From 2f93ed06a6119296ef11a4b0b0d1a886b10a5143 Mon Sep 17 00:00:00 2001
From: Manuel Guenther <manuel.guenther@idiap.ch>
Date: Mon, 11 May 2015 14:45:26 +0200
Subject: [PATCH] Updated GMM algorithm and implemented parallel UBM training
---
bob/bio/gmm/algorithm/GMM.py | 80 +-
bob/bio/gmm/config/algorithm/gmm.py | 2 -
bob/bio/gmm/config/algorithm/isv.py | 4 +-
bob/bio/gmm/script/__init__.py | 0
bob/bio/gmm/script/verify_gmm.py | 300 +++
bob/bio/gmm/test/data/scores-nonorm-gmm-dev | 2000 +++++++++++++++++++
bob/bio/gmm/test/data/scores-ztnorm-gmm-dev | 2000 +++++++++++++++++++
bob/bio/gmm/test/dummy/__init__.py | 1 +
bob/bio/gmm/test/dummy/extractor.py | 27 +
bob/bio/gmm/test/test_algorithms.py | 8 +-
bob/bio/gmm/test/test_scripts.py | 105 +
bob/bio/gmm/tools/__init__.py | 2 +
bob/bio/gmm/tools/command_line.py | 65 +
bob/bio/gmm/tools/gmm.py | 278 +++
setup.py | 2 +
15 files changed, 4798 insertions(+), 76 deletions(-)
create mode 100644 bob/bio/gmm/script/__init__.py
create mode 100644 bob/bio/gmm/script/verify_gmm.py
create mode 100644 bob/bio/gmm/test/data/scores-nonorm-gmm-dev
create mode 100644 bob/bio/gmm/test/data/scores-ztnorm-gmm-dev
create mode 100644 bob/bio/gmm/test/dummy/__init__.py
create mode 100644 bob/bio/gmm/test/dummy/extractor.py
create mode 100644 bob/bio/gmm/test/test_scripts.py
create mode 100644 bob/bio/gmm/tools/__init__.py
create mode 100644 bob/bio/gmm/tools/command_line.py
create mode 100644 bob/bio/gmm/tools/gmm.py
diff --git a/bob/bio/gmm/algorithm/GMM.py b/bob/bio/gmm/algorithm/GMM.py
index 6b48096..fc94000 100644
--- a/bob/bio/gmm/algorithm/GMM.py
+++ b/bob/bio/gmm/algorithm/GMM.py
@@ -15,21 +15,21 @@ import logging
logger = logging.getLogger("bob.bio.gmm")
class GMM (Algorithm):
- """Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features"""
+ """Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features.
+ Features must be normalized to zero mean and unit standard deviation."""
def __init__(
self,
# parameters for the GMM
number_of_gaussians,
# parameters of UBM training
- k_means_training_iterations = 500, # Maximum number of iterations for K-Means
- gmm_training_iterations = 500, # Maximum number of iterations for ML GMM Training
+ kmeans_training_iterations = 25, # Maximum number of iterations for K-Means
+ gmm_training_iterations = 25, # Maximum number of iterations for ML GMM Training
training_threshold = 5e-4, # Threshold to end the ML training
variance_threshold = 5e-4, # Minimum value that a variance can reach
update_weights = True,
update_means = True,
update_variances = True,
- normalize_before_k_means = True, # Normalize the input features before running K-Means
# parameters of the GMM enrollment
relevance_factor = 4, # Relevance factor as described in Reynolds paper
gmm_enroll_iterations = 1, # Number of iterations for the enrollment phase
@@ -47,14 +47,13 @@ class GMM (Algorithm):
use_projected_features_for_enrollment = False,
number_of_gaussians = number_of_gaussians,
- k_means_training_iterations = k_means_training_iterations,
+ kmeans_training_iterations = kmeans_training_iterations,
gmm_training_iterations = gmm_training_iterations,
training_threshold = training_threshold,
variance_threshold = variance_threshold,
update_weights = update_weights,
update_means = update_means,
update_variances = update_variances,
- normalize_before_k_means = normalize_before_k_means,
relevance_factor = relevance_factor,
gmm_enroll_iterations = gmm_enroll_iterations,
responsibility_threshold = responsibility_threshold,
@@ -67,14 +66,13 @@ class GMM (Algorithm):
# copy parameters
self.gaussians = number_of_gaussians
- self.k_means_training_iterations = k_means_training_iterations
+ self.kmeans_training_iterations = kmeans_training_iterations
self.gmm_training_iterations = gmm_training_iterations
self.training_threshold = training_threshold
self.variance_threshold = variance_threshold
self.update_weights = update_weights
self.update_means = update_means
self.update_variances = update_variances
- self.normalize_before_k_means = normalize_before_k_means
self.relevance_factor = relevance_factor
self.gmm_enroll_iterations = gmm_enroll_iterations
self.init_seed = INIT_SEED
@@ -83,6 +81,8 @@ class GMM (Algorithm):
self.scoring_function = scoring_function
self.ubm = None
+ self.kmeans_trainer = bob.learn.em.KMeansTrainer()
+ self.ubm_trainer = bob.learn.em.ML_GMMTrainer(self.update_means, self.update_variances, self.update_weights, self.responsibility_threshold)
def _check_feature(self, feature):
@@ -94,42 +94,6 @@ class GMM (Algorithm):
- #######################################################
- ################ UBM training #########################
- def _normalize_std_array(self, array):
- """Applies a unit variance normalization to an array"""
-
- # Initializes variables
- n_samples = array.shape[0]
- length = array.shape[1]
- mean = numpy.zeros((length,))
- std = numpy.zeros((length,))
-
- # Computes mean and variance
- for k in range(n_samples):
- x = array[k,:].astype('float64')
- mean += x
- std += (x ** 2)
-
- mean /= n_samples
- std /= n_samples
- std -= (mean ** 2)
- std = std ** 0.5 # sqrt(std)
-
