def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
while True:
batch = self._batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info("Decoder has finished reading dataset for single_pass.")
tf.logging.info("Output has been saved in %s and %s. Now starting ROUGE eval...", self._rouge_ref_dir,
self._rouge_dec_dir)
results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[0] # list of strings
original_category = batch.categoryWords[0]
original_tags = batch.tagWords[0]
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
category_withunks = data.show_art_oovs(original_category, self._vocab) # strin
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
# write ref summary and decoded summary to file, to eval with pyrouge later
self.write_for_rouge(original_article, original_abstract_sents, decoded_words, original_tags,original_category, counter)
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks, decoded_output,
original_category) # log output to screen
self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists,
best_hyp.p_gens) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
def process_one_article(self, original_article_sents, original_abstract_sents, \
original_selected_ids, output_ids, oovs, \
attn_dists, p_gens, log_probs, counter):
# Remove the [STOP] token from decoded_words, if necessary
decoded_words = data.outputids2words(output_ids, self._vocab, oovs)
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
decoded_sents = data.words2sents(decoded_words)
if FLAGS.single_pass:
verbose = False if FLAGS.mode == 'eval' else True
self.write_for_rouge(original_abstract_sents, decoded_sents, counter, verbose) # write ref summary and decoded summary to file, to eval with pyrouge later
if FLAGS.decode_method == 'beam' and FLAGS.save_vis:
original_article = ' '.join(original_article_sents)
original_abstract = ' '.join(original_abstract_sents)
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, oovs)
self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, \
attn_dists, p_gens, log_probs, counter, verbose)
if FLAGS.save_pkl:
self.save_result(original_article_sents, original_abstract_sents, \
original_selected_ids, decoded_sents, counter, verbose)
def decodeOneSample(self, batches):
batch = batches[0]
original_article = batch.original_articles[0]
original_abstract = batch.original_abstracts[0]
original_abstract_sents = batch.original_abstracts_sents[0]
article_withunks = data.show_art_oovs(original_article, self._vocab)
abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab,
batch.art_oovs[0])
best_hypothesis = beam_search.run_beam_search(self._session,
self._model, self._vocab,
batch, self._hps)
output_ids = [int(t) for t in best_hypothesis.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab,
batch.art_oovs[0])
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
self.write_for_rouge(original_abstract_sents, decoded_words, 0,
original_article)
self.rouge_eval()
print_results(article_withunks, abstract_withunks, decoded_output)
self.write_for_attnvis(article_withunks, abstract_withunks,
decoded_words, best_hypothesis.attn_dists,
best_hypothesis.p_gens)
def decode(self):
decode_relations = []
original_relations = []
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
# todo after finish final batch, compute precision, recall and f1_score
p, r, f1 = calculate_measure_cmp(decode_relations,
original_relations)
tf.logging.info("p: %.4f, r: %.4f, f1: %.4f", p, r, f1)
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words_webnlg(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
decode_relations.append(decoded_output)
original_relations.append(original_abstract)
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens
) # write info to .json file for visualization tool
def decode(self, ckpt_file=None):
FLAGS = self._FLAGS
# load latest checkpoint
misc_utils.load_ckpt(self._saver, self._sess, self._ckpt_dir,
ckpt_file)
counter = 0
f = open(self._decode_dir, "w")
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
break
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch, FLAGS)
# Extract the output ids from the hypothesis and convert back to
# words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
# index of the (first) [STOP] symbol
fst_stop_idx = decoded_words.index(data.STOP_DECODING)
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
# write ref summary and decoded summary to file, to eval with
# pyrouge later
# self.write_for_rouge(original_abstract_sents, decoded_words, counter)
processed = self.depreciated_processing(decoded_words)
f.write(processed + "\n")
counter += 1
if counter % 100 == 0:
print("%d sentences decoded" % counter)
f.close()
def pair_wise_decode(self):
f = os.path.join(FLAGS.data_path, "output.txt")
outputfile = codecs.open(f, "w", "utf8")
output_result = []
list_of_reference = []
while True:
batch = self._batcher.next_pairwised_decode_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
logging.info("eval_finished")
outputfile.close()
break
print(self._batcher.c_index)
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
result = self.eval_one_batch(self._sess, self._model, self._vocab,
batch)
# result = self.eval_one_batch(self._sess, self._model, self._vocab, batch)
for i, instance in enumerate(result):
if i == len(batch.art_oovs):
break
if i >= batch.real_length:
print("eval done with {} isntances".format(
len(output_result)))
break
out_words = data.outputids2words(instance,
self._model._vocab_out,
batch.art_oovs[i])
if data.STOP_DECODING in out_words:
out_words = out_words[:out_words.index(data.STOP_DECODING)]
output_now = " ".join(out_words)
output_result.append(output_now)
# refer = " ".join(refer)
refer = batch.original_abstracts[i].strip()
list_of_reference.append([refer])
outputfile.write(batch.original_articles[i] + '\t' +
output_now + '\n')
bleu = matrix.bleu_score(list_of_reference, output_result)
acc = matrix.compute_acc(list_of_reference, output_result)
print("bleu : {} acc : {}".format(bleu, acc))
return
def _decode(self):
"""
"""
t0 = time.time()
counter = 0
while True:
batch = self.batcher._next_batch()
if batch is None:
assert FLAGS.onetime, "Dataset exhausted, but we are not in onetime mode"
print('INFO: Decoder has finished reading dataset for onetime.')
