def build_feature_tensor(self):
""" Computes the inception tensor
Inputs:
self.images
Outputs:
self.inception_output
"""
self.inception_output = image_embedding.inception_v3(
self.images,
trainable=self.train_inception,
is_training=self.is_training())
# Brandon Trabucco 2018.06.13: attention is a 4 tensor of shape [batch_size, height, width, 1]
self.build_attention_tensor(
tf.zeros([
tf.shape(self.inception_output)[0],
self.config.num_lstm_units * 2
],
dtype=tf.float32))
# Compute a prelimiary context tensor for the image
self.build_context_tensor()
self.inception_variables = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope="InceptionV3")
def testTrainableTrueIsTrainingFalse(self):
embeddings = image_embedding.inception_v3(self._images,
trainable=True,
is_training=False)
self.assertEqual([self._batch_size, 2048],
embeddings.get_shape().as_list())
self._verifyParameterCounts()
self._assertCollectionSize(376, tf.GraphKeys.GLOBAL_VARIABLES)
self._assertCollectionSize(188, tf.GraphKeys.TRAINABLE_VARIABLES)
self._assertCollectionSize(0, tf.GraphKeys.UPDATE_OPS)
self._assertCollectionSize(94, tf.GraphKeys.REGULARIZATION_LOSSES)
self._assertCollectionSize(0, tf.GraphKeys.LOSSES)
self._assertCollectionSize(23, tf.GraphKeys.SUMMARIES)