def get_logits(self, image, is_train, **kwargs):
"""
"""
image = tf.reshape(image, [-1, 100, 100, 1])
# BCNN
features = self._bcnn(image, is_train)
assert features.get_shape()[1:] == (26, 26, 256)
# AON
features, clue = self._aon(features, is_train)
assert features.get_shape()[1:] == (4, 23, 512)
assert clue.get_shape()[1:] == (4, 23, 1)
# FG
features = tf.reduce_sum(features * clue, axis=1)
features = tf.nn.tanh(features)
assert features.get_shape()[1:] == (23, 512)
# LSTM
features = tf.transpose(features, [1, 0, 2], name='time_major')
features = rnn_layer(features, None, self.rnn_size, 'lstm')
logits, weights = attention_decoder(features, kwargs['label'],
len(self.out_charset),
self.rnn_size, is_train,
self.FLAGS.label_maxlen)
sequence_length = None
return logits, sequence_length
def get_logits(self, image, is_train, **kwargs):
"""
"""
widths = tf.ones(tf.shape(image)[0],
dtype=tf.int32) * tf.shape(image)[2]
features, sequence_length = self._convnet_layers(
image, widths, is_train)
features = tf.transpose(features, perm=[1, 0, 2], name='time_major')
attention_states = rnn_layer(features,
sequence_length,
self.rnn_size,
scope="rnn")
logits, weights = attention_decoder(attention_states, kwargs['label'],
len(self.out_charset),
self.rnn_size, is_train,
self.FLAGS.label_maxlen)
return logits, sequence_length
def get_logits(self, image, is_train, **kwargs):
"""
"""
widths = tf.ones(tf.shape(image)[0],
dtype=tf.int32) * tf.shape(image)[2]
features, sequence_length = self._convnet_layers(
image, widths, is_train)
attention_states = rnn_layers(features,
sequence_length,
self.rnn_size,
use_projection=True)
attention_states = dense_layer(attention_states,
self.rnn_size,
name='att_state_dense')
logits, weights = attention_decoder(attention_states,
kwargs['label'],
len(self.out_charset),
self.rnn_size,
is_train,
self.FLAGS.label_maxlen,
cell_type='gru')
return logits, sequence_length