Merge branch 'threading' into 'master'

Adds a threading option to the GMM algorithm

See merge request !22

bob/bio/gmm/algorithm/GMM.py

@@ -10,6 +10,7 @@ import bob.learn.em
 import numpy
 
 from bob.bio.base.algorithm import Algorithm
+from multiprocessing.pool import ThreadPool
 
 import logging
 logger = logging.getLogger("bob.bio.gmm")
@@ -36,7 +37,8 @@ class GMM (Algorithm):
       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
+      scoring_function = bob.learn.em.linear_scoring,
+      n_threads=None,
   ):
     """Initializes the local UBM-GMM tool chain with the given file selector object"""
 
@@ -79,6 +81,8 @@ class GMM (Algorithm):
     self.rng = bob.core.random.mt19937(self.init_seed)
     self.responsibility_threshold = responsibility_threshold
     self.scoring_function = scoring_function
+    self.n_threads = n_threads
+    self.pool = None
 
     self.ubm = None
     self.kmeans_trainer = bob.learn.em.KMeansTrainer()
@@ -101,6 +105,8 @@ class GMM (Algorithm):
   def train_ubm(self, array):
 
     logger.debug(" .... Training with %d feature vectors", array.shape[0])
+    if self.n_threads is not None:
+        self.pool = ThreadPool(self.n_threads)
 
     # Computes input size
     input_size = array.shape[1]
@@ -113,9 +119,12 @@ class GMM (Algorithm):
     # Trains using the KMeansTrainer
     logger.info("  -> Training K-Means")
 
-    # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
+    # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution.
     self.rng = bob.core.random.mt19937(self.init_seed)
-    bob.learn.em.train(self.kmeans_trainer, kmeans, array, self.kmeans_training_iterations, self.training_threshold, rng=self.rng)
+    bob.learn.em.train(
+        self.kmeans_trainer, kmeans, array, self.kmeans_training_iterations,
+        self.training_threshold, rng=self.rng, pool=self.pool,
+    )
 
     variances, weights = kmeans.get_variances_and_weights_for_each_cluster(array)
     means = kmeans.means
@@ -128,10 +137,12 @@ class GMM (Algorithm):
 
     # Trains the GMM
     logger.info("  -> Training GMM")
-    # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
+    # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution.
     self.rng = bob.core.random.mt19937(self.init_seed)
-    bob.learn.em.train(self.ubm_trainer, self.ubm, array, self.gmm_training_iterations, self.training_threshold, rng=self.rng)
-
+    bob.learn.em.train(
+        self.ubm_trainer, self.ubm, array, self.gmm_training_iterations,
+        self.training_threshold, rng=self.rng, pool=self.pool,
+    )
 
   def save_ubm(self, projector_file):
     """Save projector to file"""
@@ -140,7 +151,6 @@ class GMM (Algorithm):
     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]
@@ -154,7 +164,6 @@ class GMM (Algorithm):
 
     self.save_ubm(projector_file)
 
-
   #######################################################
   ############## GMM training using UBM #################
 
@@ -204,7 +213,10 @@ class GMM (Algorithm):
 
     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, rng=self.rng)
+    bob.learn.em.train(
+        self.enroll_trainer, gmm, array, self.gmm_enroll_iterations,
+        self.training_threshold, rng=self.rng, pool=self.pool,
+    )
     return gmm
 
   def enroll(self, feature_arrays):