...
+ return False
+ return True
+
+
+
+def verify(args, command_line_parameters, external_fake_job_id = 0):
+ """This is the main entry point for computing verification experiments.
+ You just have to specify configurations for any of the steps of the toolchain, which are:
+ -- the database
+ -- the preprocessing
+ -- feature extraction
+ -- the recognition algorithm
+ -- and the grid configuration.
+ Additionally, you can skip parts of the toolchain by selecting proper --skip-... parameters.
+ If your probe files are not too big, you can also specify the --preload-probes switch to speed up the score computation.
+ If files should be re-generated, please specify the --force option (might be combined with the --skip-... options)."""
+
+
+ # as the main entry point, check whether the sub-task is specified
+ if args.sub_task is not None:
+ # execute the desired sub-task
+ if not execute(args):
+ raise ValueError("The specified --sub-task '%s' is not known to the system" % args.sub_task)
+ return {}
+ else:
+ # add jobs
+ submitter = base_tools.GridSubmission(args, command_line_parameters, executable = 'verify_isv.py', first_fake_job_id = 0) if args.grid else None
+ retval = tools.add_jobs(args, submitter, local_job_adder = add_isv_jobs)
+ base_tools.write_info(args, command_line_parameters)
+
+ if args.grid.is_local() and args.run_local_scheduler:
+ if args.dry_run:
+ print ("Would have started the local scheduler to run the experiments with parallel jobs")
+ else:
+ # start the jman local deamon
+ submitter.execute_local()
+ return {}
+
+ else:
+ # return job ids as a dictionary
+ return retval
+
+
+def main(command_line_parameters = sys.argv):
+ """Executes the main function"""
+ try:
+ # do the command line parsing
+ args = parse_arguments(command_line_parameters[1:])
+
+ # perform face verification test
+ verify(args, command_line_parameters)
+ except Exception as e:
+ # track any exceptions as error logs (i.e., to get a time stamp)
+ logger.error("During the execution, an exception was raised: %s" % e)
+ raise
+
+if __name__ == "__main__":
+ main()
diff --git a/bob/bio/gmm/test/data/gmm_model.hdf5 b/bob/bio/gmm/test/data/gmm_model.hdf5
index 8c01d07a45d332393a8f8261811472371b1899f9..71a14c7f318639c17a7f78cf5056ac755bf9ee5f 100644
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diff --git a/bob/bio/gmm/test/data/gmm_projected.hdf5 b/bob/bio/gmm/test/data/gmm_projected.hdf5
index 8f6c44ebb6a636efc1342277d708a9f09f2fccad..2c27bc5f14f0488ab035f135881286329c8a142c 100644
Binary files a/bob/bio/gmm/test/data/gmm_projected.hdf5 and b/bob/bio/gmm/test/data/gmm_projected.hdf5 differ
diff --git a/bob/bio/gmm/test/data/gmm_projector.hdf5 b/bob/bio/gmm/test/data/gmm_projector.hdf5
index 1a5e4fd776d97bef951cf2e72d6e98aef25b818a..40d263c91c0e9b8048f2c76afa9bdb3594098080 100644
Binary files a/bob/bio/gmm/test/data/gmm_projector.hdf5 and b/bob/bio/gmm/test/data/gmm_projector.hdf5 differ
diff --git a/bob/bio/gmm/test/data/isv_model.hdf5 b/bob/bio/gmm/test/data/isv_model.hdf5
