def test_JFATrainer_updateZandD():
# test the JFATrainer for updating Z and D
d_ref = numpy.array([0.3110, 1.0138, 0.8297, 1.0382, 0.0095, 0.6320])
z1 = numpy.array([0., 0., 0., 0., 0., 0.])
z2 = numpy.array([0., 0., 0., 0., 0., 0.])
z3_ref = numpy.array([0.3256, 1.8633, 0.6480, 0.8085, -0.0432, 0.2885])
z4_ref = numpy.array([-0.3324, -0.1474, -0.4404, -0.4529, 0.0484, -0.5848])
z = [z1, z2]
# call the updateZ function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = M_y
t.__Z__ = z
t.e_step_d(m, TRAINING_STATS)
t.m_step_d(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Z__[0], z3_ref, 2e-4)
assert equals(t.__Z__[1], z4_ref, 2e-4)
assert equals(m.d, d_ref, 2e-4)
def test_JFATrainer_updateYandV():
# test the JFATrainer for updating Y and V
v_ref = numpy.array([
0.7228, 0.7892, 0.6475, 0.6080, 0.8631, 0.8416, 1.6512, 1.6068, 0.0500,
0.0101, 0.4325, 0.6719
]).reshape((6, 2))
y1 = numpy.array([0., 0.])
y2 = numpy.array([0., 0.])
y3 = numpy.array([0.9630, 1.3868])
y4 = numpy.array([0.0426, -0.3721])
y = [y1, y2]
# call the updateY function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = y
t.__Z__ = M_z
t.e_step_v(m, TRAINING_STATS)
t.m_step_v(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Y__[0], y3, 2e-4)
assert equals(t.__Y__[1], y4, 2e-4)
assert equals(m.v, v_ref, 2e-4)
def test_JFATrainer_updateXandU():
# test the JFATrainer for updating X and U
u_ref = numpy.array([
0.6729, 0.3408, 0.0544, 1.0653, 0.5399, 1.3035, 2.4995, 0.4385, 0.1292,
-0.0576, 1.1962, 0.0117
]).reshape((6, 2))
x1 = numpy.array([0., 0., 0., 0.]).reshape((2, 2))
x2 = numpy.array([0., 0., 0., 0.]).reshape((2, 2))
x3 = numpy.array([0.2143, 1.8275, 3.1979, 0.1227]).reshape((2, 2))
x4 = numpy.array([-1.3861, 0.2359, 5.3326, -0.7914]).reshape((2, 2))
x = [x1, x2]
# call the updateX function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = x
t.__Y__ = M_y
t.__Z__ = M_z
t.e_step_u(m, TRAINING_STATS)
t.m_step_u(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__X__[0], x3, 2e-4)
assert equals(t.__X__[1], x4, 2e-4)
assert equals(m.u, u_ref, 2e-4)
def test_JFATrainer_updateYandV():
# test the JFATrainer for updating Y and V
v_ref = numpy.array( [0.7228, 0.7892, 0.6475, 0.6080, 0.8631, 0.8416,
1.6512, 1.6068, 0.0500, 0.0101, 0.4325, 0.6719]).reshape((6,2))
y1 = numpy.array([0., 0.])
y2 = numpy.array([0., 0.])
y3 = numpy.array([0.9630, 1.3868])
y4 = numpy.array([0.0426, -0.3721])
y=[y1, y2]
# call the updateY function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = y
t.__Z__ = M_z
t.e_step_v(m, TRAINING_STATS)
t.m_step_v(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Y__[0], y3, 2e-4)
assert equals(t.__Y__[1], y4, 2e-4)
assert equals(m.v, v_ref, 2e-4)
def test_JFATrainer_updateZandD(): # test the JFATrainer for updating Z and D d_ref = numpy.array([0.3110, 1.0138, 0.8297, 1.0382, 0.0095, 0.6320]) z1 = numpy.array([0., 0., 0., 0., 0., 0.]) z2 = numpy.array([0., 0., 0., 0., 0., 0.]) z3_ref = numpy.array([0.3256, 1.8633, 0.6480, 0.8085, -0.0432, 0.2885]) z4_ref = numpy.array([-0.3324, -0.1474, -0.4404, -0.4529, 0.0484, -0.5848]) z=[z1, z2] # call the updateZ function ubm = GMMMachine(2,3) ubm.mean_supervector = UBM_MEAN ubm.variance_supervector = UBM_VAR m = JFABase(ubm,2,2) t = JFATrainer() t.initialize(m, TRAINING_STATS) m.u = M_u m.v = M_v m.d = M_d t.__X__ = M_x t.__Y__ = M_y t.__Z__ = z t.e_step_d(m, TRAINING_STATS) t.m_step_d(m, TRAINING_STATS) # Expected results(JFA cookbook, matlab) assert equals(t.__Z__[0], z3_ref, 2e-4) assert equals(t.__Z__[1], z4_ref, 2e-4) assert equals(m.d, d_ref, 2e-4)
