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datasets/transformers.py
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from collections import OrderedDict
import numpy
from theano import config
from fuel.transformers import Transformer
from picklable_itertools.extras import equizip
class MaximumFrameCache(Transformer):
"""Cache examples, and create batches of maximum number of frames.
Given a data stream which reads large chunks of data, this data
stream caches these chunks and returns batches with a maximum number
of acoustic frames.
Parameters
----------
max_frames : int
maximum number of frames per batch
Attributes
----------
cache : list of lists of objects
This attribute holds the cache at any given point. It is a list of
the same size as the :attr:`sources` attribute. Each element in
this list is a deque of examples that are currently in the
cache. The cache gets emptied at the start of each epoch, and gets
refilled when needed through the :meth:`get_data` method.
"""
def __init__(self, data_stream, max_frames, rng):
super(MaximumFrameCache, self).__init__(
data_stream)
self.max_frames = max_frames
self.cache = OrderedDict([(name, []) for name in self.sources])
self.num_frames = []
self.rng = rng
self.produces_examples = False
def next_request(self):
curr_max = 0
for i, n_frames in enumerate(self.num_frames):
# Select max number of frames because of future padding
curr_max = max(n_frames, curr_max)
total = curr_max * (i + 1)
if total >= self.max_frames:
return i + 1
return len(self.num_frames)
def get_data(self, request=None):
if not self.cache[self.cache.keys()[0]]:
self._cache()
data = []
request = self.next_request()
for source_name in self.cache:
data.append(numpy.asarray(self.cache[source_name][:request]))
self.cache = OrderedDict([(name, dt[request:]) for name, dt
in self.cache.iteritems()])
self.num_frames = self.num_frames[request:]
return tuple(data)
def get_epoch_iterator(self, **kwargs):
self.cache = OrderedDict([(name, []) for name in self.sources])
self.num_frames = []
return super(MaximumFrameCache, self).get_epoch_iterator(**kwargs)
def _cache(self):
data = next(self.child_epoch_iterator)
indexes = range(len(data[0]))
self.rng.shuffle(indexes)
data = [[dt[i] for i in indexes] for dt in data]
self.cache = OrderedDict([(name, self.cache[name] + dt) for name, dt
in equizip(self.data_stream.sources, data)])
self.num_frames.extend([x.shape[0] for x in data[0]])
class Transpose(Transformer):
"""Transpose axes of datastream.
"""
def __init__(self, datastream, axes_list):
super(Transpose, self).__init__(datastream)
self.axes_list = axes_list
self.produces_examples = False
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
transposed_data = []
for axes, data in zip(self.axes_list, data):
transposed_data.append(numpy.transpose(data, axes))
return transposed_data
class AddUniformAlignmentMask(Transformer):
"""Adds an uniform alignment mask to the incoming batch.
Parameters
----------
"""
def __init__(self, data_stream):
super(AddUniformAlignmentMask, self).__init__(data_stream)
self.sources = self.data_stream.sources + ('alignment',)
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
sources = self.data_stream.sources
x_idx = sources.index('x')
y_idx = sources.index('y')
x_mask_idx = sources.index('x_mask')
y_mask_idx = sources.index('y_mask')
batch_size = data[x_idx].shape[1]
max_len_output = data[y_idx].shape[0]
max_len_input = data[x_idx].shape[0]
mask_shape = (max_len_output, batch_size, max_len_input)
alignment = numpy.zeros(mask_shape, dtype=config.floatX)
for k in xrange(batch_size):
in_size = numpy.count_nonzero(data[x_mask_idx][:,k])
out_size = numpy.count_nonzero(data[y_mask_idx][:,k])
n = int(in_size/out_size) # Maybe clever way than int to do this
v = numpy.hstack([numpy.ones(n, dtype=config.floatX),
numpy.zeros(max_len_input - n,
dtype=config.floatX)])
alignment[0,k] = v
for i in xrange(1, out_size):
alignment[i,k] = numpy.roll(v, i*n)
# DEBUG
#plt.figure()
#plt.imshow(alignment[:,k,:], cmap='gray', interpolation='none')
#plt.show()
data = data + (alignment,)
return data
class AlignmentPadding(Transformer):
def __init__(self, data_stream, alignment_source):
super(AlignmentPadding, self).__init__(data_stream)
self.alignment_source = alignment_source
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(equizip(self.sources, data))
alignments = data[self.alignment_source]
input_lengths = [alignment.shape[1] for alignment in alignments]
output_lengths = [alignment.shape[0] for alignment in alignments]
max_input_length = max(input_lengths)
max_output_length = max(output_lengths)
batch_size = len(alignments)
padded_alignments = numpy.zeros((max_output_length, batch_size,
max_input_length))
padded_targets = numpy.zeros((batch_size, max_output_length))
padded_targets_mask = numpy.zeros((batch_size, max_output_length))
for i, alignment in enumerate(alignments):
out_size, inp_size = alignment.shape
padded_alignments[:out_size, i, :inp_size] = alignment
alignment_index = [list(align).index(1)
for align in alignment]
occurance = [alignment_index.count(j)
for j in set(alignment_index)]
alignment_target = data['phonemes'][i][alignment_index]
padded_targets[i, :out_size] = alignment_target
#get rid of start label
padded_targets[i, 0] = data['phonemes'][i, 1]
alignment_target_mask = sum([[occur]*num for occur, num in
zip(data['phonemes_mask'][i],
occurance)], [])
padded_targets_mask[i, :out_size] = alignment_target_mask
data[self.alignment_source] = padded_alignments
data['phonemes'] = padded_targets.astype('int')
data['phonemes_mask'] = padded_targets_mask
return data.values()
class Reshape(Transformer):
"""Reshapes data in the stream according to shape source."""