print('INFO: Output has been saved in {} and {}, start ROUGE eval...'.format(self.rouge_ref_dir, self.rouge_dec_dir))
results_dict = rouge_eval(self.rouge_ref_dir, self.rouge_dec_dir)
rouge_log(results_dict, self.decode_dir)
return
original_article = batch.original_articles[0]
original_abstract = batch.original_abstracts[0]
article_withunks = data.show_art_oovs(original_article, self.vocab)
abstract_withunks = data.show_abs_oovs(original_abstract, self.vocab, (batch.art_oovs[0] if FLAGS.pointer else None))
best_hyp = beam_search.run_beam_search(self.sess, self.model, self.vocab, batch)
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self.vocab, (batch.art_oovs[0] if FLAGS.pointer else None))
try:
fst_stop_idx = decoded_words.index(data.DECODING_END)
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words)
if FLAGS.onetime:
self._write_for_rouge(original_abstract, decoded_words, counter)
counter += 1
else:
print("")
print('INFO: ARTICLE: {}'.format(article_withunks))
print('INFO: REFERENCE SUMMARY: {}'.format(abstract_withunks))
print('INFO: GENERATED SUMMARY: {}'.format(decoded_output))
print("")
self._write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.pointers)
t1 = time.time()
if t1-t0 > SECS_UNTIL_NEW_CKPT:
print('INFO: Decoding for {} seconds, loading new checkpoint'.format(t1-t0))
while True:
try:
ckpt_state = tf.train.get_checkpoint_state(train_dir)
print('INFO: Loading checkpoint {}'.format(ckpt_state.model_checkpoint_path))
self.saver.restore(self.sess, ckpt_state.model_checkpoint_path)
break
except:
print('ERROR: Failed to restore checkpoint: {}, sleep for {} secs'.format(train_dir, 10))
time.sleep(10)
t0 = time.time()
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
out_num=0
summaries=[]
while True:
batch = self._batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert self.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info("Decoder has finished reading dataset for single_pass.")
##I commented those lines
# tf.logging.info("Output has been saved in %s and %s. Now starting ROUGE eval...", self._rouge_ref_dir, self._rouge_dec_dir)
# results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir)
# rouge_log(results_dict, self._decode_dir)
print out_num
return summaries
original_article = batch.original_articles[0] # string
# I commented those lines
# original_abstract = batch.original_abstracts[0] # string
# original_abstract_sents = batch.original_abstracts_sents[0] # list of strings
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
# I commented this line
# abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch,self.beam_size,self.max_dec_steps,self.min_dec_steps)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab, (batch.art_oovs[0] if self.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if self.single_pass:
summaries.append(decoded_output)
# open('s'+str(out_num)+'.txt','w').write(decoded_output)
# with open('output'+str(out_num)+'.txt','w') as output:
# output.write(original_article+'\n*******************************************\n\n'+decoded_output)
out_num+=1
print out_num
#this line is commented by me
# self.write_for_rouge(original_abstract_sents, decoded_words, counter) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = FLAGS.decode_after
while True:
tf.reset_default_graph()
batch = self._batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info("Decoder has finished reading dataset for single_pass.")
tf.logging.info("Output has been saved in %s and %s. Now starting ROUGE eval...", self._rouge_ref_dir, self._rouge_dec_dir)
results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[0] # list of strings
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
if FLAGS.ac_training:
best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch, self._dqn, self._dqn_sess, self._dqn_graph)
else:
best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
self.write_for_rouge(original_abstract_sents, decoded_words, counter) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks, decoded_output) # log output to screen
self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1-t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info('We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint', t1-t0)
_ = util.load_ckpt(self._saver, self._sess, FLAGS.decode_from)
t0 = time.time()
def decode2(self, batcher):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
while True:
batch = batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
results_dict = rouge_eval(self._rouge_ref_dir,
self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
return decoded_output
def decode_one_batch(self, batch, withRouge=True):
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
original_uuid = batch.uuids[0] # string
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
# write ref summary and decoded summary to file, to eval with pyrouge later
# self.write_for_rouge(original_abstract_sents, decoded_words,self._counter)
self.write_for_flink(original_uuid, original_article,
decoded_words, original_abstract_sents)
self._counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens
) # write info to .json file for visualization tool
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
while True:
batch = self._batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
tf.logging.info("Decoder has finished reading dataset for single_pass.")