- ar_std_list = []
- for k in range(n_samples):
- ar_std_list.append(array[k,:].astype('float64') / std)
- ar_std = numpy.vstack(ar_std_list)
-
- return (ar_std,std)
-
-
- def _multiply_vectors_by_factors(self, matrix, vector):
- """Used to unnormalize some data"""
- for i in range(0, matrix.shape[0]):
- for j in range(0, matrix.shape[1]):
- matrix[i, j] *= vector[j]
-
#######################################################
################ UBM training #########################
@@ -141,35 +105,18 @@ class GMM (Algorithm):
# Computes input size
input_size = array.shape[1]
- # Normalizes the array if required
- logger.debug(" .... Normalizing the array")
- if not self.normalize_before_k_means:
- normalized_array = array
- else:
- normalized_array, std_array = self._normalize_std_array(array)
-
-
# Creates the machines (KMeans and GMM)
logger.debug(" .... Creating machines")
kmeans = bob.learn.em.KMeansMachine(self.gaussians, input_size)
self.ubm = bob.learn.em.GMMMachine(self.gaussians, input_size)
- # Creates the KMeansTrainer
- kmeans_trainer = bob.learn.em.KMeansTrainer()
-
# Trains using the KMeansTrainer
logger.info(" -> Training K-Means")
- bob.learn.em.train(kmeans_trainer, kmeans, normalized_array, self.gmm_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, bob.core.random.mt19937(self.init_seed))
- variances, weights = kmeans.get_variances_and_weights_for_each_cluster(normalized_array)
+ variances, weights = kmeans.get_variances_and_weights_for_each_cluster(array)
means = kmeans.means
- # Undoes the normalization
- if self.normalize_before_k_means:
- logger.debug(" .... Undoing normalization")
- self._multiply_vectors_by_factors(means, std_array)
- self._multiply_vectors_by_factors(variances, std_array ** 2)
-
# Initializes the GMM
self.ubm.means = means
self.ubm.variances = variances
@@ -178,8 +125,7 @@ class GMM (Algorithm):
# Trains the GMM
logger.info(" -> Training GMM")
- trainer = bob.learn.em.ML_GMMTrainer(self.update_means, self.update_variances, self.update_weights)
- bob.learn.em.train(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, bob.core.random.mt19937(self.init_seed))
def _save_projector(self, projector_file):
@@ -219,7 +165,7 @@ class GMM (Algorithm):
self.load_ubm(projector_file)
# prepare MAP_GMM_Trainer
kwargs = dict(mean_var_update_responsibilities_threshold=self.responsibility_threshold) if self.responsibility_threshold > 0. else dict()
- self.trainer = bob.learn.em.MAP_GMMTrainer(self.ubm, relevance_factor = self.relevance_factor, update_means = True, update_variances = False, **kwargs)
+ self.enroll_trainer = bob.learn.em.MAP_GMMTrainer(self.ubm, relevance_factor = self.relevance_factor, update_means = True, update_variances = False, **kwargs)
self.rng = bob.core.random.mt19937(self.init_seed)
@@ -252,7 +198,7 @@ class GMM (Algorithm):
gmm = bob.learn.em.GMMMachine(self.ubm)
gmm.set_variance_thresholds(self.variance_threshold)
- bob.learn.em.train(self.trainer, gmm, array, self.gmm_enroll_iterations, self.training_threshold, self.rng)
+ bob.learn.em.train(self.enroll_trainer, gmm, array, self.gmm_enroll_iterations, self.training_threshold, self.rng)
return gmm
def enroll(self, feature_arrays):
diff --git a/bob/bio/gmm/config/algorithm/gmm.py b/bob/bio/gmm/config/algorithm/gmm.py
index e251a4d..592b30e 100644
--- a/bob/bio/gmm/config/algorithm/gmm.py
+++ b/bob/bio/gmm/config/algorithm/gmm.py
@@ -5,6 +5,4 @@ import numpy
algorithm = bob.bio.gmm.algorithm.GMM(
number_of_gaussians = 512,
- # by default, features are expected to be normalized and, hence, we don't need to re-normalize them
- normalize_before_k_means = False
)
diff --git a/bob/bio/gmm/config/algorithm/isv.py b/bob/bio/gmm/config/algorithm/isv.py
index ce5efb8..24a8be4 100644
--- a/bob/bio/gmm/config/algorithm/isv.py
+++ b/bob/bio/gmm/config/algorithm/isv.py
@@ -7,7 +7,5 @@ algorithm = bob.bio.gmm.algorithm.ISV(
# ISV parameters
subspace_dimension_of_u = 160,
# GMM parameters
- number_of_gaussians = 512,
- # by default, our features are normalized, so it does not need to be done here
- normalize_before_k_means = False
+ number_of_gaussians = 512
)
diff --git a/bob/bio/gmm/script/__init__.py b/bob/bio/gmm/script/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bob/bio/gmm/script/verify_gmm.py b/bob/bio/gmm/script/verify_gmm.py
new file mode 100644
index 0000000..f04d228
--- /dev/null
+++ b/bob/bio/gmm/script/verify_gmm.py
@@ -0,0 +1,300 @@
+#!/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)
+
+ # 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', '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', 'projection', 'enroller-training', 'enrollment', 'score-computation', 'concatenation', 'calibration']
+ )
+
+ args.skip_projector_training = True
+
+ # and add the GMM-related parameters
+ tools.initialize_parallel_gmm(args)
+
+ # assert that the algorithm is a GMM
+ if args.algorithm.__class__ not in (algorithm.GMM, algorithm.GMMRegular):
+ raise ValueError("The given algorithm %s is not a (pure) GMM algorithm" % type(args.algorithm))
+
+ return args
+
+def add_gmm_jobs(args, job_ids, deps, submitter):
+ """Adds all GMM-related jobs."""
