diff --git a/bob/learn/em/data/gmm_MAP.hdf5 b/bob/learn/em/data/gmm_MAP.hdf5
index 0f57a2bde913bba76c2d9ab5a07cf81b07346d0f..8106590f1721a6c6084c63bc9637b52a5b39b24e 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/data/gmm_ML.hdf5 b/bob/learn/em/data/gmm_ML.hdf5
index 20362881b1661a826a8773d1658a8df559099d46..5e667e2498f69cf59303fc30c675c9f6f7b1fcc3 100644
Binary files a/bob/learn/em/data/gmm_ML.hdf5 and b/bob/learn/em/data/gmm_ML.hdf5 differ
diff --git a/bob/learn/em/mixture/gmm.py b/bob/learn/em/mixture/gmm.py
index 0be5a1772bc19184cd8534312f8901192f96c575..63171cf48c610a86e0cfaf73aecb0b2468c337a5 100644
--- a/bob/learn/em/mixture/gmm.py
+++ b/bob/learn/em/mixture/gmm.py
@@ -80,42 +80,42 @@ class GMMStats:
if isinstance(hdf5, str):
hdf5 = HDF5File(hdf5, "r")
try:
- version_major, version_minor = hdf5.get("meta_file_version")[()].split(".")
+ version_major, version_minor = hdf5.attrs["file_version"].split(".")
logger.debug(
f"Reading a GMMStats HDF5 file of version {version_major}.{version_minor}"
)
- except TypeError:
+ except (KeyError, RuntimeError):
version_major, version_minor = 0, 0
if int(version_major) >= 1:
- if hdf5["meta_writer_class"][()] != str(cls):
- logger.warning(f"{hdf5['meta_writer_class'][()]} is not {cls}.")
+ if hdf5.attrs["writer_class"] != str(cls):
+ logger.warning(f"{hdf5.attrs['writer_class']} is not {cls}.")
self = cls(
n_gaussians=hdf5["n_gaussians"][()], n_features=hdf5["n_features"][()]
)
self.log_likelihood = hdf5["log_likelihood"][()]
self.t = hdf5["T"][()]
- self.n = hdf5["n"][()]
- self.sum_px = hdf5["sumPx"][()]
- self.sum_pxx = hdf5["sumPxx"][()]
+ self.n = hdf5["n"][...]
+ self.sum_px = hdf5["sumPx"][...]
+ self.sum_pxx = hdf5["sumPxx"][...]
else: # Legacy file version
logger.info("Loading a legacy HDF5 stats file.")
self = cls(
n_gaussians=int(hdf5["n_gaussians"][()]),
n_features=int(hdf5["n_inputs"][()]),
)
- self.log_likelihood = float(hdf5["log_liklihood"][()])
+ self.log_likelihood = hdf5["log_liklihood"][()]
self.t = int(hdf5["T"][()])
- self.n = hdf5["n"][()].reshape((self.n_gaussians,))
- self.sum_px = hdf5["sumPx"][()].reshape(self.shape)
- self.sum_pxx = hdf5["sumPxx"][()].reshape(self.shape)
+ self.n = np.reshape(hdf5["n"], (self.n_gaussians,))
+ self.sum_px = np.reshape(hdf5["sumPx"], (self.shape))
+ self.sum_pxx = np.reshape(hdf5["sumPxx"], (self.shape))
return self
def save(self, hdf5):
"""Saves the current statistsics in an `HDF5File` object."""
if isinstance(hdf5, str):
hdf5 = HDF5File(hdf5, "w")
- hdf5["meta_file_version"] = "1.0"
- hdf5["meta_writer_class"] = str(self.__class__)
+ hdf5.attrs["file_version"] = "1.0"
+ hdf5.attrs["writer_class"] = str(self.__class__)
hdf5["n_gaussians"] = self.n_gaussians
hdf5["n_features"] = self.n_features
hdf5["log_likelihood"] = float(self.log_likelihood)
@@ -438,16 +438,16 @@ class GMMMachine(BaseEstimator):
if isinstance(hdf5, str):
hdf5 = HDF5File(hdf5, "r")
try:
- version_major, version_minor = hdf5.get("meta_file_version")[()].split(".")
+ version_major, version_minor = hdf5.attrs["file_version"].split(".")
logger.debug(
f"Reading a GMMMachine HDF5 file of version {version_major}.{version_minor}"
)
- except (TypeError, RuntimeError):
+ except (KeyError, RuntimeError):
version_major, version_minor = 0, 0
if int(version_major) >= 1:
- if hdf5["meta_writer_class"][()] != str(cls):
- logger.warning(f"{hdf5['meta_writer_class'][()]} is not {cls}.")
- if hdf5["trainer"][()] == "map" and ubm is None:
+ if hdf5.attrs["writer_class"] != str(cls):
+ logger.warning(f"{hdf5.attrs['writer_class']} is not {cls}.")
