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augment.py
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augment.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_sample_global as nlc_model
import nlc_data
from util import get_tokenizer
tf.app.flags.DEFINE_float("learning_rate", 0.001, "Learning rate.")
tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.95, "Learning rate decays by this much.")
tf.app.flags.DEFINE_float("max_gradient_norm", 5.0, "Clip gradients to this norm.")
tf.app.flags.DEFINE_float("dropout", 0.1, "Fraction of units randomly dropped on non-recurrent connections.")
tf.app.flags.DEFINE_integer("batch_size", 128, "Batch size to use during training.")
tf.app.flags.DEFINE_integer("epochs", 0, "Number of epochs to train.")
tf.app.flags.DEFINE_integer("size", 400, "Size of each model layer.")
tf.app.flags.DEFINE_integer("num_layers", 3, "Number of layers in the model.")
tf.app.flags.DEFINE_integer("max_vocab_size", 40000, "Vocabulary size limit.")
tf.app.flags.DEFINE_integer("max_seq_len", 200, "Maximum sequence length.")
tf.app.flags.DEFINE_integer("start", 0, "Decode from.")
tf.app.flags.DEFINE_integer("end", 0, "Decode to.")
tf.app.flags.DEFINE_string("data_dir", "/tmp", "Data directory")
tf.app.flags.DEFINE_string("out_dir", "/tmp", "Output directory")
tf.app.flags.DEFINE_string("train_dir", "/tmp", "Training directory.")
tf.app.flags.DEFINE_string("tokenizer", "CHAR", "Set to WORD to train word level model.")
tf.app.flags.DEFINE_integer("beam_size", 8, "Size of beam.")
tf.app.flags.DEFINE_string("lmfile", None, "arpa file of the language model.")
tf.app.flags.DEFINE_float("alpha", 0.3, "Language model relative weight.")
tf.app.flags.DEFINE_float("gpu_frac", 0.3, "GPU Fraction to be used.")
FLAGS = tf.app.flags.FLAGS
reverse_vocab, vocab = None, None
lm = 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,
forward_only=forward_only)
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(sent, vocab, get_tokenizer(FLAGS))
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 lm_rank(strs, probs):
if lm is None:
return strs[0]
a = FLAGS.alpha
lmscores = [lm.score(s)/(1+len(s.split())) for s in strs]
probs = [ p / (len(s)+1) for (s, p) in zip(strs, probs) ]
for (s, p, l) in zip(strs, probs, lmscores):
print(s, p, l)
rescores = [(1 - a) * p + a * l for (l, p) in zip(lmscores, probs)]
rerank = [rs[0] for rs in sorted(enumerate(rescores), key=lambda x: x[1])]
generated = strs[rerank[-1]]
lm_score = lmscores[rerank[-1]]
nw_score = probs[rerank[-1]]
score = rescores[rerank[-1]]
return generated #, score, nw_score, lm_score
# if lm is None:
# return strs[0]
# a = FLAGS.alpha
# rescores = [(1-a)*p + a*lm.score(s) for (s, p) in zip(strs, probs)]
# rerank = [rs[0] for rs in sorted(enumerate(rescores), key=lambda x:x[1])]
# return strs[rerank[-1]]
def decode_beam(model, sess, encoder_output, max_beam_size, len_input):
toks, probs,_ = model.decode_beam(sess, encoder_output, max_beam_size, len_input)
return toks.tolist(), probs.tolist()
def fix_sent(model, sess, sent):
# Tokenize
input_toks, mask = tokenize(sent, vocab)
# Encode
encoder_output = model.encode(sess, input_toks, mask)
# Decode
len_input = sum(mask)
beam_toks, probs = decode_beam(model, sess, encoder_output, FLAGS.beam_size, len_input)
# De-tokenize
beam_strs = detokenize(beam_toks, reverse_vocab)
# Language Model ranking
#best_str = lm_rank(beam_strs, probs)
# Return
#return best_str
return beam_strs, probs
def decode():
# Prepare NLC data.
global reverse_vocab, vocab, lm
# if FLAGS.lmfile is not None:
# print("Loading Language model from %s" % FLAGS.lmfile)
# lm = kenlm.LanguageModel(FLAGS.lmfile)
print("Preparing NLC data in %s" % FLAGS.data_dir)
x_train, y_train, x_dev, y_dev, vocab_path = nlc_data.prepare_nlc_data(
FLAGS.data_dir, FLAGS.max_vocab_size,
tokenizer=get_tokenizer(FLAGS))
vocab, reverse_vocab = nlc_data.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)
line_id = 0
tic = time.time()
with open(pjoin(FLAGS.data_dir, 'dev.x.txt'), 'r') as f_:
lines = f_.readlines()
if not os.path.exists(FLAGS.out_dir):
os.makedirs(FLAGS.out_dir)
f_p = open(pjoin(FLAGS.out_dir, 'dev.p.txt.' + str(FLAGS.start) + '_' + str(FLAGS.end)), 'w')
f_o = open(pjoin(FLAGS.out_dir, 'dev.o.txt.' + str(FLAGS.start) + '_' + str(FLAGS.end)), 'w')
for line in lines[FLAGS.start:FLAGS.end]:
# sent = raw_input("Enter a sentence: ")
line_id += 1
print(line_id)
if line_id % 100 == 0:
toc = time.time()
print(toc - tic)
tic = time.time()
sent = line.strip()
output_sents, output_probs = fix_sent(model, sess, sent)
for sent in output_sents:
f_o.write(sent + '\n')
for prob in output_probs:
f_p.write('%.5f\n' % prob)
f_o.close()
f_p.close()
def main(_):
decode()
if __name__ == "__main__":
tf.app.run()