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run.py
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run.py
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import argparse
import os
import logging
import sys
import time
import gzip
import pickle
from shutil import copy
from math import factorial
import yaml
import numpy as np
import tensorflow as tf
from lm.stitch import init_lm_checkpoints
from lm.language_model import get_vocabs, bad_words, unk_idx
from cos.tfidf import get_idf_vector
from model import get_model
from utils import get_embeddings
from neighbor_function import get_extractive_initial_states
logging.getLogger().setLevel(logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_file', type=str, default='test/input.txt')
parser.add_argument('--config', type=str, default='configs/default.yaml')
parser.add_argument('--output_dir', type=str, default='outputs/default')
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--from_line', type=int, default=0)
parser.add_argument('--to_line', type=int, default=-1)
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
log_dir = os.path.join(args.output_dir, 'logs')
os.makedirs(log_dir, exist_ok=True)
log_file = os.path.join(log_dir, 'log_{}_{}.txt'.format(args.from_line, args.to_line))
file_handler = logging.FileHandler(log_file)
logging.getLogger().addHandler(file_handler)
if args.to_line == -1:
args.to_line = sum(1 for _ in open(args.input_file))
for k, v in vars(args).items():
logging.info('{}: {}'.format(k, v))
config = yaml.safe_load(open(args.config))
copy(args.config, os.path.join(args.output_dir, 'config.yaml'))
copy(config['vocab_file'], os.path.join(args.output_dir, 'vocab.txt'))
if config['mode'] not in ['extractive', 'extractive_norm']:
raise ValueError('mode not supported: {}'.format(config['mode']))
run(args, config)
def run(args, config):
time_start = time.time()
print('time_start: {}'.format(str(time_start)))
sess, model_inputs, model_outputs, embeddings, word2idx, idx2word = build_graph(config)
lines = get_lines(args)
all_max_scores = list()
for line_id, sentence in lines.items():
logging.info('line id: {}'.format(line_id))
line_output_dir = os.path.join(args.output_dir, 'lines', str(line_id))
os.makedirs(line_output_dir, exist_ok=True)
all_outputs = list()
line_time_start = time.time()
for batch_id, batch in enumerate(get_batches(sentence, line_id, args.batch_size, args, config, word2idx)):
x_sentence, sentence_length, initial_state, summary_length, num_steps = batch
logging.info('batch id: {}'.format(batch_id))
print(x_sentence)
feed_dict = {
model_inputs['sentence']: x_sentence,
model_inputs['sentence_length']: sentence_length,
model_inputs['initial_state']: initial_state.state,
model_inputs['initial_internal_state']: initial_state.internal_state,
model_inputs['summary_length']: summary_length,
model_inputs['num_steps']: num_steps
}
outputs = sess.run(model_outputs, feed_dict=feed_dict)
all_outputs.append(outputs)
states = np.concatenate([o['states'] for o in all_outputs], axis=1)
scores = np.concatenate([o['scores'] for o in all_outputs], axis=1)
max_idx = np.unravel_index(np.argmax(scores), scores.shape)
max_score = scores[max_idx]
all_max_scores.append(max_score)
max_state = states[max_idx]
summary = ' '.join([idx2word[idx] for idx in max_state])
logging.info('max_score: {}'.format(max_score))
logging.info('max_state: {}'.format(max_state))
logging.info('summary: {}'.format(summary))
logging.info('run_time: {}'.format(int(time.time()-line_time_start)))
outputs = dict(states=states,
scores=scores,
sentence=sentence,
summary=summary,
line_id=line_id,
time=time.time()-line_time_start)
with gzip.open(os.path.join(line_output_dir, 'outputs.pickle.gzip'), 'wb') as f:
pickle.dump(outputs, f)
with open(os.path.join(line_output_dir, 'line.txt'), 'w') as f:
f.write(summary + '\n')
logging.info('')
logging.info('avg max score: {}'.format(np.mean(all_max_scores)))
logging.info('end: {}'.format(str(time.time())))
logging.info('runtime: {}'.format(str(time.time() - time_start)))
def build_graph(config):
word2idx, idx2word = get_vocabs(config['vocab_file'])
embeddings = get_embeddings(config['s2v_file'])
weights = config.get('weights', [1 for _ in config['metrics']])
assert len(config['metrics']) == len(weights)
metrics = {m: {'weight': w} for m, w in zip(config['metrics'], weights)}
if 'lm' in metrics:
metrics['lm'].update(dict(forward=config['lm_save_dir'],
reverse=config.get('lm_rev_save_dir', None),
num_words=len(word2idx)))
if 'cos' in metrics:
idf_file = config.get('idf_file', None)
if idf_file is not None:
metrics['cos'].update(dict(idf=get_idf_vector(idf_file, word2idx), embeddings=embeddings))
else:
metrics['cos'].update(dict(embeddings=embeddings))
sess_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=sess_config)
model_inputs, model_outputs = get_model(metrics, mode=config['mode'])
if 'lm' in metrics:
init_lm_checkpoints(metrics['lm'])
sess.run(tf.global_variables_initializer())
return sess, model_inputs, model_outputs, embeddings, word2idx, idx2word
def get_lines(args):
lines = dict()
for line_id, line in enumerate(open(args.input_file)):
if line_id in range(args.from_line, args.to_line):
sentence = line.split()
lines[line_id] = sentence
return lines
def get_summary_length(config, sentence_length):
summary_length_target = config.get('summary_length', 8)
if type(summary_length_target) == str and summary_length_target.endswith('p'):
summary_percent = int(config['summary_length'][:-1]) / 100.0
summary_length_target = max(1, int(round(sentence_length * summary_percent)))
return np.asarray(summary_length_target, dtype=np.int32)
def get_batches(sentence, line_id, batch_size, args, config, word2idx):
orig_sentence_length = len(sentence)
sentence = [w for w in sentence if w not in bad_words]
x_sentence = np.asarray([word2idx.get(w, unk_idx) for w in sentence], dtype=np.int32)
sentence_length = np.asarray(len(sentence), dtype=np.int32)
summary_length = get_summary_length(config, orig_sentence_length)
logging.info('sentence: {}'.format(' '.join(sentence)))
logging.info('sentence_length: {}'.format(sentence_length))
logging.info('summary_length: {}'.format(summary_length))
num_steps = max(1, int((sentence_length * summary_length**2) * config.get('steps_factor', 0.1)))
num_restarts = max(1, int((sentence_length * summary_length**2) * config.get('restarts_factor', 0.01)))
#num_steps = min(800, num_steps)
#num_restarts = min(300, num_restarts)
#batch_size = int(batch_size * 11 / summary_length)
num_evaluations = num_steps * num_restarts
logging.info('number of evaluations: {}'.format(num_evaluations))
logging.info('number of restarts: {}'.format(num_restarts))
logging.info('number of steps: {}'.format(num_steps))
if config['mode'] == 'extractive':
num_exhaustive = int(factorial(sentence_length) / factorial(summary_length) / factorial(max(1, sentence_length - summary_length)))
exhaustive = num_evaluations >= num_exhaustive and config.get('allow_exhaustive', True)
if exhaustive:
logging.info('roughly number of exhaustive evaluations: {}'.format(num_exhaustive))
for initial_state in get_extractive_initial_states(num_restarts,
batch_size,
x_sentence,
summary_length,
exhaustive=exhaustive):
if exhaustive:
num_steps = 0
yield x_sentence, sentence_length, initial_state, summary_length, num_steps
if __name__ == '__main__':
main()