diff --git a/bob/learn/em/data/gmm_MAP.hdf5 b/bob/learn/em/data/gmm_MAP.hdf5
index 91c5e69141e3042ef5d211fc4098a8d59649d62d..0f57a2bde913bba76c2d9ab5a07cf81b07346d0f 100644
Binary files a/bob/learn/em/data/gmm_MAP.hdf5 and b/bob/learn/em/data/gmm_MAP.hdf5 differ
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index d69415d648bf21cdac42664249df42025e096157..89e96bfc98a7601c5e431d8ae30345619884ad9b 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -15,9 +15,9 @@ import os
import tempfile
from copy import deepcopy
from h5py import File as HDF5File
+from pkg_resources import resource_filename
-import bob.io.base
-from bob.io.base.test_utils import datafile
+from bob.io.base import load as load_array
from bob.learn.em.mixture import GMMMachine
from bob.learn.em.mixture import GMMStats
@@ -187,7 +187,7 @@ def test_GMMMachine_1():
def test_GMMMachine_2():
# Test a GMMMachine (statistics)
- arrayset = bob.io.base.load(datafile("faithful.torch3_f64.hdf5", __name__, path="../data/"))
+ arrayset = load_array(resource_filename("bob.learn.em", "data/faithful.torch3_f64.hdf5"))
gmm = GMMMachine(n_gaussians=2)
gmm.weights = np.array([0.5, 0.5], "float64")
gmm.means = np.array([[3, 70], [4, 72]], "float64")
@@ -197,7 +197,7 @@ def test_GMMMachine_2():
stats = gmm.acc_statistics(arrayset)
stats_ref = GMMStats(n_gaussians=2, n_features=2)
- stats_ref.load(HDF5File(datafile("stats.hdf5",__name__, path="../data/"), "r"))
+ stats_ref.load(HDF5File(resource_filename("bob.learn.em", "data/stats.hdf5"), "r"))
np.testing.assert_equal(stats.t, stats_ref.t)
np.testing.assert_almost_equal(stats.n, stats_ref.n, decimal=10)
@@ -210,11 +210,11 @@ def test_GMMMachine_2():
def test_GMMMachine_3():
# Test a GMMMachine (log-likelihood computation)
- data = bob.io.base.load(datafile("data.hdf5", __name__, path="../data/"))
+ data = load_array(resource_filename("bob.learn.em", "data/data.hdf5"))
gmm = GMMMachine(n_gaussians=2)
- gmm.weights = bob.io.base.load(datafile("weights.hdf5", __name__, path="../data/"))
- gmm.means = bob.io.base.load(datafile("means.hdf5", __name__, path="../data/"))
- gmm.variances = bob.io.base.load(datafile("variances.hdf5", __name__, path="../data/"))
+ gmm.weights = load_array(resource_filename("bob.learn.em", "data/weights.hdf5"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/means.hdf5"))
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/variances.hdf5"))
# Compare the log-likelihood with the one obtained using Chris Matlab
# implementation
@@ -229,9 +229,9 @@ def test_GMMMachine_4():
data = np.random.rand(100,50) # Doesn't matter if it is random. The average of 1D array (in python) MUST output the same result for the 2D array (in C++)
gmm = GMMMachine(n_gaussians=2)
- gmm.weights = bob.io.base.load(datafile("weights.hdf5", __name__, path="../data/"))
- gmm.means = bob.io.base.load(datafile("means.hdf5", __name__, path="../data/"))
- gmm.variances = bob.io.base.load(datafile("variances.hdf5", __name__, path="../data/"))
+ gmm.weights = load_array(resource_filename("bob.learn.em", "data/weights.hdf5"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/means.hdf5"))
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/variances.hdf5"))
ll = 0
@@ -549,9 +549,9 @@ def test_map_transformer():
def loadGMM():
gmm = GMMMachine(n_gaussians=2)
- gmm.weights = bob.io.base.load(datafile("gmm.init_weights.hdf5", __name__, path="../data/"))
- gmm.means = bob.io.base.load(datafile("gmm.init_means.hdf5", __name__, path="../data/"))
