diff --git a/bob/bio/gmm/algorithm/GMM.py b/bob/bio/gmm/algorithm/GMM.py
index ed574e28fcd24ad62fd5ab31d3edbb993b826ca2..7309c1130c61a6aef263cc4af5931de9cd4a819c 100644
--- a/bob/bio/gmm/algorithm/GMM.py
+++ b/bob/bio/gmm/algorithm/GMM.py
@@ -14,18 +14,21 @@ import logging
from typing import Callable
+import os
+
import dask.array as da
import numpy as np
+import copy
import dask
from h5py import File as HDF5File
from sklearn.base import BaseEstimator
from bob.bio.base.pipelines.vanilla_biometrics.abstract_classes import BioAlgorithm
+from bob.learn.em.cluster import KMeansMachine
from bob.learn.em.mixture import GMMMachine
from bob.learn.em.mixture import GMMStats
from bob.learn.em.mixture import linear_scoring
-from bob.pipelines.wrappers import DaskWrapper
logger = logging.getLogger(__name__)
@@ -118,8 +121,7 @@ class GMM(BioAlgorithm, BaseEstimator):
self.responsibility_threshold = responsibility_threshold
def scoring_function_wrapped(*args, **kwargs):
- with dask.config.set(scheduler="threads"):
- return scoring_function(*args, **kwargs)
+ return scoring_function(*args, **kwargs)
self.scoring_function = scoring_function_wrapped
@@ -154,7 +156,7 @@ class GMM(BioAlgorithm, BaseEstimator):
self.ubm.save(hdf5)
def load_ubm(self, ubm_file):
- hdf5file = HDF5File(ubm_file)
+ hdf5file = HDF5File(ubm_file, "r")
logger.debug("Loading model from file '%s'", ubm_file)
# read UBM
self.ubm = GMMMachine.from_hdf5(hdf5file)
@@ -163,19 +165,17 @@ class GMM(BioAlgorithm, BaseEstimator):
def project(self, array):
"""Computes GMM statistics against a UBM, given a 2D array of feature vectors"""
self._check_feature(array)
- logger.debug(" .... Projecting %d feature vectors", array.shape[0])
+ logger.warning(" .... Projecting %d feature vectors", array.shape[0])
# Accumulates statistics
- with dask.config.set(scheduler="threads"):
- gmm_stats = GMMStats(self.ubm.shape[0], self.ubm.shape[1])
- self.ubm.acc_statistics(array, gmm_stats)
- gmm_stats.compute()
+ gmm_stats = self.ubm.transform(array)
+ gmm_stats.compute()
# return the resulting statistics
return gmm_stats
def read_feature(self, feature_file):
"""Read the type of features that we require, namely GMM_Stats"""
- return GMMStats.from_hdf5(HDF5File(feature_file))
+ return GMMStats.from_hdf5(HDF5File(feature_file, "r"))
def write_feature(self, feature, feature_file):
"""Write the features (GMM_Stats)"""
@@ -184,7 +184,7 @@ class GMM(BioAlgorithm, BaseEstimator):
def enroll(self, data):
"""Enrolls a GMM using MAP adaptation, given a list of 2D np.ndarray's of feature vectors"""
[self._check_feature(feature) for feature in data]
- array = np.vstack(data)
+ array = da.vstack(data)
# Use the array to train a GMM and return it
logger.debug(" .... Enrolling with %d feature vectors", array.shape[0])
@@ -193,7 +193,7 @@ class GMM(BioAlgorithm, BaseEstimator):
gmm = GMMMachine(
n_gaussians=self.number_of_gaussians,
trainer="map",
- ubm=self.ubm,
+ ubm=copy.deepcopy(self.ubm),
convergence_threshold=self.training_threshold,
max_fitting_steps=self.gmm_enroll_iterations,
random_state=self.rng,
@@ -202,18 +202,14 @@ class GMM(BioAlgorithm, BaseEstimator):
update_weights=True, # TODO default?
)
gmm.variance_thresholds = self.variance_threshold
- gmm = gmm.fit(array)
- # info = {k: type(v) for k, v in gmm.__dict__.items()}
- # for k, v in gmm.gaussians_.__dict__.items():
- # info[k] = type(v)
- # raise ValueError(str(info))
+ gmm.fit(array)
return gmm
- def read_model(self, model_file):
+ def read_biometric_reference(self, model_file):
"""Reads the model, which is a GMM machine"""
- return GMMMachine.from_hdf5(HDF5File(model_file), ubm=self.ubm)
+ return GMMMachine.from_hdf5(HDF5File(model_file, "r"), ubm=self.ubm)
- def write_model(self, model, model_file):
+ def write_biometric_reference(self, model, model_file):
"""Write the features (GMM_Stats)"""
return model.save(model_file)
@@ -230,11 +226,13 @@ class GMM(BioAlgorithm, BaseEstimator):
The probe data to compare to the model.