index 28cf7b7c0244ddbba0fe515fbb1112cebf72903f..2fcf9425127838bcdc059e31e905c6d83a9951a3 100644
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diff --git a/bob/bio/gmm/test/data/isv_projected.hdf5 b/bob/bio/gmm/test/data/isv_projected.hdf5
index 382b5ed1b10587ed02135527210df54196f225c1..ff9cdf0ae0636d6e652d688ea541430b2dbeb3ef 100644
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diff --git a/bob/bio/gmm/test/data/isv_projector.hdf5 b/bob/bio/gmm/test/data/isv_projector.hdf5
index b8fb97eee6e8556319a46340a2d71ee84230defc..135e8dfce76c867becf61a6209ed1e9c433ade53 100644
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diff --git a/bob/bio/gmm/test/data/jfa_enroller.hdf5 b/bob/bio/gmm/test/data/jfa_enroller.hdf5
index a5588e79fe00440f040bcd1acadaf5bedaaf2f09..4dbcd81860ed003ecc5f19fe315dc6c5bc7adc00 100644
Binary files a/bob/bio/gmm/test/data/jfa_enroller.hdf5 and b/bob/bio/gmm/test/data/jfa_enroller.hdf5 differ
diff --git a/bob/bio/gmm/test/data/jfa_model.hdf5 b/bob/bio/gmm/test/data/jfa_model.hdf5
index 24e912a43b2dfa905493ebc4b3c3855e658cf6df..afaa7ddb21bdb5734b795da5fb4b3480a87cbdcd 100644
Binary files a/bob/bio/gmm/test/data/jfa_model.hdf5 and b/bob/bio/gmm/test/data/jfa_model.hdf5 differ
diff --git a/bob/bio/gmm/test/data/scores-nonorm-isv-dev b/bob/bio/gmm/test/data/scores-nonorm-isv-dev
new file mode 100644
index 0000000000000000000000000000000000000000..43e097259b90385735020c6da965abd487c3a5f0
--- /dev/null
+++ b/bob/bio/gmm/test/data/scores-nonorm-isv-dev
@@ -0,0 +1,2000 @@
+3 3 s3/1 1.62594110
+3 3 s3/3 2.02770584
+3 3 s3/6 1.00599519
+3 3 s3/8 2.37602396
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+3 32 s32/1 -1.70140390
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+3 32 s32/8 -1.19521653
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+3 35 s35/1 2.24751187
+3 35 s35/3 1.66100088
+3 35 s35/6 1.98838605
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+3 35 s35/10 0.54591192
+3 37 s37/1 0.73459582
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+3 37 s37/6 -2.28095042
+3 37 s37/8 -1.27246275
+3 37 s37/10 0.31060548
+3 38 s38/1 1.16980854
+3 38 s38/3 2.82496428
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diff --git a/bob/bio/gmm/test/data/scores-ztnorm-isv-dev b/bob/bio/gmm/test/data/scores-ztnorm-isv-dev
new file mode 100644
index 0000000000000000000000000000000000000000..8210202d0bfc5f28cf1825bb16eeed92e1856df7
--- /dev/null
+++ b/bob/bio/gmm/test/data/scores-ztnorm-isv-dev
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+40 8 s8/1 -1.04696653
+40 8 s8/3 -0.97425455
+40 8 s8/6 -0.91872241
+40 8 s8/8 -1.14109199
+40 8 s8/10 -0.95028812
+40 9 s9/1 0.51694013
+40 9 s9/3 0.31585519
+40 9 s9/6 0.16507650
+40 9 s9/8 0.65191596
+40 9 s9/10 -0.02276764
+40 13 s13/1 0.66234050
+40 13 s13/3 0.37550186
+40 13 s13/6 0.40816911
+40 13 s13/8 -0.13075537
+40 13 s13/10 -0.01190915
+40 15 s15/1 -0.72721456
+40 15 s15/3 -0.65578877
+40 15 s15/6 -0.54141976
+40 15 s15/8 -0.50398817
+40 15 s15/10 0.77057661
+40 18 s18/1 0.29679893
+40 18 s18/3 0.69726324
+40 18 s18/6 0.60686179
+40 18 s18/8 0.74492100
