/
shape_ops.py
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/
shape_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.
# ==============================================================================
"""General shape ops for frames."""
import numpy as np
from tensorflow.python.framework import constant_op
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.ops.signal import util_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
def _infer_frame_shape(signal, frame_length, frame_step, pad_end, axis):
"""Infers the shape of the return value of `frame`."""
frame_length = tensor_util.constant_value(frame_length)
frame_step = tensor_util.constant_value(frame_step)
axis = tensor_util.constant_value(axis)
if signal.shape.ndims is None:
return None
if axis is None:
return [None] * (signal.shape.ndims + 1)
signal_shape = signal.shape.as_list()
num_frames = None
frame_axis = signal_shape[axis]
outer_dimensions = signal_shape[:axis]
inner_dimensions = signal_shape[axis:][1:]
if signal_shape and frame_axis is not None:
if frame_step is not None and pad_end:
# Double negative is so that we round up.
num_frames = max(0, -(-frame_axis // frame_step))
elif frame_step is not None and frame_length is not None:
assert not pad_end
num_frames = max(
0, (frame_axis - frame_length + frame_step) // frame_step)
return outer_dimensions + [num_frames, frame_length] + inner_dimensions
@tf_export("signal.frame")
@dispatch.add_dispatch_support
def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1,
name=None):
"""Expands `signal`'s `axis` dimension into frames of `frame_length`.
Slides a window of size `frame_length` over `signal`'s `axis` dimension
with a stride of `frame_step`, replacing the `axis` dimension with
`[frames, frame_length]` frames.
If `pad_end` is True, window positions that are past the end of the `axis`
dimension are padded with `pad_value` until the window moves fully past the
end of the dimension. Otherwise, only window positions that fully overlap the
`axis` dimension are produced.
For example:
>>> # A batch size 3 tensor of 9152 audio samples.
>>> audio = tf.random.normal([3, 9152])
>>>
>>> # Compute overlapping frames of length 512 with a step of 180 (frames overlap
>>> # by 332 samples). By default, only 49 frames are generated since a frame
>>> # with start position j*180 for j > 48 would overhang the end.
>>> frames = tf.signal.frame(audio, 512, 180)
>>> frames.shape.assert_is_compatible_with([3, 49, 512])
>>>
>>> # When pad_end is enabled, the final two frames are kept (padded with zeros).
>>> frames = tf.signal.frame(audio, 512, 180, pad_end=True)
>>> frames.shape.assert_is_compatible_with([3, 51, 512])
If the dimension along `axis` is N, and `pad_end=False`, the number of frames
can be computed by:
```python
num_frames = 1 + (N - frame_size) // frame_step
```
If `pad_end=True`, the number of frames can be computed by:
```python
num_frames = -(-N // frame_step) # ceiling division
```
Args:
signal: A `[..., samples, ...]` `Tensor`. The rank and dimensions
may be unknown. Rank must be at least 1.
frame_length: The frame length in samples. An integer or scalar `Tensor`.
frame_step: The frame hop size in samples. An integer or scalar `Tensor`.
pad_end: Whether to pad the end of `signal` with `pad_value`.
pad_value: An optional scalar `Tensor` to use where the input signal
does not exist when `pad_end` is True.
axis: A scalar integer `Tensor` indicating the axis to frame. Defaults to
the last axis. Supports negative values for indexing from the end.
name: An optional name for the operation.
Returns:
A `Tensor` of frames with shape `[..., num_frames, frame_length, ...]`.
Raises:
ValueError: If `frame_length`, `frame_step`, `pad_value`, or `axis` are not
scalar.
"""
with ops.name_scope(name, "frame", [signal, frame_length, frame_step,
pad_value]):
signal = ops.convert_to_tensor(signal, name="signal")
frame_length = ops.convert_to_tensor(frame_length, name="frame_length")
frame_step = ops.convert_to_tensor(frame_step, name="frame_step")
axis = ops.convert_to_tensor(axis, name="axis")
signal.shape.with_rank_at_least(1)
frame_length.shape.assert_has_rank(0)
frame_step.shape.assert_has_rank(0)
axis.shape.assert_has_rank(0)
result_shape = _infer_frame_shape(signal, frame_length, frame_step, pad_end,
axis)
def maybe_constant(val):
val_static = tensor_util.constant_value(val)
return (val_static, True) if val_static is not None else (val, False)
signal_shape, signal_shape_is_static = maybe_constant(
array_ops.shape(signal))
axis, axis_is_static = maybe_constant(axis)
if signal_shape_is_static and axis_is_static:
