def main(unused_argv):
if len(unused_argv
) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
if FLAGS.singles_and_pairs == 'both':
FLAGS.exp_name = FLAGS.exp_name + '_both'
exp_name = _exp_name + '_both'
dataset_articles = _dataset_articles
else:
FLAGS.exp_name = FLAGS.exp_name + '_singles'
exp_name = _exp_name + '_singles'
dataset_articles = _dataset_articles + '_singles'
my_log_dir = os.path.join(log_dir, FLAGS.ssi_exp_name)
print('Running statistics on %s' % FLAGS.exp_name)
if FLAGS.dataset_name != "":
FLAGS.data_path = os.path.join(FLAGS.data_root, FLAGS.dataset_name,
FLAGS.dataset_split + '*')
if not os.path.exists(os.path.join(
FLAGS.data_root, FLAGS.dataset_name)) or len(
os.listdir(os.path.join(FLAGS.data_root,
FLAGS.dataset_name))) == 0:
print(('No TF example data found at %s so creating it from raw data.' %
os.path.join(FLAGS.data_root, FLAGS.dataset_name)))
convert_data.process_dataset(FLAGS.dataset_name)
logging.set_verbosity(
logging.INFO) # choose what level of logging you want
logging.info('Starting seq2seq_attention in %s mode...', (FLAGS.mode))
# Change log_root to FLAGS.log_root/FLAGS.exp_name and create the dir if necessary
FLAGS.exp_name = FLAGS.exp_name if FLAGS.exp_name != '' else FLAGS.dataset_name
FLAGS.actual_log_root = FLAGS.log_root
FLAGS.log_root = os.path.join(FLAGS.log_root, FLAGS.exp_name)
vocab = Vocab(FLAGS.vocab_path, FLAGS.vocab_size) # create a vocabulary
# If in decode mode, set batch_size = beam_size
# Reason: in decode mode, we decode one example at a time.
# On each step, we have beam_size-many hypotheses in the beam, so we need to make a batch of these hypotheses.
if FLAGS.mode == 'decode':
FLAGS.batch_size = FLAGS.beam_size
# If single_pass=True, check we're in decode mode
if FLAGS.single_pass and FLAGS.mode != 'decode':
raise Exception(
"The single_pass flag should only be True in decode mode")
# Make a namedtuple hps, containing the values of the hyperparameters that the model needs
hparam_list = [
'mode', 'lr', 'adagrad_init_acc', 'rand_unif_init_mag',
'trunc_norm_init_std', 'max_grad_norm', 'hidden_dim', 'emb_dim',
'batch_size', 'max_dec_steps', 'max_enc_steps', 'coverage',
'cov_loss_wt', 'pointer_gen', 'lambdamart_input'
]
hps_dict = {}
for key, val in FLAGS.__flags.items(): # for each flag
if key in hparam_list: # if it's in the list
hps_dict[key] = val.value # add it to the dict
hps = namedtuple("HParams", list(hps_dict.keys()))(**hps_dict)
tf.set_random_seed(113) # a seed value for randomness
decode_model_hps = hps._replace(
max_dec_steps=1
) # The model is configured with max_dec_steps=1 because we only ever run one step of the decoder at a time (to do beam search). Note that the batcher is initialized with max_dec_steps equal to e.g. 100 because the batches need to contain the full summaries
if len(unused_argv
) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
start_time = time.time()
np.random.seed(random_seed)
source_dir = os.path.join(data_dir, dataset_articles)
source_files = sorted(glob.glob(source_dir + '/' + dataset_split + '*'))
with open(os.path.join(my_log_dir, 'ssi.pkl')) as f:
ssi_list = pickle.load(f)
total = len(source_files
) * 1000 if 'cnn' or 'newsroom' in dataset_articles else len(
source_files)
