def forward_fixed_text(self, text_npy):
#text = tf.constant(text_npy) #smaller than 2G, then ok...
#but for safe in more application
text = melt.constant(self.sess, text_npy)
with tf.variable_scope("image_text_sim"):
text_feature = self.forward_text(text)
return text_feature
def build_fixed_text_graph(self, text_feature_npy):
"""
text features directly load to graph,
used in evaluate.py for both fixed text and fixed words
"""
with tf.variable_scope("image_text_sim"):
image_feature = self.forward_image_feature(self.image_feature_place)
text_feature = melt.constant(self.sess, text_feature_npy)
score = melt.cosine(image_feature, text_feature, nonorm=True)
return score
def init_evaluate_constant_text(self, text_npy):
#self.text = tf.constant(text_npy)
self.text = melt.constant(self.sess, text_npy)