Python data.SENTENCE_END Examples
The following are 30
code examples of data.SENTENCE_END().
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Example #1
| Source File: seq2seq_attention_decode.py From HumanRecognition with MIT License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #2
| Source File: seq2seq_attention_decode.py From hands-detection with MIT License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #3
| Source File: seq2seq_attention_decode.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #4
| Source File: seq2seq_attention_decode.py From object_detection_kitti with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #5
| Source File: seq2seq_attention_decode.py From Action_Recognition_Zoo with MIT License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #6
| Source File: seq2seq_attention_decode.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #7
| Source File: vocab_batcher.py From text2text with Apache License 2.0 | 6 votes |
|
def _AddSentenceBoundary(self, text):
"""Pads text with start end end of sentence token iff needed.
Args:
text: text to be padded.
Returns:
A text with start and end tokens.
"""
if not text.startswith(data.SENTENCE_START):
text = data.SENTENCE_START + ' ' + text
if not text.endswith(data.SENTENCE_END):
text = text + ' ' + data.SENTENCE_END
return text
Example #8
| Source File: copynet_batcher.py From text2text with Apache License 2.0 | 6 votes |
|
def _AddSentenceBoundary(self, text):
"""Pads text with start end end of sentence token iff needed.
Args:
text: text to be padded.
Returns:
A text with start and end tokens.
"""
if not text.startswith(data.SENTENCE_START):
text = data.SENTENCE_START + ' ' + text
if not text.endswith(data.SENTENCE_END):
text = text + ' ' + data.SENTENCE_END
return text
Example #9
| Source File: seq2seq_attention_decode.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #10
| Source File: seq2seq_attention_decode.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #11
| Source File: seq2seq_attention_decode.py From models with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #12
| Source File: seq2seq_attention_decode.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #13
| Source File: seq2seq_attention_decode.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #14
| Source File: seq2seq_attention_decode.py From DOTA_models with Apache License 2.0 | 6 votes |
|
def _DecodeBatch(self, article, abstract, output_ids):
"""Convert id to words and writing results.
Args:
article: The original article string.
abstract: The human (correct) abstract string.
output_ids: The abstract word ids output by machine.
"""
decoded_output = ' '.join(data.Ids2Words(output_ids, self._vocab))
end_p = decoded_output.find(data.SENTENCE_END, 0)
if end_p != -1:
decoded_output = decoded_output[:end_p]
tf.logging.info('article: %s', article)
tf.logging.info('abstract: %s', abstract)
tf.logging.info('decoded: %s', decoded_output)
self._decode_io.Write(abstract, decoded_output.strip())
Example #15
| Source File: seq2seq_attention_decode.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #16
| Source File: seq2seq_attention_decode.py From hands-detection with MIT License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #17
| Source File: seq2seq_attention_decode.py From HumanRecognition with MIT License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #18
| Source File: seq2seq_attention_decode.py From models with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #19
| Source File: seq2seq_attention_decode.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #20
| Source File: seq2seq_attention_decode.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #21
| Source File: seq2seq_attention_decode.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #22
| Source File: seq2seq_attention_decode.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #23
| Source File: seq2seq_attention_decode.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #24
| Source File: seq2seq_attention_decode.py From Action_Recognition_Zoo with MIT License | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #25
| Source File: seq2seq_attention_decode.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #26
| Source File: seq2seq_attention_decode.py From DOTA_models with Apache License 2.0 | 5 votes |
|
def _Decode(self, saver, sess):
"""Restore a checkpoint and decode it.
Args:
saver: Tensorflow checkpoint saver.
sess: Tensorflow session.
Returns:
If success, returns true, otherwise, false.
"""
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to decode yet at %s', FLAGS.log_root)
return False
tf.logging.info('checkpoint path %s', ckpt_state.model_checkpoint_path)
ckpt_path = os.path.join(
FLAGS.log_root, os.path.basename(ckpt_state.model_checkpoint_path))
tf.logging.info('renamed checkpoint path %s', ckpt_path)
saver.restore(sess, ckpt_path)
self._decode_io.ResetFiles()
for _ in xrange(FLAGS.decode_batches_per_ckpt):
(article_batch, _, _, article_lens, _, _, origin_articles,
origin_abstracts) = self._batch_reader.NextBatch()
for i in xrange(self._hps.batch_size):
bs = beam_search.BeamSearch(
self._model, self._hps.batch_size,
self._vocab.WordToId(data.SENTENCE_START),
self._vocab.WordToId(data.SENTENCE_END),
self._hps.dec_timesteps)
article_batch_cp = article_batch.copy()
article_batch_cp[:] = article_batch[i:i+1]
article_lens_cp = article_lens.copy()
article_lens_cp[:] = article_lens[i:i+1]
best_beam = bs.BeamSearch(sess, article_batch_cp, article_lens_cp)[0]
decode_output = [int(t) for t in best_beam.tokens[1:]]
self._DecodeBatch(
origin_articles[i], origin_abstracts[i], decode_output)
return True
Example #27
| Source File: seq2seq_attention.py From HumanRecognition with MIT License | 4 votes |
|
def main(unused_argv):
vocab = data.Vocab(FLAGS.vocab_path, 1000000)