def test_JFATrainer_updateXandU():
# test the JFATrainer for updating X and U
u_ref = numpy.array( [0.6729, 0.3408, 0.0544, 1.0653, 0.5399, 1.3035,
2.4995, 0.4385, 0.1292, -0.0576, 1.1962, 0.0117]).reshape((6,2))
x1 = numpy.array([0., 0., 0., 0.]).reshape((2,2))
x2 = numpy.array([0., 0., 0., 0.]).reshape((2,2))
x3 = numpy.array([0.2143, 1.8275, 3.1979, 0.1227]).reshape((2,2))
x4 = numpy.array([-1.3861, 0.2359, 5.3326, -0.7914]).reshape((2,2))
x = [x1, x2]
# call the updateX function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = x
t.__Y__ = M_y
t.__Z__ = M_z
t.e_step_u(m, TRAINING_STATS)
t.m_step_u(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__X__[0], x3, 2e-4)
assert equals(t.__X__[1], x4, 2e-4)
assert equals(m.u, u_ref, 2e-4)
def test_JFATrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
eps = 1e-10
# UBM GMM
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
## JFA
jb = JFABase(ubm, 2, 2)
# first round
rng = bob.core.random.mt19937(0)
jt = JFATrainer()
#jt.rng = rng
jt.initialize(jb, TRAINING_STATS, rng)
u1 = jb.u
v1 = jb.v
d1 = jb.d
# second round
rng = bob.core.random.mt19937(0)
jt.initialize(jb, TRAINING_STATS, rng)
u2 = jb.u
v2 = jb.v
d2 = jb.d
assert numpy.allclose(u1, u2, eps)
assert numpy.allclose(v1, v2, eps)
assert numpy.allclose(d1, d2, eps)
def test_JFABase():
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
m = JFABase(ubm, ru=1, rv=1)
_,_,ru,rv = m.shape
assert ru == 1
assert rv == 1
# Checks for correctness
m.resize(2,2)
m.u = U
m.v = V
m.d = d
n_gaussians,dim,ru,rv = m.shape
supervector_length = m.supervector_length
assert (m.u == U).all()
assert (m.v == V).all()
assert (m.d == d).all()
assert n_gaussians == 2
assert dim == 3
assert supervector_length == 6
assert ru == 2
assert rv == 2
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.base.HDF5File(filename, 'w'))
m_loaded = JFABase(bob.io.base.HDF5File(filename))
m_loaded.ubm = ubm
assert m == m_loaded
assert (m != m_loaded) is False
assert m.is_similar_to(m_loaded)
# Copy constructor
mc = JFABase(m)
assert m == mc
# Variant
#mv = JFABase()
# Checks for correctness
#mv.ubm = ubm
#mv.resize(2,2)
#mv.u = U
#mv.v = V
#mv.d = d
#assert (m.u == U).all()
#assert (m.v == V).all()
#assert (m.d == d).all()
#assert m.dim_c == 2
#assert m.dim_d == 3
#assert m.dim_cd == 6
#assert m.dim_ru == 2
#assert m.dim_rv == 2
# Clean-up
os.unlink(filename)
def test_JFAMachine():
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
base = JFABase(ubm,2,2)
base.u = U
base.v = V
base.d = d
# Creates a JFAMachine
y = numpy.array([1,2], 'float64')
z = numpy.array([3,4,1,2,0,1], 'float64')
m = JFAMachine(base)
m.y = y
m.z = z
n_gaussians,dim,ru,rv = m.shape
supervector_length = m.supervector_length
assert n_gaussians == 2
assert dim == 3
assert supervector_length == 6
assert ru == 2
assert rv == 2
assert (m.y == y).all()
assert (m.z == z).all()
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.base.HDF5File(filename, 'w'))
m_loaded = JFAMachine(bob.io.base.HDF5File(filename))
m_loaded.jfa_base = base
assert m == m_loaded
assert (m != m_loaded) is False
assert m.is_similar_to(m_loaded)
# Copy constructor
mc = JFAMachine(m)
assert m == mc
# Variant
#mv = JFAMachine()
# Checks for correctness
#mv.jfa_base = base
#m.y = y
#m.z = z
#assert m.dim_c == 2
#assert m.dim_d == 3
#assert m.dim_cd == 6
#assert m.dim_ru == 2
#assert m.dim_rv == 2
#assert (m.y == y).all()
#assert (m.z == z).all()
# Defines GMMStats
gs = GMMStats(2,3)
log_likelihood = -3.
