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ctc_model.py
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ctc_model.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jun 16 00:27:59 2019
@author: amade
"""
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
def leaky_relu(features, alpha=0.2, name=None):
with ops.name_scope(name, "LeakyRelu", [features, alpha]):
features = ops.convert_to_tensor(features, name="features")
alpha = ops.convert_to_tensor(alpha, name="alpha")
return math_ops.maximum(alpha * features, features)
def default_model_params(img_height, vocabulary_size):
params = dict()
params['img_height'] = img_height
params['img_width'] = None
params['batch_size'] = 32
params['img_channels'] = 1
params['conv_blocks'] = 4
params['conv_filter_n'] = [32, 64, 128, 256]
params['conv_filter_size'] = [ [3,3], [3,3], [3,3], [3,3] ]
params['conv_pooling_size'] = [ [2,2], [2,2], [2,2], [2,2] ]
params['rnn_units'] = 256
params['rnn_layers'] = 2
params['vocabulary_size'] = vocabulary_size
return params
def ctc_crnn(params):
# TODO Assert parameters
input = tf.placeholder(shape=(None,
params['img_height'],
params['img_width'],
params['img_channels']), # [batch, height, width, channels]
dtype=tf.float32,
name='model_input')
input_shape = tf.shape(input)
width_reduction = 1
height_reduction = 1
# Convolutional blocks
x = input
for i in range(params['conv_blocks']):
x = tf.layers.conv2d(
inputs=x,
filters=params['conv_filter_n'][i],
kernel_size=params['conv_filter_size'][i],
padding="same",
activation=None)
x = tf.layers.batch_normalization(x)
x = leaky_relu(x)
x = tf.layers.max_pooling2d(inputs=x,
pool_size=params['conv_pooling_size'][i],
strides=params['conv_pooling_size'][i])
width_reduction = width_reduction * params['conv_pooling_size'][i][1]
height_reduction = height_reduction * params['conv_pooling_size'][i][0]
# Prepare output of conv block for recurrent blocks
features = tf.transpose(x, perm=[2, 0, 3, 1]) # -> [width, batch, height, channels] (time_major=True)
feature_dim = params['conv_filter_n'][-1] * (params['img_height'] / height_reduction)
feature_width = input_shape[2] / width_reduction
features = tf.reshape(features, tf.stack([tf.cast(feature_width,'int32'), input_shape[0], tf.cast(feature_dim,'int32')])) # -> [width, batch, features]
tf.constant(params['img_height'],name='input_height')
tf.constant(width_reduction,name='width_reduction')
# Recurrent block
rnn_keep_prob = tf.placeholder(dtype=tf.float32, name="keep_prob")
rnn_hidden_units = params['rnn_units']
rnn_hidden_layers = params['rnn_layers']
rnn_outputs, _ = tf.nn.bidirectional_dynamic_rnn(
tf.contrib.rnn.MultiRNNCell(
[tf.nn.rnn_cell.DropoutWrapper(tf.contrib.rnn.BasicLSTMCell(rnn_hidden_units), input_keep_prob=rnn_keep_prob)
for _ in range(rnn_hidden_layers)]),
tf.contrib.rnn.MultiRNNCell(
[tf.nn.rnn_cell.DropoutWrapper(tf.contrib.rnn.BasicLSTMCell(rnn_hidden_units), input_keep_prob=rnn_keep_prob)
for _ in range(rnn_hidden_layers)]),
features,
dtype=tf.float32,
time_major=True,
)
rnn_outputs = tf.concat(rnn_outputs, 2)
logits = tf.contrib.layers.fully_connected(
rnn_outputs,
params['vocabulary_size'] + 1, # BLANK
activation_fn=None,
)
tf.add_to_collection("logits",logits) # for restoring purposes
# CTC Loss computation
seq_len = tf.placeholder(tf.int32, [None], name='seq_lengths')
targets = tf.sparse_placeholder(dtype=tf.int32, name='target')
ctc_loss = tf.nn.ctc_loss(labels=targets, inputs=logits, sequence_length=seq_len, time_major=True)
loss = tf.reduce_mean(ctc_loss)
# CTC decoding
decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len)
# decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits,seq_len,beam_width=50,top_paths=1,merge_repeated=True)
return input, seq_len, targets, decoded, loss, rnn_keep_prob
def ctc_crnn_custom_vgg(params):
input = tf.placeholder(shape=(None,
params['img_height'],
params['img_width'],
params['img_channels']), # [batch, height, width, channels]
dtype=tf.float32,
name='model_input')
input_shape = tf.shape(input)
width_reduction = 1
height_reduction = 1
# Convolutional blocks
x = input
x = tf.keras.layers.SeparableConv2D(32, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(32, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
# Prepare output of conv block for recurrent blocks
#features = tf.transpose(x, perm=[0, 2, 1, 3]) # -> [width, batch, height, channels] (time_major=True)
features = x
feature_dim = params['conv_filter_n'][-1] * (params['img_height'] / height_reduction)
feature_width = input_shape[2] / width_reduction
features = tf.reshape(features, tf.stack([input_shape[0], tf.cast(feature_width,'int32'), tf.cast(feature_dim,'int32')])) # -> [batch, width, features]
