def test_gmm_test():
# Tests a GMMMachine by computing scores against a model and compare to
# an old reference
ar = bob.io.base.load(datafile('dataforMAP.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
n_gaussians = 5
n_inputs = 45
gmm = GMMMachine(n_gaussians, n_inputs)
gmm.means = bob.io.base.load(datafile('meansAfterML.hdf5', __name__, path="../data/"))
gmm.variances = bob.io.base.load(datafile('variancesAfterML.hdf5', __name__, path="../data/"))
gmm.weights = bob.io.base.load(datafile('weightsAfterML.hdf5', __name__, path="../data/"))
threshold = 0.001
gmm.set_variance_thresholds(threshold)
# Test against the model
score_mean_ref = -1.50379e+06
score = 0.
for v in ar: score += gmm(v)
score /= len(ar)
# Compare current results to torch3vision
assert abs(score-score_mean_ref)/score_mean_ref<1e-4
def test_gmm_test():
# Tests a GMMMachine by computing scores against a model and compare to
# an old reference
ar = bob.io.base.load(
datafile('dataforMAP.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
n_gaussians = 5
n_inputs = 45
gmm = GMMMachine(n_gaussians, n_inputs)
gmm.means = bob.io.base.load(
datafile('meansAfterML.hdf5', __name__, path="../data/"))
gmm.variances = bob.io.base.load(
datafile('variancesAfterML.hdf5', __name__, path="../data/"))
gmm.weights = bob.io.base.load(
datafile('weightsAfterML.hdf5', __name__, path="../data/"))
threshold = 0.001
gmm.set_variance_thresholds(threshold)
# Test against the model
score_mean_ref = -1.50379e+06
score = 0.
for v in ar:
score += gmm(v)
score /= len(ar)
# Compare current results to torch3vision
assert abs(score - score_mean_ref) / score_mean_ref < 1e-4
def test_gmm_ML_2():
# Trains a GMMMachine with ML_GMMTrainer; compares to an old reference
ar = bob.io.base.load(
datafile('dataNormalized.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
gmm = GMMMachine(5, 45)
gmm.means = bob.io.base.load(
datafile('meansAfterKMeans.hdf5', __name__,
path="../data/")).astype('float64')
gmm.variances = bob.io.base.load(
datafile('variancesAfterKMeans.hdf5', __name__,
path="../data/")).astype('float64')
gmm.weights = numpy.exp(
bob.io.base.load(
datafile('weightsAfterKMeans.hdf5', __name__,
path="../data/")).astype('float64'))
threshold = 0.001
gmm.set_variance_thresholds(threshold)
# Initialize ML Trainer
prior = 0.001
max_iter_gmm = 25
accuracy = 0.00001
ml_gmmtrainer = ML_GMMTrainer(True, True, True, prior)
# Run ML
#ml_gmmtrainer.train(gmm, ar)
bob.learn.em.train(ml_gmmtrainer,
gmm,
ar,
max_iterations=max_iter_gmm,
convergence_threshold=accuracy)
# Test results
# Load torch3vision reference
meansML_ref = bob.io.base.load(
datafile('meansAfterML.hdf5', __name__, path="../data/"))
variancesML_ref = bob.io.base.load(
datafile('variancesAfterML.hdf5', __name__, path="../data/"))
weightsML_ref = bob.io.base.load(
datafile('weightsAfterML.hdf5', __name__, path="../data/"))
# Compare to current results
assert equals(gmm.means, meansML_ref, 3e-3)
assert equals(gmm.variances, variancesML_ref, 3e-3)
assert equals(gmm.weights, weightsML_ref, 1e-4)
def test_gmm_MAP_3():
# Train a GMMMachine with MAP_GMMTrainer; compares to old reference
ar = bob.io.base.load(datafile('dataforMAP.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
n_gaussians = 5
n_inputs = 45
prior_gmm = GMMMachine(n_gaussians, n_inputs)
prior_gmm.means = bob.io.base.load(datafile('meansAfterML.hdf5', __name__, path="../data/"))
prior_gmm.variances = bob.io.base.load(datafile('variancesAfterML.hdf5', __name__, path="../data/"))
prior_gmm.weights = bob.io.base.load(datafile('weightsAfterML.hdf5', __name__, path="../data/"))
threshold = 0.001