return
original_article = batch.original_articles[0] # string
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
self.write_for_rouge(decoded_words, counter) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, decoded_output) # log output to screen
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1-t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info('We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint', t1-t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
def run_beam_decoder(self,batch):
original_article=batch.original_articles[0]
original_abstract=batch.original_abstracts[0]
article_withunks=data.show_art_oovs(original_article,self.vocab)
abstract_withunks=data.show_abs_oovs(original_abstract,self.vocab,(batch.art_oovs[0] if self.hp.pointer_gen==True else None))
best_list=beam_search.run_beam_search(self.sess,self.model,self.vocab,batch)
print(best_list.tokens)
output_ids=[int(t) for t in best_list.tokens[1:]]
decoded_words=data.outputids2words(output_ids,self.vocab,(batch.art_oovs[0] if self.hp.pointer_gen==True else None))
print(decoded_words)
try:
fst_stop_idx=decoded_words.index(data.STOP_DECODING)
decoded_words=decoded_words[:fst_stop_idx]
except ValueError:
decoded_words=decoded_words
decoded_output=' '.join(decoded_words)
return article_withunks,abstract_withunks,decoded_output
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
# t0 = time.time()
batch = self._batcher.next_batch() # 1 example repeated across batch
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
# input data
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
# tf.logging.info('ARTICLE: %s', article)
# tf.logging.info('GENERATED SUMMARY: %s', decoded_output)
sys.stdout.write(decoded_output)
def decode(self, batches):
counter = 0
for batch in batches:
if (counter < 10000):
original_article = batch.original_articles[0]
original_abstract = batch.original_abstracts[0]
original_abstract_sents = batch.original_abstracts_sents[0]
article_withunks = data.show_art_oovs(original_article,
self._vocab)
abstract_withunks = data.show_abs_oovs(original_abstract,
self._vocab,
batch.art_oovs[0])
best_hypothesis = beam_search.run_beam_search(
self._session, self._model, self._vocab, batch, self._hps)
output_ids = [int(t) for t in best_hypothesis.tokens[1:]]
decoded_words = data.outputids2words(output_ids, self._vocab,
batch.art_oovs[0])
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING
) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
self.write_for_rouge(original_abstract_sents, decoded_words,
counter, original_article)
counter += 1
else:
break
self.rouge_eval()
def decode(self):
"""
Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode
indefinitely, loading latest checkpoint at regular intervals.
"""
counter = 0
scores = []
while True:
batch = self._batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info("Decoder has finished reading dataset for single_pass.")
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir,
self._rouge_dec_dir,
)
tf.logging.info("Mean score: %s", sum(scores) / len(scores))
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
article_withunks = data.show_art_oovs(original_article, self._vocab) # string
abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, batch.art_oovs[0]) # string
# Run beam search to get best Hypothesis
t_beam = time.time()
best_hyp, best_score = beam_search.run_beam_search(
self._sess, self._model, self._vocab, batch, FLAGS.beam_size, FLAGS.max_dec_steps,
FLAGS.min_dec_steps, FLAGS.trace_path
)
scores.append(best_score)
tf.logging.info("Time to decode one example: %f", time.time() - t_beam)
tf.logging.info("Mean score: %s", sum(scores) / len(scores))
# Extract the output ids from the hypothesis and convert back to words
decoded_words = best_hyp.token_strings[1:]
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
self.write_for_rouge(original_abstract, decoded_words, counter) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
else:
# log output to screen
print_results(
article_withunks, abstract_withunks, decoded_output, best_hyp, [best_score]
)
# write info to .json file for visualization tool
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists,
best_hyp.p_gens, best_hyp.log_probs
)
raw_input()
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
if not FLAGS.generate:
t0 = time.time()
counter = 0
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass."
)
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
results_dict = rouge_eval(self._rouge_ref_dir,
self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0]
if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING
) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
# write ref summary and decoded summary to file, to eval with pyrouge later
self.write_for_rouge(original_abstract_sents,
decoded_words, counter)
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens
) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
# when generate=True
else:
counter = 0
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass."