"""
+ logger.debug(f"scoring {biometric_reference}, {data}")
assert isinstance(biometric_reference, GMMMachine)
+ stats = self.project(data)
return self.scoring_function(
models_means=[biometric_reference],
ubm=self.ubm,
- test_stats=data,
+ test_stats=stats,
frame_length_normalization=True,
)[0, 0]
@@ -253,6 +251,7 @@ class GMM(BioAlgorithm, BaseEstimator):
The probe data to compare to the models.
"""
+ logger.debug(f"scoring {biometric_references}, {data}")
assert isinstance(biometric_references[0], GMMMachine), type(
biometric_references[0]
)
@@ -266,9 +265,11 @@ class GMM(BioAlgorithm, BaseEstimator):
def score_for_multiple_probes(self, model, probes):
"""This function computes the score between the given model and several given probe files."""
+ logger.debug(f"scoring {model}, {probes}")
assert isinstance(model, GMMMachine)
+ stats = []
for probe in probes:
- assert isinstance(probe, GMMStats)
+ stats.append(self.project(probe))
# logger.warn("Please verify that this function is correct")
return (
self.scoring_function(
@@ -283,31 +284,50 @@ class GMM(BioAlgorithm, BaseEstimator):
def fit(self, X, y=None, **kwargs):
"""Trains the UBM."""
+ ubm_filename = "UBM_mobio_001.hdf5" # Manually set "projector" file TODO remove
+ if not os.path.exists(ubm_filename):
- # Stack all the samples in a 2D array of features
- array = da.vstack(X)
+ # Stack all the samples in a 2D array of features
+ array = np.vstack(X).persist()
- logger.debug("UBM with %d feature vectors", array.shape[0])
+ logger.debug("UBM with %d feature vectors", array.shape[0])
- logger.debug(f"Creating UBM machine with {self.number_of_gaussians} gaussians")
+ logger.debug(f"Creating UBM machine with {self.number_of_gaussians} gaussians")
- self.ubm = GMMMachine(
- n_gaussians=self.number_of_gaussians,
- trainer="ml",
- max_fitting_steps=self.ubm_training_iterations,
- convergence_threshold=self.training_threshold,
- update_means=self.update_means,
- update_variances=self.update_variances,
- update_weights=self.update_weights,
- # TODO more params?
- )
+ self.ubm = GMMMachine(
+ n_gaussians=self.number_of_gaussians,
+ trainer="ml",
+ max_fitting_steps=self.ubm_training_iterations,
+ convergence_threshold=self.training_threshold,
+ update_means=self.update_means,
+ update_variances=self.update_variances,
+ update_weights=self.update_weights,
+ k_means_trainer=KMeansMachine(
+ self.number_of_gaussians,
+ convergence_threshold=self.training_threshold, # TODO Have a separate threshold for kmeans instead of re-using the one for GMM...
+ max_iter=self.kmeans_training_iterations, # TODO pass this param through GMMMachine instead of the full KMeansMachine?
+ init_method="k-means||",
+ init_max_iter=5,
+ )
+ # TODO more params?
+ )
+
+ # Trains the GMM
+ logger.info("Training UBM GMM")
+ # Resetting the pseudo random number generator so we can have the same initialization for serial and parallel execution.
+ # self.rng = bob.core.random.mt19937(self.init_seed)
+
+ self.ubm = self.ubm.fit(array)
- # Trains the GMM
- logger.info("Training UBM GMM")
- # Resetting the pseudo random number generator so we can have the same initialization for serial and parallel execution.
- # self.rng = bob.core.random.mt19937(self.init_seed)
- self.ubm = self.ubm.fit(array)
+ logger.warning(f"Saving trained ubm to {ubm_filename}")
+ self.save_ubm(ubm_filename)
+ if not np.all(self.ubm.weights):
+ logger.error("zero weights after gmm training")
+ raise ValueError("!! zero weights after gmm training...")
+ else:
+ logger.warning(f"Loading trained ubm from {ubm_filename}")
+ self.load_ubm(ubm_filename)
return self
def transform(self, X, **kwargs):