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cnn_hier_rnn_model.py
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cnn_hier_rnn_model.py
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import tensorflow as tf
from nets import inception
class Model(object):
def __init__(self, config, is_training=True, batch_size=26):
self.config = config
self.is_training = is_training
self.batch_size = batch_size
self.images_frontal = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, config.image_size, config.image_size, 3])
self.images_lateral = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, config.image_size, config.image_size, 3])
self.sentences = tf.placeholder(dtype=tf.int32, shape=[self.batch_size, config.max_sentence_num * config.max_sentence_length])
self.masks = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, config.max_sentence_num * config.max_sentence_length])
self.build_cnn()
self.build_rnn()
self.build_metrics()
if is_training:
self.build_optimizer()
self.build_summary()
def build_cnn(self):
net_f, _ = inception.inception_v3(self.images_frontal, trainable=True, is_training=True, add_summaries=False, scope='FrontalInceptionV3')
net_l, _ = inception.inception_v3(self.images_lateral, trainable=True, is_training=True, add_summaries=False, scope='LateralInceptionV3')
self.visual_feats = tf.concat([net_f, net_l], axis=1) # [batch_size, 4096]
print('cnn built.')
def build_rnn(self):
with tf.variable_scope("word_embedding"):
word_embedding_matrix = tf.get_variable(
name='weights',
shape=[self.config.vocabulary_size, self.config.word_embedding_size],
trainable=True)
# 1. build hierarchical rnn
SentRNN = tf.nn.rnn_cell.LSTMCell(
name = 'sent_rnn',
num_units=self.config.rnn_units)
if self.is_training:
SentRNN = tf.nn.rnn_cell.DropoutWrapper(
SentRNN,
input_keep_prob = 1.0 - self.config.rnn_dropout_rate,
output_keep_prob = 1.0 - self.config.rnn_dropout_rate,
state_keep_prob = 1.0 - self.config.rnn_dropout_rate)
WordRNN = tf.nn.rnn_cell.LSTMCell(
name='word_rnn',
num_units=self.config.rnn_units)
if self.is_training:
WordRNN = tf.nn.rnn_cell.DropoutWrapper(
WordRNN,
input_keep_prob=1.0 - self.config.rnn_dropout_rate,
output_keep_prob=1.0 - self.config.rnn_dropout_rate,
state_keep_prob=1.0 - self.config.rnn_dropout_rate)
# 2. init Sent RNN
with tf.variable_scope('sent_rnn_initialize'):
context = tf.layers.dropout(self.visual_feats, rate=self.config.dropout_rate, training=self.is_training, name='drop_v')
init_c = tf.layers.dense(context, units=self.config.rnn_units, activation=tf.tanh, use_bias=True, name='fc_c')
init_h = tf.layers.dense(context, units=self.config.rnn_units, activation=tf.tanh, use_bias=True, name='fc_h')
SentRNN_last_state = init_c, init_h
# 3. generate sentence one by one
predicts = []
cross_entropies = []
corrects = []
for sent_id in range(self.config.max_sentence_num):
# 3.1 sent rnn
with tf.variable_scope('sent_rnn'):
SentRNN_output, SentRNN_state = SentRNN(self.visual_feats, SentRNN_last_state)
SentRNN_last_state = SentRNN_state
# 3.2 init Word RNN
with tf.variable_scope('word_rnn_initialize'):
context = tf.layers.dropout(SentRNN_output, rate=self.config.dropout_rate, training=self.is_training, name='drop_s')
init_c = tf.layers.dense(context, units=self.config.rnn_units, activation=tf.tanh, use_bias=True, name='fc_c')
init_h = tf.layers.dense(context, units=self.config.rnn_units, activation=tf.tanh, use_bias=True, name='fc_h')
WordRNN_last_state = init_c, init_h
WordRNN_last_word = tf.zeros([self.batch_size], tf.int32)
# 3.3 generate word one by one
for word_id in range(self.config.max_sentence_length):
with tf.variable_scope("word_embedding"):
word_embedding = tf.nn.embedding_lookup(word_embedding_matrix, WordRNN_last_word)
with tf.variable_scope('word_rnn'):
WordRNN_output, WordRNN_state = WordRNN(word_embedding, WordRNN_last_state)
WordRNN_last_state = WordRNN_state
with tf.variable_scope('decode'):
WordRNN_output = tf.layers.dropout(WordRNN_output, rate=self.config.dropout_rate, training=self.is_training, name='drop_d')
logits = tf.layers.dense(WordRNN_output, units=self.config.vocabulary_size, use_bias=True, name='fc_d')
predict = tf.argmax(logits, 1)
predicts.append(predict)
tf.get_variable_scope().reuse_variables()
if self.is_training:
WordRNN_last_word = self.sentences[:, sent_id*self.config.max_sentence_length + word_id]
else:
WordRNN_last_word = predict
# compute cross entropy loss
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.sentences[:, sent_id*self.config.max_sentence_length + word_id], logits=logits)
masked_cross_entropy = cross_entropy * self.masks[:, sent_id*self.config.max_sentence_length + word_id]
cross_entropies.append(masked_cross_entropy)
# compute acc
ground_truth = tf.cast(self.sentences[:, sent_id*self.config.max_sentence_length + word_id], tf.int64)
correct = tf.where(
tf.equal(predict, ground_truth),
tf.cast(self.masks[:, sent_id*self.config.max_sentence_length + word_id], tf.float32),
tf.cast(tf.zeros_like(predict), tf.float32)
)
corrects.append(correct)
self.predicts = predicts
self.cross_entropies = cross_entropies
self.corrects = corrects
print('rnn built.')
def build_metrics(self):
corrects = tf.stack(self.corrects, axis=1)
self.accuracy = tf.reduce_sum(corrects) / tf.reduce_sum(self.masks)
cross_entropies = tf.stack(self.cross_entropies, axis=1)
self.cross_entropy_loss = tf.reduce_sum(cross_entropies) / tf.reduce_sum(self.masks)
self.reg_loss = tf.losses.get_regularization_loss()
self.loss = self.cross_entropy_loss + self.reg_loss
print('metrics built.')
def build_optimizer(self):
self.global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.constant(self.config.learning_rate)
def _learning_rate_decay_fn(learning_rate, global_step):
return tf.train.exponential_decay(
learning_rate=learning_rate,
global_step=global_step,
decay_steps=self.config.decay_iters,
decay_rate=self.config.decay_rate,
staircase=True
)
learning_rate_decay_fn = _learning_rate_decay_fn
with tf.variable_scope('optimizer', reuse=tf.AUTO_REUSE):
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-8
)
self.step_op = tf.contrib.layers.optimize_loss(
loss=self.loss,
global_step=self.global_step,
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=5.0,
learning_rate_decay_fn=learning_rate_decay_fn,
# variables=other_var_list
)
print('optimizer built.')
def build_summary(self):
with tf.name_scope("metrics"):
tf.summary.scalar('cross entropy loss', self.cross_entropy_loss)
tf.summary.scalar('reg loss', self.reg_loss)
tf.summary.scalar('acc', self.accuracy)
self.summary = tf.summary.merge_all()
print('summary built.')