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decode_line_simple_prior_uni.py
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# Copyright 2016 Stanford University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import random
import sys
import time
import random
import string
import numpy as np
from six.moves import xrange
import tensorflow as tf
from os.path import join as pjoin
import nlc_model_prior as nlc_model
#import nlc_model_global as nlc_model
import nlc_data
#import nlc_data_no_filter as nlc_data
from util import initialize_vocabulary, get_tokenizer
from multiprocessing import Pool
import pdb
from scipy.signal import gaussian as gs
from flag import FLAGS
import re
def remove_nonascii(text):
return re.sub(r'[^\x00-\x7F]', '', text)
reverse_vocab, vocab = None, None
def create_model(session, vocab_size, forward_only):
model = nlc_model.NLCModel(
vocab_size, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor, FLAGS.dropout,
weight=FLAGS.weight, forward_only=True)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
return model
def tokenize(sent, vocab, depth=FLAGS.num_layers):
align = pow(2, depth - 1)
token_ids = nlc_data.sentence_to_token_ids(remove_nonascii(sent), vocab, get_tokenizer(FLAGS.tokenizer))
ones = [1] * len(token_ids)
pad = (align - len(token_ids)) % align
token_ids += [nlc_data.PAD_ID] * pad
ones += [0] * pad
source = np.array(token_ids).reshape([-1, 1])
mask = np.array(ones).reshape([-1, 1])
return source, mask
def detokenize(sents, reverse_vocab):
# TODO: char vs word
def detok_sent(sent):
outsent = ''
for t in sent:
if t >= len(nlc_data._START_VOCAB):
outsent += reverse_vocab[t]
return outsent
return [detok_sent(s) for s in sents]
def decode_beam(model, sess, encoder_output, probs, max_beam_size, len_input):
toks, probs, prob_trans = model.decode_beam(sess, encoder_output, probs, max_beam_size, len_input)
return toks.tolist(), probs.tolist(), prob_trans
def decode_lattice(beamstrs, probs, prob_trans):
num_str = len(beamstrs)
dict_str = {}
for i in range(num_str):
cur_str = beamstrs[i]
for j in range(0, len(cur_str)):
prefix = cur_str[:j]
if prefix not in dict_str:
dict_str[prefix] = {}
dict_str[prefix][cur_str[j]] = prob_trans[i][j + 1]
else:
dict_str[prefix][cur_str[j]] = prob_trans[i][j + 1]
if cur_str not in dict_str:
dict_str[cur_str] = {}
dict_str[cur_str]['<eos>'] = prob_trans[i][len(cur_str) + 1]
dict_str[cur_str + '<eos>'] = probs[i]
return dict_str
def fix_sent(model, sess, sent):
# Tokenize
input_toks, mask = tokenize(sent, vocab)
# Encode
encoder_output = model.encode(sess, input_toks, mask)
len_input = encoder_output.shape[0]
probs_v = np.zeros(len_input + 13 + 1 + 13 + len_input)
probs_v[len_input + 13 - int(FLAGS.variance): len_input + 13 + int(FLAGS.variance) + 1] = 1
probs_v = np.exp(probs_v * FLAGS.scalar)/ sum(np.exp(probs_v * FLAGS.scalar))
len_input = sum(mask)
# Decode
model.weight=FLAGS.weight
beam_toks, probs, prob_trans = decode_beam(model, sess, encoder_output, probs_v, FLAGS.beam_size, len_input)
# De-tokenize
beam_strs = detokenize(beam_toks, reverse_vocab)
# model.weight = 1
# probs_v2 = np.ones(probs_v.shape[0]) * (1. / probs_v.shape[0])
# beam_toks2, probs2, prob_trans2 = decode_beam(model, sess, encoder_output, probs_v2, FLAGS.beam_size, len_input)
# beam_strs2 = detokenize(beam_toks2, reverse_vocab)
return beam_strs, probs
def decode():
# Prepare NLC data.
global reverse_vocab, vocab
folder_out = FLAGS.out_dir
if not os.path.exists(folder_out):
os.makedirs(folder_out)
print("Preparing NLC data in %s" % FLAGS.data_dir)
vocab_path = pjoin(FLAGS.data_dir, "vocab.dat")
vocab, reverse_vocab = initialize_vocabulary(vocab_path)
vocab_size = len(vocab)
print("Vocabulary size: %d" % vocab_size)
if FLAGS.gpu_frac == 1:
sess = tf.Session()
else:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_frac)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))
print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model = create_model(sess, vocab_size, False)
tic = time.time()
with open(pjoin(FLAGS.data_dir, FLAGS.dev + '.x.txt'), 'r') as f_:
lines = [ele.strip('\n') for ele in f_.readlines()]
f_o = open(pjoin(folder_out, FLAGS.dev + '.prior.uni.o.txt.' + str(FLAGS.start) + '_' + str(FLAGS.end)), 'w')
f_p = open(pjoin(folder_out, FLAGS.dev + '.prior.uni.p.txt.' + str(FLAGS.start) + '_' + str(FLAGS.end)), 'w')
for line_id in range(FLAGS.start, FLAGS.end):
line = lines[line_id].split('\t')[0]
sent = line.strip()
if len(sent) == 0:
f_o.write('\n' * 100)
continue
output_sents, output_probs = fix_sent(model, sess, sent)
#output_sents, output_probs = fix_sent(model, sess, sent.replace('-', '_'))
for i in range(len(output_sents)):
sent = output_sents[i]
#sent = output_sents[i].replace('_', '-')
prob = output_probs[i]
f_o.write(sent + '\n')
f_p.write(str(prob) + '\n')
# f_o.write(sent + '\t' + str(prob) + '\n')
if line_id % 100 == 0:
toc = time.time()
print(toc - tic)
tic = time.time()
f_o.close()
f_p.close()
def main(_):
decode()
if __name__ == "__main__":
tf.app.run()