From 63a73cdf58d0d2a9cdbc8c33dbc84a68b201b774 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Wed, 30 Mar 2022 14:16:36 +0200
Subject: [PATCH] Optimized some functions
---
bob/learn/em/factor_analysis.py | 79 +++++++++++++--------------------
1 file changed, 32 insertions(+), 47 deletions(-)
diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 06d868c..6eb2cd4 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -459,22 +459,15 @@ class FactorAnalysisBase(BaseEstimator):
inv_A1 = np.linalg.inv(acc_U_A1)
# Iterating over the gaussians to update U
-
- for c in range(self.ubm.n_gaussians):
-
- U_c = (
- acc_U_A2[
- c
- * self.feature_dimension : (c + 1)
- * self.feature_dimension,
- :,
- ]
- @ inv_A1[c, :, :]
+ U_c = (
+ acc_U_A2.reshape(
+ self.ubm.n_gaussians, self.feature_dimension, self.r_U
)
- self._U[
- c * self.feature_dimension : (c + 1) * self.feature_dimension,
- :,
- ] = U_c
+ @ inv_A1
+ )
+ self._U = U_c.reshape(
+ self.ubm.n_gaussians * self.feature_dimension, self.r_U
+ )
def _compute_uprod(self):
"""
@@ -483,14 +476,15 @@ class FactorAnalysisBase(BaseEstimator):
### https://gitlab.idiap.ch/bob/bob.learn.em/-/blob/da92d0e5799d018f311f1bf5cdd5a80e19e142ca/bob/learn/em/cpp/FABaseTrainer.cpp#L325
"""
- UProd = np.zeros((self.ubm.n_gaussians, self.r_U, self.r_U))
- for c in range(self.ubm.n_gaussians):
- # U_c.T
- U_c = self._U[
- c * self.feature_dimension : (c + 1) * self.feature_dimension, :
- ]
- sigma_c = self.ubm.variances[c].flatten()
- UProd[c, :, :] = U_c.T @ (U_c.T / sigma_c).T
+ # UProd = (self.ubm.n_gaussians, self.r_U, self.r_U)
+
+ Uc = self._U.reshape(
+ (self.ubm.n_gaussians, self.feature_dimension, self.r_U)
+ )
+ UcT = Uc.transpose(0, 2, 1)
+
+ sigma_c = self.ubm.variances[:, np.newaxis]
+ UProd = (UcT / sigma_c) @ Uc
return UProd
@@ -893,14 +887,13 @@ class FactorAnalysisBase(BaseEstimator):
### https://gitlab.idiap.ch/bob/bob.learn.em/-/blob/da92d0e5799d018f311f1bf5cdd5a80e19e142ca/bob/learn/em/cpp/FABaseTrainer.cpp#L193
"""
- VProd = np.zeros((self.ubm.n_gaussians, self.r_V, self.r_V))
- for c in range(self.ubm.n_gaussians):
- # V_c.T
- V_c = self._V[
- c * self.feature_dimension : (c + 1) * self.feature_dimension, :
- ]
- sigma_c = self.ubm.variances[c].flatten()
- VProd[c, :, :] = V_c.T @ (V_c.T / sigma_c).T
+ Vc = self._V.reshape(
+ (self.ubm.n_gaussians, self.feature_dimension, self.r_V)
+ )
+ VcT = Vc.transpose(0, 2, 1)
+
+ sigma_c = self.ubm.variances[:, np.newaxis]
+ VProd = (VcT / sigma_c) @ Vc
return VProd
@@ -1408,23 +1401,15 @@ class JFAMachine(FactorAnalysisBase):
# https://stackoverflow.com/questions/11972102/is-there-a-way-to-efficiently-invert-an-array-of-matrices-with-numpy
inv_A1 = np.linalg.inv(acc_V_A1)
- # Iterating over the gaussians to update V
-
- for c in range(self.ubm.n_gaussians):
-
- V_c = (
- acc_V_A2[
- c
- * self.feature_dimension : (c + 1)
- * self.feature_dimension,
- :,
- ]
- @ inv_A1[c, :, :]
+ V_c = (
+ acc_V_A2.reshape(
+ (self.ubm.n_gaussians, self.feature_dimension, self.r_V)
)
- self._V[
- c * self.feature_dimension : (c + 1) * self.feature_dimension,
- :,
- ] = V_c
+ @ inv_A1
+ )
+ self._V = V_c.reshape(
+ (self.ubm.n_gaussians * self.feature_dimension, self.r_V)
+ )
def finalize_v(self, X, y, n_acc, f_acc):
"""
--
GitLab