From 78b62b408bb4d4a232b604bcc957e9663b69e165 Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Mon, 6 Dec 2021 12:56:31 +0100
Subject: [PATCH] Remove projection in score, fix ubm not set in fit
---
bob/bio/gmm/bioalgorithm/GMM.py | 59 +++------------------------------
1 file changed, 5 insertions(+), 54 deletions(-)
diff --git a/bob/bio/gmm/bioalgorithm/GMM.py b/bob/bio/gmm/bioalgorithm/GMM.py
index 0247428..ed574e2 100644
--- a/bob/bio/gmm/bioalgorithm/GMM.py
+++ b/bob/bio/gmm/bioalgorithm/GMM.py
@@ -21,8 +21,6 @@ from h5py import File as HDF5File
from sklearn.base import BaseEstimator
-import bob.core
-
from bob.bio.base.pipelines.vanilla_biometrics.abstract_classes import BioAlgorithm
from bob.learn.em.mixture import GMMMachine
from bob.learn.em.mixture import GMMStats
@@ -121,7 +119,7 @@ class GMM(BioAlgorithm, BaseEstimator):
def scoring_function_wrapped(*args, **kwargs):
with dask.config.set(scheduler="threads"):
- return scoring_function(*args, **kwargs).compute()
+ return scoring_function(*args, **kwargs)
self.scoring_function = scoring_function_wrapped
@@ -233,11 +231,10 @@ class GMM(BioAlgorithm, BaseEstimator):
"""
assert isinstance(biometric_reference, GMMMachine)
- stats = self.project(data)
return self.scoring_function(
models_means=[biometric_reference],
ubm=self.ubm,
- test_stats=stats,
+ test_stats=data,
frame_length_normalization=True,
)[0, 0]
@@ -309,7 +306,7 @@ class GMM(BioAlgorithm, BaseEstimator):
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.fit(array)
+ self.ubm = self.ubm.fit(array)
return self
@@ -321,51 +318,5 @@ class GMM(BioAlgorithm, BaseEstimator):
# `project` is applied in the score function directly.
return X
-
-
-
-def delayed_to_da(delayed, meta=None):
- """Converts one dask.delayed object to a dask.array"""
- if meta is None:
- meta = np.array(delayed.data.compute())
-
- darray = da.from_delayed(delayed.data, meta.shape, dtype=meta.dtype, name=False)
- return darray, meta
-
-
-def delayed_samples_to_dask_arrays(delayed_samples, meta=None):
- output = []
- for ds in delayed_samples:
- d_array, meta = delayed_to_da(ds, meta)
- output.append(d_array)
- return output, meta
-
-
-def delayeds_to_dask_array(delayeds, meta=None):
- """Converts a set of dask.delayed to a list of dask.array"""
- output = []
- for d in delayeds:
- d_array, meta = delayed_samples_to_dask_arrays(d, meta)
- output.extend(d_array)
- return output
-
-
-class GMMDaskWrapper(DaskWrapper):
- def fit(self, X, y=None, **fit_params):
- # convert X which is a dask bag to a dask array
- X = X.persist()
- delayeds = X.to_delayed()
- lengths = X.map_partitions(lambda samples: [len(samples)]).compute()
- shapes = X.map_partitions(
- lambda samples: [[s.data.shape for s in samples]]
- ).compute()
- dtype, X = None, []
- for l, s, d in zip(lengths, shapes, delayeds):
- d._length = l
- for shape, ds in zip(s, d):
- if dtype is None:
- dtype = np.array(ds.data.compute()).dtype
- darray = da.from_delayed(ds.data, shape, dtype=dtype, name=False)
- X.append(darray)
- self.estimator.fit(X, y, **fit_params)
- return self
+ def _more_tags(self):
+ return {"bob_fit_supports_dask_array": True}
--
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