diff --git a/bob/bio/base/extractor/stacks.py b/bob/bio/base/extractor/stacks.py
index 511b7d04106cbf62af745bdaf6447fc9fa7a3a89..8bc5aee5db9f755fc6520d5b68b764c9ae811e50 100644
--- a/bob/bio/base/extractor/stacks.py
+++ b/bob/bio/base/extractor/stacks.py
@@ -1,5 +1,6 @@
from ..utils.processors import SequentialProcessor, ParallelProcessor
from .Extractor import Extractor
+from bob.io.base import HDF5File
class MultipleExtractor(Extractor):
@@ -8,34 +9,35 @@ class MultipleExtractor(Extractor):
def get_attributes(self, processors):
requires_training = any((p.requires_training for p in processors))
- split_training_data_by_client = any(
- (p.split_training_data_by_client for p in processors))
- min_extractor_file_size = min(
- (p.min_extractor_file_size for p in processors))
- min_feature_file_size = min(
- (p.min_feature_file_size for p in processors))
+ split_training_data_by_client = any(p.split_training_data_by_client for p
+ in processors)
+ min_extractor_file_size = min(p.min_extractor_file_size for p in
+ processors)
+ min_feature_file_size = min(p.min_feature_file_size for p in processors)
return (requires_training, split_training_data_by_client,
min_extractor_file_size, min_feature_file_size)
- def get_extractor_files(self, extractor_file):
- paths = [extractor_file]
- paths += [extractor_file +
- '_{}.hdf5'.format(i) for i in range(1, len(self.processors))]
- return paths
+ def get_extractor_groups(self):
+ groups = ['E_{}'.format(i + 1) for i in range(len(self.processors))]
+ return groups
def train_one(self, e, training_data, extractor_file, apply=False):
if not e.requires_training:
return
+ # if any of the extractors require splitting the data, the
+ # split_training_data_by_client is True.
if e.split_training_data_by_client:
e.train(training_data, extractor_file)
if not apply:
return
training_data = [[e(d) for d in datalist] for datalist in training_data]
+ # when no extractor needs splitting
elif not self.split_training_data_by_client:
e.train(training_data, extractor_file)
if not apply:
return
training_data = [e(d) for d in training_data]
+ # when e here wants it flat but the data is split
else:
# make training_data flat
training_data_len = [len(datalist) for datalist in training_data]
@@ -55,9 +57,12 @@ class MultipleExtractor(Extractor):
return training_data
def load(self, extractor_file):
- paths = self.get_extractor_files(extractor_file)
- for e, path in zip(self.processors, paths):
- e.load(path)
+ with HDF5File(extractor_file) as f:
+ groups = self.get_extractor_groups()
+ for e, group in zip(self.processors, groups):
+ f.cd(group)
+ e.load(f)
+ f.cd('..')
class SequentialExtractor(SequentialProcessor, MultipleExtractor):
@@ -66,21 +71,24 @@ class SequentialExtractor(SequentialProcessor, MultipleExtractor):
def __init__(self, processors):
(requires_training, split_training_data_by_client,
- min_extractor_file_size, min_feature_file_size) = self.get_attributes(
- processors)
+ min_extractor_file_size, min_feature_file_size) = \
+ self.get_attributes(processors)
- SequentialProcessor.__init__(self, processors)
- MultipleExtractor.__init__(
- self,
+ super(SequentialExtractor, self).__init__(
+ processors=processors,
requires_training=requires_training,
split_training_data_by_client=split_training_data_by_client,
min_extractor_file_size=min_extractor_file_size,
min_feature_file_size=min_feature_file_size)
def train(self, training_data, extractor_file):
- paths = self.get_extractor_files(extractor_file)
- for e, path in zip(self.processors, paths):
- training_data = self.train_one(e, training_data, path, apply=True)
+ with HDF5File(extractor_file, 'w') as f:
+ groups = self.get_extractor_groups()
+ for e, group in zip(self.processors, groups):
+ f.create_group(group)
+ f.cd(group)
+ training_data = self.train_one(e, training_data, f, apply=True)
+ f.cd('..')