example_generator = data.example_generator(source_dir + '/' +
dataset_split + '*',
True,
False,
should_check_valid=False)
# batcher = Batcher(None, vocab, hps, single_pass=FLAGS.single_pass)
model = SummarizationModel(decode_model_hps, vocab)
decoder = BeamSearchDecoder(model, None, vocab)
decoder.decode_iteratively(example_generator, total, names_to_types,
ssi_list, hps)
a = 0
def main(unused_argv):
if len(unused_argv
) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
extractor = 'bert' if FLAGS.use_bert else 'lambdamart'
pretrained_dataset = FLAGS.dataset_name
if FLAGS.dataset_name == 'duc_2004':
pretrained_dataset = 'cnn_dm'
if FLAGS.singles_and_pairs == 'both':
FLAGS.exp_name = FLAGS.dataset_name + '_' + FLAGS.exp_name + extractor + '_both'
FLAGS.pretrained_path = os.path.join(FLAGS.log_root,
pretrained_dataset + '_both')
dataset_articles = FLAGS.dataset_name
else:
FLAGS.exp_name = FLAGS.dataset_name + '_' + FLAGS.exp_name + extractor + '_singles'
FLAGS.pretrained_path = os.path.join(FLAGS.log_root,
pretrained_dataset + '_singles')
dataset_articles = FLAGS.dataset_name + '_singles'
if FLAGS.upper_bound:
FLAGS.exp_name = FLAGS.exp_name + '_upperbound'
ssi_list = None # this is if we are doing the upper bound evaluation (ssi_list comes straight from the groundtruth)
else:
my_log_dir = os.path.join(
log_dir, '%s_%s_%s' %
(FLAGS.dataset_name, extractor, FLAGS.singles_and_pairs))
with open(os.path.join(my_log_dir, 'ssi.pkl'), 'rb') as f:
ssi_list = pickle.load(f)
print('Running statistics on %s' % FLAGS.exp_name)
if FLAGS.dataset_name != "":
FLAGS.data_path = os.path.join(FLAGS.data_root, FLAGS.dataset_name,
FLAGS.dataset_split + '*')
if not os.path.exists(os.path.join(
FLAGS.data_root, FLAGS.dataset_name)) or len(
os.listdir(os.path.join(FLAGS.data_root,
FLAGS.dataset_name))) == 0:
raise Exception('No TF example data found at %s.' %
os.path.join(FLAGS.data_root, FLAGS.dataset_name))
logging.set_verbosity(
logging.INFO) # choose what level of logging you want
logging.info('Starting seq2seq_attention in %s mode...', (FLAGS.mode))
# Change log_root to FLAGS.log_root/FLAGS.exp_name and create the dir if necessary
FLAGS.exp_name = FLAGS.exp_name if FLAGS.exp_name != '' else FLAGS.dataset_name
FLAGS.actual_log_root = FLAGS.log_root
FLAGS.log_root = os.path.join(FLAGS.log_root, FLAGS.exp_name)
print(util.bcolors.OKGREEN + "Experiment path: " + FLAGS.log_root +
util.bcolors.ENDC)
if FLAGS.dataset_name == 'duc_2004':
vocab = Vocab(FLAGS.vocab_path + '_' + 'cnn_dm',
FLAGS.vocab_size) # create a vocabulary
else:
vocab = Vocab(FLAGS.vocab_path + '_' + FLAGS.dataset_name,
FLAGS.vocab_size) # create a vocabulary
# If in decode mode, set batch_size = beam_size
# Reason: in decode mode, we decode one example at a time.
# On each step, we have beam_size-many hypotheses in the beam, so we need to make a batch of these hypotheses.
if FLAGS.mode == 'decode':
FLAGS.batch_size = FLAGS.beam_size
# If single_pass=True, check we're in decode mode
if FLAGS.single_pass and FLAGS.mode != 'decode':
raise Exception(
"The single_pass flag should only be True in decode mode")
# Make a namedtuple hps, containing the values of the hyperparameters that the model needs
hparam_list = [
item for item in list(FLAGS.flag_values_dict().keys()) if item != '?'