+
+ # KMeans
+ if not args.skip_kmeans:
+ # initialization
+ if not args.kmeans_start_iteration:
+ job_ids['kmeans-init'] = submitter.submit(
+ '--sub-task kmeans-init',
+ name = 'k-init',
+ dependencies = deps,
+ **args.grid.training_queue)
+ deps.append(job_ids['kmeans-init'])
+
+ # several iterations of E and M steps
+ for iteration in range(args.kmeans_start_iteration, args.algorithm.kmeans_training_iterations):
+ # E-step
+ job_ids['kmeans-e-step'] = submitter.submit(
+ '--sub-task kmeans-e-step --iteration %d' % iteration,
+ name='k-e-%d' % iteration,
+ number_of_parallel_jobs = args.grid.number_of_projection_jobs,
+ dependencies = [job_ids['kmeans-m-step']] if iteration != args.kmeans_start_iteration else deps,
+ **args.grid.projection_queue)
+
+ # M-step
+ job_ids['kmeans-m-step'] = submitter.submit(
+ '--sub-task kmeans-m-step --iteration %d' % iteration,
+ name='k-m-%d' % iteration,
+ dependencies = [job_ids['kmeans-e-step']],
+ **args.grid.training_queue)
+
+ # add dependence to the last m step
+ deps.append(job_ids['kmeans-m-step'])
+
+ # GMM
+ if not args.skip_gmm:
+ # initialization
+ if not args.gmm_start_iteration:
+ job_ids['gmm-init'] = submitter.submit(
+ '--sub-task gmm-init',
+ name = 'g-init',
+ dependencies = deps,
+ **args.grid.training_queue)
+ deps.append(job_ids['gmm-init'])
+
+ # several iterations of E and M steps
+ for iteration in range(args.gmm_start_iteration, args.algorithm.gmm_training_iterations):
+ # E-step
+ job_ids['gmm-e-step'] = submitter.submit(
+ '--sub-task gmm-e-step --iteration %d' % iteration,
+ name='g-e-%d' % iteration,
+ number_of_parallel_jobs = args.grid.number_of_projection_jobs,
+ dependencies = [job_ids['gmm-m-step']] if iteration != args.gmm_start_iteration else deps,
+ **args.grid.projection_queue)
+
+ # M-step
+ job_ids['gmm-m-step'] = submitter.submit(
+ '--sub-task gmm-m-step --iteration %d' % iteration,
+ name='g-m-%d' % iteration,
+ dependencies = [job_ids['gmm-e-step']],
+ **args.grid.training_queue)
+
+ # add dependence to the last m step
+ deps.append(job_ids['gmm-m-step'])
+ return job_ids, deps
+
+
+
+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.
+ 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 bob.bio.base.script.verify.execute(args):
+ return True
+
+ # now, check what we can do
+
+ # the file selector object
+ fs = tools.FileSelector.instance()
+
+ # train the feature projector
+ if args.sub_task == 'kmeans-init':
+ tools.kmeans_initialize(
+ args.algorithm,
+ args.extractor,
+ args.limit_training_data,
+ force = args.force)
+
+ # train the feature projector
+ elif args.sub_task == 'kmeans-e-step':
+ tools.kmeans_estep(
+ args.algorithm,
+ args.extractor,
+ args.iteration,
+ 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 == 'kmeans-m-step':
+ tools.kmeans_mstep(
+ args.algorithm,
+ args.iteration,
+ number_of_parallel_jobs = args.grid.number_of_projection_jobs,
+ clean = args.clean_intermediate,
+ force = args.force)
+
+ elif args.sub_task == 'gmm-init':
+ tools.gmm_initialize(
+ args.algorithm,
+ args.extractor,
+ args.limit_training_data,
+ force = args.force)
+
+ # train the feature projector
+ elif args.sub_task == 'gmm-e-step':
+ tools.gmm_estep(
+ args.algorithm,
+ args.extractor,
+ args.iteration,
+ 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 == 'gmm-m-step':
+ tools.gmm_mstep(
+ args.algorithm,
+ args.iteration,
+ number_of_parallel_jobs = args.grid.number_of_projection_jobs,
+ clean = args.clean_intermediate,
+ 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_gmm.py', first_fake_job_id = 0) if args.grid else None
+ retval = add_jobs(args, submitter)
+ 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/scores-nonorm-gmm-dev b/bob/bio/gmm/test/data/scores-nonorm-gmm-dev
new file mode 100644
index 0000000..f2ef723
--- /dev/null
+++ b/bob/bio/gmm/test/data/scores-nonorm-gmm-dev
@@ -0,0 +1,2000 @@
+3 3 s3/1 12.12236988
+3 3 s3/3 9.84751712
+3 3 s3/6 4.75518987