prior_gmm.set_variance_thresholds(threshold)
# Initialize MAP Trainer
relevance_factor = 0.1
prior = 0.001
max_iter_gmm = 1
accuracy = 0.00001
map_factor = 0.5
map_gmmtrainer = MAP_GMMTrainer(prior_gmm, alpha=map_factor, update_means=True, update_variances=False, update_weights=False, mean_var_update_responsibilities_threshold=accuracy)
#map_gmmtrainer.max_iterations = max_iter_gmm
#map_gmmtrainer.convergence_threshold = accuracy
gmm = GMMMachine(n_gaussians, n_inputs)
gmm.set_variance_thresholds(threshold)
# Train
#map_gmmtrainer.train(gmm, ar)
bob.learn.em.train(map_gmmtrainer, gmm, ar, max_iterations = max_iter_gmm, convergence_threshold=prior)
# Test results
# Load torch3vision reference
meansMAP_ref = bob.io.base.load(datafile('meansAfterMAP.hdf5', __name__, path="../data/"))
variancesMAP_ref = bob.io.base.load(datafile('variancesAfterMAP.hdf5', __name__, path="../data/"))
weightsMAP_ref = bob.io.base.load(datafile('weightsAfterMAP.hdf5', __name__, path="../data/"))
# Compare to current results
# Gaps are quite large. This might be explained by the fact that there is no
# adaptation of a given Gaussian in torch3 when the corresponding responsibilities
# are below the responsibilities threshold
assert equals(gmm.means, meansMAP_ref, 2e-1)
assert equals(gmm.variances, variancesMAP_ref, 1e-4)
assert equals(gmm.weights, weightsMAP_ref, 1e-4)
def test_gmm_ML_2():
# Trains a GMMMachine with ML_GMMTrainer; compares to an old reference
ar = bob.io.base.load(datafile('dataNormalized.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
gmm = GMMMachine(5, 45)
gmm.means = bob.io.base.load(datafile('meansAfterKMeans.hdf5', __name__, path="../data/")).astype('float64')
gmm.variances = bob.io.base.load(datafile('variancesAfterKMeans.hdf5', __name__, path="../data/")).astype('float64')
gmm.weights = numpy.exp(bob.io.base.load(datafile('weightsAfterKMeans.hdf5', __name__, path="../data/")).astype('float64'))
threshold = 0.001
gmm.set_variance_thresholds(threshold)
# Initialize ML Trainer
prior = 0.001
max_iter_gmm = 25
accuracy = 0.00001
ml_gmmtrainer = ML_GMMTrainer(True, True, True, prior)
# Run ML
#ml_gmmtrainer.train(gmm, ar)
bob.learn.em.train(ml_gmmtrainer, gmm, ar, max_iterations = max_iter_gmm, convergence_threshold=accuracy)
# Test results
# Load torch3vision reference
meansML_ref = bob.io.base.load(datafile('meansAfterML.hdf5', __name__, path="../data/"))
variancesML_ref = bob.io.base.load(datafile('variancesAfterML.hdf5', __name__, path="../data/"))
weightsML_ref = bob.io.base.load(datafile('weightsAfterML.hdf5', __name__, path="../data/"))
# Compare to current results
assert equals(gmm.means, meansML_ref, 3e-3)
assert equals(gmm.variances, variancesML_ref, 3e-3)
assert equals(gmm.weights, weightsML_ref, 1e-4)
def test_gmm_MAP_3():
# Train a GMMMachine with MAP_GMMTrainer; compares to old reference
ar = bob.io.base.load(
datafile('dataforMAP.hdf5', __name__, path="../data/"))
# Initialize GMMMachine
n_gaussians = 5
n_inputs = 45
prior_gmm = GMMMachine(n_gaussians, n_inputs)
prior_gmm.means = bob.io.base.load(
datafile('meansAfterML.hdf5', __name__, path="../data/"))
prior_gmm.variances = bob.io.base.load(
datafile('variancesAfterML.hdf5', __name__, path="../data/"))
prior_gmm.weights = bob.io.base.load(
datafile('weightsAfterML.hdf5', __name__, path="../data/"))
threshold = 0.001
prior_gmm.set_variance_thresholds(threshold)
# Initialize MAP Trainer
relevance_factor = 0.1
prior = 0.001
max_iter_gmm = 1
accuracy = 0.00001
map_factor = 0.5
map_gmmtrainer = MAP_GMMTrainer(
prior_gmm,
alpha=map_factor,
update_means=True,
update_variances=False,
update_weights=False,