+40 18 s18/10 0.73003619
+40 19 s19/1 -0.26645334
+40 19 s19/3 -0.16139666
+40 19 s19/6 0.13255027
+40 19 s19/8 -0.85131453
+40 19 s19/10 -0.10739114
+40 22 s22/1 -0.73680099
+40 22 s22/3 -0.70491177
+40 22 s22/6 -1.09723933
+40 22 s22/8 -0.61603530
+40 22 s22/10 -0.43814793
+40 23 s23/1 -0.08264126
+40 23 s23/3 0.70820623
+40 23 s23/6 0.94118503
+40 23 s23/8 -1.26436495
+40 23 s23/10 -0.75397897
+40 25 s25/1 0.84848788
+40 25 s25/3 0.90731162
+40 25 s25/6 0.78437774
+40 25 s25/8 0.91335850
+40 25 s25/10 0.53874091
+40 28 s28/1 -1.26267884
+40 28 s28/3 -0.84703947
+40 28 s28/6 -1.24074566
+40 28 s28/8 -0.63620469
+40 28 s28/10 -1.45598422
+40 30 s30/1 -0.36455870
+40 30 s30/3 -0.10113275
+40 30 s30/6 -0.88747181
+40 30 s30/8 -0.67029154
+40 30 s30/10 -0.41531352
+40 31 s31/1 0.36554526
+40 31 s31/3 -0.05405135
+40 31 s31/6 0.28548285
+40 31 s31/8 0.40389417
+40 31 s31/10 0.02543310
+40 32 s32/1 -0.42715416
+40 32 s32/3 0.45479259
+40 32 s32/6 -0.54264840
+40 32 s32/8 -0.15520306
+40 32 s32/10 -0.45625365
+40 35 s35/1 0.80140491
+40 35 s35/3 1.26083259
+40 35 s35/6 0.36464563
+40 35 s35/8 0.77367750
+40 35 s35/10 0.99934721
+40 37 s37/1 -0.15735343
+40 37 s37/3 -0.21495410
+40 37 s37/6 -0.89835345
+40 37 s37/8 -0.85266223
+40 37 s37/10 -0.54982542
+40 38 s38/1 -0.58354578
+40 38 s38/3 0.35819201
+40 38 s38/6 -0.61575653
+40 38 s38/8 -0.09514153
+40 38 s38/10 -0.11471385
+40 40 s40/1 0.89405923
+40 40 s40/3 1.19425984
+40 40 s40/6 0.86949211
+40 40 s40/8 0.99603519
+40 40 s40/10 0.93328583
diff --git a/bob/bio/gmm/test/test_algorithms.py b/bob/bio/gmm/test/test_algorithms.py
index 03613b11e9fd754969c90c38347c8486587fbbe6..b358496f4765cbc907849e889a575b7c3473803b 100644
--- a/bob/bio/gmm/test/test_algorithms.py
+++ b/bob/bio/gmm/test/test_algorithms.py
@@ -118,7 +118,7 @@ def test_gmm():
# compare model with probe
probe = gmm1.read_probe(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/gmm_projected.hdf5'))
- reference_score = -0.02335195
+ reference_score = -0.01676570
assert abs(gmm1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (gmm1.score(model, probe), reference_score)
assert abs(gmm1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -171,7 +171,7 @@ def test_gmm_regular():
probe = utils.random_array((20,45), -5., 5., seed=84)
# compare model with probe
- reference_score = -0.41556023
+ reference_score = -0.40840148
assert abs(gmm1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (gmm1.score(model, probe), reference_score)
# TODO: not implemented
#assert abs(gmm1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -236,7 +236,7 @@ def test_isv():
# compare model with probe
probe = isv1.read_probe(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_projected.hdf5'))
- reference_score = 0.01732122
+ reference_score = 0.02136784
assert abs(isv1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (isv1.score(model, probe), reference_score)
# assert abs(isv1.score_for_multiple_probes(model, [probe]*4) - reference_score) < 1e-5, isv1.score_for_multiple_probes(model, [probe, probe])