+3 3 s3/8 7.34467862
+3 3 s3/10 5.84208782
+3 4 s4/1 3.09607379
+3 4 s4/3 2.48938722
+3 4 s4/6 2.63333268
+3 4 s4/8 1.40062007
+3 4 s4/10 2.58779227
+3 7 s7/1 0.03026203
+3 7 s7/3 0.05371084
+3 7 s7/6 0.73314783
+3 7 s7/8 1.25630276
+3 7 s7/10 0.16822658
+3 8 s8/1 5.81056890
+3 8 s8/3 5.75233918
+3 8 s8/6 6.44847608
+3 8 s8/8 5.69238999
+3 8 s8/10 5.86999901
+3 9 s9/1 5.45359187
+3 9 s9/3 5.17747819
+3 9 s9/6 5.73711804
+3 9 s9/8 4.93655799
+3 9 s9/10 2.85098127
+3 13 s13/1 5.62485853
+3 13 s13/3 4.69495514
+3 13 s13/6 3.55635679
+3 13 s13/8 2.59760528
+3 13 s13/10 2.37059830
+3 15 s15/1 -1.70142152
+3 15 s15/3 -1.69356476
+3 15 s15/6 -1.33033951
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diff --git a/bob/bio/gmm/test/data/scores-ztnorm-gmm-dev b/bob/bio/gmm/test/data/scores-ztnorm-gmm-dev
new file mode 100644
index 0000000..699b19d
--- /dev/null
+++ b/bob/bio/gmm/test/data/scores-ztnorm-gmm-dev
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+38 28 s28/8 0.04231748
+38 28 s28/10 1.83954626
+38 30 s30/1 0.33794990
+38 30 s30/3 0.39139063
+38 30 s30/6 0.34120431
+38 30 s30/8 -0.17142861
+38 30 s30/10 0.40667002
+38 31 s31/1 0.05644954
+38 31 s31/3 -1.27272131
+38 31 s31/6 -0.08964086
+38 31 s31/8 0.10124706
+38 31 s31/10 -1.40039358
+38 32 s32/1 -1.79019670
+38 32 s32/3 -1.60918385
+38 32 s32/6 -0.52069776
+38 32 s32/8 -1.25764940
+38 32 s32/10 -0.62417347
+38 35 s35/1 -1.39942528
+38 35 s35/3 -1.36550208
+38 35 s35/6 -1.39690969
+38 35 s35/8 -1.64817588
+38 35 s35/10 -1.89374635
+38 37 s37/1 0.08342767
+38 37 s37/3 0.15969474
+38 37 s37/6 -0.11885445
+38 37 s37/8 0.16820096
+38 37 s37/10 -0.05410606
+38 38 s38/1 1.87471089
+38 38 s38/3 1.95141077
+38 38 s38/6 1.74221205
+38 38 s38/8 1.98588432
+38 38 s38/10 2.17910838
+38 40 s40/1 -0.87799651
+38 40 s40/3 -1.08762577
+38 40 s40/6 -0.05492982
+38 40 s40/8 -0.81392371
+38 40 s40/10 0.07995599
+40 3 s3/1 0.51637170
+40 3 s3/3 0.64860899
+40 3 s3/6 -0.44882742
+40 3 s3/8 0.34932772
+40 3 s3/10 0.43240206
+40 4 s4/1 0.46063527
+40 4 s4/3 0.16012312
+40 4 s4/6 0.05840162
+40 4 s4/8 0.09479120
+40 4 s4/10 0.26872669
+40 7 s7/1 -0.50236994
+40 7 s7/3 -0.57872182
+40 7 s7/6 -0.59928377
+40 7 s7/8 -0.48562501
+40 7 s7/10 0.03356254
+40 8 s8/1 -0.72784396
+40 8 s8/3 -0.65408256
+40 8 s8/6 -0.55713994
+40 8 s8/8 -0.68363972
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+40 9 s9/1 0.27685346
+40 9 s9/3 0.34761244
+40 9 s9/6 0.55505748
+40 9 s9/8 0.67307236
+40 9 s9/10 0.53172221
+40 13 s13/1 0.70340455
+40 13 s13/3 0.52593826
+40 13 s13/6 0.63664529
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+40 30 s30/6 -0.99308896
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+40 31 s31/1 0.22223696
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+40 31 s31/8 0.25100159
+40 31 s31/10 0.01844171
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+40 32 s32/8 -0.16907488
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+40 35 s35/1 0.89543358
+40 35 s35/3 1.40488718
+40 35 s35/6 0.68117396
+40 35 s35/8 1.09961333
+40 35 s35/10 0.97750186
+40 37 s37/1 0.65609166
+40 37 s37/3 0.79906075
+40 37 s37/6 -1.16413966
+40 37 s37/8 -1.14823398
+40 37 s37/10 0.15681889
+40 38 s38/1 -0.46298136
+40 38 s38/3 -0.66460668
+40 38 s38/6 -0.52422730
+40 38 s38/8 -0.54380387
+40 38 s38/10 -0.70446398
+40 40 s40/1 0.58579289
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diff --git a/bob/bio/gmm/test/dummy/__init__.py b/bob/bio/gmm/test/dummy/__init__.py
new file mode 100644
index 0000000..51221ae
--- /dev/null
+++ b/bob/bio/gmm/test/dummy/__init__.py
@@ -0,0 +1 @@
+from . import extractor
diff --git a/bob/bio/gmm/test/dummy/extractor.py b/bob/bio/gmm/test/dummy/extractor.py
new file mode 100644
index 0000000..2c58557
--- /dev/null
+++ b/bob/bio/gmm/test/dummy/extractor.py
@@ -0,0 +1,27 @@
+import numpy
+import bob.io.base
+
+from bob.bio.base.extractor import Extractor
+
+_data = [0., 1., 2., 3., 4.]
