Python data.show_art_oovs方法代码示例

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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) 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
def process_one_article(self, original_article_sents, original_abstract_sents, \
                          original_selected_ids, output_ids, oovs, attn_dists_norescale, \
                          attn_dists, p_gens, log_probs, sent_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:
        sent_probs_per_word = []
        for sent_id, sent in enumerate(original_article_sents):
          sent_len = len(sent.split(' '))
          for _ in range(sent_len):
            if sent_id < FLAGS.max_art_len:
              sent_probs_per_word.append(sent_probs[sent_id])
            else:
              sent_probs_per_word.append(0)
        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_norescale, \
                               attn_dists, p_gens, log_probs, sent_probs_per_word, counter, verbose)
      if FLAGS.save_pkl:
        self.save_result(original_article_sents, original_abstract_sents, \
                         original_selected_ids, decoded_sents, counter, verbose) 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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) 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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
      if len(original_abstract_sents) == 0:
        print("NOOOOO!!!!, An empty abstract :(")
        continue

      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() 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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() 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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
    all_decoded = {} # a dictionary keeping the decoded files to be written for visualization
    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)
        if FLAGS.single_pass:
          self.write_all_for_attnvis(all_decoded)
        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_id = batch.article_ids[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
#       import pdb; pdb.set_trace()
      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, article_id) # write ref summary and decoded summary to file, to eval with pyrouge later
        print_results(article_withunks, abstract_withunks, decoded_output, article_id) # log output to screen
        all_decoded[article_id] = self.prepare_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens, best_hyp.attn_dists_sec)
        counter += 1 # this is how many examples we've decoded
        self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens, best_hyp.attn_dists_sec) # write info to .json file for visualization tool        
      else:
        print_results(article_withunks, abstract_withunks, decoded_output, article_id) # log output to screen
        self.write_for_attnvis(article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens, best_hyp.attn_dists_sec) # 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() 

# 需要导入模块: import data [as 别名]
# 或者: from data import show_art_oovs [as 别名]
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

      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:
        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)
          t0 = time.time()