# TODO: Why is the score not identical for multiple copies of the same probe?
@@ -320,7 +320,7 @@ def test_jfa():
# compare model with probe
probe = jfa1.read_probe(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/gmm_projected.hdf5'))
- reference_score = 0.01763115
+ reference_score = 0.02225812
assert abs(jfa1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (jfa1.score(model, probe), reference_score)
# TODO: implement that
# assert abs(jfa1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5, jfa1.score_for_multiple_probes(model, [probe, probe])
diff --git a/bob/bio/gmm/test/test_scripts.py b/bob/bio/gmm/test/test_scripts.py
index 18f39f7be534d293a20605214fdfc3ae09c0888a..f175f2a27d224af88e683a943bbe9faddbb82938 100644
--- a/bob/bio/gmm/test/test_scripts.py
+++ b/bob/bio/gmm/test/test_scripts.py
@@ -1,5 +1,3 @@
-
-
from __future__ import print_function
import bob.measure
@@ -10,11 +8,9 @@ import shutil
import tempfile
import numpy
-import bob.io.base.test_utils
import bob.io.image
import bob.bio.base
import bob.bio.gmm
-from . import utils
from nose.plugins.skip import SkipTest
@@ -63,8 +59,8 @@ def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('sco
shutil.rmtree(test_dir)
-def test_gmm_base():
- test_dir = tempfile.mkdtemp(prefix='frltest_')
+def test_gmm_sequential():
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
# define dummy parameters
parameters = [
'-d', 'dummy',
@@ -72,7 +68,7 @@ def test_gmm_base():
'-e', 'dummy',
'-a', 'bob.bio.gmm.algorithm.GMM(2, 2, 2)', '--import', 'bob.bio.gmm',
'--zt-norm',
- '-s', 'test_gmm_sequential', '-vv',
+ '-s', 'test_gmm_sequential',
'--temp-directory', test_dir,
'--result-directory', test_dir
]
@@ -84,7 +80,7 @@ def test_gmm_base():
def test_gmm_parallel():
from bob.bio.gmm.script.verify_gmm import main
- test_dir = tempfile.mkdtemp(prefix='frltest_')
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
test_database = os.path.join(test_dir, "submitted.sql3")
# define dummy parameters
parameters = [
@@ -95,7 +91,7 @@ def test_gmm_parallel():
'-g', 'bob.bio.base.grid.Grid(grid = "local", number_of_parallel_processes = 2, scheduler_sleep_time = 0.1)', '-G', test_database, '--run-local-scheduler', '-R',
'--clean-intermediate',
'--zt-norm',
- '-s', 'test_gmm_parallel', '-vv',
+ '-s', 'test_gmm_parallel',
'--temp-directory', test_dir,
'--result-directory', test_dir,
]
@@ -103,3 +99,45 @@ def test_gmm_parallel():
print (bob.bio.base.tools.command_line(parameters))
_verify(parameters, test_dir, 'test_gmm_parallel', executable=main, ref_modifier='-gmm')
+
+
+def test_isv_sequential():
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
+ # define dummy parameters
+ parameters = [
+ '-d', 'dummy',
+ '-p', 'dummy',
+ '-e', 'dummy',
+ '-a', 'bob.bio.gmm.algorithm.ISV(10, number_of_gaussians=2, kmeans_training_iterations=2, gmm_training_iterations=2, isv_training_iterations=2)', '--import', 'bob.bio.gmm',
+ '--zt-norm',
+ '-s', 'test_isv_sequential',
+ '--temp-directory', test_dir,
+ '--result-directory', test_dir
+ ]
+
+ print (bob.bio.base.tools.command_line(parameters))
+
+ _verify(parameters, test_dir, 'test_isv_sequential', ref_modifier='-isv')
+
+
+def test_isv_parallel():
+ from bob.bio.gmm.script.verify_isv import main
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
+ test_database = os.path.join(test_dir, "submitted.sql3")
+ # define dummy parameters
+ parameters = [
+ '-d', 'dummy',
+ '-p', 'dummy',
+ '-e', 'dummy',