)
return
original_article = batch.original_articles[0] # string
# original_abstract = batch.original_abstracts[0] # string
# original_abstract_sents = batch.original_abstracts_sents[0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
# abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING
) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
counter += 1
# log output to screen
print(
"---------------------------------------------------------------------------"
)
tf.logging.info('ARTICLE: %s', article_withunks)
tf.logging.info('GENERATED SUMMARY: %s', decoded_output)
print(
"---------------------------------------------------------------------------"
)
# self.write_for_rouge(original_abstract_sents, decoded_words, counter)
# Write to file
decoded_sents = []
while len(decoded_words) > 0:
try:
fst_period_idx = decoded_words.index(".")
except ValueError: # there is text remaining that doesn't end in "."
fst_period_idx = len(decoded_words)
sent = decoded_words[:fst_period_idx +
1] # sentence up to and including the period
decoded_words = decoded_words[fst_period_idx +
1:] # everything else
decoded_sents.append(' '.join(sent))
# pyrouge calls a perl script that puts the data into HTML files.
# Therefore we need to make our output HTML safe.
decoded_sents = [make_html_safe(w) for w in decoded_sents]
# Write to file
result_file = os.path.join(self._result_dir,
"%06d_summary.txt" % counter)
with open(result_file, "w") as f:
for idx, sent in enumerate(decoded_sents):
f.write(sent) if idx == len(
decoded_sents) - 1 else f.write(sent + "\n")
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely,
loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
if FLAGS.decode_bleu:
ref_file = os.path.join(self._bleu_dec_dir, "reference.txt")
decoded_file = os.path.join(self._bleu_dec_dir, "decoded.txt")
if os.path.exists(decoded_file):
tf.logging.info('正在删除 %s', decoded_file)
os.remove(decoded_file)
if os.path.exists(ref_file):
tf.logging.info('正在删除 %s', ref_file)
os.remove(ref_file)
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
if FLAGS.decode_rouge:
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
try:
t0 = time.time()
results_dict = rouge_eval(self._rouge_ref_dir,
self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
t1 = time.time()
tf.logging.info(
'calculate Rouge score cost %d seconds', t1 - t0)
except Exception as e:
tf.logging.error('计算ROUGE出错 %s', e)
if FLAGS.decode_bleu:
ref_file = os.path.join(self._bleu_dec_dir,
"reference.txt")
decoded_file = os.path.join(self._bleu_dec_dir,
"decoded.txt")
t0 = time.time()
bleu, bleu1, bleu2, bleu3, bleu4 = calcu_bleu(
decoded_file, ref_file)
sys_bleu = sys_bleu_file(decoded_file, ref_file)
sys_bleu_perl = sys_bleu_perl_file(decoded_file, ref_file)
t1 = time.time()
tf.logging.info(bcolors.HEADER +
'-----------BLEU SCORE-----------' +
bcolors.ENDC)
tf.logging.info(
bcolors.OKGREEN + '%f \t %f \t %f \t %f \t %f' +
bcolors.ENDC, bleu, bleu1, bleu2, bleu3, bleu4)
tf.logging.info(
bcolors.OKGREEN + 'sys_bleu %f' + bcolors.ENDC,
sys_bleu)
tf.logging.info(
bcolors.OKGREEN + 'sys_bleu_perl %s' + bcolors.ENDC,
sys_bleu_perl)
tf.logging.info(bcolors.HEADER +
'-----------BLEU SCORE-----------' +
bcolors.ENDC)
tf.logging.info('calculate BLEU score cost %d seconds',
t1 - t0)
break
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ''.join(decoded_words) # single string
if FLAGS.single_pass:
print_results(article_withunks, abstract_withunks,
decoded_output, counter) # log output to screen
if FLAGS.decode_rouge:
self.write_for_rouge(
original_abstract_sents, decoded_words, counter
) # write ref summary and decoded summary to file, to eval with pyrouge later
if FLAGS.decode_bleu:
self.write_for_bleu(original_abstract_sents, decoded_words)
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens
) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
def decode(self, output_dir=None):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
idx = 0
# used to store values during decoding in a list each
outut_str = ""
beam_search_str = ""
metadata = []
# evaluate over a fixed number of test set
while True: #idx <=100 :
print("[%d]" % idx)
batch = self._batcher.next_batch(
) # 1 example repeated across batch
# if idx < 11000:
# idx += 1
# continue
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
results_dict = rouge_eval(self._rouge_ref_dir,
self._rouge_dec_dir)
rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get all the Hypothesis
all_hyp = beam_search.run_beam_search(
self._sess, self._model, self._vocab, batch, counter,
self._lm_model, self._lm_word2idx, self._lm_idx2word
) #TODO changed the method signature just to look at the outputs of beam search
if FLAGS.save_values:
for h in all_hyp:
output_ids = [int(t) for t in h.tokens[1:]]
search_str = str(
data.outputids2words(output_ids, self._vocab,
(batch.art_oovs[0]
if FLAGS.pointer_gen else None)))
beam_search_str += search_str
beam_search_str += "\n"
# Extract the get best Hypothesis
best_hyp = all_hyp[0]
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
metadata.append(decoded_words)
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(
data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
###########################
#print best hyp statistics
hyp_stat = ""
# log prob
hyp_stat += "\navg log prob: %s.\n" % best_hyp.avg_log_prob
# words overlap with article: this is buggy
# tri, bi, uni = word_overlap.gram_search(ngrams(nltk.pos_tag(article_withunks.strip().split()), 3), ngrams(nltk.pos_tag(decoded_output.strip().split()), 3))
# hyp_stat += "trigram overlap: %s. bigram overlap: %s. unigram overlap: %s.\n"%(uni, bi, tri)
print_statistics.get_overlap(article_withunks.strip(),
decoded_output.strip(),
match_count=self.overlap_dict)
hyp_stat += "word overlap: "
for key, value in self.overlap_dict.iteritems():
hyp_stat += "\n%d-gram avg overlap: %d" % (key, value /
(counter + 1))
# num sentences and avg length
self.total_nsentence += len(decoded_output.strip().split(
".")) #sentences are seperated by "."