+ if hdf5["trainer"] == "map" and ubm is None:
raise ValueError("The UBM is needed when loading a MAP machine.")
self = cls(
n_gaussians=hdf5["n_gaussians"][()],
@@ -455,30 +455,30 @@ class GMMMachine(BaseEstimator):
ubm=ubm,
convergence_threshold=1e-5,
max_fitting_steps=hdf5["max_fitting_steps"][()],
- weights=hdf5["weights"][()],
+ weights=hdf5["weights"][...],
k_means_trainer=None,
update_means=hdf5["update_means"][()],
update_variances=hdf5["update_variances"][()],
update_weights=hdf5["update_weights"][()],
)
gaussians_group = hdf5["gaussians"]
- self.means = gaussians_group["means"][()]
- self.variances = gaussians_group["variances"][()]
- self.variance_thresholds = gaussians_group["variance_thresholds"][()]
+ 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 machine file.")
- n_gaussians = int(hdf5["m_n_gaussians"][()])
+ n_gaussians = hdf5["m_n_gaussians"][()]
g_means = []
g_variances = []
g_variance_thresholds = []
for i in range(n_gaussians):
gaussian_group = hdf5[f"m_gaussians{i}"]
- g_means.append(gaussian_group["m_mean"][()])
- g_variances.append(gaussian_group["m_variance"][()])
+ g_means.append(gaussian_group["m_mean"][...])
+ g_variances.append(gaussian_group["m_variance"][...])
g_variance_thresholds.append(
- gaussian_group["m_variance_thresholds"][()]
+ gaussian_group["m_variance_thresholds"][...]
)
- weights = hdf5["m_weights"][()].reshape(n_gaussians)
+ weights = np.reshape(hdf5["m_weights"], (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)
@@ -491,8 +491,8 @@ class GMMMachine(BaseEstimator):
"""Saves the current statistics in an `HDF5File` object."""
if isinstance(hdf5, str):
hdf5 = HDF5File(hdf5, "w")
- hdf5["meta_file_version"] = "1.0"
- hdf5["meta_writer_class"] = str(self.__class__)
+ hdf5.attrs["file_version"] = "1.0"
+ hdf5.attrs["writer_class"] = str(self.__class__)
hdf5["n_gaussians"] = self.n_gaussians
hdf5["trainer"] = self.trainer
hdf5["convergence_threshold"] = self.convergence_threshold
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index 785b0ae760049013d889a3e8723ba099b73e35d4..e99e1601fb5cc0a9e1ef3b89b6ce1c3c0bcd5d3a 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -55,6 +55,10 @@ def test_GMMStats():
assert (gs != gs_loaded ) is False
assert gs.is_similar_to(gs_loaded)
+ assert type(gs_loaded.n_gaussians) is np.int64
+ assert type(gs_loaded.n_features) is np.int64
+ assert type(gs_loaded.log_likelihood) is np.float64
+
# Saves and load from file using `load`
filename = str(tempfile.mkstemp(".hdf5")[1])
gs.save(hdf5=HDF5File(filename, "w"))
@@ -183,14 +187,22 @@ def test_GMMMachine():
assert gmm.is_similar_to(gmm6) is False
# Saving and loading
- filename = tempfile.mkstemp(suffix=".hdf5")[1]
- gmm.save(HDF5File(filename, "w"))
- gmm1 = GMMMachine.from_hdf5(HDF5File(filename, "r"))
- assert gmm == gmm1
- gmm.save(filename)
- gmm1 = GMMMachine.from_hdf5(filename)
- assert gmm == gmm1
- os.unlink(filename)
+ with tempfile.NamedTemporaryFile(suffix=".hdf5") as f:
+ filename = f.name
+ gmm.save(HDF5File(filename, "w"))
+ gmm1 = GMMMachine.from_hdf5(HDF5File(filename, "r"))
+ assert type(gmm1.n_gaussians) is np.int64
+ assert type(gmm1.update_means) is np.bool_
+ assert type(gmm1.update_variances) is np.bool_
+ assert type(gmm1.update_weights) is np.bool_
+ assert type(gmm1.trainer) is str
+ assert gmm1.ubm is None
+ assert gmm == gmm1
+ with tempfile.NamedTemporaryFile(suffix=".hdf5") as f:
+ filename = f.name
+ gmm.save(filename)
+ gmm1 = GMMMachine.from_hdf5(filename)
+ assert gmm == gmm1
# Weights
n_gaussians = 5
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 37bcc615cf5ef6b85465592622c7152a62b01dbc..8ba3b142709c9ef386eddd185f79ca6442567161 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -35,6 +35,7 @@ requirements:
- dask {{ dask }}
- dask-ml {{ dask_ml }}
- h5py {{ h5py }}
+ - h5py >=3
run:
- python
- setuptools
diff --git a/requirements.txt b/requirements.txt
index 8a86a69f50e30281292a5f1c6a71ecd22fb71f01..0a01048ef7e4c6adf624b636f594fd27df5ac9d0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,4 +6,4 @@ bob.learn.activation
bob.learn.linear
dask
dask-ml
-h5py
+h5py >= 3