From 615a5061e58beace95d21d199e9b544a7f211f2b Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Mon, 29 Nov 2021 10:43:47 +0100
Subject: [PATCH] fix GMM_ML test and legacy files loading
---
bob/learn/em/data/gmm_ML.hdf5 | Bin 9128 -> 12920 bytes
bob/learn/em/mixture/gmm.py | 25 +++++++++++--------------
bob/learn/em/test/test_gmm.py | 19 +++++++++----------
3 files changed, 20 insertions(+), 24 deletions(-)
diff --git a/bob/learn/em/data/gmm_ML.hdf5 b/bob/learn/em/data/gmm_ML.hdf5
index 74269fe3d824aa877e59609097828ee922d5dbc4..0326c186f11ad38387b3a4c4b1e3cc7a66d32b80 100644
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diff --git a/bob/learn/em/mixture/gmm.py b/bob/learn/em/mixture/gmm.py
index d64b6f1..4370d5b 100644
--- a/bob/learn/em/mixture/gmm.py
+++ b/bob/learn/em/mixture/gmm.py
@@ -439,7 +439,7 @@ class GMMMachine(BaseEstimator):
try:
version_major, version_minor = hdf5.get("meta_file_version")[()].split(".")
logger.debug(
- f"Reading a GMMStats HDF5 file of version {version_major}.{version_minor}"
+ f"Reading a GMMMachine HDF5 file of version {version_major}.{version_minor}"
)
except (TypeError, RuntimeError):
version_major, version_minor = 0, 0
@@ -460,12 +460,12 @@ class GMMMachine(BaseEstimator):
update_variances=hdf5["update_variances"][()],
update_weights=hdf5["update_weights"][()],
)
- gaussians = hdf5["gaussians"]
- self.means = gaussians["means"][()]
- self.variances = gaussians["variances"][()]
- self.variance_thresholds = gaussians["variance_thresholds"][()]
+ gaussians_group = hdf5["gaussians"]
+ self.means = gaussians_group["means"][()]
+ self.variances = gaussians_group["variances"][()]
+ self.variance_thresholds = gaussians_group["variance_thresholds"][()]
else: # Legacy file version
- logger.info("Loading a legacy HDF5 stats file.")
+ logger.info("Loading a legacy HDF5 machine file.")
n_gaussians = int(hdf5["m_n_gaussians"][()])
g_means = []
g_variances = []
@@ -475,14 +475,11 @@ class GMMMachine(BaseEstimator):
g_means.append(gaussian_group["m_mean"][()])
g_variances.append(gaussian_group["m_variance"][()])
g_variance_thresholds.append(gaussian_group["m_variance_thresholds"][()])
- self = cls(
- n_gaussians=n_gaussians,
- ubm=ubm,
- weights=hdf5["m_weights"][()],
- )
- self.means = np.array(g_means)
- self.variances = np.array(g_variances)
- self.variance_thresholds = np.array(g_variance_thresholds)
+ weights = hdf5["m_weights"][()].reshape(n_gaussians)
+ self = cls(n_gaussians=n_gaussians, ubm=ubm, weights=weights)
+ self.means = np.array(g_means).reshape(n_gaussians,-1)
+ self.variances = np.array(g_variances).reshape(n_gaussians,-1)
+ self.variance_thresholds = np.array(g_variance_thresholds).reshape(n_gaussians,-1)
return self
def save(self, hdf5):
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index 40d535a..d69415d 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -547,7 +547,7 @@ def test_map_transformer():
## Tests from `test_em.py`
def loadGMM():
- gmm = GMMMachine(2)
+ 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/"))
@@ -578,13 +578,11 @@ def test_gmm_ML_1():
gmm.update_weights = True
gmm = gmm.fit(ar)
- #config = HDF5File(datafile("gmm_ML.hdf5", __name__), "w")
- #gmm.save(config)
+ # Generate reference
+ # gmm.save(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data"), "w"))
- gmm_ref = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data"), "r")) # TODO update the ref file(s)
- gmm_ref_32bit_debug = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML_32bit_debug.hdf5", __name__, path="../data/"), "r"))
- gmm_ref_32bit_release = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML_32bit_release.hdf5", __name__, path="../data/"), "r"))
- assert (gmm == gmm_ref) # or (gmm == gmm_ref_32bit_release) or (gmm == gmm_ref_32bit_debug)
+ gmm_ref = GMMMachine.from_hdf5(HDF5File(datafile("gmm_ML.hdf5", __name__, path="../data"), "r"))
+ assert gmm == gmm_ref
def test_gmm_ML_2():
@@ -592,7 +590,7 @@ def test_gmm_ML_2():
ar = bob.io.base.load(datafile("dataNormalized.hdf5", __name__, path="../data/"))
# Initialize GMMMachine
- gmm = GMMMachine(5, 45)
+ 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"))
@@ -628,7 +626,7 @@ def test_gmm_ML_parallel():
ar = da.array(bob.io.base.load(datafile("dataNormalized.hdf5", __name__, path="../data/")))
# Initialize GMMMachine
- gmm = GMMMachine(5, 45)
+ 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"))
@@ -703,6 +701,8 @@ def test_gmm_MAP_2():
gmm.variances = variances
gmm.weights = weights
+ gmm = gmm.fit(data)
+
gmm_adapted = GMMMachine(
n_gaussians=2,
trainer="map",
@@ -717,7 +717,6 @@ def test_gmm_MAP_2():
gmm_adapted.variances = variances
gmm_adapted.weights = weights
- gmm = gmm.fit(data)
gmm_adapted = gmm_adapted.fit(data)
--
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