self.total_length += len(decoded_output.strip().split())
avg_nsentence, avg_length = self.total_nsentence / (
counter + 1), self.total_length / (counter + 1)
hyp_stat += "\nnum sentences: %s. avg len: %s.\n" % (avg_nsentence,
avg_length)
# entropy??
if FLAGS.print_info:
print(hyp_stat)
###########################
# saves data into numpy files for analysis
if FLAGS.save_values:
save_decode_data.save_data_iteration(self._decode_dir, counter,
best_hyp)
if FLAGS.single_pass: #change to counter later
self.write_for_rouge(
original_abstract_sents, decoded_words, counter
) # write ref summary and decoded summary to file, to eval with pyrouge later
# writing all the output combined to a file
if FLAGS.print_info:
output = '\nARTICLE: %s\n REFERENCE SUMMARY: %s\n' 'GENERATED SUMMARY: %s\n' % (
article_withunks, abstract_withunks, decoded_output)
print(output)
outut_str += output
# Leena: modifying this to save more stuff
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens, counter,
best_hyp.log_prob, best_hyp.avg_log_prob,
best_hyp.average_pgen) #change to counter later
counter += 1 # this is how many examples we've decoded
else: #Leena: I use the above condition so might have neglected making change to the below condition
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens,
counter) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
idx += 1
#Leena: saving entire output and beam output as a string to write to a file
if FLAGS.save_values:
save_decode_data.save_data_once(self._decode_dir,
FLAGS.result_path, outut_str,
beam_search_str, metadata)
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass.")
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
# results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir)
# rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
original_topic = batch.original_topics[0]
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
all_sencent = []
best_hyps = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
put_ids = [int(t) for t in best_hyps[0].tokens[1:]]
standard_words = data.outputids2words(
put_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
# Extract the output ids from the hypothesis and convert back to words
score = []
for best_hyp in best_hyps:
output_ids = [int(t) for t in best_hyp.tokens[1:]]
tmp_decoded = data.outputids2words(
output_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
all_sencent.extend(self.removes(tmp_decoded))
all_sencent.append('\n@next\n')
score.append(self.get_score(original_topic, tmp_decoded))
all_sencent.extend(self.removes(standard_words))
all_sencent.append('\n@next\n')
max_index = score.index(max(score))
put_ids = [int(t) for t in best_hyps[max_index].tokens[1:]]
decoded_words = data.outputids2words(
put_ids, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None))
all_sencent.extend(self.removes(decoded_words))
decoded_words = all_sencent
# Remove the [STOP] token from decoded_words, if necessary
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
self.write_for_rouge(
original_abstract_sents, decoded_words, counter
) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.topic_attn_dists, best_hyp.
p_gens) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
total = len(glob.glob(self._batcher._data_path)) * 1000
pbar = tqdm(total=total)
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
logging.info(
"Decoder has finished reading dataset for single_pass.")
logging.info("Output has been saved in %s and %s.",
self._rouge_ref_dir, self._rouge_dec_dir)
if len(os.listdir(self._rouge_ref_dir)) != 0:
logging.info("Now starting ROUGE eval...")
results_dict = rouge_functions.rouge_eval(
self._rouge_ref_dir, self._rouge_dec_dir)
rouge_functions.rouge_log(results_dict, self._decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
all_original_abstract_sents = batch.all_original_abstracts_sents[0]
raw_article_sents = batch.raw_article_sents[0]
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
decoded_words, decoded_output, best_hyp = decode_example(
self._sess, self._model, self._vocab, batch, counter,
self._batcher._hps)
if FLAGS.single_pass:
if counter < 1000:
self.write_for_human(raw_article_sents,
all_original_abstract_sents,
decoded_words, counter)
rouge_functions.write_for_rouge(
all_original_abstract_sents,
None,
counter,
self._rouge_ref_dir,
self._rouge_dec_dir,
decoded_words=decoded_words
) # write ref summary and decoded summary to file, to eval with pyrouge later
if FLAGS.attn_vis:
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens, counter
) # write info to .json file for visualization tool
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks,
decoded_output) # log output to screen
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens,
counter) # write info to .json file for visualization tool
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()
pbar.update(1)
pbar.close()
def run_decode(model, batcher, vocab):
print "build graph..."