class ParallelExtractor(ParallelProcessor, MultipleExtractor):
@@ -92,18 +100,21 @@ class ParallelExtractor(ParallelProcessor, MultipleExtractor):
min_extractor_file_size, min_feature_file_size) = self.get_attributes(
processors)
- ParallelProcessor.__init__(self, processors)
- MultipleExtractor.__init__(
- self,
+ super(ParallelExtractor, self).__init__(
+ processors=processors,
requires_training=requires_training,
split_training_data_by_client=split_training_data_by_client,
min_extractor_file_size=min_extractor_file_size,
min_feature_file_size=min_feature_file_size)
def train(self, training_data, extractor_file):
- paths = self.get_extractor_files(extractor_file)
- for e, path in zip(self.processors, paths):
- self.train_one(e, training_data, path)
+ with HDF5File(extractor_file, 'w') as f:
+ groups = self.get_extractor_groups()
+ for e, group in zip(self.processors, groups):
+ f.create_group(group)
+ f.cd(group)
+ self.train_one(e, training_data, f, apply=False)
+ f.cd('..')
class CallableExtractor(Extractor):
diff --git a/bob/bio/base/preprocessor/stacks.py b/bob/bio/base/preprocessor/stacks.py
index 1c8be13db4a6e48a1a396bdca0e207360504a0be..af96a736f02dc39b8deaaa25dd072b79e0428a8c 100644
--- a/bob/bio/base/preprocessor/stacks.py
+++ b/bob/bio/base/preprocessor/stacks.py
@@ -26,12 +26,8 @@ class ParallelPreprocessor(ParallelProcessor, Preprocessor):
__doc__ = ParallelProcessor.__doc__
def __init__(self, processors, **kwargs):
- min_preprocessed_file_size = 1000
- try:
- min_preprocessed_file_size = min(
- (p.min_preprocessed_file_size for p in processors))
- except AttributeError:
- pass
+ min_preprocessed_file_size = min((p.min_preprocessed_file_size for p in
+ processors))
ParallelProcessor.__init__(self, processors)
Preprocessor.__init__(
@@ -48,14 +44,21 @@ class CallablePreprocessor(Preprocessor):
Attributes
----------
+ accepts_annotations : bool
+ If False, annotations are not passed to the callable.
callable : object
Anything that is callable. It will be used as a preprocessor in
bob.bio.base.
"""
- def __init__(self, callable, **kwargs):
- super(CallablePreprocessor, self).__init__(**kwargs)
+ def __init__(self, callable, accepts_annotations=True, **kwargs):
+ super(CallablePreprocessor, self).__init__(
+ callable=callable, accepts_annotations=accepts_annotations, **kwargs)
self.callable = callable
+ self.accepts_annotations = accepts_annotations
def __call__(self, data, annotations):
- return self.callable(data)
+ if self.accepts_annotations:
+ return self.callable(data, annotations)
+ else:
+ return self.callable(data)
diff --git a/bob/bio/base/test/test_stacks.py b/bob/bio/base/test/test_stacks.py
index 3db57f56b14e74cd71584603c01d623f3d212166..3628902de8f39dba21d79e4a5a0498b940cd22e6 100644
--- a/bob/bio/base/test/test_stacks.py
+++ b/bob/bio/base/test/test_stacks.py
@@ -24,7 +24,7 @@ def test_processors():
def test_preprocessors():
- processors = [CallablePreprocessor(p) for p in PROCESSORS]
+ processors = [CallablePreprocessor(p, False) for p in PROCESSORS]
proc = SequentialPreprocessor(processors)
data = proc(DATA, None)
assert np.allclose(data, SEQ_DATA)
diff --git a/bob/bio/base/utils/processors.py b/bob/bio/base/utils/processors.py
index 64549d767fd07dacb5d7de329ee7f604472d5dee..03dd2e98cf8f6eb12d7e6b69cdfccc55af6e717c 100644
--- a/bob/bio/base/utils/processors.py
+++ b/bob/bio/base/utils/processors.py
@@ -11,7 +11,7 @@ class SequentialProcessor(object):
A list of processors to apply.
"""
- def __init__(self, processors):
+ def __init__(self, processors, **kwargs):
super(SequentialProcessor, self).__init__()
self.processors = processors
@@ -32,10 +32,7 @@ class SequentialProcessor(object):
The processed data.
"""
for processor in self.processors:
- try:
- data = processor(data, **kwargs)
- except ValueError:
- data = processor(data)
+ data = processor(data, **kwargs)
return data
@@ -51,7 +48,7 @@ class ParallelProcessor(object):
If True (default), :any:`numpy.hstack` is called on the list of outputs.
"""
- def __init__(self, processors, stack=True):
+ def __init__(self, processors, stack=True, **kwargs):
super(ParallelProcessor, self).__init__()
self.processors = processors
self.stack = stack
@@ -74,10 +71,7 @@ class ParallelProcessor(object):
"""
output = []
for processor in self.processors:
- try:
- out = processor(data, **kwargs)
- except ValueError:
- out = processor(data)
+ out = processor(data, **kwargs)
output.append(out)
if self.stack:
output = numpy.hstack(output)