+ '-a', 'bob.bio.gmm.algorithm.ISV(10, number_of_gaussians=2, kmeans_training_iterations=2, gmm_training_iterations=2, isv_training_iterations=2)', '--import', 'bob.bio.gmm', 'bob.io.image',
+ '-g', 'bob.bio.base.grid.Grid(grid = "local", number_of_parallel_processes = 2, scheduler_sleep_time = 0.1)', '-G', test_database, '--run-local-scheduler', '-R',
+ '--clean-intermediate',
+ '--zt-norm',
+ '-s', 'test_isv_parallel',
+ '--temp-directory', test_dir,
+ '--result-directory', test_dir,
+ ]
+
+ print (bob.bio.base.tools.command_line(parameters))
+
+ _verify(parameters, test_dir, 'test_isv_parallel', executable=main, ref_modifier='-isv')
diff --git a/bob/bio/gmm/tools/__init__.py b/bob/bio/gmm/tools/__init__.py
index 1903eb6d546039803703d15ad842f5d217886267..581eabab61293291cd17ee07a242575bd58f519e 100644
--- a/bob/bio/gmm/tools/__init__.py
+++ b/bob/bio/gmm/tools/__init__.py
@@ -1,2 +1,4 @@
+from .utils import *
from .command_line import *
from .gmm import *
+from .isv import *
diff --git a/bob/bio/gmm/tools/command_line.py b/bob/bio/gmm/tools/command_line.py
index e81c358814b39b918971392036d9fa70c34efbcc..8673398703902dfa7be6276e9012447c8eeabe6a 100644
--- a/bob/bio/gmm/tools/command_line.py
+++ b/bob/bio/gmm/tools/command_line.py
@@ -7,7 +7,7 @@ logger = bob.core.log.setup("bob.bio.gmm")
from bob.bio.base.tools import FileSelector
-def add_parallel_gmm_options(parsers, additional_functions = ['gmm']):
+def add_parallel_gmm_options(parsers, sub_module = None):
"""Add the options for parallel UBM training to the given parsers."""
flag_group = parsers['flag']
@@ -27,23 +27,27 @@ def add_parallel_gmm_options(parsers, additional_functions = ['gmm']):
sub_dir_group.add_argument('--gmm-directory', default = 'gmm_temp',
help = 'The sub-directory (relative to --temp-directory), where intermediate gmm files should be stored')
+ if sub_module == 'isv':
+ sub_dir_group.add_argument('--isv-directory', default = 'isv_temp',
+ help = 'The sub-directory (relative to --temp-directory), where intermediate isv training files should be stored')
+
# Functions to be added to the FileSelector class, once it is instantiated
def _kmeans_intermediate_file(self, round):
- return os.path.join(self.kmeans_temp_directory, 'round_%05d' % round, 'kmeans.hdf5')
+ return os.path.join(self.directories['kmeans'], 'round_%05d' % round, 'kmeans.hdf5')
def _kmeans_stats_file(self, round, start_index, end_index):
- return os.path.join(self.kmeans_temp_directory, 'round_%05d' % round, 'stats-%05d-%95d.hdf5' % (start_index, end_index))
+ return os.path.join(self.directories['kmeans'], 'round_%05d' % round, 'stats-%05d-%95d.hdf5' % (start_index, end_index))
def _gmm_intermediate_file(self, round):
- return os.path.join(self.gmm_temp_directory, 'round_%05d' % round, 'gmm.hdf5')
+ return os.path.join(self.directories['gmm'], 'round_%05d' % round, 'gmm.hdf5')
def _gmm_stats_file(self, round, start_index, end_index):
- return os.path.join(self.gmm_temp_directory, 'round_%05d' % round, 'stats-%05d-%95d.hdf5' % (start_index, end_index))
+ return os.path.join(self.directories['gmm'], 'round_%05d' % round, 'stats-%05d-%95d.hdf5' % (start_index, end_index))
-def initialize_parallel_gmm(args):
+def initialize_parallel_gmm(args, sub_module = None):
# get the relevant sub_directory, which depends on the database and the prorocol
protocol = 'None' if args.database.protocol is None else args.database.protocol
extractor_sub_dir = protocol if args.database.training_depends_on_protocol and args.extractor.requires_training else '.'