+
+class DummyExtractor (Extractor):
+ def __init__(self):
+ Extractor.__init__(self, requires_training=True)
+ self.model = False
+
+ def train(self, train_data, extractor_file):
+ assert isinstance(train_data, list)
+ bob.io.base.save(_data, extractor_file)
+
+ def load(self, extractor_file):
+ data = bob.io.base.load(extractor_file)
+ assert (_data == data).all()
+ self.model = True
+
+ def __call__(self, data):
+ """Does nothing, simply converts the data type of the data, ignoring any annotation."""
+ assert self.model
+ return data.astype(numpy.float)
+
+extractor = DummyExtractor()
diff --git a/bob/bio/gmm/test/test_algorithms.py b/bob/bio/gmm/test/test_algorithms.py
index 5bac224..03613b1 100644
--- a/bob/bio/gmm/test/test_algorithms.py
+++ b/bob/bio/gmm/test/test_algorithms.py
@@ -81,7 +81,7 @@ def test_gmm():
# create smaller GMM object
gmm2 = bob.bio.gmm.algorithm.GMM(
number_of_gaussians = 2,
- k_means_training_iterations = 1,
+ kmeans_training_iterations = 1,
gmm_training_iterations = 1,
INIT_SEED = seed_value,
)
@@ -138,7 +138,7 @@ def test_gmm_regular():
# create smaller GMM object
gmm2 = bob.bio.gmm.algorithm.GMMRegular(
number_of_gaussians = 2,
- k_means_training_iterations = 1,
+ kmeans_training_iterations = 1,
gmm_training_iterations = 1,
INIT_SEED = seed_value,
)
@@ -193,7 +193,7 @@ def test_isv():
isv2 = bob.bio.gmm.algorithm.ISV(
number_of_gaussians = 2,
subspace_dimension_of_u = 10,
- k_means_training_iterations = 1,
+ kmeans_training_iterations = 1,
gmm_training_iterations = 1,
isv_training_iterations = 1,
INIT_SEED = seed_value
@@ -260,7 +260,7 @@ def test_jfa():
number_of_gaussians = 2,
subspace_dimension_of_u = 2,
subspace_dimension_of_v = 2,
- k_means_training_iterations = 1,
+ kmeans_training_iterations = 1,
gmm_training_iterations = 1,
jfa_training_iterations = 1,
INIT_SEED = seed_value
diff --git a/bob/bio/gmm/test/test_scripts.py b/bob/bio/gmm/test/test_scripts.py
new file mode 100644
index 0000000..18f39f7
--- /dev/null
+++ b/bob/bio/gmm/test/test_scripts.py
@@ -0,0 +1,105 @@
+
+
+from __future__ import print_function
+
+import bob.measure
+
+import os
+import sys
+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
+
+import pkg_resources
+
+regenerate_reference = False
+
+from bob.bio.base.script.verify import main
+
+data_dir = pkg_resources.resource_filename('bob.bio.gmm', 'test/data')
+
+def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('scores',''), executable = main):
+ try:
+ executable([sys.argv[0]] + parameters)
+
+ # assert that the score file exists
+ score_files = [os.path.join(test_dir, sub_dir, 'Default', norm, '%s-dev%s'%score_modifier) for norm in ('nonorm', 'ztnorm')]
+ assert os.path.exists(score_files[0]), "Score file %s does not exist" % score_files[0]
+ assert os.path.exists(score_files[1]), "Score file %s does not exist" % score_files[1]
+
+ # also assert that the scores are still the same -- though they have no real meaning
+ reference_files = [os.path.join(data_dir, 'scores-%s%s-dev'%(norm, ref_modifier)) for norm in ('nonorm', 'ztnorm')]
+
+ if regenerate_reference:
+ for i in (0,1):
+ shutil.copy(score_files[i], reference_files[i])
+
+ for i in (0,1):
+ d = []
+ # read reference and new data
+ for score_file in (score_files[i], reference_files[i]):
+ f = bob.measure.load.open_file(score_file)
+ d_ = []
+ for line in f:
+ if isinstance(line, bytes): line = line.decode('utf-8')
+ d_.append(line.rstrip().split())
+ d.append(numpy.array(d_))
+
+ assert d[0].shape == d[1].shape
+ # assert that the data order is still correct
+ assert (d[0][:,0:3] == d[1][:, 0:3]).all()
+ # assert that the values are OK
+ assert numpy.allclose(d[0][:,3].astype(float), d[1][:,3].astype(float), 1e-5)
+
+ finally:
+ shutil.rmtree(test_dir)
+
+
+def test_gmm_base():
+ test_dir = tempfile.mkdtemp(prefix='frltest_')
+ # define dummy parameters
+ parameters = [
+ '-d', 'dummy',
+ '-p', 'dummy',
+ '-e', 'dummy',
+ '-a', 'bob.bio.gmm.algorithm.GMM(2, 2, 2)', '--import', 'bob.bio.gmm',
+ '--zt-norm',
+ '-s', 'test_gmm_sequential', '-vv',
+ '--temp-directory', test_dir,
+ '--result-directory', test_dir
+ ]
+
+ print (bob.bio.base.tools.command_line(parameters))
+
+ _verify(parameters, test_dir, 'test_gmm_sequential', ref_modifier='-gmm')
+
+
+def test_gmm_parallel():
+ from bob.bio.gmm.script.verify_gmm import main
+ test_dir = tempfile.mkdtemp(prefix='frltest_')
+ test_database = os.path.join(test_dir, "submitted.sql3")
+ # define dummy parameters
+ parameters = [
+ '-d', 'dummy',