# Check for presence of required special tokens.
assert vocab.CheckVocab(data.PAD_TOKEN) > 0
assert vocab.CheckVocab(data.UNKNOWN_TOKEN) >= 0
assert vocab.CheckVocab(data.SENTENCE_START) > 0
assert vocab.CheckVocab(data.SENTENCE_END) > 0
batch_size = 4
if FLAGS.mode == 'decode':
batch_size = FLAGS.beam_size
hps = seq2seq_attention_model.HParams(
mode=FLAGS.mode, # train, eval, decode
min_lr=0.01, # min learning rate.
lr=0.15, # learning rate
batch_size=batch_size,
enc_layers=4,
enc_timesteps=120,
dec_timesteps=30,
min_input_len=2, # discard articles/summaries < than this
num_hidden=256, # for rnn cell
emb_dim=128, # If 0, don't use embedding
max_grad_norm=2,
num_softmax_samples=4096) # If 0, no sampled softmax.
batcher = batch_reader.Batcher(
FLAGS.data_path, vocab, hps, FLAGS.article_key,
FLAGS.abstract_key, FLAGS.max_article_sentences,
FLAGS.max_abstract_sentences, bucketing=FLAGS.use_bucketing,
truncate_input=FLAGS.truncate_input)
tf.set_random_seed(FLAGS.random_seed)
if hps.mode == 'train':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Train(model, batcher)
elif hps.mode == 'eval':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Eval(model, batcher, vocab=vocab)
elif hps.mode == 'decode':
decode_mdl_hps = hps
# Only need to restore the 1st step and reuse it since
# we keep and feed in state for each step's output.
decode_mdl_hps = hps._replace(dec_timesteps=1)
model = seq2seq_attention_model.Seq2SeqAttentionModel(
decode_mdl_hps, vocab, num_gpus=FLAGS.num_gpus)
decoder = seq2seq_attention_decode.BSDecoder(model, batcher, hps, vocab)
decoder.DecodeLoop()
Example #28
| Source File: seq2seq_attention.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
|
def main(unused_argv):
vocab = data.Vocab(FLAGS.vocab_path, 1000000)
# Check for presence of required special tokens.
assert vocab.CheckVocab(data.PAD_TOKEN) > 0
assert vocab.CheckVocab(data.UNKNOWN_TOKEN) >= 0
assert vocab.CheckVocab(data.SENTENCE_START) > 0
assert vocab.CheckVocab(data.SENTENCE_END) > 0
batch_size = 4
if FLAGS.mode == 'decode':
batch_size = FLAGS.beam_size
hps = seq2seq_attention_model.HParams(
mode=FLAGS.mode, # train, eval, decode
min_lr=0.01, # min learning rate.
lr=0.15, # learning rate
batch_size=batch_size,
enc_layers=4,
enc_timesteps=120,
dec_timesteps=30,
min_input_len=2, # discard articles/summaries < than this
num_hidden=256, # for rnn cell
emb_dim=128, # If 0, don't use embedding
max_grad_norm=2,
num_softmax_samples=4096) # If 0, no sampled softmax.
batcher = batch_reader.Batcher(
FLAGS.data_path, vocab, hps, FLAGS.article_key,
FLAGS.abstract_key, FLAGS.max_article_sentences,
FLAGS.max_abstract_sentences, bucketing=FLAGS.use_bucketing,
truncate_input=FLAGS.truncate_input)
tf.set_random_seed(FLAGS.random_seed)
if hps.mode == 'train':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Train(model, batcher)
elif hps.mode == 'eval':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Eval(model, batcher, vocab=vocab)
elif hps.mode == 'decode':