- gmm.variances = bob.io.base.load(datafile("gmm.init_variances.hdf5", __name__, path="../data/"))
+ gmm.weights = load_array(resource_filename("bob.learn.em", "data/gmm.init_weights.hdf5"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/gmm.init_means.hdf5"))
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/gmm.init_variances.hdf5"))
return gmm
@@ -560,7 +560,7 @@ def equals(x, y, epsilon):
def test_gmm_ML_1():
"""Trains a GMMMachine with ML_GMMTrainer"""
- ar = bob.io.base.load(datafile("faithful.torch3_f64.hdf5", __name__, path="../data/"))
+ ar = load_array(resource_filename("bob.learn.em", "data/faithful.torch3_f64.hdf5"))
gmm = loadGMM()
# test rng handling
@@ -579,29 +579,29 @@ def test_gmm_ML_1():
gmm = gmm.fit(ar)
# Generate reference
- # gmm.save(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data"), "w"))
+ # gmm.save(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "w"))
- gmm_ref = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data"), "r"))
+ gmm_ref = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r"))
assert gmm == gmm_ref
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/"))
+ ar = load_array(resource_filename("bob.learn.em", "data/dataNormalized.hdf5"))
# Initialize GMMMachine
gmm = GMMMachine(n_gaussians=5)
- 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 = np.exp(bob.io.base.load(datafile("weightsAfterKMeans.hdf5", __name__, path="../data/")).astype("float64"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/meansAfterKMeans.hdf5")).astype("float64")
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/variancesAfterKMeans.hdf5")).astype("float64")
+ gmm.weights = np.exp(load_array(resource_filename("bob.learn.em", "data/weightsAfterKMeans.hdf5")).astype("float64"))
threshold = 0.001
gmm.variance_thresholds = threshold
# Initialize ML Trainer
gmm.mean_var_update_threshold = 0.001
- gmm.max_fitting_steps = 26
- gmm.convergence_threshold = 0.00001
+ gmm.max_fitting_steps = 25
+ gmm.convergence_threshold = 0.000001
gmm.update_means = True
gmm.update_variances = True
gmm.update_weights = True
@@ -610,26 +610,26 @@ def test_gmm_ML_2():
gmm = gmm.fit(ar)
# 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/"))
+ meansML_ref = load_array(resource_filename("bob.learn.em", "data/meansAfterML.hdf5"))
+ variancesML_ref = load_array(resource_filename("bob.learn.em", "data/variancesAfterML.hdf5"))
+ weightsML_ref = load_array(resource_filename("bob.learn.em", "data/weightsAfterML.hdf5"))
# Compare to current results
- np.testing.assert_allclose(gmm.means, meansML_ref, rtol=3e-3)
- np.testing.assert_allclose(gmm.variances, variancesML_ref, rtol=3e-3)
- np.testing.assert_allclose(gmm.weights, weightsML_ref, rtol=1e-4)
+ np.testing.assert_allclose(gmm.means, meansML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.variances, variancesML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.weights, weightsML_ref, atol=1e-4)
def test_gmm_ML_parallel():
"""Trains a GMMMachine with ML_GMMTrainer; compares to an old reference"""
- ar = da.array(bob.io.base.load(datafile("dataNormalized.hdf5", __name__, path="../data/")))
+ ar = da.array(load_array(resource_filename("bob.learn.em", "data/dataNormalized.hdf5")))
# Initialize GMMMachine
gmm = GMMMachine(n_gaussians=5)
- 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 = np.exp(bob.io.base.load(datafile("weightsAfterKMeans.hdf5", __name__, path="../data/")).astype("float64"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/meansAfterKMeans.hdf5")).astype("float64")
