diff --git a/bob/bio/gmm/config/algorithm/gmm.py b/bob/bio/gmm/config/algorithm/gmm.py
index 592b30e63d760dfcef8a505a941c7da45d49869e..87b43acdcd8cc1780f74c0267f7f5b7841c10887 100644
--- a/bob/bio/gmm/config/algorithm/gmm.py
+++ b/bob/bio/gmm/config/algorithm/gmm.py
@@ -1,7 +1,4 @@
-#!/usr/bin/env python
-
import bob.bio.gmm
-import numpy
algorithm = bob.bio.gmm.algorithm.GMM(
number_of_gaussians = 512,
diff --git a/bob/bio/gmm/config/algorithm/isv.py b/bob/bio/gmm/config/algorithm/isv.py
index 3ae069d881764d904ea8d4419c5b5b01760e0b35..390e8294691b46f46f002290a4de096b9181075b 100644
--- a/bob/bio/gmm/config/algorithm/isv.py
+++ b/bob/bio/gmm/config/algorithm/isv.py
@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-
import bob.bio.gmm
algorithm = bob.bio.gmm.algorithm.ISV(
diff --git a/bob/bio/gmm/script/verify_gmm.py b/bob/bio/gmm/script/verify_gmm.py
index c1683b293440db0473a59ff2d41f43d403068ab9..8501ecc561700d997494dfbe390283203435aae6 100644
--- a/bob/bio/gmm/script/verify_gmm.py
+++ b/bob/bio/gmm/script/verify_gmm.py
@@ -55,7 +55,7 @@ def parse_arguments(command_line_parameters, exclude_resources_from = []):
tools.initialize_parallel_gmm(args)
# assert that the algorithm is a GMM
- if args.algorithm.__class__ not in (algorithm.GMM, algorithm.GMMRegular):
+ if tools.base(args.algorithm).__class__ not in (algorithm.GMM, algorithm.GMMRegular):
raise ValueError("The given algorithm %s is not a (pure) GMM algorithm" % type(args.algorithm))
return args
@@ -63,6 +63,8 @@ def parse_arguments(command_line_parameters, exclude_resources_from = []):
def add_gmm_jobs(args, job_ids, deps, submitter):
"""Adds all GMM-related jobs."""
+ algorithm = tools.base(args.algorithm)
+
# KMeans
if not args.skip_kmeans:
# initialization
@@ -75,7 +77,7 @@ def add_gmm_jobs(args, job_ids, deps, submitter):
deps.append(job_ids['kmeans-init'])
# several iterations of E and M steps
- for iteration in range(args.kmeans_start_iteration, args.algorithm.kmeans_training_iterations):
+ for iteration in range(args.kmeans_start_iteration, algorithm.kmeans_training_iterations):
# E-step
job_ids['kmeans-e-step'] = submitter.submit(
'--sub-task kmeans-e-step --iteration %d' % iteration,
@@ -106,7 +108,7 @@ def add_gmm_jobs(args, job_ids, deps, submitter):
deps.append(job_ids['gmm-init'])
# several iterations of E and M steps
- for iteration in range(args.gmm_start_iteration, args.algorithm.gmm_training_iterations):
+ for iteration in range(args.gmm_start_iteration, algorithm.gmm_training_iterations):
# E-step
job_ids['gmm-e-step'] = submitter.submit(
'--sub-task gmm-e-step --iteration %d' % iteration,
@@ -138,6 +140,7 @@ def execute(args):
return True
# now, check what we can do
+ algorithm = tools.base(args.algorithm)
# the file selector object
fs = tools.FileSelector.instance()
@@ -145,7 +148,7 @@ def execute(args):
# train the feature projector
if args.sub_task == 'kmeans-init':
tools.kmeans_initialize(
- args.algorithm,
+ algorithm,
args.extractor,
args.limit_training_data,
force = args.force)
@@ -153,7 +156,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'kmeans-e-step':
tools.kmeans_estep(
- args.algorithm,
+ algorithm,
args.extractor,
args.iteration,
indices = base_tools.indices(fs.training_list('extracted', 'train_projector'), args.grid.number_of_projection_jobs),
@@ -162,7 +165,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'kmeans-m-step':
tools.kmeans_mstep(
- args.algorithm,
+ algorithm,
args.iteration,
number_of_parallel_jobs = args.grid.number_of_projection_jobs,
clean = args.clean_intermediate,
@@ -170,7 +173,7 @@ def execute(args):
elif args.sub_task == 'gmm-init':
tools.gmm_initialize(
- args.algorithm,
+ algorithm,
args.extractor,
args.limit_training_data,
force = args.force)
@@ -178,7 +181,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'gmm-e-step':
tools.gmm_estep(
- args.algorithm,
+ algorithm,
args.extractor,
args.iteration,
indices = base_tools.indices(fs.training_list('extracted', 'train_projector'), args.grid.number_of_projection_jobs),
@@ -187,7 +190,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'gmm-m-step':
tools.gmm_mstep(
- args.algorithm,
+ algorithm,
args.iteration,
number_of_parallel_jobs = args.grid.number_of_projection_jobs,