]
hps_dict = {}
for key, val in FLAGS.__flags.items(): # for each flag
if key in hparam_list: # if it's in the list
hps_dict[key] = val.value # add it to the dict
hps = namedtuple("HParams", list(hps_dict.keys()))(**hps_dict)
tf.set_random_seed(113) # a seed value for randomness
decode_model_hps = hps._replace(
max_dec_steps=1
) # The model is configured with max_dec_steps=1 because we only ever run one step of the decoder at a time (to do beam search). Note that the batcher is initialized with max_dec_steps equal to e.g. 100 because the batches need to contain the full summaries
if len(unused_argv
) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
start_time = time.time()
np.random.seed(random_seed)
source_dir = os.path.join(FLAGS.data_root, dataset_articles)
source_files = sorted(glob.glob(source_dir + '/' + dataset_split + '*'))
total = len(
source_files
) * 1000 if 'cnn' in dataset_articles or 'xsum' in dataset_articles else len(
source_files)
example_generator = data.example_generator(source_dir + '/' +
dataset_split + '*',
True,
False,
should_check_valid=False)
# batcher = Batcher(None, vocab, hps, single_pass=FLAGS.single_pass)
model = SummarizationModel(decode_model_hps, vocab)
decoder = BeamSearchDecoder(model, None, vocab)
decoder.decode_iteratively(example_generator, total, names_to_types,
ssi_list, hps)
def main(unused_argv):
if len(unused_argv) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
extractor = 'bert' if FLAGS.use_bert else 'lambdamart'
if FLAGS.cnn_dm_pg:
pretrained_dataset = 'cnn_dm'
elif FLAGS.websplit:
pretrained_dataset = 'websplit'
else:
pretrained_dataset = FLAGS.dataset_name
if FLAGS.dataset_name == 'duc_2004':
pretrained_dataset = 'cnn_dm'
if FLAGS.singles_and_pairs == 'both':
FLAGS.exp_name = FLAGS.dataset_name + '_' + FLAGS.exp_name + extractor + '_both'
FLAGS.pretrained_path = os.path.join(FLAGS.log_root, pretrained_dataset + '_both')
dataset_articles = FLAGS.dataset_name
else:
FLAGS.exp_name = FLAGS.dataset_name + '_' + FLAGS.exp_name + extractor + '_singles'
FLAGS.pretrained_path = os.path.join(FLAGS.log_root, pretrained_dataset + '_singles')
dataset_articles = FLAGS.dataset_name + '_singles'
if FLAGS.word_imp_reg:
FLAGS.pretrained_path += '_imp' + str(FLAGS.imp_loss_wt)
FLAGS.exp_name += '_imp' + str(FLAGS.imp_loss_wt)
if FLAGS.imp_loss_oneminus:
FLAGS.pretrained_path += '_oneminus'
FLAGS.exp_name += '_oneminus'
if FLAGS.sep:
FLAGS.pretrained_path += '_sep'
FLAGS.exp_name += '_sep'
if FLAGS.tag_tokens:
FLAGS.pretrained_path += '_tag'
FLAGS.exp_name += '_tag' + str(FLAGS.tag_loss_wt)
bert_suffix = ''
# if FLAGS.use_bert:
# if FLAGS.sentemb:
# FLAGS.exp_name += '_sentemb'
# bert_suffix += '_sentemb'
# if FLAGS.artemb:
# FLAGS.exp_name += '_artemb'
# bert_suffix += '_artemb'
# if FLAGS.plushidden:
# FLAGS.exp_name += '_plushidden'
# bert_suffix += '_plushidden'
if FLAGS.tag_tokens:
bert_suffix += '_tag' + str(FLAGS.tag_loss_wt)
else:
bert_suffix += '_tag' + '0.0'
if FLAGS.upper_bound:
FLAGS.exp_name = FLAGS.exp_name + '_upperbound'
ssi_list = None # this is if we are doing the upper bound evaluation (ssi_list comes straight from the groundtruth)
else:
my_log_dir = os.path.join(log_dir, '%s_%s_%s%s' % (FLAGS.dataset_name, extractor, FLAGS.singles_and_pairs, bert_suffix))
print(util.bcolors.OKGREEN + "BERT path: " + my_log_dir + util.bcolors.ENDC)
with open(os.path.join(my_log_dir, 'ssi.pkl'), 'rb') as f:
ssi_list = pickle.load(f)
FLAGS.ssi_data_path = my_log_dir
if FLAGS.cnn_dm_pg:
FLAGS.exp_name = FLAGS.exp_name + '_cnntrained'
if FLAGS.websplit:
FLAGS.exp_name = FLAGS.exp_name + '_websplittrained'
if FLAGS.first_intact:
FLAGS.exp_name = FLAGS.exp_name + '_firstintact'
print('Running statistics on %s' % FLAGS.exp_name)
if FLAGS.dataset_name != "":
FLAGS.data_path = os.path.join(FLAGS.data_root, FLAGS.dataset_name, FLAGS.dataset_split + '*')
if not os.path.exists(os.path.join(FLAGS.data_root, FLAGS.dataset_name)) or len(os.listdir(os.path.join(FLAGS.data_root, FLAGS.dataset_name))) == 0:
print(('No TF example data found at %s so creating it from raw data.' % os.path.join(FLAGS.data_root, FLAGS.dataset_name)))
convert_data.process_dataset(FLAGS.dataset_name)
logging.set_verbosity(logging.INFO) # choose what level of logging you want
logging.info('Starting seq2seq_attention in %s mode...', (FLAGS.mode))
# Change log_root to FLAGS.log_root/FLAGS.exp_name and create the dir if necessary
FLAGS.exp_name = FLAGS.exp_name if FLAGS.exp_name != '' else FLAGS.dataset_name
FLAGS.actual_log_root = FLAGS.log_root
FLAGS.log_root = os.path.join(FLAGS.log_root, FLAGS.exp_name)
print(util.bcolors.OKGREEN + "Experiment path: " + FLAGS.log_root + util.bcolors.ENDC)
if FLAGS.dataset_name == 'duc_2004':
vocab = Vocab(FLAGS.vocab_path + '_' + 'cnn_dm', FLAGS.vocab_size, add_sep=FLAGS.sep) # create a vocabulary
else:
vocab_datasets = [os.path.basename(file_path).split('vocab_')[1] for file_path in glob.glob(FLAGS.vocab_path + '_*')]
original_dataset_name = [file_name for file_name in vocab_datasets if file_name in FLAGS.dataset_name]
if len(original_dataset_name) > 1:
raise Exception('Too many choices for vocab file')
if len(original_dataset_name) < 1:
raise Exception('No vocab file for dataset created. Run make_vocab.py --dataset_name=<my original dataset name>')
original_dataset_name = original_dataset_name[0]
FLAGS.original_dataset_name = original_dataset_name
vocab = Vocab(FLAGS.vocab_path + '_' + original_dataset_name, FLAGS.vocab_size, add_sep=FLAGS.sep) # create a vocabulary