tf.constant(params['img_height'],name='input_height')
tf.constant(width_reduction,name='width_reduction')
# Recurrent block
rnn_keep_prob = tf.keras.backend.placeholder(dtype=tf.float32, name="keep_prob")
rnn_hidden_units = params['rnn_units']
#rnn_hidden_layers = params['rnn_layers']
x1 = tf.keras.layers.GRU(rnn_hidden_units, return_sequences = True, activation = 'relu',
kernel_initializer = 'he_normal', dropout = 0.5)(features)
x2 = tf.keras.layers.GRU(rnn_hidden_units, return_sequences = True, go_backwards = True,
activation = 'relu', kernel_initializer = 'he_normal', dropout = 0.5)(features)
x = tf.keras.layers.Add()([x1, x2])
x1 = tf.keras.layers.GRU(rnn_hidden_units, return_sequences = True, activation = 'relu',
kernel_initializer = 'he_normal', dropout = 0.5)(x)
x2 = tf.keras.layers.GRU(rnn_hidden_units, return_sequences = True, go_backwards = True,
activation = 'relu', kernel_initializer = 'he_normal', dropout = 0.5)(x)
rnn_outputs = tf.keras.layers.Concatenate()([x1, x2])
logits = tf.keras.layers.Dense(params['vocabulary_size'] + 1)(rnn_outputs)
tf.add_to_collection("logits",logits) # for restoring purposes
# CTC Loss computation
seq_len = tf.placeholder(tf.int32, [None], name='seq_lengths')
targets = tf.sparse_placeholder(dtype=tf.int32, name='target')
ctc_loss = tf.nn.ctc_loss(labels=targets, inputs=logits, sequence_length=seq_len, time_major=False)
loss = tf.reduce_mean(ctc_loss)
# CTC decoding
decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len)
# decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits,seq_len,beam_width=50,top_paths=1,merge_repeated=True)
return input, seq_len, targets, decoded, loss, rnn_keep_prob
def ctc_ctcn_custom_vgg(params):
input = tf.placeholder(shape=(None,
params['img_height'],
params['img_width'],
params['img_channels']), # [batch, height, width, channels]
dtype=tf.float32,
name='model_input')
input_shape = tf.shape(input)
width_reduction = 1
height_reduction = 1
# Convolutional blocks
x = input
x = tf.keras.layers.SeparableConv2D(32, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(32, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(64, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(128, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.SeparableConv2D(256, (3, 3), padding="same")(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU()(x)
x = tf.keras.layers.MaxPooling2D()(x)
width_reduction = width_reduction * 2
height_reduction = height_reduction * 2
# Prepare output of conv block for recurrent blocks
#features = tf.transpose(x, perm=[0, 2, 1, 3]) # -> [width, batch, height, channels] (time_major=True)
features = x
feature_dim = params['conv_filter_n'][-1] * (params['img_height'] / height_reduction)
feature_width = input_shape[2] / width_reduction
features = tf.reshape(features, tf.stack([input_shape[0], tf.cast(feature_width,'int32'), tf.cast(feature_dim,'int32')])) # -> [batch, width, features]
tf.constant(params['img_height'],name='input_height')
tf.constant(width_reduction,name='width_reduction')
# Recurrent block
rnn_keep_prob = tf.keras.backend.placeholder(dtype=tf.float32, name="keep_prob")
rnn_hidden_units = params['rnn_units']
#rnn_hidden_layers = params['rnn_layers']
x = tf.keras.layers.Dense(64, activation = 'relu')(features)
x = tf.keras.layers.Conv1D(rnn_hidden_units, 1, padding='causal')(x)
for s in range(1):#number of stacks
for d in [1, 2, 4, 8, 16, 32]: #dilation rate
x1 = tf.keras.layers.Conv1D(rnn_hidden_units, 2, dilation_rate=d, padding='causal')(x)
x1 = tf.keras.layers.Activation('relu')(x1)
x1 = tf.keras.layers.SpatialDropout1D(rate=0.2)(x1)
x1 = tf.keras.layers.Conv1D(rnn_hidden_units, 2, dilation_rate=d, padding='causal')(x1)
x1 = tf.keras.layers.Activation('relu')(x1)
x1 = tf.keras.layers.SpatialDropout1D(rate=0.2)(x1)
x2 = tf.keras.layers.Conv1D(rnn_hidden_units, 1, padding='causal')(x)
x = tf.keras.layers.add([x1, x2])
rnn_outputs = x
logits = tf.keras.layers.Dense(params['vocabulary_size'] + 1)(rnn_outputs)
tf.add_to_collection("logits",logits) # for restoring purposes
# CTC Loss computation
seq_len = tf.placeholder(tf.int32, [None], name='seq_lengths')
targets = tf.sparse_placeholder(dtype=tf.int32, name='target')
ctc_loss = tf.nn.ctc_loss(labels=targets, inputs=logits, sequence_length=seq_len, time_major=False)
loss = tf.reduce_mean(ctc_loss)
# CTC decoding
decoded, log_prob = tf.nn.ctc_greedy_decoder(logits, seq_len)
# decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits,seq_len,beam_width=50,top_paths=1,merge_repeated=True)
return input, seq_len, targets, decoded, loss, rnn_keep_prob