/
reconstruction_ops.py
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/
reconstruction_ops.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Signal reconstruction via overlapped addition of frames."""
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
@tf_export("signal.overlap_and_add")
@dispatch.add_dispatch_support
def overlap_and_add(signal, frame_step, name=None):
"""Reconstructs a signal from a framed representation.
Adds potentially overlapping frames of a signal with shape
`[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
The resulting tensor has shape `[..., output_size]` where
output_size = (frames - 1) * frame_step + frame_length
Args:
signal: A [..., frames, frame_length] `Tensor`. All dimensions may be
unknown, and rank must be at least 2.
frame_step: An integer or scalar `Tensor` denoting overlap offsets. Must be
less than or equal to `frame_length`.
name: An optional name for the operation.
Returns:
A `Tensor` with shape `[..., output_size]` containing the overlap-added
frames of `signal`'s inner-most two dimensions.
Raises:
ValueError: If `signal`'s rank is less than 2, or `frame_step` is not a
scalar integer.
"""
with ops.name_scope(name, "overlap_and_add", [signal, frame_step]):
signal = ops.convert_to_tensor(signal, name="signal")
signal.shape.with_rank_at_least(2)
frame_step = ops.convert_to_tensor(frame_step, name="frame_step")
frame_step.shape.assert_has_rank(0)
if not frame_step.dtype.is_integer:
raise ValueError("frame_step must be an integer. Got %s" %
frame_step.dtype)
frame_step_static = tensor_util.constant_value(frame_step)
frame_step_is_static = frame_step_static is not None
frame_step = frame_step_static if frame_step_is_static else frame_step
signal_shape = array_ops.shape(signal)
signal_shape_static = tensor_util.constant_value(signal_shape)
if signal_shape_static is not None:
signal_shape = signal_shape_static
# All dimensions that are not part of the overlap-and-add. Can be empty for
# rank 2 inputs.
outer_dimensions = signal_shape[:-2]
outer_rank = array_ops.size(outer_dimensions)
outer_rank_static = tensor_util.constant_value(outer_rank)
if outer_rank_static is not None:
outer_rank = outer_rank_static
def full_shape(inner_shape):
return array_ops.concat([outer_dimensions, inner_shape], 0)
frame_length = signal_shape[-1]
frames = signal_shape[-2]
# Compute output length.
output_length = frame_length + frame_step * (frames - 1)
# If frame_length is equal to frame_step, there's no overlap so just
# reshape the tensor.
if (frame_step_is_static and signal.shape.dims is not None and
frame_step == signal.shape.dims[-1].value):
output_shape = full_shape([output_length])
return array_ops.reshape(signal, output_shape, name="fast_path")
# The following code is documented using this example:
#
# frame_step = 2
# signal.shape = (3, 5)
# a b c d e
# f g h i j
# k l m n o
# Compute the number of segments, per frame.
segments = -(-frame_length // frame_step) # Divide and round up.
# Pad the frame_length dimension to a multiple of the frame step.
# Pad the frames dimension by `segments` so that signal.shape = (6, 6)
# a b c d e 0
# f g h i j 0
# k l m n o 0
# 0 0 0 0 0 0
# 0 0 0 0 0 0
# 0 0 0 0 0 0
paddings = [[0, segments], [0, segments * frame_step - frame_length]]
outer_paddings = array_ops.zeros([outer_rank, 2], dtypes.int32)
paddings = array_ops.concat([outer_paddings, paddings], 0)
signal = array_ops.pad(signal, paddings)
# Reshape so that signal.shape = (3, 6, 2)
# ab cd e0
# fg hi j0
# kl mn o0
# 00 00 00
# 00 00 00
# 00 00 00
shape = full_shape([frames + segments, segments, frame_step])
signal = array_ops.reshape(signal, shape)
# Transpose dimensions so that signal.shape = (3, 6, 2)
# ab fg kl 00 00 00
# cd hi mn 00 00 00
# e0 j0 o0 00 00 00
perm = array_ops.concat(
[math_ops.range(outer_rank), outer_rank + [1, 0, 2]], 0)
perm_static = tensor_util.constant_value(perm)
perm = perm_static if perm_static is not None else perm
signal = array_ops.transpose(signal, perm)
# Reshape so that signal.shape = (18, 2)
# ab fg kl 00 00 00 cd hi mn 00 00 00 e0 j0 o0 00 00 00
shape = full_shape([(frames + segments) * segments, frame_step])
signal = array_ops.reshape(signal, shape)
# Truncate so that signal.shape = (15, 2)
# ab fg kl 00 00 00 cd hi mn 00 00 00 e0 j0 o0
signal = signal[..., :(frames + segments - 1) * segments, :]
# Reshape so that signal.shape = (3, 5, 2)
# ab fg kl 00 00
# 00 cd hi mn 00
# 00 00 e0 j0 o0
shape = full_shape([segments, (frames + segments - 1), frame_step])
signal = array_ops.reshape(signal, shape)
# Now, reduce over the columns, to achieve the desired sum.
signal = math_ops.reduce_sum(signal, -3)
# Flatten the array.
shape = full_shape([(frames + segments - 1) * frame_step])
signal = array_ops.reshape(signal, shape)
# Truncate to final length.
signal = signal[..., :output_length]
return signal