def test_ivector_plda():
temp_file = bob.io.base.test_utils.temporary_filename()
ivec1 = bob.bio.base.load_resource("ivector-plda", "algorithm", preferred_package='bob.bio.gmm')
ivec1.use_plda = True
# create smaller IVector object
ivec2 = bob.bio.gmm.algorithm.IVector(
number_of_gaussians = 2,
subspace_dimension_of_t = 10,
kmeans_training_iterations = 1,
tv_training_iterations = 1,
INIT_SEED = seed_value,
use_plda = True,
plda_dim_F = 2,
plda_dim_G = 2,
plda_training_iterations = 2
)
train_data = utils.random_training_set_by_id((100,45), count=5, minimum=-5., maximum=5.)
# reference is the same as for GMM projection
reference_file = pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_projector.hdf5')
try:
# train the projector
ivec2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
ivec1.load_projector(reference_file)
ivec2.load_projector(temp_file)
assert ivec1.ubm.is_similar_to(ivec2.ubm)
assert ivec1.tv.is_similar_to(ivec2.tv)
assert ivec1.whitener.is_similar_to(ivec2.whitener)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20,45), -5., 5., seed=84)
projected = ivec1.project(feature)
_compare(projected, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_projected.hdf5'), ivec1.write_feature, ivec1.read_feature)
# enroll model from random features
random_features = utils.random_training_set((20,45), count=5, minimum=-5., maximum=5.)
enroll_features = [ivec1.project(feature) for feature in random_features]
model = ivec1.enroll(enroll_features)
_compare(model, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_model.hdf5'), ivec1.write_model, ivec1.read_model)
# compare model with probe
probe = ivec1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_projected.hdf5'))
logger.info("%f" %ivec1.score(model, probe))
reference_score = 1.21879822
assert abs(ivec1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (ivec1.score(model, probe), reference_score)
assert abs(ivec1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
def test_ivector_lda_wccn_plda():
temp_file = bob.io.base.test_utils.temporary_filename()
ivec1 = bob.bio.base.load_resource("ivector-lda-wccn-plda",
"algorithm",
preferred_package='bob.bio.gmm')
ivec1.use_lda = True
ivec1.use_wccn = True
ivec1.use_plda = True
# create smaller IVector object
ivec2 = bob.bio.gmm.algorithm.IVector(number_of_gaussians=2,
subspace_dimension_of_t=10,
kmeans_training_iterations=1,
tv_training_iterations=1,
INIT_SEED=seed_value,
use_lda=True,
lda_dim=3,
use_wccn=True,
use_plda=True,
plda_dim_F=2,
plda_dim_G=2,
plda_training_iterations=2)
train_data = utils.random_training_set_by_id((100, 45),
count=5,
minimum=-5.,
maximum=5.)
# reference is the same as for GMM projection
reference_file = pkg_resources.resource_filename(
'bob.bio.gmm.test', 'data/ivector3_projector.hdf5')
try:
# train the projector
ivec2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
ivec1.load_projector(reference_file)
ivec2.load_projector(temp_file)
assert ivec1.ubm.is_similar_to(ivec2.ubm)
assert ivec1.tv.is_similar_to(ivec2.tv)
assert ivec1.whitener.is_similar_to(ivec2.whitener)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20, 45), -5., 5., seed=84)
projected = ivec1.project(feature)
_compare(
projected,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/ivector3_projected.hdf5'),
ivec1.write_feature, ivec1.read_feature)
# enroll model from random features
random_features = utils.random_training_set((20, 45),
count=5,
minimum=-5.,
maximum=5.)
enroll_features = [ivec1.project(feature) for feature in random_features]
model = ivec1.enroll(enroll_features)
_compare(
model,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/ivector3_model.hdf5'),
ivec1.write_model, ivec1.read_model)
# compare model with probe
probe = ivec1.read_probe(
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/ivector3_projected.hdf5'))
reference_score = 0.338051
assert abs(ivec1.score(model, probe) -
reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (
ivec1.score(model, probe), reference_score)
assert abs(
ivec1.score_for_multiple_probes(model, [probe, probe]) -
reference_score) < 1e-5
def test_jfa():
temp_file = bob.io.base.test_utils.temporary_filename()
jfa1 = bob.bio.base.load_resource("jfa",
"algorithm",
preferred_package='bob.bio.gmm')
assert isinstance(jfa1, bob.bio.gmm.algorithm.JFA)
assert isinstance(jfa1, bob.bio.gmm.algorithm.GMM)
assert isinstance(jfa1, bob.bio.base.algorithm.Algorithm)
assert jfa1.performs_projection
assert jfa1.requires_projector_training
assert jfa1.use_projected_features_for_enrollment
assert not jfa1.split_training_features_by_client
assert jfa1.requires_enroller_training
# create smaller JFA object
jfa2 = bob.bio.gmm.algorithm.JFA(number_of_gaussians=2,
subspace_dimension_of_u=2,
subspace_dimension_of_v=2,
kmeans_training_iterations=1,
gmm_training_iterations=1,
jfa_training_iterations=1,
INIT_SEED=seed_value)
train_data = utils.random_training_set((100, 45),
count=5,
minimum=-5.,
maximum=5.)