@@ -51,15 +55,18 @@ def initialize_parallel_gmm(args):
fs = FileSelector.instance()
- # add relevant directories to file selector object
- fs.kmeans_temp_directory = os.path.join(args.temp_directory, sub_dir, args.kmeans_directory)
- fs.kmeans_file = os.path.join(args.temp_directory, sub_dir, "kmeans.hdf5")
- fs.gmm_temp_directory = os.path.join(args.temp_directory, sub_dir, args.gmm_directory)
-# fs.gmm_file = os.path.join(args.temp_directory, sub_dir, "gmm.hdf5")
- fs.gmm_file = fs.projector_file
-
# add relevant **functions** to file selector object
fs.kmeans_intermediate_file = types.MethodType(_kmeans_intermediate_file, fs)
fs.kmeans_stats_file = types.MethodType(_kmeans_stats_file, fs)
fs.gmm_intermediate_file = types.MethodType(_gmm_intermediate_file, fs)
fs.gmm_stats_file = types.MethodType(_gmm_stats_file, fs)
+
+ # add relevant directories to file selector object
+ fs.directories['kmeans'] = os.path.join(args.temp_directory, sub_dir, args.kmeans_directory)
+ fs.kmeans_file = os.path.join(args.temp_directory, sub_dir, "kmeans.hdf5")
+ fs.directories['gmm'] = os.path.join(args.temp_directory, sub_dir, args.gmm_directory)
+ if sub_module is None:
+ fs.ubm_file = fs.projector_file
+ else:
+ fs.ubm_file = os.path.join(args.temp_directory, sub_dir, "ubm.hdf5")
+ fs.directories['isv'] = os.path.join(args.temp_directory, sub_dir, args.isv_directory)
diff --git a/bob/bio/gmm/tools/gmm.py b/bob/bio/gmm/tools/gmm.py
index 6c443c46ecb6b72b2194096ddedd140ee4e771df..951e6c0d32e29beff76d5411f5993f811bcd03c5 100644
--- a/bob/bio/gmm/tools/gmm.py
+++ b/bob/bio/gmm/tools/gmm.py
@@ -269,8 +269,8 @@ def gmm_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=
gmm_machine.save(bob.io.base.HDF5File(new_machine_file, 'w'))
if iteration == algorithm.gmm_training_iterations-1:
- shutil.copy(new_machine_file, fs.gmm_file)
- logger.info("UBM training: Wrote new GMM machine '%s'", fs.gmm_file)
+ shutil.copy(new_machine_file, fs.ubm_file)
+ logger.info("UBM training: Wrote new GMM machine '%s'", fs.ubm_file)
if clean and iteration > 0:
old_dir = os.path.dirname(fs.gmm_intermediate_file(iteration-1))
diff --git a/bob/bio/gmm/tools/isv.py b/bob/bio/gmm/tools/isv.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ec7f546d6b094577fdb4adc1a3aa4184437c94d
--- /dev/null
+++ b/bob/bio/gmm/tools/isv.py
@@ -0,0 +1,55 @@
+import logging
+logger = logging.getLogger("bob.bio.gmm")
+
+import bob.io.base
+import os
+
+from bob.bio.base.tools.FileSelector import FileSelector
+from bob.bio.base import utils, tools
+
+def gmm_project(algorithm, extractor, indices, force=False):
+ """Performs GMM projection"""
+ fs = FileSelector.instance()
+
+ algorithm.load_ubm(fs.ubm_file)
+
+ feature_files = fs.training_list('extracted', 'train_projector')
+ projected_files = fs.training_list('isv', 'train_projector')
+
+ logger.info("ISV training: Project features range (%d, %d) from '%s' to '%s'", indices, fs.directories['extracted'], fs.directories['isv'])
+
+ # extract the features
+ for i in range(indices[0], indices[1]):
+ feature_file = feature_files[i]
+ projected_file = projected_files[i]
+
+ if not utils.check_file(projected_file, force):
+ # load feature
+ feature = extractor.read_feature(feature_file)
+ # project feature
+ projected = algorithm.project_ubm(feature)
+ # write it
+ bob.io.base.create_directories_safe(os.path.dirname(projected_file))
+ bob.bio.base.save(projected, projected_file)
+
+
+def isv_training(algorithm, force=False):
+ """Finally, the UBM is used to train the ISV projector/enroller."""
