org.apache.tika.parser.captioning.tf.vocabulary.py Maven / Gradle / Ivy
#!/usr/bin/env python
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# to you under the Apache License, Version 2.0 (the
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class Vocabulary(object):
"""
Vocabulary class for an image-to-text model
"""
def __init__(self,
vocab_file,
start_word="",
end_word="",
unk_word=""):
"""Initializes the vocabulary"""
if not tf.gfile.Exists(vocab_file):
tf.logging.fatal("Vocab file %s not found.", vocab_file)
tf.logging.info("Initializing vocabulary from file: %s", vocab_file)
with tf.gfile.GFile(vocab_file, mode="r") as f:
reverse_vocab = list(f.readlines())
reverse_vocab = [line.split()[0] for line in reverse_vocab]
assert start_word in reverse_vocab
assert end_word in reverse_vocab
if unk_word not in reverse_vocab:
reverse_vocab.append(unk_word)
vocab = dict([(x, y) for (y, x) in enumerate(reverse_vocab)])
tf.logging.info("Created vocabulary with %d words" % len(vocab))
# vocab[word] = id
self.vocab = vocab
# reverse_vocab[id] = word
self.reverse_vocab = reverse_vocab
# save special word ids
self.start_id = vocab[start_word]
self.end_id = vocab[end_word]
self.unk_id = vocab[unk_word]
def id_to_word(self, word_id):
"""Returns the word string of an integer word id"""
if word_id >= len(self.reverse_vocab):
return self.reverse_vocab[self.unk_id]
else:
return self.reverse_vocab[word_id]