Select Git revision
GMM.py 13.45 KiB
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Manuel Guenther <Manuel.Guenther@idiap.ch>
import bob.core
import bob.io.base
import bob.learn.em
import numpy
from bob.bio.base.algorithm import Algorithm
import logging
logger = logging.getLogger("bob.bio.gmm")
class GMM(Algorithm):
"""
Trains an UBM/GMM system in the same as in [Reynolds2000]_:
**Parameters**:
number_of_gaussians:
Number of gaussians in the model
kmeans_training_iterations:
Maximum number of iterations for the KMeans
kmeans_initialization_method:
Initialization method for the kmeans. Possible values are: 'RANDOM', 'RANDOM_NO_DUPLICATE', 'KMEANS_PLUS_PLUS'.
gmm_training_iterations:
Maximum number of iterations for the ML Estimator in the GMM Training.
training_threshold:
Convergence threshold for the ML Estimator.
variance_threshold:
Variance flooring
update_weights:
If **True** update the weights during the GMM Training
update_variances:
If **True** update the variances during the GMM Training
update_means:
If **True** update the means during the GMM Training
relevance_factor:
The relevance factor for the GMM MAP estimation
gmm_enroll_iterations:
Maximum number of iterations for the MAP Estimation.
responsibility_threshold:
Threshold for the responsibilities
INIT_SEED:
Seed for the pseudo random number generator
scoring_function:
Set the GMM scoring function
"""
def __init__(
self,
# parameters for the GMM
number_of_gaussians,
# parameters of UBM training
kmeans_training_iterations=25, # Maximum number of iterations for K-Means
kmeans_initialization_method="RANDOM_NO_DUPLICATE",
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,
# 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
responsibility_threshold=0,
# If set, the weight of a particular Gaussian will at least be greater than this threshold. In the case the real weight is lower, the prior mean value will be used to estimate the current mean and variance.
INIT_SEED=5489,
# scoring
scoring_function=bob.learn.em.linear_scoring
):
"""Initializes the local UBM-GMM tool chain with the given file selector object"""
# call base class constructor and register that this tool performs projection
Algorithm.__init__(
self,
performs_projection=True,
use_projected_features_for_enrollment=False,
number_of_gaussians=number_of_gaussians,
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,
relevance_factor=relevance_factor,
gmm_enroll_iterations=gmm_enroll_iterations,
responsibility_threshold=responsibility_threshold,
INIT_SEED=INIT_SEED,
scoring_function=str(scoring_function),
multiple_model_scoring=None,
multiple_probe_scoring='average'
)
# copy parameters
self.gaussians = number_of_gaussians
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.relevance_factor = relevance_factor
self.gmm_enroll_iterations = gmm_enroll_iterations
self.init_seed = INIT_SEED
self.rng = bob.core.random.mt19937(self.init_seed)
self.responsibility_threshold = responsibility_threshold
self.scoring_function = scoring_function
self.ubm = None
self.kmeans_trainer = bob.learn.em.KMeansTrainer(kmeans_initialization_method)
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):
"""Checks that the features are appropriate"""
if not isinstance(feature, numpy.ndarray) or feature.ndim != 2 or feature.dtype != numpy.float64:
raise ValueError("The given feature is not appropriate")
if self.ubm is not None and feature.shape[1] != self.ubm.shape[1]:
raise ValueError("The given feature is expected to have %d elements, but it has %d" % (
self.ubm.shape[1], feature.shape[1]))
#######################################################
################ UBM training #########################
def train_ubm(self, array):
logger.debug(" .... Training with %d feature vectors", array.shape[0])
# Computes input size
input_size = array.shape[1]
# 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)
# 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,
self.rng)
variances, weights = kmeans.get_variances_and_weights_for_each_cluster(array)
means = kmeans.means
# Initializes the GMM
self.ubm.means = means
self.ubm.variances = variances
self.ubm.weights = weights
self.ubm.set_variance_thresholds(self.variance_threshold)
# Trains the GMM
logger.info(" -> Training GMM")
bob.learn.em.train(self.ubm_trainer, self.ubm, array, self.gmm_training_iterations, self.training_threshold,
self.rng)
def save_ubm(self, projector_file):
"""Save projector to file"""
# Saves the UBM to file
logger.debug(" .... Saving model to file '%s'", projector_file)
hdf5 = projector_file if isinstance(projector_file, bob.io.base.HDF5File) else bob.io.base.HDF5File(
projector_file, 'w')
self.ubm.save(hdf5)
def train_projector(self, train_features, projector_file):
"""Computes the Universal Background Model from the training ("world") data"""
[self._check_feature(feature) for feature in train_features]
logger.info(" -> Training UBM model with %d training files", len(train_features))
# Loads the data into an array
array = numpy.vstack(train_features)
self.train_ubm(array)
self.save_ubm(projector_file)
#######################################################
############## GMM training using UBM #################
def load_ubm(self, ubm_file):
hdf5file = bob.io.base.HDF5File(ubm_file)
# read UBM
self.ubm = bob.learn.em.GMMMachine(hdf5file)
self.ubm.set_variance_thresholds(self.variance_threshold)
def load_projector(self, projector_file):
"""Reads the UBM model from file"""
# read UBM
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.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)
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])
self.ubm.acc_statistics(array, gmm_stats)
# return the resulting statistics
return gmm_stats
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_ubm(feature)
def read_gmm_stats(self, gmm_stats_file):
"""Reads GMM stats from file."""
