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Remove sparse image warp, fix boolean flags type, rebase to master

This commit is contained in:
Reuben Morais 2019-09-09 12:11:28 +02:00
parent 0e4eed7be3
commit d051d4fd0e
4 changed files with 3 additions and 218 deletions
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7
util/feeding.py
View File

@ -16,7 +16,7 @@ from util.config import Config
from util.logging import log_error
from util.text import text_to_char_array
from util.flags import FLAGS
from util.spectrogram_augmentations import augment_sparse_deform, augment_freq_time_mask, augment_dropout, augment_pitch_and_tempo, augment_speed_up
from util.spectrogram_augmentations import augment_freq_time_mask, augment_dropout, augment_pitch_and_tempo, augment_speed_up
def read_csvs(csv_files):
source_data = None
@ -40,11 +40,6 @@ def samples_to_mfccs(samples, sample_rate, train_phase=False):
# Data Augmentations
if train_phase:
if FLAGS.augmention_sparse_deform:
spectrogram = augment_sparse_deform(spectrogram,
time_warping_para=FLAGS.augmentation_time_warp_max_warping,
normal_around_warping_std=FLAGS.augmentation_sparse_deform_std_warp)
if FLAGS.augmentation_spec_dropout_keeprate < 1:
spectrogram = augment_dropout(spectrogram,
keep_prob=FLAGS.augmentation_spec_dropout_keeprate)

8
util/flags.py
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@ -27,13 +27,9 @@ def create_flags():
f.DEFINE_float('data_aug_features_additive', 0, 'std of the Gaussian additive noise')
f.DEFINE_float('data_aug_features_multiplicative', 0, 'std of normal distribution around 1 for multiplicative noise')
f.DEFINE_integer('augmention_sparse_deform', 0, 'whether to use time-warping augmentation')
f.DEFINE_integer('augmentation_time_warp_max_warping', 12, 'max value for warping')
f.DEFINE_float('augmentation_sparse_deform_std_warp', 0.5, 'std for warping different values to different frequencies')
f.DEFINE_float('augmentation_spec_dropout_keeprate', 1, 'keep rate of dropout augmentation on spectrogram (if 1, no dropout will be performed on spectrogram)')
f.DEFINE_integer('augmentation_freq_and_time_masking', 0, 'whether to use frequency and time masking augmentation')
f.DEFINE_boolean('augmentation_freq_and_time_masking', False, 'whether to use frequency and time masking augmentation')
f.DEFINE_integer('augmentation_freq_and_time_masking_freq_mask_range', 5, 'max range of masks in the frequency domain when performing freqtime-mask augmentation')
f.DEFINE_integer('augmentation_freq_and_time_masking_number_freq_masks', 3, 'number of masks in the frequency domain when performing freqtime-mask augmentation')
f.DEFINE_integer('augmentation_freq_and_time_masking_time_mask_range', 2, 'max range of masks in the time domain when performing freqtime-mask augmentation')
@ -41,7 +37,7 @@ def create_flags():
f.DEFINE_float('augmentation_speed_up_std', 0, 'std for speeding-up tempo. If std is 0, this augmentation is not performed')
f.DEFINE_integer('augmentation_pitch_and_tempo_scaling', 0, 'whether to use spectrogram speed and tempo scaling')
f.DEFINE_boolean('augmentation_pitch_and_tempo_scaling', False, 'whether to use spectrogram speed and tempo scaling')
f.DEFINE_float('augmentation_pitch_and_tempo_scaling_min_pitch', 0.95, 'min value of pitch scaling')
f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_pitch', 1.2, 'max value of pitch scaling')
f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_tempo', 1.2, 'max vlaue of tempo scaling')