+ fs = FileSelector.instance()
+
+ if utils.check_file(fs.projector_file, force, 800):
+ logger.info("ISV training: Skipping ISV training since '%s' already exists", fs.isv_file)
+ else:
+ # read UBM into the ISV class
+ algorithm.load_ubm(fs.ubm_file)
+
+ # read training data
+ training_list = fs.training_list('isv', 'train_projector', arrange_by_client = True)
+ train_gmm_stats = [[algorithm.read_gmm_stats(filename) for filename in client_files] for client_files in training_list]
+
+ # perform ISV training
+ logger.info("ISV training: training ISV with %d clients", len(train_gmm_stats))
+ algorithm.train_isv(train_gmm_stats)
+ # save result
+ bob.io.base.create_directories_safe(os.path.dirname(fs.projector_file))
+ algorithm.save_projector(fs.projector_file)
diff --git a/bob/bio/gmm/tools/utils.py b/bob/bio/gmm/tools/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..df76c4bf4c13c6e807748aef2fb2054e3f8c6c14
--- /dev/null
+++ b/bob/bio/gmm/tools/utils.py
@@ -0,0 +1,47 @@
+import bob.bio.base
+
+def add_jobs(args, submitter, local_job_adder):
+ """Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed."""
+
+ assert args.grid is not None
+
+ # Here, we use the default bob.bio.base add_jobs function, but intercept it for adding the training
+ SKIPS = ['preprocessing', 'extractor_training', 'extraction', 'projector_training', 'projection', 'enroller_training', 'enrollment', 'score_computation', 'concatenation', 'calibration']
+ original_skips = {key : args.__dict__["skip_%s" % key] for key in SKIPS}
+
+ # first, submit preprocessing and feature extraction; skip all others
+ for key in SKIPS[3:]:
+ setattr(args, "skip_%s" % key, True)
+
+ job_ids = bob.bio.base.script.verify.add_jobs(args, submitter)
+
+ for key in SKIPS[3:]:
+ setattr(args, "skip_%s" % key, original_skips[key])
+
+ # reset skips
+ args.skip_preprocessing = original_skips['preprocessing']
+ args.skip_extractor_training = original_skips['extractor_training']
+ args.skip_extraction = original_skips['extraction']
+
+ # if there are any external dependencies, we need to respect them
+ deps = args.external_dependencies[:]
+ # also, we depend on all previous steps
+ for n in ['preprocessing', 'extractor-training', 'extraction']:
+ if n in job_ids:
+ deps.append(job_ids[n])
+
+ # now, add our jobs
+ job_ids, deps = local_job_adder(args, job_ids, deps, submitter)
+
+ # alright, finish the remaining bits
+ for key in SKIPS[:4]:
+ setattr(args, "skip_%s" % key, True)
+
+ args.external_dependencies = deps
+ job_ids.update(bob.bio.base.script.verify.add_jobs(args, submitter))
+
+ # alright, finish the remaining bits
+ for key in SKIPS[:4]:
+ setattr(args, "skip_%s" % key, original_skips[key])
+
+ return job_ids
diff --git a/setup.py b/setup.py
index 0ed1518e2de1fb936174ad1035ee133bec7d7ab8..6b180159d8d2b50855dbf046860cd4d9ba1d6a0c 100644
--- a/setup.py
+++ b/setup.py
@@ -103,6 +103,7 @@ setup(
# scripts should be declared using this entry:
'console_scripts' : [
'verify_gmm.py = bob.bio.gmm.script.verify_gmm:main',
+ 'verify_isv.py = bob.bio.gmm.script.verify_isv:main',
],
'bob.bio.database': [