# If in decode mode, set batch_size = beam_size
# Reason: in decode mode, we decode one example at a time.
# On each step, we have beam_size-many hypotheses in the beam, so we need to make a batch of these hypotheses.
if FLAGS.mode == 'decode':
FLAGS.batch_size = FLAGS.beam_size
# If single_pass=True, check we're in decode mode
if FLAGS.single_pass and FLAGS.mode!='decode':
raise Exception("The single_pass flag should only be True in decode mode")
# Make a namedtuple hps, containing the values of the hyperparameters that the model needs
# hparam_list = ['mode', 'lr', 'adagrad_init_acc', 'rand_unif_init_mag', 'trunc_norm_init_std',
# 'max_grad_norm', 'hidden_dim', 'emb_dim', 'batch_size', 'max_dec_steps',
# 'max_enc_steps', 'coverage', 'cov_loss_wt', 'pointer_gen', 'lambdamart_input', 'pg_mmr', 'singles_and_pairs', 'skip_with_less_than_3',
# 'ssi_data_path', 'word_imp_reg', 'imp_loss_wt']
hparam_list = [item for item in list(FLAGS.flag_values_dict().keys()) if item != '?']
hps_dict = {}
for key,val in FLAGS.__flags.items(): # for each flag
if key in hparam_list: # if it's in the list
hps_dict[key] = val.value # add it to the dict
hps = namedtuple("HParams", list(hps_dict.keys()))(**hps_dict)
tf.set_random_seed(113) # a seed value for randomness
decode_model_hps = hps._replace(
max_dec_steps=1) # The model is configured with max_dec_steps=1 because we only ever run one step of the decoder at a time (to do beam search). Note that the batcher is initialized with max_dec_steps equal to e.g. 100 because the batches need to contain the full summaries
if len(unused_argv) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
start_time = time.time()
np.random.seed(random_seed)
source_dir = os.path.join(FLAGS.data_root, dataset_articles)
source_files = sorted(glob.glob(source_dir + '/' + dataset_split + '*'))
total = len(source_files) * 1000 if 'cnn' in dataset_articles or 'xsum' in dataset_articles else len(source_files)
example_generator = data.example_generator(source_dir + '/' + dataset_split + '*', True, False,
should_check_valid=False)
# batcher = Batcher(None, vocab, hps, single_pass=FLAGS.single_pass)
model = SummarizationModel(decode_model_hps, vocab)
decoder = BeamSearchDecoder(model, None, vocab)
decoder.decode_iteratively(example_generator, total, names_to_types, ssi_list, hps)
# num_outside = []
# for example_idx, example in enumerate(tqdm(example_generator, total=total)):
# raw_article_sents, groundtruth_similar_source_indices_list, groundtruth_summary_text, corefs = util.unpack_tf_example(
# example, names_to_types)
# article_sent_tokens = [util.process_sent(sent) for sent in raw_article_sents]
# cur_token_idx = 0
# for sent_idx, sent_tokens in enumerate(article_sent_tokens):
# for token in sent_tokens:
# cur_token_idx += 1
# if cur_token_idx >= 400:
# sent_idx_at_400 = sent_idx
# break
# if cur_token_idx >= 400:
# break
#
# my_num_outside = 0
# for ssi in groundtruth_similar_source_indices_list:
# for source_idx in ssi:
# if source_idx >= sent_idx_at_400:
# my_num_outside += 1
# num_outside.append(my_num_outside)
# print "num_outside = %d" % np.mean(num_outside)
a=0