# Axis can be negative. Convert it to positive.
axis = range(len(signal_shape))[axis]
outer_dimensions, length_samples, inner_dimensions = np.split(
signal_shape, indices_or_sections=[axis, axis + 1])
length_samples = length_samples.item()
else:
signal_rank = array_ops.rank(signal)
# Axis can be negative. Convert it to positive.
axis = math_ops.range(signal_rank)[axis]
outer_dimensions, length_samples, inner_dimensions = array_ops.split(
signal_shape, [axis, 1, signal_rank - 1 - axis])
length_samples = array_ops.reshape(length_samples, [])
num_outer_dimensions = array_ops.size(outer_dimensions)
num_inner_dimensions = array_ops.size(inner_dimensions)
# If padding is requested, pad the input signal tensor with pad_value.
if pad_end:
pad_value = ops.convert_to_tensor(pad_value, signal.dtype)
pad_value.shape.assert_has_rank(0)
# Calculate number of frames, using double negatives to round up.
num_frames = -(-length_samples // frame_step)
# Pad the signal by up to frame_length samples based on how many samples
# are remaining starting from last_frame_position.
pad_samples = math_ops.maximum(
0, frame_length + frame_step * (num_frames - 1) - length_samples)
# Pad the inner dimension of signal by pad_samples.
paddings = array_ops.concat([
array_ops.zeros([num_outer_dimensions, 2], dtype=pad_samples.dtype),
ops.convert_to_tensor([[0, pad_samples]]),
array_ops.zeros([num_inner_dimensions, 2], dtype=pad_samples.dtype)
], 0)
signal = array_ops.pad(signal, paddings, constant_values=pad_value)
signal_shape = array_ops.shape(signal)
length_samples = signal_shape[axis]
else:
num_frames = math_ops.maximum(
constant_op.constant(0, dtype=frame_length.dtype),
1 + (length_samples - frame_length) // frame_step)
subframe_length, _ = maybe_constant(util_ops.gcd(frame_length, frame_step))
subframes_per_frame = frame_length // subframe_length
subframes_per_hop = frame_step // subframe_length
num_subframes = length_samples // subframe_length
slice_shape = array_ops.concat([outer_dimensions,
[num_subframes * subframe_length],
inner_dimensions], 0)
subframe_shape = array_ops.concat([outer_dimensions,
[num_subframes, subframe_length],
inner_dimensions], 0)
subframes = array_ops.reshape(array_ops.strided_slice(
signal, array_ops.zeros_like(signal_shape),
slice_shape), subframe_shape)
# frame_selector is a [num_frames, subframes_per_frame] tensor
# that indexes into the appropriate frame in subframes. For example:
# [[0, 0, 0, 0], [2, 2, 2, 2], [4, 4, 4, 4]]
frame_selector = array_ops.reshape(
math_ops.range(num_frames, dtype=frame_length.dtype) *
subframes_per_hop, [num_frames, 1])
# subframe_selector is a [num_frames, subframes_per_frame] tensor
# that indexes into the appropriate subframe within a frame. For example:
# [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
subframe_selector = array_ops.reshape(
math_ops.range(subframes_per_frame, dtype=frame_length.dtype),
[1, subframes_per_frame])
# Adding the 2 selector tensors together produces a [num_frames,
# subframes_per_frame] tensor of indices to use with tf.gather to select
# subframes from subframes. We then reshape the inner-most
# subframes_per_frame dimension to stitch the subframes together into
# frames. For example: [[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7]].
selector = frame_selector + subframe_selector
# Dtypes have to match.
outer_dimensions = ops.convert_to_tensor(outer_dimensions)
inner_dimensions = ops.convert_to_tensor(
inner_dimensions, dtype=outer_dimensions.dtype)
mid_dimensions = ops.convert_to_tensor([num_frames, frame_length],
dtype=outer_dimensions.dtype)
frames = array_ops.reshape(
array_ops.gather(subframes, selector, axis=axis),
array_ops.concat([outer_dimensions, mid_dimensions, inner_dimensions],
0))
if result_shape:
frames.set_shape(result_shape)
return frames