'Loads a human readable English name for each softmax node.
Args:
label_lookup_path: string UID to integer node ID.
uid_lookup_path: string UID to human-readable string.
Returns:
dict from integer node ID to human-readable string.'

def load(self, label_lookup_path, uid_lookup_path):

    if (not tf.gfile.Exists(uid_lookup_path)):
        tf.logging.fatal('File    does    not    exist    %s', uid_lookup_path)
    if (not tf.gfile.Exists(label_lookup_path)):
        tf.logging.fatal('File    does    not    exist    %s', label_lookup_path)
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile('[n\\d]*[    \\S,]*')
    for line in proto_as_ascii_lines:
        parsed_items = p.findall(line)
        uid = parsed_items[0]
        human_string = parsed_items[2]
        uid_to_human[uid] = human_string
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
        if line.startswith('        target_class:'):
            target_class = int(line.split(':    ')[1])
        if line.startswith('        target_class_string:'):
            target_class_string = line.split(':    ')[1]
            node_id_to_uid[target_class] = target_class_string[1:(-2)]
    node_id_to_name = {}
    for (key, val) in node_id_to_uid.items():
        if (val not in uid_to_human):
            tf.logging.fatal('Failed    to    locate:    %s', val)
        name = uid_to_human[val]
        node_id_to_name[key] = name
    return node_id_to_name