return bob.learn.em.GMMStats(bob.io.base.HDF5File(gmm_stats_file))
def read_feature(self, feature_file):
"""Read the type of features that we require, namely GMM_Stats"""
return self.read_gmm_stats(feature_file)
def enroll_gmm(self, array):
logger.debug(" .... Enrolling with %d feature vectors", array.shape[0])
gmm = bob.learn.em.GMMMachine(self.ubm)
gmm.set_variance_thresholds(self.variance_threshold)
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):
"""Enrolls a GMM using MAP adaptation, given a list of 2D numpy.ndarray's of feature vectors"""
[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_gmm(array)
######################################################
################ Feature comparison ##################
def read_model(self, model_file):
"""Reads the model, which is a GMM machine"""
return bob.learn.em.GMMMachine(bob.io.base.HDF5File(model_file))
read_probe = read_feature
def score(self, model, probe):
"""Computes the score for the given model and the given probe using the scoring function from the config file"""
assert isinstance(model, bob.learn.em.GMMMachine)
assert isinstance(probe, bob.learn.em.GMMStats)
return self.scoring_function([model], self.ubm, [probe], [], frame_length_normalisation=True)[0][0]
def score_for_multiple_probes(self, model, probes):
"""This function computes the score between the given model and several given probe files."""
assert isinstance(model, bob.learn.em.GMMMachine)
for probe in probes:
assert isinstance(probe, bob.learn.em.GMMStats)
# logger.warn("Please verify that this function is correct")
return self.probe_fusion_function(
self.scoring_function([model], self.ubm, probes, [], frame_length_normalisation=True))
class GMMRegular(GMM):
"""Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features"""
def __init__(self, **kwargs):
"""Initializes the local UBM-GMM tool chain with the given file selector object"""
# logger.warn("This class must be checked. Please verify that I didn't do any mistake here. I had to rename 'train_projector' into a 'train_enroller'!")
# initialize the UBMGMM base class
GMM.__init__(self, **kwargs)
# register a different set of functions in the Tool base class
Algorithm.__init__(self, requires_enroller_training=True, performs_projection=False)
#######################################################
################ UBM training #########################
def train_enroller(self, train_features, enroller_file):
"""Computes the Universal Background Model from the training ("world") data"""
train_features = [feature for client in train_features for feature in client]
return self.train_projector(train_features, enroller_file)
#######################################################
############## GMM training using UBM #################
def load_enroller(self, enroller_file):
"""Reads the UBM model from file"""
return self.load_projector(enroller_file)
######################################################
################ Feature comparison ##################
def read_probe(self, probe_file):
"""Reads a feature from file, which is supposed to be a simple 2D array"""
return bob.bio.base.load(probe_file)
def score(self, model, probe):
"""Computes the score for the given model and the given probe.
The score are Log-Likelihood.
Therefore, the log of the likelihood ratio is obtained by computing the following difference."""
assert isinstance(model, bob.learn.em.GMMMachine)
self._check_feature(probe)
score = sum(
model.log_likelihood(probe[i, :]) - self.ubm.log_likelihood(probe[i, :]) for i in range(probe.shape[0]))
return score / probe.shape[0]
def score_for_multiple_probes(self, model, probes):
raise NotImplementedError("Implement Me!")