Commit 824b0b6c authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
Browse files

Correctly reorganized the input for the PLDA

Optimized PCA training

Testing PLDA with no PCA
parent 655b88cf
Pipeline #5642 passed with stages
in 7 minutes and 22 seconds
......@@ -73,7 +73,7 @@ class PLDA (Algorithm):
def _train_pca(self, training_set):
"""Trains and returns a LinearMachine that is trained using PCA"""
data = numpy.vstack([feature for client in training_set for feature in client])
data = numpy.vstack([feature for feature in training_set])" -> Training LinearMachine using PCA ")
trainer = bob.learn.linear.PCATrainer()
......@@ -92,20 +92,30 @@ class PLDA (Algorithm):
machine.resize(machine.shape[0], self.subspace_dimension_pca)
return machine
def _perform_pca_client(self, client):
"""Perform PCA on an array"""
return numpy.vstack([self.pca_machine(feature) for feature in client])
def _perform_pca(self, training_set):
"""Perform PCA on data"""
return [self._perform_pca_client(client) for client in training_set]
return [self.pca_machine(client) for client in training_set]
def _arrange_data(self, training_files):
"""Arranges the data to train the PLDA """
data = []
for client_files in training_files:
# at least two files per client are required!
if len(client_files) < 2:
logger.warn("Skipping one client since the number of client files is only %d", len(client_files))
data.append(numpy.vstack([feature.flatten() for feature in client_files]))
# Returns the list of lists of arrays
return data
def train_enroller(self, training_features, projector_file):
"""Generates the PLDA base model from a list of arrays (one per identity),
and a set of training parameters. If PCA is requested, it is trained on the same data.
Both the trained PLDABase and the PCA machine are written."""
# arrange PLDA training data
training_features = self._arrange_data(training_features)
# train PCA and perform PCA on training data
if self.subspace_dimension_pca is not None:
......@@ -113,6 +123,7 @@ class PLDA (Algorithm):
training_features = self._perform_pca(training_features)
input_dimension = training_features[0].shape[1]" -> Training PLDA base machine")
# train machine
......@@ -146,7 +157,7 @@ class PLDA (Algorithm):
plda_machine = bob.learn.em.PLDAMachine(self.plda_base)
# project features, if enabled
if self.pca_machine is not None:
enroll_features = self._perform_pca_client(enroll_features)
enroll_features = self._perform_pca(enroll_features)
# enroll
self.plda_trainer.enroll(plda_machine, enroll_features)
return plda_machine
......@@ -358,3 +358,42 @@ def test_plda():
reference_score = 0.
assert abs(plda1.score(model, feature) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (plda1.score(model, feature), reference_score)
assert abs(plda1.score_for_multiple_probes(model, [feature, feature]) - reference_score) < 1e-5
def test_plda_nopca():
temp_file =
plda_ref ="plda", "algorithm", preferred_package = '')
reference_file = pkg_resources.resource_filename('', 'data/plda_nopca_enroller.hdf5')
# generate a smaller PCA subspcae
plda = = 2, subspace_dimension_of_g = 2, plda_training_iterations = 1, INIT_SEED = seed_value)
# create random training set
train_set = utils.random_training_set_by_id(200, count=20, minimum=0., maximum=255.)
# train the projector
# train projector
plda.train_enroller(train_set, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
assert plda.plda_base.is_similar_to(plda_ref.plda_base)
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array(200, 0., 255., seed=84)
# enroll model from random features
reference = pkg_resources.resource_filename('', 'data/plda_nopca_model.hdf5')
model = plda.enroll([feature])
# execute the preprocessor
if regenerate_refs:
plda.write_model(model, reference)
reference = plda.read_model(reference)
assert model.is_similar_to(reference)
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