mean_var_update_responsibilities_threshold=accuracy)
#map_gmmtrainer.max_iterations = max_iter_gmm
#map_gmmtrainer.convergence_threshold = accuracy
gmm = GMMMachine(n_gaussians, n_inputs)
gmm.set_variance_thresholds(threshold)
# Train
#map_gmmtrainer.train(gmm, ar)
bob.learn.em.train(map_gmmtrainer,
gmm,
ar,
max_iterations=max_iter_gmm,
convergence_threshold=prior)
# Test results
# Load torch3vision reference
meansMAP_ref = bob.io.base.load(
datafile('meansAfterMAP.hdf5', __name__, path="../data/"))
variancesMAP_ref = bob.io.base.load(
datafile('variancesAfterMAP.hdf5', __name__, path="../data/"))
weightsMAP_ref = bob.io.base.load(
datafile('weightsAfterMAP.hdf5', __name__, path="../data/"))
# Compare to current results
# Gaps are quite large. This might be explained by the fact that there is no
# adaptation of a given Gaussian in torch3 when the corresponding responsibilities
# are below the responsibilities threshold
assert equals(gmm.means, meansMAP_ref, 2e-1)
assert equals(gmm.variances, variancesMAP_ref, 1e-4)
assert equals(gmm.weights, weightsMAP_ref, 1e-4)
def test_GMMMachine_1():
# Test a GMMMachine basic features
weights = numpy.array([0.5, 0.5], 'float64')
weights2 = numpy.array([0.6, 0.4], 'float64')
means = numpy.array([[3, 70, 0], [4, 72, 0]], 'float64')
means2 = numpy.array([[3, 7, 0], [4, 72, 0]], 'float64')
variances = numpy.array([[1, 10, 1], [2, 5, 2]], 'float64')
variances2 = numpy.array([[10, 10, 1], [2, 5, 2]], 'float64')
varianceThresholds = numpy.array([[0, 0, 0], [0, 0, 0]], 'float64')
varianceThresholds2 = numpy.array([[0.0005, 0.0005, 0.0005], [0, 0, 0]],
'float64')
# Initializes a GMMMachine
gmm = GMMMachine(2, 3)
# Sets the weights, means, variances and varianceThresholds and
# Checks correctness
gmm.weights = weights
gmm.means = means
gmm.variances = variances
gmm.variance_thresholds = varianceThresholds
assert gmm.shape == (2, 3)
assert (gmm.weights == weights).all()
assert (gmm.means == means).all()
assert (gmm.variances == variances).all()
assert (gmm.variance_thresholds == varianceThresholds).all()
# Checks supervector-like accesses
assert (gmm.mean_supervector == means.reshape(means.size)).all()
assert (gmm.variance_supervector == variances.reshape(
variances.size)).all()
newMeans = numpy.array([[3, 70, 2], [4, 72, 2]], 'float64')
newVariances = numpy.array([[1, 1, 1], [2, 2, 2]], 'float64')
# Checks particular varianceThresholds-related methods
varianceThresholds1D = numpy.array([0.3, 1, 0.5], 'float64')
gmm.set_variance_thresholds(varianceThresholds1D)
assert (gmm.variance_thresholds[0, :] == varianceThresholds1D).all()
assert (gmm.variance_thresholds[1, :] == varianceThresholds1D).all()
gmm.set_variance_thresholds(0.005)
assert (gmm.variance_thresholds == 0.005).all()
# Checks Gaussians access
gmm.means = newMeans
gmm.variances = newVariances
assert (gmm.get_gaussian(0).mean == newMeans[0, :]).all()
assert (gmm.get_gaussian(1).mean == newMeans[1, :]).all()
assert (gmm.get_gaussian(0).variance == newVariances[0, :]).all()
assert (gmm.get_gaussian(1).variance == newVariances[1, :]).all()
# Checks resize
gmm.resize(4, 5)
assert gmm.shape == (4, 5)
# Checks comparison
gmm2 = GMMMachine(gmm)
gmm3 = GMMMachine(2, 3)
gmm3.weights = weights2
gmm3.means = means
gmm3.variances = variances
#gmm3.varianceThresholds = varianceThresholds
gmm4 = GMMMachine(2, 3)
gmm4.weights = weights
gmm4.means = means2
gmm4.variances = variances
#gmm4.varianceThresholds = varianceThresholds
gmm5 = GMMMachine(2, 3)
gmm5.weights = weights
gmm5.means = means
gmm5.variances = variances2
#gmm5.varianceThresholds = varianceThresholds
gmm6 = GMMMachine(2, 3)