def __init__(self, data_source, shape_source, **kwargs):
super(Reshape, self).__init__(**kwargs)
self.data_source = data_source
self.shape_source = shape_source
self.sources = tuple(source for source in self.data_stream.sources
if source != shape_source)
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(zip(self.data_stream.sources, data))
shapes = data.pop(self.shape_source)
reshaped_data = []
for dt, shape in zip(data[self.data_source], shapes):
reshaped_data.append(dt.reshape(shape))
data[self.data_source] = reshaped_data
return data.values()
class ConvReshape(Transformer):
def __init__(self, data_source, quaternion, **kwargs):
super(ConvReshape, self).__init__(**kwargs)
self.data_source = data_source
self.sources = tuple(source for source in self.data_stream.sources)
self.quaternion = quaternion
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(zip(self.data_stream.sources, data))
#shapes = data.pop(self.shape_source)
reshaped_data = []
if self.data_source in ['features', 'X']:
for dt in data[self.data_source]:
shape = (1, dt.shape[0], 3, dt.shape[1] / 3)
if self.quaternion:
empty_channel = numpy.zeros((1, shape[1], 1, shape[3]))
reshaped_data.append(numpy.concatenate([dt.reshape(shape),
empty_channel],
axis=2))
else:
reshaped_data.append(dt.reshape(shape))
else:
for dt in data[self.data_source]:
shape = (numpy.prod(dt.shape), 1)
reshaped_data.append(dt.reshape(shape))
if len(reshaped_data) == 1:
reshaped_data = reshaped_data * 2
data['phonemes'] = numpy.repeat(data['phonemes'], 2, axis=0)
data['features_mask'] = numpy.repeat(data['features_mask'],
2, axis=1)
data['phonemes_mask'] = numpy.repeat(data['phonemes_mask'],
2, axis=1)
data[self.data_source] = numpy.vstack(reshaped_data)
# remove the start label
data['phonemes'] = data['phonemes'][:, 1:]
# get the indice starts at 0
data['phonemes'] = data['phonemes'] - 1.
# remove the end label
data['phonemes'][data['phonemes']==61] = 0
# recover original padding
data['phonemes'][data['phonemes']==-1] = 0
data['features_mask'] = numpy.sum(data['features_mask'], axis=0)[:, None]
data['phonemes_mask'] = numpy.sum(data['phonemes_mask'], axis=0)[:, None] - 2
return data.values()
class DictRep(Transformer):
def __init__(self, data_source):
super(DictRep, self).__init__(data_source)
self.data_source = data_source
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(zip(self.data_stream.sources, data))
return (data.values(), numpy.zeros([data['phonemes_mask'].shape[0]]))
class Subsample(Transformer):
def __init__(self, data_stream, source, step):
super(Subsample, self).__init__(data_stream)
self.source = source
self.step = step
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(equizip(self.sources, data))
dt = data[self.source]
indexes = ((slice(None, None, self.step),) +
(slice(None),) * (len(dt.shape) - 1))
subsampled = dt[indexes]
data[self.source] = subsampled
return data.values()
class WindowFeatures(Transformer):
def __init__(self, data_stream, source, window_size):
super(WindowFeatures, self).__init__(data_stream)
self.source = source
self.window_size = window_size
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(equizip(self.sources, data))
feature_batch = data[self.source]
windowed_features = []
for features in feature_batch:
features_padded = features.copy()
features_shifted = [features]
# shift forward
for i in xrange(self.window_size / 2):
feats = numpy.roll(features_padded, i + 1, axis=0)
feats[:i + 1, :] = 0
features_shifted.append(feats)
features_padded = features.copy()
# shift backward
for i in xrange(self.window_size / 2):
feats = numpy.roll(features_padded, -i - 1, axis=0)
feats[-i - 1:, :] = 0
features_shifted.append(numpy.roll(features_padded, -i - 1,
axis=0))
windowed_features.append(numpy.concatenate(
features_shifted, axis=1))
data[self.source] = windowed_features
return data.values()
class Normalize(Transformer):
"""Normalizes each features : x = (x - means)/stds"""
def __init__(self, data_stream, means, stds, over='features'):
super(Normalize, self).__init__(data_stream)
self.means = means
self.stds = stds
self.over = over
self.produces_examples = False
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(zip(self.data_stream.sources, data))
for i in range(len(data[self.over])):
data[self.over][i] -= self.means
data[self.over][i] /= self.stds
return data.values()
def length_getter(dt):
def get_length(k):
return dt[k].shape[0]
return get_length
class SortByLegth(Transformer):
def __init__(self, data_stream, source='features'):
super(SortByLegth, self).__init__(data_stream)
self.source = source
def get_data(self, request=None):
data = next(self.child_epoch_iterator)
data = OrderedDict(zip(self.data_stream.sources, data))
dt = data[self.source]
indexes = sorted(range(len(dt)), key=length_getter(dt))
for source in self.sources:
data[source] = [data[source][k] for k in indexes]
return data.values()
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