decode_mdl_hps = hps
# Only need to restore the 1st step and reuse it since
# we keep and feed in state for each step's output.
decode_mdl_hps = hps._replace(dec_timesteps=1)
model = seq2seq_attention_model.Seq2SeqAttentionModel(
decode_mdl_hps, vocab, num_gpus=FLAGS.num_gpus)
decoder = seq2seq_attention_decode.BSDecoder(model, batcher, hps, vocab)
decoder.DecodeLoop()
Example #29
| Source File: seq2seq_attention.py From DOTA_models with Apache License 2.0 | 4 votes |
|
def main(unused_argv):
vocab = data.Vocab(FLAGS.vocab_path, 1000000)
# Check for presence of required special tokens.
assert vocab.CheckVocab(data.PAD_TOKEN) > 0
assert vocab.CheckVocab(data.UNKNOWN_TOKEN) >= 0
assert vocab.CheckVocab(data.SENTENCE_START) > 0
assert vocab.CheckVocab(data.SENTENCE_END) > 0
batch_size = 4
if FLAGS.mode == 'decode':
batch_size = FLAGS.beam_size
hps = seq2seq_attention_model.HParams(
mode=FLAGS.mode, # train, eval, decode
min_lr=0.01, # min learning rate.
lr=0.15, # learning rate
batch_size=batch_size,
enc_layers=4,
enc_timesteps=120,
dec_timesteps=30,
min_input_len=2, # discard articles/summaries < than this
num_hidden=256, # for rnn cell
emb_dim=128, # If 0, don't use embedding
max_grad_norm=2,
num_softmax_samples=4096) # If 0, no sampled softmax.
batcher = batch_reader.Batcher(
FLAGS.data_path, vocab, hps, FLAGS.article_key,
FLAGS.abstract_key, FLAGS.max_article_sentences,
FLAGS.max_abstract_sentences, bucketing=FLAGS.use_bucketing,
truncate_input=FLAGS.truncate_input)
tf.set_random_seed(FLAGS.random_seed)
if hps.mode == 'train':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Train(model, batcher)
elif hps.mode == 'eval':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Eval(model, batcher, vocab=vocab)
elif hps.mode == 'decode':
decode_mdl_hps = hps
# Only need to restore the 1st step and reuse it since
# we keep and feed in state for each step's output.
decode_mdl_hps = hps._replace(dec_timesteps=1)
model = seq2seq_attention_model.Seq2SeqAttentionModel(
decode_mdl_hps, vocab, num_gpus=FLAGS.num_gpus)
decoder = seq2seq_attention_decode.BSDecoder(model, batcher, hps, vocab)
decoder.DecodeLoop()
Example #30
| Source File: seq2seq_attention.py From models with Apache License 2.0 | 4 votes |
|
def main(unused_argv):
vocab = data.Vocab(FLAGS.vocab_path, 1000000)
# Check for presence of required special tokens.
assert vocab.CheckVocab(data.PAD_TOKEN) > 0
assert vocab.CheckVocab(data.UNKNOWN_TOKEN) >= 0
assert vocab.CheckVocab(data.SENTENCE_START) > 0
assert vocab.CheckVocab(data.SENTENCE_END) > 0
batch_size = 4
if FLAGS.mode == 'decode':
batch_size = FLAGS.beam_size
hps = seq2seq_attention_model.HParams(
mode=FLAGS.mode, # train, eval, decode
min_lr=0.01, # min learning rate.
lr=0.15, # learning rate
batch_size=batch_size,
enc_layers=4,
enc_timesteps=120,
dec_timesteps=30,
min_input_len=2, # discard articles/summaries < than this
num_hidden=256, # for rnn cell
emb_dim=128, # If 0, don't use embedding
max_grad_norm=2,
num_softmax_samples=4096) # If 0, no sampled softmax.
batcher = batch_reader.Batcher(
FLAGS.data_path, vocab, hps, FLAGS.article_key,
FLAGS.abstract_key, FLAGS.max_article_sentences,
FLAGS.max_abstract_sentences, bucketing=FLAGS.use_bucketing,
truncate_input=FLAGS.truncate_input)
tf.set_random_seed(FLAGS.random_seed)
if hps.mode == 'train':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Train(model, batcher)
elif hps.mode == 'eval':
model = seq2seq_attention_model.Seq2SeqAttentionModel(
hps, vocab, num_gpus=FLAGS.num_gpus)
_Eval(model, batcher, vocab=vocab)
elif hps.mode == 'decode':
decode_mdl_hps = hps
# Only need to restore the 1st step and reuse it since
# we keep and feed in state for each step's output.
decode_mdl_hps = hps._replace(dec_timesteps=1)
model = seq2seq_attention_model.Seq2SeqAttentionModel(
decode_mdl_hps, vocab, num_gpus=FLAGS.num_gpus)
decoder = seq2seq_attention_decode.BSDecoder(model, batcher, hps, vocab)
decoder.DecodeLoop()