gmm6.weights = weights
gmm6.means = means
gmm6.variances = variances
#gmm6.varianceThresholds = varianceThresholds2
assert gmm == gmm2
assert (gmm != gmm2) is False
assert gmm.is_similar_to(gmm2)
assert gmm != gmm3
assert (gmm == gmm3) is False
assert gmm.is_similar_to(gmm3) is False
assert gmm != gmm4
assert (gmm == gmm4) is False
assert gmm.is_similar_to(gmm4) is False
assert gmm != gmm5
assert (gmm == gmm5) is False
assert gmm.is_similar_to(gmm5) is False
assert gmm != gmm6
assert (gmm == gmm6) is False
assert gmm.is_similar_to(gmm6) is False
def test_GMMMachine_1(): # Test a GMMMachine basic features weights = numpy.array([0.5, 0.5], 'float64') weights2 = numpy.array([0.6, 0.4], 'float64') means = numpy.array([[3, 70, 0], [4, 72, 0]], 'float64') means2 = numpy.array([[3, 7, 0], [4, 72, 0]], 'float64') variances = numpy.array([[1, 10, 1], [2, 5, 2]], 'float64') variances2 = numpy.array([[10, 10, 1], [2, 5, 2]], 'float64') varianceThresholds = numpy.array([[0, 0, 0], [0, 0, 0]], 'float64') varianceThresholds2 = numpy.array([[0.0005, 0.0005, 0.0005], [0, 0, 0]], 'float64') # Initializes a GMMMachine gmm = GMMMachine(2,3) # Sets the weights, means, variances and varianceThresholds and # Checks correctness gmm.weights = weights gmm.means = means gmm.variances = variances gmm.variance_thresholds = varianceThresholds assert gmm.shape == (2,3) assert (gmm.weights == weights).all() assert (gmm.means == means).all() assert (gmm.variances == variances).all() assert (gmm.variance_thresholds == varianceThresholds).all() # Checks supervector-like accesses assert (gmm.mean_supervector == means.reshape(means.size)).all() assert (gmm.variance_supervector == variances.reshape(variances.size)).all() newMeans = numpy.array([[3, 70, 2], [4, 72, 2]], 'float64') newVariances = numpy.array([[1, 1, 1], [2, 2, 2]], 'float64') # Checks particular varianceThresholds-related methods varianceThresholds1D = numpy.array([0.3, 1, 0.5], 'float64') gmm.set_variance_thresholds(varianceThresholds1D) assert (gmm.variance_thresholds[0,:] == varianceThresholds1D).all() assert (gmm.variance_thresholds[1,:] == varianceThresholds1D).all() gmm.set_variance_thresholds(0.005) assert (gmm.variance_thresholds == 0.005).all() # Checks Gaussians access gmm.means = newMeans gmm.variances = newVariances assert (gmm.get_gaussian(0).mean == newMeans[0,:]).all() assert (gmm.get_gaussian(1).mean == newMeans[1,:]).all() assert (gmm.get_gaussian(0).variance == newVariances[0,:]).all() assert (gmm.get_gaussian(1).variance == newVariances[1,:]).all() # Checks resize gmm.resize(4,5) assert gmm.shape == (4,5) # Checks comparison gmm2 = GMMMachine(gmm) gmm3 = GMMMachine(2,3) gmm3.weights = weights2 gmm3.means = means gmm3.variances = variances #gmm3.varianceThresholds = varianceThresholds gmm4 = GMMMachine(2,3) gmm4.weights = weights gmm4.means = means2 gmm4.variances = variances #gmm4.varianceThresholds = varianceThresholds gmm5 = GMMMachine(2,3) gmm5.weights = weights gmm5.means = means gmm5.variances = variances2 #gmm5.varianceThresholds = varianceThresholds gmm6 = GMMMachine(2,3) gmm6.weights = weights gmm6.means = means gmm6.variances = variances #gmm6.varianceThresholds = varianceThresholds2 assert gmm == gmm2 assert (gmm != gmm2) is False assert gmm.is_similar_to(gmm2) assert gmm != gmm3 assert (gmm == gmm3) is False assert gmm.is_similar_to(gmm3) is False assert gmm != gmm4 assert (gmm == gmm4) is False assert gmm.is_similar_to(gmm4) is False assert gmm != gmm5 assert (gmm == gmm5) is False assert gmm.is_similar_to(gmm5) is False assert gmm != gmm6 assert (gmm == gmm6) is False assert gmm.is_similar_to(gmm6) is False