model.build_graph()
saver = tf.train.Saver(max_to_keep=3)
sess = tf.Session(config=util.get_config())
saver = tf.train.Saver()
ckpt_path = util.load_ckpt(saver, sess)
if FLAGS.single_pass:
ckpt_name = "ckpt-" + ckpt_path.split('-')[-1]
dirname = "decode_maxenc_%ibeam_%imindec_%imaxdec_%i" % (FLAGS.max_enc_steps, FLAGS.beam_size, FLAGS.min_dec_steps, FLAGS.max_dec_steps)
decode_dir = os.path.join(FLAGS.log_root, dirname + ckpt_name)
if os.path.exists(decode_dir):
raise Exception('single_pass decode directory %s should not exist', decode_dir)
else:
decode_dir = os.path.join(FLAGS.log_root, 'decode')
if not os.path.exists(decode_dir): os.mkdir(decode_dir)
if FLAGS.single_pass:
rouge_ref_dir = os.path.join(decode_dir, "reference")
if not os.path.exists(rouge_ref_dir): os.mkdir(rouge_ref_dir)
rouge_dec_dir = os.path.join(decode_dir, "decoded")
if not os.path.exists(rouge_dec_dir): os.mkdir(rouge_dec_dir)
counter = 0
t0 = time.time()
while True:
batch = batcher.next_batch() # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode"
print("Decoder has finished reading dataset for single_pass.")
print("Output has been saved in %s and %s. Now starting ROUGE eval...", rouge_ref_dir,
rouge_dec_dir)
results_dict = rouge_eval(rouge_ref_dir, rouge_dec_dir)
rouge_log(results_dict, decode_dir)
return
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[0] # list of strings
article_withunks = data.show_art_oovs(original_article, vocab) # string
abstract_withunks = data.show_abs_oovs(original_abstract, vocab, None) # string
# Run beam search to get best Hypothesis
output = model.run_beam_decode_step(sess, batch, vocab)
output_ids = [int(t) for t in output]
decoded_words = data.outputids2words(output_ids, vocab, None)
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(data.STOP_DECODING) # index of the (first) [STOP] symbol
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ' '.join(decoded_words) # single string
if FLAGS.single_pass:
write_for_rouge(original_abstract_sents, decoded_words, counter, rouge_ref_dir, rouge_dec_dir) # write ref summary and decoded summary to file, to eval with pyrouge later
counter += 1 # this is how many examples we've decoded
else:
print_results(article_withunks, abstract_withunks, decoded_output) # log output to screen
# Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info('We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(saver, sess)
t0 = time.time()
def decode_iteratively(self, example_generator, total, names_to_types,
ssi_list, hps):
attn_vis_idx = 0
for example_idx, example in enumerate(
tqdm(example_generator, total=total)):
raw_article_sents, groundtruth_similar_source_indices_list, groundtruth_summary_text, corefs, groundtruth_article_lcs_paths_list = util.unpack_tf_example(
example, names_to_types)
article_sent_tokens = [
util.process_sent(sent) for sent in raw_article_sents
]
groundtruth_summ_sents = [[
sent.strip()
for sent in groundtruth_summary_text.strip().split('\n')
]]
groundtruth_summ_sent_tokens = [
sent.split(' ') for sent in groundtruth_summ_sents[0]
]
if ssi_list is None: # this is if we are doing the upper bound evaluation (ssi_list comes straight from the groundtruth)
sys_ssi = groundtruth_similar_source_indices_list
sys_alp_list = groundtruth_article_lcs_paths_list
if FLAGS.singles_and_pairs == 'singles':
sys_ssi = util.enforce_sentence_limit(sys_ssi, 1)
sys_alp_list = util.enforce_sentence_limit(sys_alp_list, 1)
elif FLAGS.singles_and_pairs == 'both':
sys_ssi = util.enforce_sentence_limit(sys_ssi, 2)
sys_alp_list = util.enforce_sentence_limit(sys_alp_list, 2)
sys_ssi, sys_alp_list = util.replace_empty_ssis(
sys_ssi, raw_article_sents, sys_alp_list=sys_alp_list)
else:
gt_ssi, sys_ssi, ext_len, sys_token_probs_list = ssi_list[
example_idx]
sys_alp_list = ssi_functions.list_labels_from_probs(
sys_token_probs_list, FLAGS.tag_threshold)
if FLAGS.singles_and_pairs == 'singles':
sys_ssi = util.enforce_sentence_limit(sys_ssi, 1)
sys_alp_list = util.enforce_sentence_limit(sys_alp_list, 1)
groundtruth_similar_source_indices_list = util.enforce_sentence_limit(
groundtruth_similar_source_indices_list, 1)