177
util/sparse_image_warp.py
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@ -1,177 +0,0 @@
## Implementation of sparse_image_warp that handles dynamic shapes
from tensorflow.contrib.image.python.ops import dense_image_warp
from tensorflow.contrib.image.python.ops import interpolate_spline
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
def _get_grid_locations(image_height, image_width):
"""Wrapper for array_ops.meshgrid."""
y_range = math_ops.linspace(0.0, math_ops.to_float(image_height) - 1,
image_height)
x_range = math_ops.linspace(0.0, math_ops.to_float(image_width) - 1,
image_width)
y_grid, x_grid = array_ops.meshgrid(y_range, x_range, indexing='ij')
return array_ops.stack((y_grid, x_grid), -1)
def _expand_to_minibatch(array, batch_size):
"""Tile arbitrarily-sized array to include new batch dimension."""
batch_size = array_ops.expand_dims(batch_size, 0)
array_ones = array_ops.ones((array_ops.rank(array)), dtype=dtypes.int32)
tiles = array_ops.concat([batch_size, array_ones], axis=0)
return array_ops.tile(array_ops.expand_dims(array, 0), tiles)
def _get_boundary_locations(image_height, image_width, num_points_per_edge):
"""Compute evenly-spaced indices along edge of image."""
image_height = math_ops.to_float(image_height)
image_width = math_ops.to_float(image_width)
y_range = math_ops.linspace(0.0, image_height - 1, num_points_per_edge + 2)
x_range = math_ops.linspace(0.0, image_width - 1, num_points_per_edge + 2)
ys, xs = array_ops.meshgrid(y_range, x_range, indexing='ij')
is_boundary = math_ops.logical_or(
math_ops.logical_or(math_ops.equal(xs, 0), # pylint: disable=bad-continuation
math_ops.equal(xs, image_width - 1)),
math_ops.logical_or(math_ops.equal(ys, 0), # pylint: disable=bad-continuation
math_ops.equal(ys, image_height - 1)))
return array_ops.stack([array_ops.boolean_mask(ys, is_boundary),
array_ops.boolean_mask(xs, is_boundary)], axis=-1)
def _add_zero_flow_controls_at_boundary(control_point_locations,
control_point_flows, image_height,
image_width, boundary_points_per_edge):
"""Add control points for zero-flow boundary conditions.
Augment the set of control points with extra points on the
boundary of the image that have zero flow.
Args:
control_point_locations: input control points
control_point_flows: their flows
image_height: image height
image_width: image width
boundary_points_per_edge: number of points to add in the middle of each
edge (not including the corners).
The total number of points added is
4 + 4*(boundary_points_per_edge).
Returns:
merged_control_point_locations: augmented set of control point locations
merged_control_point_flows: augmented set of control point flows
"""
batch_size = tensor_shape.dimension_value(control_point_locations.shape[0])
boundary_point_locations = _get_boundary_locations(image_height, image_width,
boundary_points_per_edge)
boundary_point_flows = array_ops.zeros([array_ops.shape(boundary_point_locations)[0], 2])
boundary_point_locations = _expand_to_minibatch(boundary_point_locations,
batch_size)
boundary_point_flows = _expand_to_minibatch(boundary_point_flows, batch_size)
merged_control_point_locations = array_ops.concat([control_point_locations, boundary_point_locations], 1)
merged_control_point_flows = array_ops.concat([control_point_flows, boundary_point_flows], 1)
return merged_control_point_locations, merged_control_point_flows
def sparse_image_warp(image,
source_control_point_locations,
dest_control_point_locations,
interpolation_order=2,
regularization_weight=0.0,
num_boundary_points=0,
name='sparse_image_warp'):
"""Image warping using correspondences between sparse control points.
Apply a non-linear warp to the image, where the warp is specified by
the source and destination locations of a (potentially small) number of
control points. First, we use a polyharmonic spline
(`tf.contrib.image.interpolate_spline`) to interpolate the displacements
between the corresponding control points to a dense flow field.
Then, we warp the image using this dense flow field
(`tf.contrib.image.dense_image_warp`).
Let t index our control points. For regularization_weight=0, we have:
warped_image[b, dest_control_point_locations[b, t, 0],
dest_control_point_locations[b, t, 1], :] =
image[b, source_control_point_locations[b, t, 0],
source_control_point_locations[b, t, 1], :].
For regularization_weight > 0, this condition is met approximately, since
regularized interpolation trades off smoothness of the interpolant vs.
reconstruction of the interpolant at the control points.
See `tf.contrib.image.interpolate_spline` for further documentation of the
interpolation_order and regularization_weight arguments.
Args:
image: `[batch, height, width, channels]` float `Tensor`
source_control_point_locations: `[batch, num_control_points, 2]` float
`Tensor`
dest_control_point_locations: `[batch, num_control_points, 2]` float
`Tensor`
interpolation_order: polynomial order used by the spline interpolation
regularization_weight: weight on smoothness regularizer in interpolation
num_boundary_points: How many zero-flow boundary points to include at
each image edge.Usage:
num_boundary_points=0: don't add zero-flow points
num_boundary_points=1: 4 corners of the image
num_boundary_points=2: 4 corners and one in the middle of each edge
(8 points total)
num_boundary_points=n: 4 corners and n-1 along each edge
name: A name for the operation (optional).
Note that image and offsets can be of type tf.half, tf.float32, or
tf.float64, and do not necessarily have to be the same type.
Returns:
warped_image: `[batch, height, width, channels]` float `Tensor` with same
type as input image.
flow_field: `[batch, height, width, 2]` float `Tensor` containing the dense
flow field produced by the interpolation.
"""
image = ops.convert_to_tensor(image)
source_control_point_locations = ops.convert_to_tensor(
source_control_point_locations)
dest_control_point_locations = ops.convert_to_tensor(
dest_control_point_locations)
control_point_flows = (
dest_control_point_locations - source_control_point_locations)
clamp_boundaries = num_boundary_points > 0
boundary_points_per_edge = num_boundary_points - 1
with ops.name_scope(name):
batch_size, image_height, image_width = (array_ops.shape(image)[0],
array_ops.shape(image)[1],
array_ops.shape(image)[2])
# This generates the dense locations where the interpolant
# will be evaluated.
grid_locations = _get_grid_locations(image_height, image_width)
flattened_grid_locations = array_ops.reshape(grid_locations,
[image_height*image_width, 2])
flattened_grid_locations = _expand_to_minibatch(flattened_grid_locations,
batch_size)
if clamp_boundaries:
(dest_control_point_locations,
control_point_flows) = _add_zero_flow_controls_at_boundary(dest_control_point_locations,
control_point_flows, image_height,
image_width, boundary_points_per_edge)
flattened_flows = interpolate_spline.interpolate_spline(dest_control_point_locations, control_point_flows,
flattened_grid_locations, interpolation_order,
regularization_weight)
dense_flows = array_ops.reshape(flattened_flows,
[batch_size, image_height, image_width, 2])
warped_image = dense_image_warp.dense_image_warp(image, dense_flows)
return warped_image, dense_flows