# reference is the same as for GMM projection
reference_file = pkg_resources.resource_filename(
'bob.bio.gmm.test', 'data/gmm_projector.hdf5')
try:
# train the projector
jfa2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
jfa1.load_projector(reference_file)
jfa2.load_projector(temp_file)
assert jfa1.ubm.is_similar_to(jfa2.ubm)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20, 45), -5., 5., seed=84)
projected = jfa1.project(feature)
assert isinstance(projected, bob.learn.em.GMMStats)
_compare(
projected,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/gmm_projected.hdf5'),
jfa1.write_feature, jfa1.read_feature)
# enroll model from random features
random_features = utils.random_training_set_by_id((20, 45),
count=5,
minimum=-5.,
maximum=5.)
train_data = [[jfa1.project(feature) for feature in client_features]
for client_features in random_features]
reference_file = pkg_resources.resource_filename('bob.bio.gmm.test',
'data/jfa_enroller.hdf5')
try:
# train the projector
jfa2.train_enroller(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
jfa1.load_enroller(reference_file)
jfa2.load_enroller(temp_file)
assert jfa1.jfa_base.is_similar_to(jfa2.jfa_base)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# enroll model from random features
random_features = utils.random_training_set((20, 45),
count=5,
minimum=-5.,
maximum=5.)
enroll_features = [jfa1.project(feature) for feature in random_features]
model = jfa1.enroll(enroll_features)
assert isinstance(model, bob.learn.em.JFAMachine)
_compare(
model,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/jfa_model.hdf5'),
jfa1.write_model, jfa1.read_model)
# compare model with probe
probe = jfa1.read_probe(
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/gmm_projected.hdf5'))
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)
def test_isv():
temp_file = bob.io.base.test_utils.temporary_filename()
isv1 = bob.bio.base.load_resource("isv",
"algorithm",
preferred_package='bob.bio.gmm')
assert isinstance(isv1, bob.bio.gmm.algorithm.ISV)
assert isinstance(isv1, bob.bio.gmm.algorithm.GMM)
assert isinstance(isv1, bob.bio.base.algorithm.Algorithm)
assert isv1.performs_projection
assert isv1.requires_projector_training
assert isv1.use_projected_features_for_enrollment
assert isv1.split_training_features_by_client
assert not isv1.requires_enroller_training
# create smaller GMM object
isv2 = bob.bio.gmm.algorithm.ISV(number_of_gaussians=2,
subspace_dimension_of_u=10,
kmeans_training_iterations=1,
gmm_training_iterations=1,
isv_training_iterations=1,
INIT_SEED=seed_value)
train_data = utils.random_training_set_by_id((100, 45),
count=5,
minimum=-5.,
maximum=5.)
reference_file = pkg_resources.resource_filename(
'bob.bio.gmm.test', 'data/isv_projector.hdf5')
try:
# train the projector
isv2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
isv1.load_projector(reference_file)
isv2.load_projector(temp_file)
assert isv1.ubm.is_similar_to(isv2.ubm)
assert isv1.isvbase.is_similar_to(isv2.isvbase)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20, 45), -5., 5., seed=84)
projected = isv1.project(feature)
assert isinstance(projected, (list, tuple))
assert len(projected) == 2
assert isinstance(projected[0], bob.learn.em.GMMStats)
assert isinstance(projected[1], numpy.ndarray)
_compare_complex(
projected,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/isv_projected.hdf5'),
isv1.write_feature, isv1.read_probe)
# enroll model from random features
random_features = utils.random_training_set((20, 45),
count=5,
minimum=-5.,
maximum=5.)
enroll_features = [isv1.project(feature)[0] for feature in random_features]
model = isv1.enroll(enroll_features)
assert isinstance(model, bob.learn.em.ISVMachine)
_compare(
model,
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/isv_model.hdf5'),
isv1.write_model, isv1.read_model)
# compare model with probe
probe = isv1.read_probe(
pkg_resources.resource_filename('bob.bio.gmm.test',
'data/isv_projected.hdf5'))
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?