clean = args.clean_intermediate,
diff --git a/bob/bio/gmm/script/verify_isv.py b/bob/bio/gmm/script/verify_isv.py
index cd65f5504155caee6369960b599592957657fce8..1c55f65b5a169735d141652b4663f7c03396fa42 100644
--- a/bob/bio/gmm/script/verify_isv.py
+++ b/bob/bio/gmm/script/verify_isv.py
@@ -55,7 +55,7 @@ def parse_arguments(command_line_parameters, exclude_resources_from = []):
tools.initialize_parallel_gmm(args, sub_module = 'isv')
# assert that the algorithm is a GMM
- if args.algorithm.__class__ != algorithm.ISV:
+ if tools.base(args.algorithm).__class__ != algorithm.ISV:
raise ValueError("The given algorithm %s is not a (pure) ISV algorithm" % type(args.algorithm))
return args
@@ -101,13 +101,14 @@ def execute(args):
return True
# now, check what we can do
+ algorithm = tools.base(args.algorithm)
# the file selector object
fs = tools.FileSelector.instance()
if args.sub_task == 'gmm-project':
tools.gmm_project(
- args.algorithm,
+ algorithm,
args.extractor,
indices = base_tools.indices(fs.training_list('extracted', 'train_projector'), args.grid.number_of_projection_jobs),
force = args.force)
@@ -115,7 +116,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'train-isv':
tools.train_isv(
- args.algorithm,
+ algorithm,
force = args.force)
else:
diff --git a/bob/bio/gmm/script/verify_ivector.py b/bob/bio/gmm/script/verify_ivector.py
index 5081c5af20be1e6e4d30136a2bc38496c24a8f16..fbe2f87fc89d3550f45d96da10819a5a411102f2 100644
--- a/bob/bio/gmm/script/verify_ivector.py
+++ b/bob/bio/gmm/script/verify_ivector.py
@@ -55,7 +55,7 @@ def parse_arguments(command_line_parameters, exclude_resources_from = []):
tools.initialize_parallel_gmm(args, sub_module = 'ivector')
# assert that the algorithm is a GMM
- if args.algorithm.__class__ != algorithm.IVector:
+ if tools.base(args.algorithm).__class__ != algorithm.IVector:
raise ValueError("The given algorithm %s is not a (pure) IVector algorithm" % type(args.algorithm))
return args
@@ -69,6 +69,8 @@ def add_ivector_jobs(args, job_ids, deps, submitter):
job_ids, deps = add_gmm_jobs(args, job_ids, deps, submitter)
# now, add the extra steps for ivector
+ algorithm = tools.base(args.algorithm)
+
if not args.skip_ivector:
# gmm projection
job_ids['gmm-projection'] = submitter.submit(
@@ -80,7 +82,7 @@ def add_ivector_jobs(args, job_ids, deps, submitter):
deps.append(job_ids['gmm-projection'])
# several iterations of E and M steps
- for iteration in range(args.tv_start_iteration, args.algorithm.tv_training_iterations):
+ for iteration in range(args.tv_start_iteration, algorithm.tv_training_iterations):
# E-step
job_ids['ivector-e-step'] = submitter.submit(
'--sub-task ivector-e-step --iteration %d' % iteration,
@@ -131,20 +133,21 @@ def execute(args):
return True
# now, check what we can do
+ algorithm = tools.base(args.algorithm)
# the file selector object
fs = tools.FileSelector.instance()
if args.sub_task == 'gmm-project':
tools.gmm_project(
- args.algorithm,
+ algorithm,
args.extractor,
indices = base_tools.indices(fs.training_list('extracted', 'train_projector'), args.grid.number_of_projection_jobs),
force = args.force)
elif args.sub_task == 'ivector-e-step':
tools.ivector_estep(
- args.algorithm,
+ algorithm,
args.iteration,
indices = base_tools.indices(fs.training_list('projected_gmm', 'train_projector'), args.grid.number_of_projection_jobs),
force = args.force)
@@ -152,7 +155,7 @@ def execute(args):
# train the feature projector
elif args.sub_task == 'ivector-m-step':
tools.ivector_mstep(
- args.algorithm,
+ algorithm,
args.iteration,
number_of_parallel_jobs = args.grid.number_of_projection_jobs,
clean = args.clean_intermediate,
@@ -160,13 +163,13 @@ def execute(args):
elif args.sub_task == 'ivector-project':
tools.ivector_project(
- args.algorithm,
+ algorithm,
indices = base_tools.indices(fs.training_list('projected_gmm', 'train_projector'), args.grid.number_of_projection_jobs),
force = args.force)
elif args.sub_task == 'train-whitener':
tools.train_whitener(
- args.algorithm,
+ algorithm,
force = args.force)
else:
diff --git a/bob/bio/gmm/tools/gmm.py b/bob/bio/gmm/tools/gmm.py
index fba7ee8fff417f7249b0cd3455be764a81cb9e30..dd18ccedc08660b31bbcc902c2d1350ada7b9895 100644
--- a/bob/bio/gmm/tools/gmm.py
