research/attention_ocr/python/sequence_layers.py
# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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.
# ==============================================================================
"""Various implementations of sequence layers for character prediction.
A 'sequence layer' is a part of a computation graph which is responsible of
producing a sequence of characters using extracted image features. There are
many reasonable ways to implement such layers. All of them are using RNNs.
This module provides implementations which uses 'attention' mechanism to
spatially 'pool' image features and also can use a previously predicted
character to predict the next (aka auto regression).
Usage:
Select one of available classes, e.g. Attention or use a wrapper function to
pick one based on your requirements:
layer_class = sequence_layers.get_layer_class(use_attention=True,
use_autoregression=True)
layer = layer_class(net, labels_one_hot, model_params, method_params)
char_logits = layer.create_logits()
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import abc
import logging
import numpy as np
import tensorflow as tf
from tensorflow.contrib import slim
def orthogonal_initializer(shape, dtype=tf.float32, *args, **kwargs):
"""Generates orthonormal matrices with random values.
Orthonormal initialization is important for RNNs:
http://arxiv.org/abs/1312.6120
http://smerity.com/articles/2016/orthogonal_init.html
For non-square shapes the returned matrix will be semi-orthonormal: if the
number of columns exceeds the number of rows, then the rows are orthonormal
vectors; but if the number of rows exceeds the number of columns, then the
columns are orthonormal vectors.
We use SVD decomposition to generate an orthonormal matrix with random
values. The same way as it is done in the Lasagne library for Theano. Note
that both u and v returned by the svd are orthogonal and random. We just need
to pick one with the right shape.
Args:
shape: a shape of the tensor matrix to initialize.
dtype: a dtype of the initialized tensor.
*args: not used.
**kwargs: not used.
Returns:
An initialized tensor.
"""
del args
del kwargs
flat_shape = (shape[0], np.prod(shape[1:]))
w = np.random.randn(*flat_shape)
u, _, v = np.linalg.svd(w, full_matrices=False)
w = u if u.shape == flat_shape else v
return tf.constant(w.reshape(shape), dtype=dtype)
SequenceLayerParams = collections.namedtuple('SequenceLogitsParams', [
'num_lstm_units', 'weight_decay', 'lstm_state_clip_value'
])
class SequenceLayerBase(object):
"""A base abstruct class for all sequence layers.
A child class has to define following methods:
get_train_input
get_eval_input
unroll_cell
"""
__metaclass__ = abc.ABCMeta
def __init__(self, net, labels_one_hot, model_params, method_params):
"""Stores argument in member variable for further use.
Args:
net: A tensor with shape [batch_size, num_features, feature_size] which
contains some extracted image features.
labels_one_hot: An optional (can be None) ground truth labels for the
input features. Is a tensor with shape
[batch_size, seq_length, num_char_classes]
model_params: A namedtuple with model parameters (model.ModelParams).
method_params: A SequenceLayerParams instance.
"""
self._params = model_params
self._mparams = method_params
self._net = net
self._labels_one_hot = labels_one_hot
self._batch_size = tf.shape(input=net)[0]
# Initialize parameters for char logits which will be computed on the fly
# inside an LSTM decoder.
self._char_logits = {}
regularizer = tf.keras.regularizers.l2(0.5 * (self._mparams.weight_decay))
self._softmax_w = slim.model_variable(
'softmax_w',
[self._mparams.num_lstm_units, self._params.num_char_classes],
initializer=orthogonal_initializer,
regularizer=regularizer)
self._softmax_b = slim.model_variable(
'softmax_b', [self._params.num_char_classes],
initializer=tf.compat.v1.zeros_initializer(),
regularizer=regularizer)
@abc.abstractmethod
def get_train_input(self, prev, i):
"""Returns a sample to be used to predict a character during training.
This function is used as a loop_function for an RNN decoder.
Args:
prev: output tensor from previous step of the RNN. A tensor with shape:
[batch_size, num_char_classes].
i: index of a character in the output sequence.
Returns:
A tensor with shape [batch_size, ?] - depth depends on implementation
details.
"""
pass
@abc.abstractmethod
def get_eval_input(self, prev, i):
"""Returns a sample to be used to predict a character during inference.
This function is used as a loop_function for an RNN decoder.