gt_ssi = util.enforce_sentence_limit(gt_ssi, 1)
elif FLAGS.singles_and_pairs == 'both':
sys_ssi = util.enforce_sentence_limit(sys_ssi, 2)
sys_alp_list = util.enforce_sentence_limit(sys_alp_list, 2)
groundtruth_similar_source_indices_list = util.enforce_sentence_limit(
groundtruth_similar_source_indices_list, 2)
gt_ssi = util.enforce_sentence_limit(gt_ssi, 2)
# if gt_ssi != groundtruth_similar_source_indices_list:
# raise Exception('Example %d has different groundtruth source indices: ' + str(groundtruth_similar_source_indices_list) + ' || ' + str(gt_ssi))
if FLAGS.dataset_name == 'xsum':
sys_ssi = [sys_ssi[0]]
final_decoded_words = []
final_decoded_outpus = ''
best_hyps = []
highlight_html_total = '<u>System Summary</u><br><br>'
for ssi_idx, ssi in enumerate(sys_ssi):
# selected_article_lcs_paths = None
selected_article_lcs_paths = sys_alp_list[ssi_idx]
ssi, selected_article_lcs_paths = util.make_ssi_chronological(
ssi, selected_article_lcs_paths)
selected_article_lcs_paths = [selected_article_lcs_paths]
selected_raw_article_sents = util.reorder(
raw_article_sents, ssi)
selected_article_text = ' '.join([
' '.join(sent)
for sent in util.reorder(article_sent_tokens, ssi)
])
selected_doc_indices_str = '0 ' * len(
selected_article_text.split())
if FLAGS.upper_bound:
selected_groundtruth_summ_sent = [[
groundtruth_summ_sents[0][ssi_idx]
]]
else:
selected_groundtruth_summ_sent = groundtruth_summ_sents
batch = create_batch(selected_article_text,
selected_groundtruth_summ_sent,
selected_doc_indices_str,
selected_raw_article_sents,
selected_article_lcs_paths,
FLAGS.batch_size, hps, self._vocab)
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0]
if FLAGS.pointer_gen else None)) # string
# article_withunks = data.show_art_oovs(original_article, self._vocab) # string
# abstract_withunks = data.show_abs_oovs(original_abstract, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
if FLAGS.first_intact and ssi_idx == 0:
decoded_words = selected_article_text.strip().split()
decoded_output = selected_article_text
else:
decoded_words, decoded_output, best_hyp = decode_example(
self._sess, self._model, self._vocab, batch,
example_idx, hps)
best_hyps.append(best_hyp)
final_decoded_words.extend(decoded_words)
final_decoded_outpus += decoded_output
if example_idx < 100 or (example_idx >= 2000
and example_idx < 2100):
min_matched_tokens = 2
selected_article_sent_tokens = [
util.process_sent(sent)
for sent in selected_raw_article_sents
]
highlight_summary_sent_tokens = [decoded_words]
highlight_ssi_list, lcs_paths_list, highlight_article_lcs_paths_list, highlight_smooth_article_lcs_paths_list = ssi_functions.get_simple_source_indices_list(
highlight_summary_sent_tokens,
selected_article_sent_tokens, None, 2,
min_matched_tokens)
highlighted_html = ssi_functions.html_highlight_sents_in_article(
highlight_summary_sent_tokens,
highlight_ssi_list,
selected_article_sent_tokens,
lcs_paths_list=lcs_paths_list,
article_lcs_paths_list=
highlight_smooth_article_lcs_paths_list)
highlight_html_total += highlighted_html + '<br>'
if FLAGS.attn_vis and example_idx < 200:
self.write_for_attnvis(
article_withunks, abstract_withunks, decoded_words,
best_hyp.attn_dists, best_hyp.p_gens, attn_vis_idx
) # write info to .json file for visualization tool
attn_vis_idx += 1
if len(final_decoded_words) >= 100:
break
gt_ssi_list, gt_alp_list = util.replace_empty_ssis(
groundtruth_similar_source_indices_list,
raw_article_sents,
sys_alp_list=groundtruth_article_lcs_paths_list)
highlight_html_gt = '<u>Reference Summary</u><br><br>'
for ssi_idx, ssi in enumerate(gt_ssi_list):
selected_article_lcs_paths = gt_alp_list[ssi_idx]
try:
ssi, selected_article_lcs_paths = util.make_ssi_chronological(
ssi, selected_article_lcs_paths)
except:
util.print_vars(ssi, example_idx,
selected_article_lcs_paths)
raise
selected_raw_article_sents = util.reorder(
raw_article_sents, ssi)
if example_idx < 100 or (example_idx >= 2000
and example_idx < 2100):
min_matched_tokens = 2
selected_article_sent_tokens = [
util.process_sent(sent)
for sent in selected_raw_article_sents
]
highlight_summary_sent_tokens = [
