From 6b84f7ddbb9524e4d2b7e88ad9d350175355dfb0 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Wed, 16 Mar 2022 11:18:32 +0100
Subject: [PATCH] [gmm] fix gmm training when dask workers are in a different
process
---
bob/learn/em/gmm.py | 8 +++++---
bob/learn/em/test/test_gmm.py | 26 +++++++++++++++++++-------
2 files changed, 24 insertions(+), 10 deletions(-)
diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index 436d169..88efe81 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -119,7 +119,7 @@ def m_step(
relevance_factor=map_relevance_factor,
)
average_output = float(statistics.log_likelihood / statistics.t)
- return average_output
+ return machine, average_output
class GMMStats:
@@ -888,9 +888,11 @@ class GMMMachine(BaseEstimator):
)
for xx in X
]
- average_output = dask.compute(
+ new_machine, average_output = dask.compute(
dask.delayed(m_step_func)(self, stats)
)[0]
+ for attr in ["weights", "means", "variances"]:
+ setattr(self, attr, getattr(new_machine, attr))
else:
stats = [
e_step(
@@ -902,7 +904,7 @@ class GMMMachine(BaseEstimator):
log_weights=self.log_weights,
)
]
- average_output = m_step_func(self, stats)
+ _, average_output = m_step_func(self, stats)
logger.debug(f"log likelihood = {average_output}")
if step > 1:
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index 7eda1c6..3ad780a 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -16,11 +16,14 @@ from copy import deepcopy
import dask.array as da
import numpy as np
+from dask.distributed import Client
from h5py import File as HDF5File
from pkg_resources import resource_filename
from bob.learn.em import GMMMachine, GMMStats, KMeansMachine
+from .test_kmeans import to_dask_array, to_numpy
+
def load_array(filename):
with HDF5File(filename, "r") as f:
@@ -464,13 +467,22 @@ def test_gmm_kmeans_parallel_init():
data = np.array(
[[1.5, 1], [1, 1.5], [-1, 0.5], [-1.5, 0], [2, 2], [2.5, 2.5]]
)
- machine = machine.fit(data)
- expected_means = np.array([[1.25, 1.25], [-1.25, 0.25], [2.25, 2.25]])
- expected_variances = np.array(
- [[1 / 16, 1 / 16], [1 / 16, 1 / 16], [1 / 16, 1 / 16]]
- )
- np.testing.assert_almost_equal(machine.means, expected_means, decimal=3)
- np.testing.assert_almost_equal(machine.variances, expected_variances)
+ with Client().as_current():
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = machine.fit(data)
+ expected_means = np.array(
+ [[1.25, 1.25], [-1.25, 0.25], [2.25, 2.25]]
+ )
+ expected_variances = np.array(
+ [[1 / 16, 1 / 16], [1 / 16, 1 / 16], [1 / 16, 1 / 16]]
+ )
+ np.testing.assert_almost_equal(
+ machine.means, expected_means, decimal=3
+ )
+ np.testing.assert_almost_equal(
+ machine.variances, expected_variances
+ )
def test_likelihood():
--
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