Args:
prev: output tensor from previous step of the RNN. A tensor with shape:
[batch_size, num_char_classes].
i: index of a character in the output sequence.
Returns:
A tensor with shape [batch_size, ?] - depth depends on implementation
details.
"""
raise AssertionError('Not implemented')
@abc.abstractmethod
def unroll_cell(self, decoder_inputs, initial_state, loop_function, cell):
"""Unrolls an RNN cell for all inputs.
This is a placeholder to call some RNN decoder. It has a similar to
tf.seq2seq.rnn_decode interface.
Args:
decoder_inputs: A list of 2D Tensors* [batch_size x input_size]. In fact,
most of existing decoders in presence of a loop_function use only the
first element to determine batch_size and length of the list to
determine number of steps.
initial_state: 2D Tensor with shape [batch_size x cell.state_size].
loop_function: function will be applied to the i-th output in order to
generate the i+1-st input (see self.get_input).
cell: rnn_cell.RNNCell defining the cell function and size.
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of character logits of the same length as
decoder_inputs of 2D Tensors with shape [batch_size x num_characters].
state: The state of each cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
"""
pass
def is_training(self):
"""Returns True if the layer is created for training stage."""
return self._labels_one_hot is not None
def char_logit(self, inputs, char_index):
"""Creates logits for a character if required.
Args:
inputs: A tensor with shape [batch_size, ?] (depth is implementation
dependent).
char_index: A integer index of a character in the output sequence.
Returns:
A tensor with shape [batch_size, num_char_classes]
"""
if char_index not in self._char_logits:
self._char_logits[char_index] = tf.compat.v1.nn.xw_plus_b(inputs, self._softmax_w,
self._softmax_b)
return self._char_logits[char_index]
def char_one_hot(self, logit):
"""Creates one hot encoding for a logit of a character.
Args:
logit: A tensor with shape [batch_size, num_char_classes].
Returns:
A tensor with shape [batch_size, num_char_classes]
"""
prediction = tf.argmax(input=logit, axis=1)
return slim.one_hot_encoding(prediction, self._params.num_char_classes)
def get_input(self, prev, i):
"""A wrapper for get_train_input and get_eval_input.
Args:
prev: output tensor from previous step of the RNN. A tensor with shape:
[batch_size, num_char_classes].
i: index of a character in the output sequence.
Returns:
A tensor with shape [batch_size, ?] - depth depends on implementation
details.
"""
if self.is_training():
return self.get_train_input(prev, i)
else:
return self.get_eval_input(prev, i)
def create_logits(self):
"""Creates character sequence logits for a net specified in the constructor.
A "main" method for the sequence layer which glues together all pieces.
Returns:
A tensor with shape [batch_size, seq_length, num_char_classes].
"""
with tf.compat.v1.variable_scope('LSTM'):
first_label = self.get_input(prev=None, i=0)
decoder_inputs = [first_label] + [None] * (self._params.seq_length - 1)
lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
self._mparams.num_lstm_units,
use_peepholes=False,
cell_clip=self._mparams.lstm_state_clip_value,
state_is_tuple=True,
initializer=orthogonal_initializer)
lstm_outputs, _ = self.unroll_cell(
decoder_inputs=decoder_inputs,
initial_state=lstm_cell.zero_state(self._batch_size, tf.float32),
loop_function=self.get_input,
cell=lstm_cell)
with tf.compat.v1.variable_scope('logits'):
logits_list = [
tf.expand_dims(self.char_logit(logit, i), axis=1)
for i, logit in enumerate(lstm_outputs)
]
return tf.concat(logits_list, 1)
class NetSlice(SequenceLayerBase):
"""A layer which uses a subset of image features to predict each character.
"""
def __init__(self, *args, **kwargs):
super(NetSlice, self).__init__(*args, **kwargs)
self._zero_label = tf.zeros(
tf.stack([self._batch_size, self._params.num_char_classes]))
def get_image_feature(self, char_index):
"""Returns a subset of image features for a character.
Args:
char_index: an index of a character.
Returns:
A tensor with shape [batch_size, ?]. The output depth depends on the
depth of input net.