groundtruth_summ_sent_tokens[ssi_idx]
]
highlight_ssi_list, lcs_paths_list, highlight_article_lcs_paths_list, highlight_smooth_article_lcs_paths_list = ssi_functions.get_simple_source_indices_list(
highlight_summary_sent_tokens,
selected_article_sent_tokens, None, 2,
min_matched_tokens)
highlighted_html = ssi_functions.html_highlight_sents_in_article(
highlight_summary_sent_tokens,
highlight_ssi_list,
selected_article_sent_tokens,
lcs_paths_list=lcs_paths_list,
article_lcs_paths_list=
highlight_smooth_article_lcs_paths_list)
highlight_html_gt += highlighted_html + '<br>'
if example_idx < 100 or (example_idx >= 2000
and example_idx < 2100):
self.write_for_human(raw_article_sents, groundtruth_summ_sents,
final_decoded_words, example_idx)
highlight_html_total = ssi_functions.put_html_in_two_columns(
highlight_html_total, highlight_html_gt)
ssi_functions.write_highlighted_html(highlight_html_total,
self._highlight_dir,
example_idx)
# if example_idx % 100 == 0:
# attn_dir = os.path.join(self._decode_dir, 'attn_vis_data')
# attn_selections.process_attn_selections(attn_dir, self._decode_dir, self._vocab)
rouge_functions.write_for_rouge(
groundtruth_summ_sents,
None,
example_idx,
self._rouge_ref_dir,
self._rouge_dec_dir,
decoded_words=final_decoded_words,
log=False
) # write ref summary and decoded summary to file, to eval with pyrouge later
# if FLAGS.attn_vis:
# self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens, example_idx) # write info to .json file for visualization tool
example_idx += 1 # this is how many examples we've decoded
logging.info("Decoder has finished reading dataset for single_pass.")
logging.info("Output has been saved in %s and %s.",
self._rouge_ref_dir, self._rouge_dec_dir)
if len(os.listdir(self._rouge_ref_dir)) != 0:
if FLAGS.dataset_name == 'xsum':
l_param = 100
else:
l_param = 100
logging.info("Now starting ROUGE eval...")
results_dict = rouge_functions.rouge_eval(self._rouge_ref_dir,
self._rouge_dec_dir,
l_param=l_param)
rouge_functions.rouge_log(results_dict, self._decode_dir)
def decode(self):
"""Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals"""
t0 = time.time()
counter = 0
f = os.path.join(FLAGS.log_root, "output.txt")
# print("----------------"+f)
outputfile = codecs.open(f, "w", "utf8")
output_result = []
list_of_reference = []
while True:
batch = self._batcher.next_batch(
) # 1 example repeated across batch
if batch is None: # finished decoding dataset in single_pass mode
logging.info("eval_finished")
outputfile.close()
break
print(self._batcher.c_index)
original_article = batch.original_articles[0] # string
original_abstract = batch.original_abstracts[0] # string
original_abstract_sents = batch.original_abstracts_sents[
0] # list of strings
article_withunks = data.show_art_oovs(original_article,
self._vocab) # string
abstract_withunks = data.show_abs_oovs(
original_abstract, self._vocab,
(batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
result, all_candidate = self.eval_one_batch_with_candidate(
self._sess, self._model, self._vocab, batch)
#result = self.eval_one_batch(self._sess, self._model, self._vocab, batch)
for i, instance in enumerate(result):
if i == len(batch.art_oovs):
break
if i >= batch.real_length:
print("eval done with {} isntances".format(
len(output_result)))
break
out_words = data.outputids2words(instance,
self._model._vocab_out,
batch.art_oovs[i])
if data.STOP_DECODING in out_words:
out_words = out_words[:out_words.index(data.STOP_DECODING)]
candidates_value = self.get_condidate_predicate(
out_words, all_candidate[i], batch.art_oovs[i])
candidates_value = "_||_".join(candidates_value)
output_now = " ".join(out_words)
output_result.append(output_now)
# refer = " ".join(refer)
refer = batch.original_abstracts[i].strip()
list_of_reference.append([refer])
outputfile.write(batch.original_articles[i] + '\t' +
batch.original_abstracts[i] + '\t' +
output_now + '\t' + candidates_value + '\n')
bleu = matrix.bleu_score(list_of_reference, output_result)
acc = matrix.compute_acc(list_of_reference, output_result)
print("bleu : {} acc : {}".format(bleu, acc))
return