29
util/spectrogram_augmentations.py
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@ -1,33 +1,4 @@
import tensorflow as tf
from util.sparse_image_warp import sparse_image_warp
def augment_sparse_deform(mel_spectrogram,
time_warping_para=12,
normal_around_warping_std=0.5):
mel_spectrogram = tf.expand_dims(mel_spectrogram, -1)
freq_max = tf.shape(mel_spectrogram)[1]
time_max = tf.shape(mel_spectrogram)[2]
center_freq = tf.cast(freq_max, tf.float32)/2.0
random_time_point = tf.random.uniform(shape=(), minval=time_warping_para, maxval=tf.cast(time_max, tf.float32) - time_warping_para)
chosen_warping = tf.random.uniform(shape=(), minval=0, maxval=time_warping_para)
#add different warping values to different frequencies
normal_around_warping = tf.random.normal(mean=chosen_warping, stddev=normal_around_warping_std, shape=(3,))
control_point_freqs = tf.stack([0.0, center_freq, tf.cast(freq_max, tf.float32)], axis=0)
control_point_times_src = tf.stack([random_time_point, random_time_point, random_time_point], axis=0)
control_point_times_dst = control_point_times_src+normal_around_warping
control_src = tf.expand_dims(tf.stack([control_point_freqs, control_point_times_src], axis=-1), 0)
control_dst = tf.expand_dims(tf.stack([control_point_freqs, control_point_times_dst], axis=1), 0)
warped_mel_spectrogram, _ = sparse_image_warp(mel_spectrogram,
source_control_point_locations=control_src,
dest_control_point_locations=control_dst,
interpolation_order=2,
regularization_weight=0,
num_boundary_points=1
)
warped_mel_spectrogram = warped_mel_spectrogram[:, :, :, 0]
return warped_mel_spectrogram
def augment_freq_time_mask(mel_spectrogram,
frequency_masking_para=30,