File size: 1,634 Bytes
18ddfe2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
#!/usr/bin/env python
# Copyright 2017, 2018 Google, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts a text embedding file into a binary format for quicker loading."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
tf.flags.DEFINE_string('input', '', 'text file containing embeddings')
tf.flags.DEFINE_string('output_vocab', '', 'output file for vocabulary')
tf.flags.DEFINE_string('output_npy', '', 'output file for binary')
FLAGS = tf.flags.FLAGS
def main(_):
vecs = []
vocab = []
with tf.gfile.GFile(FLAGS.input) as fh:
for line in fh:
parts = line.strip().split()
vocab.append(parts[0])
vecs.append([float(x) for x in parts[1:]])
with tf.gfile.GFile(FLAGS.output_vocab, 'w') as fh:
fh.write('\n'.join(vocab))
fh.write('\n')
vecs = np.array(vecs, dtype=np.float32)
np.save(FLAGS.output_npy, vecs, allow_pickle=False)
if __name__ == '__main__':
tf.app.run()
|