+ '-p', 'dummy',
+ '-e', 'dummy',
+ '-a', 'bob.bio.gmm.algorithm.GMM(2, 2, 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_gmm_parallel', '-vv',
+ '--temp-directory', test_dir,
+ '--result-directory', test_dir,
+ ]
+
+ print (bob.bio.base.tools.command_line(parameters))
+
+ _verify(parameters, test_dir, 'test_gmm_parallel', executable=main, ref_modifier='-gmm')
diff --git a/bob/bio/gmm/tools/__init__.py b/bob/bio/gmm/tools/__init__.py
new file mode 100644
index 0000000..1903eb6
--- /dev/null
+++ b/bob/bio/gmm/tools/__init__.py
@@ -0,0 +1,2 @@
+from .command_line import *
+from .gmm import *
diff --git a/bob/bio/gmm/tools/command_line.py b/bob/bio/gmm/tools/command_line.py
new file mode 100644
index 0000000..e81c358
--- /dev/null
+++ b/bob/bio/gmm/tools/command_line.py
@@ -0,0 +1,65 @@
+import os
+import sys
+import types
+
+import bob.core
+logger = bob.core.log.setup("bob.bio.gmm")
+
+from bob.bio.base.tools import FileSelector
+
+def add_parallel_gmm_options(parsers, additional_functions = ['gmm']):
+ """Add the options for parallel UBM training to the given parsers."""
+
+ flag_group = parsers['flag']
+ flag_group.add_argument('-l', '--limit-training-data', type=int,
+ help = 'Limit the number of training examples used for KMeans initialization and the GMM initialization')
+
+ flag_group.add_argument('-k', '--kmeans-start-iteration', type=int, default=0,
+ help = 'Specify the first iteration for the KMeans training (i.e. to restart from there)')
+ flag_group.add_argument('-m', '--gmm-start-iteration', type=int, default=0,
+ help = 'Specify the first iteration for the GMM training (i.e. to restart from there)')
+ flag_group.add_argument('-C', '--clean-intermediate', action='store_true',
+ help = 'Clean up temporary files of older iterations?')
+
+ sub_dir_group = parsers['sub-dir']
+ sub_dir_group.add_argument('--kmeans-directory', default = 'kmeans_temp',
+ help = 'The sub-directory (relative to --temp-directory), where intermediate kmeans files should be stored')
+ 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')
+
+
+
+# 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')
+
+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))
+
+def _gmm_intermediate_file(self, round):
+ return os.path.join(self.gmm_temp_directory, '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))
+
+
+def initialize_parallel_gmm(args):
+ # 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 '.'
+ sub_dir = protocol if args.database.training_depends_on_protocol else '.'
+
+ 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)
diff --git a/bob/bio/gmm/tools/gmm.py b/bob/bio/gmm/tools/gmm.py
new file mode 100644
index 0000000..6c443c4
--- /dev/null
+++ b/bob/bio/gmm/tools/gmm.py
@@ -0,0 +1,278 @@
+import bob.io.base
+import bob.learn.em
+import shutil
+import numpy
+import os
+
+import logging
+logger = logging.getLogger("bob.bio.gmm")
+
+from bob.bio.base.tools.FileSelector import FileSelector
+from bob.bio.base import utils, tools
+
+
+def kmeans_initialize(algorithm, extractor, limit_data = None, force = False):
+ """Initializes the K-Means training (non-parallel)."""
+ fs = FileSelector.instance()
+
+ output_file = fs.kmeans_intermediate_file(0)
+
+ if utils.check_file(output_file, force, 1000):
+ logger.info("UBM training: Skipping KMeans initialization since the file '%s' already exists", output_file)
+ else:
+ # read data
+ logger.info("UBM training: initializing kmeans")
+ training_list = utils.selected_elements(fs.training_list('extracted', 'train_projector'), limit_data)
+ data = numpy.vstack([extractor.read_feature(feature_file) for feature_file in training_list])
+
+ # Perform KMeans initialization
+ kmeans_machine = bob.learn.em.KMeansMachine(algorithm.gaussians, data.shape[1])
+ # Creates the KMeansTrainer and call the initialization procedure
+ algorithm.kmeans_trainer.initialize(kmeans_machine, data)
+ bob.io.base.create_directories_safe(os.path.dirname(output_file))
+ kmeans_machine.save(bob.io.base.HDF5File(output_file, 'w'))
+ logger.info("UBM training: saved initial KMeans machine to '%s'", output_file)
+
+
+def kmeans_estep(algorithm, extractor, iteration, indices, force=False):
+ """Performs a single E-step of the K-Means algorithm (parallel)"""
+ if indices[0] > indices[1]:
+ return
+
+ fs = FileSelector.instance()
+
+ # check if we need to compute this step
+ stats_file = fs.kmeans_stats_file(iteration, indices[0], indices[1])
+ new_machine_file = fs.kmeans_intermediate_file(iteration + 1)
+