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/variancesAfterKMeans.hdf5")).astype("float64")
+ gmm.weights = np.exp(load_array(resource_filename("bob.learn.em", "data/weightsAfterKMeans.hdf5")).astype("float64"))
threshold = 0.001
gmm.variance_thresholds = threshold
@@ -647,24 +647,24 @@ def test_gmm_ML_parallel():
# 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/"))
+ meansML_ref = load_array(resource_filename("bob.learn.em", "data/meansAfterML.hdf5"))
+ variancesML_ref = load_array(resource_filename("bob.learn.em", "data/variancesAfterML.hdf5"))
+ weightsML_ref = load_array(resource_filename("bob.learn.em", "data/weightsAfterML.hdf5"))
# Compare to current results
- np.testing.assert_allclose(gmm.means, meansML_ref, rtol=3e-3)
- np.testing.assert_allclose(gmm.variances, variancesML_ref, rtol=3e-3)
- np.testing.assert_allclose(gmm.weights, weightsML_ref, rtol=1e-4)
+ np.testing.assert_allclose(gmm.means, meansML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.variances, variancesML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.weights, weightsML_ref, atol=1e-4)
def test_gmm_MAP_1():
"""Train a GMMMachine with MAP_GMMTrainer"""
- ar = bob.io.base.load(datafile("faithful.torch3_f64.hdf5", __name__, path="../data/"))
+ ar = load_array(resource_filename("bob.learn.em", "data/faithful.torch3_f64.hdf5"))
# test with rng
- gmmprior = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data/"), "r"))
- gmm = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data/"), "r"), ubm = gmmprior)
+ gmmprior = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r"))
+ gmm = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r"), ubm = gmmprior)
gmm.update_means = True
gmm.update_variances = False
gmm.update_weights = False
@@ -672,37 +672,38 @@ def test_gmm_MAP_1():
gmm.random_state = rng
gmm = gmm.fit(ar)
- gmmprior = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data/"), "r"))
- gmm = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data/"), "r"), ubm = gmmprior)
+ gmmprior = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r"))
+ gmm = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r"), ubm = gmmprior)
gmm.update_means = True
gmm.update_variances = False
gmm.update_weights = False
gmm = gmm.fit(ar)
- gmm_ref = GMMMachine.from_hdf5(HDF5File(datafile("gmm_MAP.hdf5", __name__, path="../data/"), "r"))
+ # Generate reference
+ # gmm.save(HDF5File(resource_filename("bob.learn.em", "data/gmm_MAP.hdf5"), "w"))
+
+ gmm_ref = GMMMachine.from_hdf5(HDF5File(resource_filename("bob.learn.em", "data/gmm_MAP.hdf5"), "r"))
- np.testing.assert_allclose(gmm.means, gmm_ref.means, rtol=1e-3)
- np.testing.assert_allclose(gmm.variances, gmm_ref.variances, rtol=1e-3)
- np.testing.assert_allclose(gmm.weights, gmm_ref.weights, rtol=1e-3)
+ np.testing.assert_almost_equal(gmm.means, gmm_ref.means, decimal=3)
+ np.testing.assert_almost_equal(gmm.variances, gmm_ref.variances, decimal=3)
+ np.testing.assert_almost_equal(gmm.weights, gmm_ref.weights, decimal=3)
def test_gmm_MAP_2():
"""Train a GMMMachine with MAP_GMMTrainer and compare with matlab reference"""
- data = bob.io.base.load(datafile("data.hdf5", __name__, path="../data/"))
- data = data.reshape((-1, 1)) # make a 2D array out of it
- means = bob.io.base.load(datafile("means.hdf5", __name__, path="../data/"))
- variances = bob.io.base.load(datafile("variances.hdf5", __name__, path="../data/"))
- weights = bob.io.base.load(datafile("weights.hdf5", __name__, path="../data/"))
+ data = load_array(resource_filename("bob.learn.em", "data/data.hdf5"))
