diff --git a/bob/bio/base/extractor/stacks.py b/bob/bio/base/extractor/stacks.py
index 98cb83ce3ea4d18470a6b1ffca85900e4c99312f..0f85207a9fbdee3b9d89ea1609f0e5996ca57096 100644
--- a/bob/bio/base/extractor/stacks.py
+++ b/bob/bio/base/extractor/stacks.py
@@ -4,133 +4,141 @@ from bob.io.base import HDF5File
class MultipleExtractor(Extractor):
- """Base class for SequentialExtractor and ParallelExtractor. This class is
- not meant to be used directly."""
-
- 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)
- return (requires_training, split_training_data_by_client,
- min_extractor_file_size, min_feature_file_size)
-
- 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]
- training_data = [d for datalist in training_data for d in datalist]
- e.train(training_data, extractor_file)
- if not apply:
- return
- # split training data
- new_training_data, i = [], 0
- for length in training_data_len:
- class_data = []
- for _ in range(length):
- class_data.append(e(training_data[i]))
- i += 1
- new_training_data.append(class_data)
- training_data = new_training_data
- return training_data
-
- def load(self, extractor_file):
- 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('..')
+ """Base class for SequentialExtractor and ParallelExtractor. This class is
+ not meant to be used directly."""
+
+ 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)
+ return (requires_training, split_training_data_by_client,
+ min_extractor_file_size, min_feature_file_size)
+
+ 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]
+ training_data = [d for datalist in training_data for d in datalist]
+ e.train(training_data, extractor_file)
+ if not apply:
+ return
+ # split training data
+ new_training_data, i = [], 0
+ for length in training_data_len:
+ class_data = []
+ for _ in range(length):
+ class_data.append(e(training_data[i]))
+ i += 1
+ new_training_data.append(class_data)
+ training_data = new_training_data
+ return training_data
+
+ def load(self, extractor_file):
+ 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):
- __doc__ = SequentialProcessor.__doc__
+ __doc__ = SequentialProcessor.__doc__
- def __init__(self, processors):
+ def __init__(self, processors):
- (requires_training, split_training_data_by_client,
- min_extractor_file_size, min_feature_file_size) = \
- self.get_attributes(processors)
+ (requires_training, split_training_data_by_client,
+ min_extractor_file_size, min_feature_file_size) = \
+ self.get_attributes(processors)
- 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)
+ 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):
- 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('..')
+ def train(self, training_data, extractor_file):
+ 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('..')
+
+ def read_feature(self, feature_file):
+ return self.processors[-1].read_feature(feature_file)
+
+ def write_feature(self, feature, feature_file):
+ return self.processors[-1].write_feature(feature, feature_file)
class ParallelExtractor(ParallelProcessor, MultipleExtractor):
- __doc__ = ParallelProcessor.__doc__
+ __doc__ = ParallelProcessor.__doc__
- def __init__(self, processors):
+ def __init__(self, processors):
- (requires_training, split_training_data_by_client,
- min_extractor_file_size, min_feature_file_size) = self.get_attributes(
- processors)
+ (requires_training, split_training_data_by_client,
+ min_extractor_file_size, min_feature_file_size) = self.get_attributes(
+ processors)
- 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)
+ 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):
- 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('..')
+ def train(self, training_data, extractor_file):
+ 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):
- """A simple extractor that takes a callable and applies that callable to the
- input.
-
- Attributes
- ----------
- callable : object
- Anything that is callable. It will be used as an extractor in
- bob.bio.base.
- """
-
- def __init__(self, callable, **kwargs):
- super(CallableExtractor, self).__init__(**kwargs)
- self.callable = callable
-
- def __call__(self, data):
- return self.callable(data)
+ """A simple extractor that takes a callable and applies that callable to
+ the input.
+
+ Attributes
+ ----------
+ callable : object
+ Anything that is callable. It will be used as an extractor in
+ bob.bio.base.
+ """
+
+ def __init__(self, callable, **kwargs):
+ super(CallableExtractor, self).__init__(**kwargs)
+ self.callable = callable
+
+ def __call__(self, data):
+ return self.callable(data)
diff --git a/bob/bio/base/preprocessor/stacks.py b/bob/bio/base/preprocessor/stacks.py
index d782291514210a7518d2655b38e48370a01284f9..1a4b31005b7d357c4f17a605a9dd1d58e3af1405 100644
--- a/bob/bio/base/preprocessor/stacks.py
+++ b/bob/bio/base/preprocessor/stacks.py
@@ -3,62 +3,71 @@ from .Preprocessor import Preprocessor
class SequentialPreprocessor(SequentialProcessor, Preprocessor):
- __doc__ = SequentialProcessor.__doc__
+ __doc__ = SequentialProcessor.__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
+ 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
- SequentialProcessor.__init__(self, processors)
- Preprocessor.__init__(
- self, min_preprocessed_file_size=min_preprocessed_file_size, **kwargs)
+ SequentialProcessor.__init__(self, processors)
+ Preprocessor.__init__(
+ self, min_preprocessed_file_size=min_preprocessed_file_size,
+ **kwargs)
- def __call__(self, data, annotations):
- return super(SequentialPreprocessor, self).__call__(
- data, annotations=annotations)
+ def __call__(self, data, annotations):
+ return super(SequentialPreprocessor, self).__call__(
+ data, annotations=annotations)
+
+ def read_data(self, data_file):
+ return self.processors[-1].read_data(data_file)
+
+ def write_data(self, data, data_file):
+ return self.processors[-1].write_data(data, data_file)
class ParallelPreprocessor(ParallelProcessor, Preprocessor):
- __doc__ = ParallelProcessor.__doc__
+ __doc__ = ParallelProcessor.__doc__
- def __init__(self, processors, **kwargs):
- min_preprocessed_file_size = min(p.min_preprocessed_file_size for p in
- processors)
+ def __init__(self, processors, **kwargs):
+ min_preprocessed_file_size = min(p.min_preprocessed_file_size for p in
+ processors)
- ParallelProcessor.__init__(self, processors)
- Preprocessor.__init__(
- self, min_preprocessed_file_size=min_preprocessed_file_size, **kwargs)
+ ParallelProcessor.__init__(self, processors)
+ Preprocessor.__init__(
+ self, min_preprocessed_file_size=min_preprocessed_file_size,
+ **kwargs)
- def __call__(self, data, annotations):
- return super(ParallelPreprocessor, self).__call__(
- data, annotations=annotations)
+ def __call__(self, data, annotations):
+ return super(ParallelPreprocessor, self).__call__(
+ data, annotations=annotations)
class CallablePreprocessor(Preprocessor):
- """A simple preprocessor that takes a callable and applies that callable to
- the input.
