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# Copyright 2016 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.
# ==============================================================================
"""A small library of functions dealing with LSTMs applied to images.
Tensors in this library generally have the shape (num_images, height, width,
depth).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.ndlstm.python import lstm1d
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import variable_scope
def _shape(tensor):
"""Get the shape of a tensor as an int list."""
return tensor.get_shape().as_list()
def images_to_sequence(tensor):
"""Convert a batch of images into a batch of sequences.
Args:
tensor: a (num_images, height, width, depth) tensor
Returns:
(width, num_images*height, depth) sequence tensor
"""
num_image_batches, height, width, depth = _shape(tensor)
transposed = array_ops.transpose(tensor, [2, 0, 1, 3])
return array_ops.reshape(transposed,
[width, num_image_batches * height, depth])
def sequence_to_images(tensor, num_image_batches):
"""Convert a batch of sequences into a batch of images.
Args:
tensor: (num_steps, num_batches, depth) sequence tensor
num_image_batches: the number of image batches
Returns:
(num_images, height, width, depth) tensor
"""
width, num_batches, depth = _shape(tensor)
height = num_batches // num_image_batches
reshaped = array_ops.reshape(tensor,
[width, num_image_batches, height, depth])
return array_ops.transpose(reshaped, [1, 2, 0, 3])
def horizontal_lstm(images, num_filters_out, scope=None):
"""Run an LSTM bidirectionally over all the rows of each image.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output depth
scope: optional scope name
Returns:
(num_images, height, width, num_filters_out) tensor, where
num_steps is width and new num_batches is num_image_batches * height
"""
with variable_scope.variable_scope(scope, "HorizontalLstm", [images]):
batch_size, _, _, _ = _shape(images)
sequence = images_to_sequence(images)
with variable_scope.variable_scope("lr"):
hidden_sequence_lr = lstm1d.ndlstm_base(sequence, num_filters_out // 2)
with variable_scope.variable_scope("rl"):
hidden_sequence_rl = (lstm1d.ndlstm_base(
sequence, num_filters_out - num_filters_out // 2, reverse=1))
output_sequence = array_ops.concat([hidden_sequence_lr, hidden_sequence_rl],
2)
output = sequence_to_images(output_sequence, batch_size)
return output
def get_blocks(images, kernel_size):
"""Split images in blocks
Args:
images: (num_images, height, width, depth) tensor
kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
of the pooling. Can be an int if both values are the same.
Returns:
(num_images, height/kernel_height, width/kernel_width,
depth*kernel_height*kernel_width) tensor
"""
with variable_scope.variable_scope("image_blocks"):
batch_size, height, width, chanels = _shape(images)
if height % kernel_size[0] != 0:
offset = array_ops.zeros([batch_size,
kernel_size[0] - (height % kernel_size[0]),
width,
chanels])
images = array_ops.concat([images, offset], 1)
batch_size, height, width, chanels = _shape(images)
if width % kernel_size[1] != 0:
offset = array_ops.zeros([batch_size,
height,
kernel_size[1] - (width % kernel_size[1]),
chanels])
images = array_ops.concat([images, offset], 2)
batch_size, height, width, chanels = _shape(images)
h, w = int(height / kernel_size[0]), int(width / kernel_size[1])
features = kernel_size[1] * kernel_size[0] * chanels
lines = array_ops.split(images, h, axis=1)
line_blocks = []
for line in lines:
line = array_ops.transpose(line, [0, 2, 3, 1])
line = array_ops.reshape(line, [batch_size, w, features])
line_blocks.append(line)
return array_ops.stack(line_blocks, axis=1)
def separable_lstm(images, num_filters_out,
kernel_size=None, nhidden=None, scope=None):
"""Run bidirectional LSTMs first horizontally then vertically.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output layer depth
kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of
of the pooling. Can be an int if both values are the same. Set to None for
not using blocks
nhidden: hidden layer depth
scope: optional scope name
Returns:
(num_images, height/kernel_height, width/kernel_width,
num_filters_out) tensor
"""
with variable_scope.variable_scope(scope, "SeparableLstm", [images]):
if nhidden is None:
nhidden = num_filters_out
if kernel_size is not None:
images = get_blocks(images, kernel_size)
hidden = horizontal_lstm(images, nhidden)
with variable_scope.variable_scope("vertical"):
transposed = array_ops.transpose(hidden, [0, 2, 1, 3])
output_transposed = horizontal_lstm(transposed, num_filters_out)
output = array_ops.transpose(output_transposed, [0, 2, 1, 3])
return output
def reduce_to_sequence(images, num_filters_out, scope=None):
"""Reduce an image to a sequence by scanning an LSTM vertically.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output layer depth
scope: optional scope name
Returns:
A (width, num_images, num_filters_out) sequence.
"""
with variable_scope.variable_scope(scope, "ReduceToSequence", [images]):
batch_size, height, width, depth = _shape(images)
transposed = array_ops.transpose(images, [1, 0, 2, 3])
reshaped = array_ops.reshape(transposed,
[height, batch_size * width, depth])
reduced = lstm1d.sequence_to_final(reshaped, num_filters_out)
output = array_ops.reshape(reduced, [batch_size, width, num_filters_out])
return output
def reduce_to_final(images, num_filters_out, nhidden=None, scope=None):
"""Reduce an image to a final state by running two LSTMs.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output layer depth
nhidden: hidden layer depth (defaults to num_filters_out)
scope: optional scope name
Returns:
A (num_images, num_filters_out) batch.
"""
with variable_scope.variable_scope(scope, "ReduceToFinal", [images]):
nhidden = nhidden or num_filters_out
batch_size, height, width, depth = _shape(images)
transposed = array_ops.transpose(images, [1, 0, 2, 3])
reshaped = array_ops.reshape(transposed,
[height, batch_size * width, depth])
with variable_scope.variable_scope("reduce1"):
reduced = lstm1d.sequence_to_final(reshaped, nhidden)
transposed_hidden = array_ops.reshape(reduced,
[batch_size, width, nhidden])
hidden = array_ops.transpose(transposed_hidden, [1, 0, 2])
with variable_scope.variable_scope("reduce2"):
output = lstm1d.sequence_to_final(hidden, num_filters_out)
return output
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