assert abs(
isv1.score_for_multiple_probes(model, [probe, probe]) -
reference_score) < 1e-4, isv1.score_for_multiple_probes(
model, [probe, probe])
def test_jfa():
temp_file = bob.io.base.test_utils.temporary_filename()
jfa1 = bob.bio.base.load_resource("jfa", "algorithm", preferred_package='bob.bio.gmm')
assert isinstance(jfa1, bob.bio.gmm.algorithm.JFA)
assert isinstance(jfa1, bob.bio.gmm.algorithm.GMM)
assert isinstance(jfa1, bob.bio.base.algorithm.Algorithm)
assert jfa1.performs_projection
assert jfa1.requires_projector_training
assert jfa1.use_projected_features_for_enrollment
assert not jfa1.split_training_features_by_client
assert jfa1.requires_enroller_training
# create smaller JFA object
jfa2 = bob.bio.gmm.algorithm.JFA(
number_of_gaussians = 2,
subspace_dimension_of_u = 2,
subspace_dimension_of_v = 2,
kmeans_training_iterations = 1,
gmm_training_iterations = 1,
jfa_training_iterations = 1,
INIT_SEED = seed_value
)
train_data = utils.random_training_set((100,45), count=5, minimum=-5., maximum=5.)
# reference is the same as for GMM projection
reference_file = pkg_resources.resource_filename('bob.bio.gmm.test', 'data/gmm_projector.hdf5')
try:
# train the projector
jfa2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
jfa1.load_projector(reference_file)
jfa2.load_projector(temp_file)
assert jfa1.ubm.is_similar_to(jfa2.ubm)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20,45), -5., 5., seed=84)
projected = jfa1.project(feature)
assert isinstance(projected, bob.learn.em.GMMStats)
_compare(projected, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/gmm_projected.hdf5'), jfa1.write_feature, jfa1.read_feature)
# enroll model from random features
random_features = utils.random_training_set_by_id((20,45), count=5, minimum=-5., maximum=5.)
train_data = [[jfa1.project(feature) for feature in client_features] for client_features in random_features]
reference_file = pkg_resources.resource_filename('bob.bio.gmm.test', 'data/jfa_enroller.hdf5')
try:
# train the projector
jfa2.train_enroller(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
jfa1.load_enroller(reference_file)
jfa2.load_enroller(temp_file)
assert jfa1.jfa_base.is_similar_to(jfa2.jfa_base)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# enroll model from random features
random_features = utils.random_training_set((20,45), count=5, minimum=-5., maximum=5.)
enroll_features = [jfa1.project(feature) for feature in random_features]
model = jfa1.enroll(enroll_features)
assert isinstance(model, bob.learn.em.JFAMachine)
_compare(model, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/jfa_model.hdf5'), jfa1.write_model, jfa1.read_model)
# compare model with probe
probe = jfa1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/gmm_projected.hdf5'))
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)
def test_isv():
temp_file = bob.io.base.test_utils.temporary_filename()
isv1 = bob.bio.base.load_resource("isv", "algorithm", preferred_package='bob.bio.gmm')
assert isinstance(isv1, bob.bio.gmm.algorithm.ISV)
assert isinstance(isv1, bob.bio.gmm.algorithm.GMM)
assert isinstance(isv1, bob.bio.base.algorithm.Algorithm)
assert isv1.performs_projection
assert isv1.requires_projector_training
assert isv1.use_projected_features_for_enrollment
assert isv1.split_training_features_by_client
assert not isv1.requires_enroller_training
# create smaller GMM object
isv2 = bob.bio.gmm.algorithm.ISV(
number_of_gaussians = 2,
subspace_dimension_of_u = 10,
kmeans_training_iterations = 1,
gmm_training_iterations = 1,
isv_training_iterations = 1,
INIT_SEED = seed_value
)
train_data = utils.random_training_set_by_id((100,45), count=5, minimum=-5., maximum=5.)
reference_file = pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_projector.hdf5')
try:
# train the projector
isv2.train_projector(train_data, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
# check projection matrix
isv1.load_projector(reference_file)
isv2.load_projector(temp_file)
assert isv1.ubm.is_similar_to(isv2.ubm)
assert isv1.isvbase.is_similar_to(isv2.isvbase)
finally:
if os.path.exists(temp_file): os.remove(temp_file)
# generate and project random feature
feature = utils.random_array((20,45), -5., 5., seed=84)
projected = isv1.project(feature)
assert isinstance(projected, (list, tuple))
assert len(projected) == 2
assert isinstance(projected[0], bob.learn.em.GMMStats)
assert isinstance(projected[1], numpy.ndarray)
_compare_complex(projected, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_projected.hdf5'), isv1.write_feature, isv1.read_feature)
# enroll model from random features
random_features = utils.random_training_set((20,45), count=5, minimum=-5., maximum=5.)
enroll_features = [isv1.project(feature) for feature in random_features]
model = isv1.enroll(enroll_features)
assert isinstance(model, bob.learn.em.ISVMachine)
_compare(model, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_model.hdf5'), isv1.write_model, isv1.read_model)
# compare model with probe
probe = isv1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_projected.hdf5'))
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?
assert abs(isv1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-4, isv1.score_for_multiple_probes(model, [probe, probe])