-
- 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, 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):
- if self.accepts_annotations:
- return self.callable(data, annotations)
- else:
- return self.callable(data)
+ """A simple preprocessor that takes a callable and applies that callable to
+ the input.
+
+ 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, 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):
+ if self.accepts_annotations:
+ return self.callable(data, annotations)
+ else:
+ return self.callable(data)
diff --git a/bob/bio/base/utils/processors.py b/bob/bio/base/utils/processors.py
index 03dd2e98cf8f6eb12d7e6b69cdfccc55af6e717c..1939d860b63bcabf6e9944c35dce30a5960a0b1d 100644
--- a/bob/bio/base/utils/processors.py
+++ b/bob/bio/base/utils/processors.py
@@ -2,77 +2,77 @@ import numpy
class SequentialProcessor(object):
- """A helper class which takes several processors and applies them one by one
- sequentially
+ """A helper class which takes several processors and applies them one by
+ one sequentially
- Attributes
- ----------
- processors : list
- A list of processors to apply.
- """
+ Attributes
+ ----------
+ processors : list
+ A list of processors to apply.
+ """
- def __init__(self, processors, **kwargs):
- super(SequentialProcessor, self).__init__()
- self.processors = processors
+ def __init__(self, processors, **kwargs):
+ super(SequentialProcessor, self).__init__()
+ self.processors = processors
- def __call__(self, data, **kwargs):
- """Applies the processors on the data sequentially. The output of the first
- one goes as input to the next one.
+ def __call__(self, data, **kwargs):
+ """Applies the processors on the data sequentially. The output of the
+ first one goes as input to the next one.
- Parameters
- ----------
- data : object
- The data that needs to be processed.
- **kwargs
- Any kwargs are passed to the processors.
+ Parameters
+ ----------
+ data : object
+ The data that needs to be processed.
+ **kwargs
+ Any kwargs are passed to the processors.
- Returns
- -------
- object
- The processed data.
- """
- for processor in self.processors:
- data = processor(data, **kwargs)
- return data
+ Returns
+ -------
+ object
+ The processed data.
+ """
+ for processor in self.processors:
+ data = processor(data, **kwargs)
+ return data
class ParallelProcessor(object):
- """A helper class which takes several processors and applies them on each
- processor separately and outputs a list of their outputs in the end.
+ """A helper class which takes several processors and applies them on each
+ processor separately and outputs a list of their outputs in the end.
- Attributes
- ----------
- processors : list
- A list of processors to apply.
- stack : bool
- If True (default), :any:`numpy.hstack` is called on the list of outputs.
- """
+ Attributes
+ ----------
+ processors : list
+ A list of processors to apply.
+ stack : bool
+ If True (default), :any:`numpy.hstack` is called on the list of outputs
+ """
- def __init__(self, processors, stack=True, **kwargs):
- super(ParallelProcessor, self).__init__()
- self.processors = processors
- self.stack = stack
+ def __init__(self, processors, stack=True, **kwargs):
+ super(ParallelProcessor, self).__init__()
+ self.processors = processors
+ self.stack = stack
- def __call__(self, data, **kwargs):
- """Applies the processors on the data independently and outputs a list of
- their outputs.
+ def __call__(self, data, **kwargs):
+ """Applies the processors on the data independently and outputs a list of
+ their outputs.
- Parameters
- ----------
- data : object
- The data that needs to be processed.
- **kwargs
- Any kwargs are passed to the processors.
+ Parameters
+ ----------
+ data : object
+ The data that needs to be processed.
+ **kwargs
+ Any kwargs are passed to the processors.
- Returns
- -------
- object
- The processed data.
- """
- output = []
- for processor in self.processors:
- out = processor(data, **kwargs)
- output.append(out)
- if self.stack:
- output = numpy.hstack(output)
- return output
+ Returns
+ -------
+ object
+ The processed data.
+ """
+ output = []
+ for processor in self.processors:
+ out = processor(data, **kwargs)
+ output.append(out)
+ if self.stack:
+ output = numpy.hstack(output)
+ return output