+ if utils.check_file(stats_file, force, 1000) or utils.check_file(new_machine_file, force, 1000):
+ logger.info("UBM training: Skipping KMeans E-Step since the file '%s' or '%s' already exists", stats_file, new_machine_file)
+ else:
+ training_list = fs.training_list('extracted', 'train_projector')
+ last_machine_file = fs.kmeans_intermediate_file(iteration)
+ kmeans_machine = bob.learn.em.KMeansMachine(bob.io.base.HDF5File(last_machine_file))
+
+ logger.info("UBM training: KMeans E-Step round %d from range(%d, %d)", iteration, *indices)
+
+ # read data
+ data = numpy.vstack([extractor.read_feature(training_list[index]) for index in range(indices[0], indices[1])])
+
+ # Performs the E-step
+ trainer = algorithm.kmeans_trainer
+ trainer.e_step(kmeans_machine, data)
+
+ # write results to file
+ dist = numpy.array(trainer.average_min_distance)
+ nsamples = numpy.array([indices[1] - indices[0]], dtype=numpy.float64)
+
+ # write statistics
+ bob.io.base.create_directories_safe(os.path.dirname(stats_file))
+ hdf5 = bob.io.base.HDF5File(stats_file, 'w')
+ hdf5.set('zeros', trainer.zeroeth_order_statistics)
+ hdf5.set('first', trainer.first_order_statistics)
+ hdf5.set('dist', dist * nsamples)
+ hdf5.set('nsamples', nsamples)
+
+ logger.info("UBM training: Wrote Stats file '%s'", stats_file)
+
+
+
+def _read_stats(filename):
+ """Reads accumulated K-Means statistics from file"""
+ logger.debug("UBM training: Reading stats file '%s'", filename)
+ hdf5 = bob.io.base.HDF5File(filename)
+ zeroeth = hdf5.read('zeros')
+ first = hdf5.read('first')
+ nsamples = hdf5.read('nsamples')
+ dist = hdf5.read('dist')
+ return (zeroeth, first, nsamples, dist)
+
+def _accumulate(filenames):
+ zeroeth, first, nsamples, dist = _read_stats(filenames[0])
+ for stat in filenames[1:]:
+ zeroeth_, first_, nsamples_, dist_ = _read_stats(filenames[1])
+ zeroeth += zeroeth_
+ first += first_
+ nsamples += nsamples_
+ dist += dist_
+ return (zeroeth, first, nsamples, dist)
+
+def kmeans_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False):
+ """Performs a single M-step of the K-Means algorithm (non-parallel)"""
+ fs = FileSelector.instance()
+
+ old_machine_file = fs.kmeans_intermediate_file(iteration)
+ new_machine_file = fs.kmeans_intermediate_file(iteration+1)
+
+ if utils.check_file(new_machine_file, force, 1000):
+ logger.info("UBM training: Skipping KMeans M-Step since the file '%s' already exists", new_machine_file)
+ else:
+ # get the files from e-step
+ training_list = fs.training_list('extracted', 'train_projector')
+
+ # try if there is one file containing all data
+ if os.path.exists(fs.kmeans_stats_file(iteration, 0, len(training_list))):
+ stats_file = fs.kmeans_stats_file(iteration, 0, len(training_list))
+ # load stats file
+ statistics = _read_stats(stats_file)
+ else:
+ # load several files
+ filenames = []
+ for job in range(number_of_parallel_jobs):
+ job_indices = tools.indices(training_list, number_of_parallel_jobs, job+1)
+ if job_indices[-1] >= job_indices[0]:
+ filenames.append(fs.kmeans_stats_file(iteration, job_indices[0], job_indices[-1]))
+ statistics = _accumulate(filenames)
+
+ # Creates the KMeansMachine
+ kmeans_machine = bob.learn.em.KMeansMachine(bob.io.base.HDF5File(old_machine_file))
+ trainer = algorithm.kmeans_trainer
+ trainer.reset_accumulators(kmeans_machine)
+
+ trainer.zeroeth_order_statistics = statistics[0]
+ trainer.first_order_statistics = statistics[1]
+ trainer.average_min_distance = statistics[3]
+ error = statistics[3] / statistics[2]
+
+ # Performs the M-step
+ trainer.m_step(kmeans_machine, None) # data is not used in M-step
+ logger.info("UBM training: Performed M step %d with result %f" % (iteration, error))
+
+ # Save the K-Means model
+ bob.io.base.create_directories_safe(os.path.dirname(new_machine_file))
+ kmeans_machine.save(bob.io.base.HDF5File(new_machine_file, 'w'))
+
+ # copy the k_means file in any case
+ if iteration == algorithm.kmeans_training_iterations-1:
+ shutil.copy(new_machine_file, fs.kmeans_file)
+ logger.info("UBM training: Wrote new KMeans machine '%s'", fs.kmeans_file)
+
+ if clean and iteration > 0:
+ old_dir = os.path.dirname(fs.kmeans_intermediate_file(iteration-1))
+ logger.info("Removing old intermediate directory '%s'", old_dir)
+ shutil.rmtree(old_dir)
+
+
+
+def gmm_initialize(algorithm, extractor, limit_data = None, force = False):
+ """Initializes the GMM calculation with the result of the K-Means algorithm (non-parallel).
+ This might require a lot of memory."""