+++ b/bob/bio/gmm/tools/gmm.py
@@ -9,6 +9,7 @@ logger = logging.getLogger("bob.bio.gmm")
from bob.bio.base.tools.FileSelector import FileSelector
from bob.bio.base import utils, tools
+from .utils import read_feature
def kmeans_initialize(algorithm, extractor, limit_data = None, force = False):
@@ -23,7 +24,7 @@ def kmeans_initialize(algorithm, extractor, limit_data = None, force = False):
# read data
logger.info("UBM training: initializing kmeans")
training_list = utils.selected_elements(fs.training_list('extracted', 'train_projector'), limit_data)
- data = numpy.vstack([extractor.read_feature(feature_file) for feature_file in training_list])
+ data = numpy.vstack([read_feature(extractor, feature_file) for feature_file in training_list])
# Perform KMeans initialization
kmeans_machine = bob.learn.em.KMeansMachine(algorithm.gaussians, data.shape[1])
@@ -55,7 +56,7 @@ def kmeans_estep(algorithm, extractor, iteration, indices, force=False):
logger.info("UBM training: KMeans E-Step round %d from range(%d, %d)", iteration, *indices)
# read data
- data = numpy.vstack([extractor.read_feature(training_list[index]) for index in range(indices[0], indices[1])])
+ data = numpy.vstack([read_feature(extractor, training_list[index]) for index in range(indices[0], indices[1])])
# Performs the E-step
trainer = algorithm.kmeans_trainer
@@ -169,7 +170,7 @@ def gmm_initialize(algorithm, extractor, limit_data = None, force = False):
# read features
training_list = utils.selected_elements(fs.training_list('extracted', 'train_projector'), limit_data)
- data = numpy.vstack([extractor.read_feature(feature_file) for feature_file in training_list])
+ data = numpy.vstack([read_feature(extractor, feature_file) for feature_file in training_list])
# get means and variances of kmeans result
kmeans_machine = bob.learn.em.KMeansMachine(bob.io.base.HDF5File(fs.kmeans_file))
@@ -209,7 +210,7 @@ def gmm_estep(algorithm, extractor, iteration, indices, force=False):
logger.info("UBM training: GMM E-Step from range(%d, %d)", indices)
# read data
- data = numpy.vstack([extractor.read_feature(training_list[index]) for index in range(indices[0], indices[1])])
+ data = numpy.vstack([read_feature(extractor, training_list[index]) for index in range(indices[0], indices[1])])
trainer = algorithm.ubm_trainer
trainer.initialize(gmm_machine, None)
@@ -299,7 +300,7 @@ def gmm_project(algorithm, extractor, indices, force=False):
if not utils.check_file(projected_file, force):
# load feature
- feature = extractor.read_feature(feature_file)
+ feature = read_feature(extractor, feature_file)
# project feature
projected = algorithm.project_ubm(feature)
# write it
diff --git a/bob/bio/gmm/tools/utils.py b/bob/bio/gmm/tools/utils.py
index fe773e0136518d8d52f22741f488b06341316987..146fb50e811f1aa88ab8ca17ebe0ed930e3a96d6 100644
--- a/bob/bio/gmm/tools/utils.py
+++ b/bob/bio/gmm/tools/utils.py
@@ -1,4 +1,5 @@
import bob.bio.base
+import numpy
def add_jobs(args, submitter, local_job_adder):
"""Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed."""
@@ -47,3 +48,28 @@ def add_jobs(args, submitter, local_job_adder):
setattr(args, "skip_%s" % key, original_skips[key])
return job_ids
+
+
+def is_video_extension(algorithm):
+ try:
+ import bob.bio.video
+ if isinstance(algorithm, bob.bio.video.algorithm.Algorithm):
+ return True
+ except ImportError:
+ pass
+ return False
+
+def base(algorithm):
+ """Returns the base algorithm, if it is a video extension, otherwise returns the algorithm itself"""
+ return algorithm.algorithm if is_video_extension(algorithm) else algorithm
+
+def read_feature(extractor, feature_file):
+ feature = extractor.read_feature(feature_file)
+ try:
+ import bob.bio.video
+ if isinstance(extractor, bob.bio.video.extractor.Extractor):
+ assert isinstance(feature, bob.bio.video.FrameContainer)
+ return numpy.vstack([frame for _,frame,_ in feature])
+ except ImportError:
+ pass
+ return feature
diff --git a/buildout.cfg b/buildout.cfg
index 3771d80635ba39bb94f1841b504bce18c923eda3..0710f0569057c9d8d04872f068e9a8245dbb52c6 100644
--- a/buildout.cfg
+++ b/buildout.cfg
@@ -14,7 +14,7 @@ develop = src/bob.bio.base
.
; options for bob.buildout
-debug = true
+debug = false
verbose = true
newest = false