From cc846902e598619a9b8161e0babcece406e2c24c Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Fri, 6 Nov 2020 10:52:13 +0100
Subject: [PATCH] Pickle iVectorMachine
---
bob/learn/em/__init__.py | 33 +++++++-
bob/learn/em/ivector_machine.cpp | 2 +-
bob/learn/em/test/test_picklability.py | 109 ++++++++++++++++++-------
3 files changed, 111 insertions(+), 33 deletions(-)
diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index b982c35..44e6b51 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -15,12 +15,14 @@ from ._library import ISVBase as _ISVBase_C
from ._library import ISVMachine as _ISVMachine_C
from ._library import KMeansMachine as _KMeansMachine_C
from ._library import GMMStats as _GMMStats_C
+from ._library import IVectorMachine as _IVectorMachine_C
from . import version
from .version import module as __version__
from .version import api as __api_version__
from .train import *
+
def get_config():
"""Returns a string containing the configuration information.
"""
@@ -37,7 +39,7 @@ class GMMMachine(_GMMMachine_C):
def update_dict(self, d):
self.means = d["means"]
self.variances = d["variances"]
- self.means = d["means"]
+ self.weights = d["weights"]
@staticmethod
def gmm_shape_from_dict(d):
@@ -182,3 +184,32 @@ class GMMStats(_GMMStats_C):
self.log_likelihood = d["log_likelihood"]
self.sum_px = d["sum_px"]
self.sum_pxx = d["sum_pxx"]
+
+
+class IVectorMachine(_IVectorMachine_C):
+ __doc__ = _IVectorMachine_C.__doc__
+
+ @staticmethod
+ def to_dict(ivector_machine):
+ ivector_data = dict()
+ ivector_data["gmm"] = GMMMachine.to_dict(ivector_machine.ubm)
+ ivector_data["sigma"] = ivector_machine.sigma
+ ivector_data["t"] = ivector_machine.t
+
+ return ivector_data
+
+ def update_dict(self, d):
+ ubm = GMMMachine.create_from_dict(d["gmm"])
+ t = d["t"]
+ self.__init__(ubm, t.shape[1])
+ self.sigma = d["sigma"]
+ self.t = t
+
+ def __getstate__(self):
+ d = dict(self.__dict__)
+ d.update(self.__class__.to_dict(self))
+ return d
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+ self.update_dict(d)
diff --git a/bob/learn/em/ivector_machine.cpp b/bob/learn/em/ivector_machine.cpp
index 99d98d2..159bec7 100644
--- a/bob/learn/em/ivector_machine.cpp
+++ b/bob/learn/em/ivector_machine.cpp
@@ -651,7 +651,7 @@ bool init_BobLearnEMIVectorMachine(PyObject* module)
// initialize the type struct
PyBobLearnEMIVectorMachine_Type.tp_name = IVectorMachine_doc.name();
PyBobLearnEMIVectorMachine_Type.tp_basicsize = sizeof(PyBobLearnEMIVectorMachineObject);
- PyBobLearnEMIVectorMachine_Type.tp_flags = Py_TPFLAGS_DEFAULT;
+ PyBobLearnEMIVectorMachine_Type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
PyBobLearnEMIVectorMachine_Type.tp_doc = IVectorMachine_doc.doc();
// set the functions
diff --git a/bob/learn/em/test/test_picklability.py b/bob/learn/em/test/test_picklability.py
index b9fa4cf..b3c6948 100644
--- a/bob/learn/em/test/test_picklability.py
+++ b/bob/learn/em/test/test_picklability.py
@@ -2,26 +2,39 @@
# vim: set fileencoding=utf-8 :
# Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-from bob.learn.em import GMMMachine, ISVBase, ISVMachine, KMeansMachine, GMMStats
+from bob.learn.em import (
+ GMMMachine,
+ ISVBase,
+ ISVMachine,
+ KMeansMachine,
+ GMMStats,
+ IVectorMachine,
+)
import numpy
import pickle
def test_gmm_machine():
- gmm_machine = GMMMachine(3,3)
- gmm_machine.means = numpy.arange(9).reshape(3,3).astype("float")
+ gmm_machine = GMMMachine(3, 3)
+ gmm_machine.means = numpy.arange(9).reshape(3, 3).astype("float")
gmm_machine_after_pickle = pickle.loads(pickle.dumps(gmm_machine))
-
- assert numpy.allclose(gmm_machine_after_pickle.means, gmm_machine_after_pickle.means, 10e-3)
- assert numpy.allclose(gmm_machine_after_pickle.variances, gmm_machine_after_pickle.variances, 10e-3)
- assert numpy.allclose(gmm_machine_after_pickle.weights, gmm_machine_after_pickle.weights, 10e-3)
+
+ assert numpy.allclose(
+ gmm_machine_after_pickle.means, gmm_machine_after_pickle.means, 10e-3
+ )
+ assert numpy.allclose(
+ gmm_machine_after_pickle.variances, gmm_machine_after_pickle.variances, 10e-3
+ )
+ assert numpy.allclose(
+ gmm_machine_after_pickle.weights, gmm_machine_after_pickle.weights, 10e-3
+ )
def test_isv_base():