"""
batch_size, features_num, _ = [d.value for d in self._net.get_shape()]
slice_len = int(features_num / self._params.seq_length)
# In case when features_num != seq_length, we just pick a subset of image
# features, this choice is arbitrary and there is no intuitive geometrical
# interpretation. If features_num is not dividable by seq_length there will
# be unused image features.
net_slice = self._net[:, char_index:char_index + slice_len, :]
feature = tf.reshape(net_slice, [batch_size, -1])
logging.debug('Image feature: %s', feature)
return feature
def get_eval_input(self, prev, i):
"""See SequenceLayerBase.get_eval_input for details."""
del prev
return self.get_image_feature(i)
def get_train_input(self, prev, i):
"""See SequenceLayerBase.get_train_input for details."""
return self.get_eval_input(prev, i)
def unroll_cell(self, decoder_inputs, initial_state, loop_function, cell):
"""See SequenceLayerBase.unroll_cell for details."""
return tf.contrib.legacy_seq2seq.rnn_decoder(
decoder_inputs=decoder_inputs,
initial_state=initial_state,
cell=cell,
loop_function=self.get_input)
class NetSliceWithAutoregression(NetSlice):
"""A layer similar to NetSlice, but it also uses auto regression.
The "auto regression" means that we use network output for previous character
as a part of input for the current character.
"""
def __init__(self, *args, **kwargs):
super(NetSliceWithAutoregression, self).__init__(*args, **kwargs)
def get_eval_input(self, prev, i):
"""See SequenceLayerBase.get_eval_input for details."""
if i == 0:
prev = self._zero_label
else:
logit = self.char_logit(prev, char_index=i - 1)
prev = self.char_one_hot(logit)
image_feature = self.get_image_feature(char_index=i)
return tf.concat([image_feature, prev], 1)
def get_train_input(self, prev, i):
"""See SequenceLayerBase.get_train_input for details."""
if i == 0:
prev = self._zero_label
else:
prev = self._labels_one_hot[:, i - 1, :]
image_feature = self.get_image_feature(i)
return tf.concat([image_feature, prev], 1)
class Attention(SequenceLayerBase):
"""A layer which uses attention mechanism to select image features."""
def __init__(self, *args, **kwargs):
super(Attention, self).__init__(*args, **kwargs)
self._zero_label = tf.zeros(
tf.stack([self._batch_size, self._params.num_char_classes]))
def get_eval_input(self, prev, i):
"""See SequenceLayerBase.get_eval_input for details."""
del prev, i
# The attention_decoder will fetch image features from the net, no need for
# extra inputs.
return self._zero_label
def get_train_input(self, prev, i):
"""See SequenceLayerBase.get_train_input for details."""
return self.get_eval_input(prev, i)
def unroll_cell(self, decoder_inputs, initial_state, loop_function, cell):
return tf.contrib.legacy_seq2seq.attention_decoder(
decoder_inputs=decoder_inputs,
initial_state=initial_state,
attention_states=self._net,
cell=cell,
loop_function=self.get_input)
class AttentionWithAutoregression(Attention):
"""A layer which uses both attention and auto regression."""
def __init__(self, *args, **kwargs):
super(AttentionWithAutoregression, self).__init__(*args, **kwargs)
def get_train_input(self, prev, i):
"""See SequenceLayerBase.get_train_input for details."""
if i == 0:
return self._zero_label
else:
# TODO(gorban): update to gradually introduce gt labels.
return self._labels_one_hot[:, i - 1, :]
def get_eval_input(self, prev, i):
"""See SequenceLayerBase.get_eval_input for details."""
if i == 0:
return self._zero_label
else:
logit = self.char_logit(prev, char_index=i - 1)
return self.char_one_hot(logit)
def get_layer_class(use_attention, use_autoregression):
"""A convenience function to get a layer class based on requirements.
Args:
use_attention: if True a returned class will use attention.
use_autoregression: if True a returned class will use auto regression.
Returns:
One of available sequence layers (child classes for SequenceLayerBase).
"""
if use_attention and use_autoregression:
layer_class = AttentionWithAutoregression
elif use_attention and not use_autoregression:
layer_class = Attention
elif not use_attention and not use_autoregression:
layer_class = NetSlice
elif not use_attention and use_autoregression:
layer_class = NetSliceWithAutoregression
else:
raise AssertionError('Unsupported sequence layer class')
logging.debug('Use %s as a layer class', layer_class.__name__)
return layer_class