+ data = data.reshape((1, -1)) # make a 2D array out of it
+ means = load_array(resource_filename("bob.learn.em", "data/means.hdf5"))
+ variances = load_array(resource_filename("bob.learn.em", "data/variances.hdf5"))
+ weights = load_array(resource_filename("bob.learn.em", "data/weights.hdf5"))
gmm = GMMMachine(n_gaussians=2)
gmm.means = means
gmm.variances = variances
gmm.weights = weights
- gmm = gmm.fit(data)
-
gmm_adapted = GMMMachine(
n_gaussians=2,
trainer="map",
@@ -717,38 +718,36 @@ def test_gmm_MAP_2():
gmm_adapted.variances = variances
gmm_adapted.weights = weights
-
gmm_adapted = gmm_adapted.fit(data)
- new_means = bob.io.base.load(datafile("new_adapted_mean.hdf5", __name__, path="../data/"))
+ new_means = load_array(resource_filename("bob.learn.em", "data/new_adapted_mean.hdf5"))
# Compare to matlab reference
- np.testing.assert_allclose(new_means[0,:], gmm_adapted.means[:,0], rtol=1e-4)
- np.testing.assert_allclose(new_means[1,:], gmm_adapted.means[:,1], rtol=1e-4)
+ np.testing.assert_allclose(new_means.T, gmm_adapted.means, rtol=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/"))
+ ar = load_array(resource_filename("bob.learn.em", "data/dataforMAP.hdf5"))
# Initialize GMMMachine
n_gaussians = 5
prior_gmm = GMMMachine(n_gaussians)
- 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/"))
+ prior_gmm.means = load_array(resource_filename("bob.learn.em", "data/meansAfterML.hdf5"))
+ prior_gmm.variances = load_array(resource_filename("bob.learn.em", "data/variancesAfterML.hdf5"))
+ prior_gmm.weights = load_array(resource_filename("bob.learn.em", "data/weightsAfterML.hdf5"))
threshold = 0.001
prior_gmm.variance_thresholds = threshold
# Initialize MAP Trainer
+ prior = 0.001
accuracy = 0.00001
-
gmm = GMMMachine(
n_gaussians,
trainer="map",
ubm=prior_gmm,
- convergence_threshold=threshold,
+ convergence_threshold=prior,
max_fitting_steps=1,
update_means=True,
update_variances=False,
@@ -762,32 +761,31 @@ def test_gmm_MAP_3():
# 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/"))
+ meansMAP_ref = load_array(resource_filename("bob.learn.em", "data/meansAfterMAP.hdf5"))
+ variancesMAP_ref = load_array(resource_filename("bob.learn.em", "data/variancesAfterMAP.hdf5"))
+ weightsMAP_ref = load_array(resource_filename("bob.learn.em", "data/weightsAfterMAP.hdf5"))
# 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
- np.testing.assert_allclose(gmm.means, meansMAP_ref, rtol=2e-1)
- np.testing.assert_allclose(gmm.variances, variancesMAP_ref, rtol=1e-4)
- np.testing.assert_allclose(gmm.weights, weightsMAP_ref, rtol=1e-4)
+ np.testing.assert_allclose(gmm.means, meansMAP_ref, atol=2e-1)
+ np.testing.assert_allclose(gmm.variances, variancesMAP_ref, atol=1e-4)
+ np.testing.assert_allclose(gmm.weights, weightsMAP_ref, atol=1e-4)
def test_gmm_test():
+ """ Tests a GMMMachine by computing scores against a model and comparing to a reference
+ """
- # 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/"))
+ ar = load_array(resource_filename("bob.learn.em", "data/dataforMAP.hdf5"))
# Initialize GMMMachine
n_gaussians = 5
gmm = GMMMachine(n_gaussians)
- 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/"))
+ gmm.means = load_array(resource_filename("bob.learn.em", "data/meansAfterML.hdf5"))
+ gmm.variances = load_array(resource_filename("bob.learn.em", "data/variancesAfterML.hdf5"))
+ gmm.weights = load_array(resource_filename("bob.learn.em", "data/weightsAfterML.hdf5"))
threshold = 0.001
gmm.variance_thresholds = threshold