T = 1
n = numpy.array([0.4, 0.6], 'float64')
sumpx = numpy.array([[1., 2., 3.], [4., 5., 6.]], 'float64')
sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], 'float64')
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
# Forward GMMStats and check estimated value of the x speaker factor
eps = 1e-10
x_ref = numpy.array([0.291042849767692, 0.310273618998444], 'float64')
score_ref = -2.111577181208289
score = m.log_likelihood(gs)
assert numpy.allclose(m.x, x_ref, eps)
assert abs(score_ref-score) < eps
# x and Ux
x = numpy.ndarray((2,), numpy.float64)
m.estimate_x(gs, x)
n_gaussians, dim,_,_ = m.shape
x_py = estimate_x(n_gaussians, dim, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
assert numpy.allclose(x, x_py, eps)
ux = numpy.ndarray((6,), numpy.float64)
m.estimate_ux(gs, ux)
n_gaussians, dim,_,_ = m.shape
ux_py = estimate_ux(n_gaussians, dim, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
assert numpy.allclose(ux, ux_py, eps)
assert numpy.allclose(m.x, x, eps)
score = m.forward_ux(gs, ux)
assert abs(score_ref-score) < eps
# Clean-up
os.unlink(filename)
def test_JFATrainAndEnrol():
# Train and enroll a JFAMachine
# Calls the train function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
mb.v = M_v
mb.d = M_d
bob.learn.em.train_jfa(t, mb, TRAINING_STATS, initialize=False)
v_ref = numpy.array([[0.245364911936476, 0.978133261775424],
[0.769646805052223, 0.940070736856596],
[0.310779202800089, 1.456332053893072],
[0.184760934399551, 2.265139705602147],
[0.701987784039800, 0.081632150899400],
[0.074344030229297, 1.090248340917255]], 'float64')
u_ref = numpy.array([[0.049424652628448, 0.060480486336896],
[0.178104127464007, 1.884873813495153],
[1.204011484266777, 2.281351307871720],
[7.278512126426286, -0.390966087173334],
[-0.084424326581145, -0.081725474934414],
[4.042143689831097, -0.262576386580701]], 'float64')
d_ref = numpy.array([
9.648467e-18, 2.63720683155e-12, 2.11822157653706e-10, 9.1047243e-17,
1.41163442535567e-10, 3.30581e-19
], 'float64')
eps = 1e-10
assert numpy.allclose(mb.v, v_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
assert numpy.allclose(mb.d, d_ref, eps)
# Calls the enroll function
m = JFAMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2, 2))
Fe = numpy.array([
0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345,
0.2463, 0.4789, 0.5236
]).reshape((6, 2))
gse1 = GMMStats(2, 3)
gse1.n = Ne[:, 0]
gse1.sum_px = Fe[:, 0].reshape(2, 3)
gse2 = GMMStats(2, 3)
gse2.n = Ne[:, 1]
gse2.sum_px = Fe[:, 1].reshape(2, 3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
y_ref = numpy.array([0.555991469319657, 0.002773650670010], 'float64')
z_ref = numpy.array([
8.2228e-20, 3.15216909492e-13, -1.48616735364395e-10, 1.0625905e-17,
3.7150503117895e-11, 1.71104e-19
], 'float64')
assert numpy.allclose(m.y, y_ref, eps)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
def test_JFATrainAndEnrol():
# Train and enroll a JFAMachine
# Calls the train function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
mb.v = M_v
mb.d = M_d
bob.learn.em.train_jfa(t,mb, TRAINING_STATS, initialize=False)
v_ref = numpy.array([[0.245364911936476, 0.978133261775424], [0.769646805052223, 0.940070736856596], [0.310779202800089, 1.456332053893072],
[0.184760934399551, 2.265139705602147], [0.701987784039800, 0.081632150899400], [0.074344030229297, 1.090248340917255]], 'float64')
u_ref = numpy.array([[0.049424652628448, 0.060480486336896], [0.178104127464007, 1.884873813495153], [1.204011484266777, 2.281351307871720],
[7.278512126426286, -0.390966087173334], [-0.084424326581145, -0.081725474934414], [4.042143689831097, -0.262576386580701]], 'float64')
d_ref = numpy.array([9.648467e-18, 2.63720683155e-12, 2.11822157653706e-10, 9.1047243e-17, 1.41163442535567e-10, 3.30581e-19], 'float64')
eps = 1e-10
assert numpy.allclose(mb.v, v_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
assert numpy.allclose(mb.d, d_ref, eps)
# Calls the enroll function
m = JFAMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2,2))
Fe = numpy.array([0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345, 0.2463, 0.4789, 0.5236]).reshape((6,2))
gse1 = GMMStats(2,3)
gse1.n = Ne[:,0]
gse1.sum_px = Fe[:,0].reshape(2,3)
gse2 = GMMStats(2,3)
gse2.n = Ne[:,1]
gse2.sum_px = Fe[:,1].reshape(2,3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
y_ref = numpy.array([0.555991469319657, 0.002773650670010], 'float64')
z_ref = numpy.array([8.2228e-20, 3.15216909492e-13, -1.48616735364395e-10, 1.0625905e-17, 3.7150503117895e-11, 1.71104e-19], 'float64')
assert numpy.allclose(m.y, y_ref, eps)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1,2,2]],5)