+ fs = FileSelector.instance()
+
+ output_file = fs.gmm_intermediate_file(0)
+
+ if utils.check_file(output_file, force, 800):
+ logger.info("UBM Training: Skipping GMM initialization since '%s' already exists", output_file)
+ else:
+ logger.info("UBM Training: Initializing GMM")
+
+ # read features
+ training_list = utils.selected_elements(fs.training_list('extracted', 'train_projector'), limit_data)
+ data = numpy.vstack([extractor.read_feature(feature_file) for feature_file in training_list])
+
+ # get means and variances of kmeans result
+ kmeans_machine = bob.learn.em.KMeansMachine(bob.io.base.HDF5File(fs.kmeans_file))
+ [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
+
+ # Create initial GMM Machine
+ gmm_machine = bob.learn.em.GMMMachine(algorithm.gaussians, data.shape[1])
+
+ # Initializes the GMM
+ gmm_machine.means = kmeans_machine.means
+ gmm_machine.variances = variances
+ gmm_machine.weights = weights
+ gmm_machine.set_variance_thresholds(algorithm.variance_threshold)
+
+ # write gmm machine to file
+ bob.io.base.create_directories_safe(os.path.dirname(output_file))
+ gmm_machine.save(bob.io.base.HDF5File(output_file, 'w'))
+ logger.info("UBM Training: Wrote GMM file '%s'", output_file)
+
+
+def gmm_estep(algorithm, extractor, iteration, indices, force=False):
+ """Performs a single E-step of the GMM training (parallel)."""
+ if indices[0] > indices[1]:
+ return
+ fs = FileSelector.instance()
+
+ stats_file = fs.gmm_stats_file(iteration, indices[0], indices[1])
+ new_machine_file = fs.gmm_intermediate_file(iteration + 1)
+
+ if utils.check_file(stats_file, force, 1000) or utils.check_file(new_machine_file, force, 1000):
+ loggerinfo("UBM training: Skipping GMM E-Step since the file '%s' or '%s' already exists", stats_file, new_machine_file)
+ else:
+ training_list = fs.training_list('extracted', 'train_projector')
+ last_machine_file = fs.gmm_intermediate_file(iteration)
+ gmm_machine = bob.learn.em.GMMMachine(bob.io.base.HDF5File(last_machine_file))
+
+ logger.info("UBM training: GMM E-Step from range(%d, %d)", indices)
+
+ # read data
+ data = numpy.vstack([extractor.read_feature(training_list[index]) for index in range(indices[0], indices[1])])
+ trainer = algorithm.ubm_trainer
+ trainer.initialize(gmm_machine, None)
+
+ # Calls the E-step and extracts the GMM statistics
+ algorithm.ubm_trainer.e_step(gmm_machine, data)
+ gmm_stats = algorithm.ubm_trainer.gmm_statistics
+
+ # Saves the GMM statistics to the file
+ bob.io.base.create_directories_safe(os.path.dirname(stats_file))
+ gmm_stats.save(bob.io.base.HDF5File(stats_file, 'w'))
+ logger.info("UBM training: Wrote GMM stats '%s'", stats_file)
+
+
+def gmm_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False):
+ """Performs a single M-step of the GMM training (non-parallel)"""
+ fs = FileSelector.instance()
+
+ old_machine_file = fs.gmm_intermediate_file(iteration)
+ new_machine_file = fs.gmm_intermediate_file(iteration + 1)
+
+ if utils.check_file(new_machine_file, force, 1000):
+ logger.info("UBM training: Skipping GMM M-Step since the file '%s' already exists", new_machine_file)
+ else:
+ # get the files from e-step
+ training_list = fs.training_list('extracted', 'train_projector')
+
+ # try if there is one file containing all data
+ if os.path.exists(fs.gmm_stats_file(iteration, 0, len(training_list))):
+ stats_file = fs.gmm_stats_file(iteration, 0, len(training_list))
+ # load stats file
+ gmm_stats = bob.learn.em.GMMStats(bob.io.base.HDF5File(stats_file))
+ else:
+ # load several files
+ stats_files = []
+ for job in range(number_of_parallel_jobs):
+ job_indices = tools.indices(training_list, number_of_parallel_jobs, job+1)
+ if job_indices[-1] >= job_indices[0]:
+ stats_files.append(fs.gmm_stats_file(iteration, job_indices[0], job_indices[-1]))
+
+ # read all stats files
+ gmm_stats = bob.learn.em.GMMStats(bob.io.base.HDF5File(stats_files[0]))
+ for stats_file in stats_files[1:]:
+ gmm_stats += bob.learn.em.GMMStats(bob.io.base.HDF5File(stats_file))
+
+ # load the old gmm machine
+ gmm_machine = bob.learn.em.GMMMachine(bob.io.base.HDF5File(old_machine_file))
+
+ # initialize the trainer
+ trainer = algorithm.ubm_trainer
+ trainer.initialize(gmm_machine)
+ trainer.gmm_statistics = gmm_stats
+
+ # Calls M-step (no data required)
+ trainer.m_step(gmm_machine)
+
+ # Saves the GMM statistics to the file
+ bob.io.base.create_directories_safe(os.path.dirname(new_machine_file))
+ 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)
+
+ if clean and iteration > 0:
+ old_dir = os.path.dirname(fs.gmm_intermediate_file(iteration-1))
+ logger.info("Removing old intermediate directory '%s'", old_dir)
+ shutil.rmtree(old_dir)
diff --git a/setup.py b/setup.py
index eeb46e9..0ed1518 100644
--- a/setup.py
+++ b/setup.py
@@ -102,6 +102,7 @@ setup(
# scripts should be declared using this entry:
'console_scripts' : [
+ 'verify_gmm.py = bob.bio.gmm.script.verify_gmm:main',
],
'bob.bio.database': [
@@ -111,6 +112,7 @@ setup(
],
'bob.bio.extractor': [
+ 'dummy = bob.bio.gmm.test.dummy.extractor:extractor', # for test purposes only
],
'bob.bio.algorithm': [
--
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