- ubm = GMMMachine(3,3)
- ubm.means = numpy.arange(9).reshape(3,3).astype("float")
+ ubm = GMMMachine(3, 3)
+ ubm.means = numpy.arange(9).reshape(3, 3).astype("float")
isv_base = ISVBase(ubm, 2)
- isv_base.u = numpy.arange(18).reshape(9,2).astype("float")
+ isv_base.u = numpy.arange(18).reshape(9, 2).astype("float")
isv_base.d = numpy.arange(9).astype("float")
isv_base_after_pickle = pickle.loads(pickle.dumps(isv_base))
@@ -33,32 +46,36 @@ def test_isv_base():
def test_isv_machine():
# 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)
+ 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 ISVBaseMachine
- U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
- #V = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
- d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
- base = ISVBase(ubm,2)
+ U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64")
+ # V = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
+ d = numpy.array([0, 1, 0, 1, 0, 1], "float64")
+ base = ISVBase(ubm, 2)
base.u = U
base.d = d
# Creates a ISVMachine
- z = numpy.array([3,4,1,2,0,1], 'float64')
- x = numpy.array([1,2], 'float64')
+ z = numpy.array([3, 4, 1, 2, 0, 1], "float64")
+ x = numpy.array([1, 2], "float64")
isv_machine = ISVMachine(base)
isv_machine.z = z
isv_machine.x = x
isv_machine_after_pickle = pickle.loads(pickle.dumps(isv_machine))
- assert numpy.allclose(isv_machine_after_pickle.isv_base.u, isv_machine.isv_base.u, 10e-3)
- assert numpy.allclose(isv_machine_after_pickle.isv_base.d, isv_machine.isv_base.d, 10e-3)
+ assert numpy.allclose(
+ isv_machine_after_pickle.isv_base.u, isv_machine.isv_base.u, 10e-3
+ )
+ assert numpy.allclose(
+ isv_machine_after_pickle.isv_base.d, isv_machine.isv_base.d, 10e-3
+ )
assert numpy.allclose(isv_machine_after_pickle.x, isv_machine.x, 10e-3)
assert numpy.allclose(isv_machine_after_pickle.z, isv_machine.z, 10e-3)
@@ -66,30 +83,60 @@ def test_isv_machine():
def test_kmeans_machine():
# Test a KMeansMachine
- means = numpy.array([[3, 70, 0], [4, 72, 0]], 'float64')
- mean = numpy.array([3,70,1], 'float64')
+ means = numpy.array([[3, 70, 0], [4, 72, 0]], "float64")
+ mean = numpy.array([3, 70, 1], "float64")
# Initializes a KMeansMachine
- kmeans_machine = KMeansMachine(2,3)
+ kmeans_machine = KMeansMachine(2, 3)
kmeans_machine.means = means
kmeans_machine_after_pickle = pickle.loads(pickle.dumps(kmeans_machine))
- assert numpy.allclose(kmeans_machine_after_pickle.means, kmeans_machine.means, 10e-3)
+ assert numpy.allclose(
+ kmeans_machine_after_pickle.means, kmeans_machine.means, 10e-3
+ )
def test_gmmstats():
- gs = GMMStats(2,3)
- log_likelihood = -3.
+ gs = GMMStats(2, 3)
+ log_likelihood = -3.0
T = 1
n = numpy.array([0.4, 0.6], numpy.float64)
- sumpx = numpy.array([[1., 2., 3.], [2., 4., 3.]], numpy.float64)
- sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], numpy.float64)
+ sumpx = numpy.array([[1.0, 2.0, 3.0], [2.0, 4.0, 3.0]], numpy.float64)
+ sumpxx = numpy.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], numpy.float64)
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
-
+
gs_after_pickle = pickle.loads(pickle.dumps(gs))
assert gs == gs_after_pickle
+
+
+def test_ivector_machine():
+
+ # Ubm
+ ubm = GMMMachine(2, 3)
+ ubm.weights = numpy.array([0.4, 0.6])
+ ubm.means = numpy.array([[1.0, 7, 4], [4, 5, 3]])
+ ubm.variances = numpy.array([[0.5, 1.0, 1.5], [1.0, 1.5, 2.0]])
+
+ ivector_machine = IVectorMachine(ubm, 2)
+ t = numpy.array([[1.0, 2], [4, 1], [0, 3], [5, 8], [7, 10], [11, 1]])
+ sigma = numpy.array([1.0, 2.0, 1.0, 3.0, 2.0, 4.0])
+ ivector_machine.t = t
+ ivector_machine.sigma = sigma
+
+ ivector_after_pickle = pickle.loads(pickle.dumps(ivector_machine))
+ assert numpy.allclose(ivector_after_pickle.sigma, ivector_machine.sigma, 10e-3)
+ assert numpy.allclose(ivector_after_pickle.t, ivector_machine.t, 10e-3)
+ assert numpy.allclose(
+ ivector_after_pickle.ubm.means, ivector_machine.ubm.means, 10e-3
+ )
+ assert numpy.allclose(
+ ivector_after_pickle.ubm.variances, ivector_machine.ubm.variances, 10e-3
+ )
+ assert numpy.allclose(
+ ivector_after_pickle.ubm.weights, ivector_machine.ubm.weights, 10e-3
+ )
--
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