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enc-dec-tranceration.py
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enc-dec-tranceration.py
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#!/usr/bin/python
# coding: UTF-8
USE_GPU = True
DEBUG = True
import datetime
from argparse import ArgumentParser
from Vocabulary import Vocabulary
import random
from chainer import Chain, cuda, Variable, links, optimizers, serializers
from chainer import functions as F
if USE_GPU:
import cupy as xp
else:
import numpy as xp
if not DEBUG:
import os
os.environ["CHAINER_TYPE_CHECK"] = "0" #type_checkをしない
def parse_args():
p = ArgumentParser(description='Encoder-decoder neural machine translation')
p.add_argument("model", type=str)
p.add_argument("mode", type=str, help="train mode or test mode")
p.add_argument("source", type=str)
p.add_argument("target", type=str)
p.add_argument("s_test", type=str)
p.add_argument("t_test", type=str)
p.add_argument("s_vocab", type=str)
p.add_argument("t_vocab", type=str)
p.add_argument("--embed_size", dest="embed_size", type=int, default=100)
p.add_argument("--hidden_size", dest="hidden_size", type=int, default=200,
help="the number of cells at hidden layer")
p.add_argument("--epochs", dest="epochs", type=int, default=1)
p.add_argument("--batch_size", dest="batch_size", type=int, default=100)
return p.parse_args()
## todo:ファイル名がハードコーティングなのを直す
def trace(*args):
output_file = 'output_1.txt'
with open(output_file, 'a') as fp:
print('[', datetime.datetime.now(), ']', *args, file=fp)
def my_zeros(shape, dtype):
return Variable(xp.zeros(shape, dtype=dtype))
def my_array(array, dtype):
return Variable(xp.array(array, dtype=dtype))
def load_vocab(file):
v = Vocabulary()
v.load(file)
return v
def load_input(file, vocab):
with open(file, "r") as f:
for line in f:
line = line.strip().split()
yield [vocab.w2id(e) for e in line]
## todo: test時(学習中のvalidation時ではなく)のときにすべて出力するようにする
def show_outputs(pre_trained, trained, sources, targets, outputs, s_vocab, t_vocab, train):
if int(pre_trained/20000) != int(trained/20000):
trace('------- trained:', trained, ' --------')
for s, t, o in zip(sources, targets, outputs):
trace('source:', " ".join([s_vocab.id2w(e) for e in s if e != -1]))
trace('target:', " ".join([t_vocab.id2w(e) for e in t if e != -1]))
trace('outputs:', " ".join([t_vocab.id2w(e) for e in o if e != -1]))
trace('----')
elif train == False and int(pre_trained/200) != int(trained/200):
trace('------- tested:', trained, ' --------')
for s, t, o in zip(sources, targets, outputs):
trace('t_source:', " ".join([s_vocab.id2w(e) for e in s if e != -1]))
trace('t_target:', " ".join([t_vocab.id2w(e) for e in t if e != -1]))
trace('t_outputs:', " ".join([t_vocab.id2w(e) for e in o if e != -1]))
trace('----')
return
def batch(gen, batch_size):
batch = []
for e in gen:
batch.append(e)
if len(batch) == batch_size:
yield batch
batch = []
if batch:
yield batch
def parallel_batch(gen, batch_size):
batch = [[],[]]
for e in gen:
batch[0].append(e[0])
batch[1].append(e[1])
if len(batch[0]) == batch_size:
yield batch
batch = [[],[]]
if batch != [[],[]]:
yield batch
def sort_gen(s_gen, t_gen, sort_size):
gen1 = batch(s_gen, sort_size)
gen2 = batch(t_gen, sort_size)
for e1, e2 in zip(gen1, gen2):
for x in sorted(zip(e1, e2), key=lambda x: (len(x[1]), len(x[0]))):
yield list(x)
def suffle_gen(gen, suffle_size):
gen = batch(gen, suffle_size)
for e in gen:
for i in sorted(e, key=lambda i: random.random()):
yield i
def generater(s_gen, t_gen, batch_size, sort_block=100):
sorted_gen = sort_gen(s_gen, t_gen, batch_size*sort_block)
batch_gen = parallel_batch(sorted_gen, batch_size)
gen = suffle_gen(batch_gen, sort_block)
return gen
def fill_batch(batch, token='</s>'):
max_len = max(len(x) for x in batch)
return [x + [token] * (max_len - len(x) + 1) for x in batch]
class Encoder(Chain):
def __init__(self, embed_size, hidden_size, source_vocab):
super(Encoder, self).__init__(
word_id_2_embed=F.EmbedID(source_vocab, embed_size, ignore_label=-1),
embed_2_lstm_input=F.Linear(embed_size, hidden_size*4),
pre_hidden_2_lstm_input=F.Linear(hidden_size, hidden_size*4),
)
def __call__(self, x, c, p):
word_embed = self.word_id_2_embed(x)
lstm_input = self.embed_2_lstm_input(word_embed) + self.pre_hidden_2_lstm_input(p)
c, p = F.lstm(c, lstm_input)
return c, p
class Decoder(Chain):
def __init__(self, embed_size, hidden_size, target_vocab):
super(Decoder, self).__init__(
word_id_2_embed=F.EmbedID(target_vocab, embed_size, ignore_label=-1),
embed_2_lstm_input=F.Linear(embed_size, hidden_size*4),
pre_hidden_2_lstm_input=F.Linear(hidden_size, hidden_size*4),
hidden_2_word_id=F.Linear(hidden_size, target_vocab),
)
def __call__(self, x, c, q):
word_embed = self.word_id_2_embed(x)
lstm_input = self.embed_2_lstm_input(word_embed) + self.pre_hidden_2_lstm_input(q)
c, q = F.lstm(c, lstm_input)
y = self.hidden_2_word_id(q)
return c, q, y
## todo: h, cを外部からいじれるようにする(今は1-best、beam-searchを使わないことが前提の書き方になっている)
class EncoderDecoder(Chain):
def __init__(self, embed_size, hidden_size, s_vocab_size, t_vocab_size):
super(EncoderDecoder, self).__init__(
enc = Encoder(embed_size, hidden_size, s_vocab_size),
dec = Decoder(embed_size, hidden_size, t_vocab_size),
)
self.embed_size = embed_size
self.hidden_size = hidden_size
def reset_state(self, batch_size):
self.zerograds()
self.c = my_zeros((batch_size, self.hidden_size), xp.float32)
self.h = my_zeros((batch_size, self.hidden_size), xp.float32)
def encode(self, x):
self.c, self.h = self.enc(x, self.c, self.h)
def decode(self, x):
self.c, self.h, y = self.dec(x, self.c, self.h)
return y
def save_spec(self, filename):
with open(filename, 'w') as fp:
print(self.embed_size, file=fp)
print(self.hidden_size, file=fp)
@staticmethod
def load_spec(filename, s_vocab_size, t_vocab_size):
with open(filename) as fp:
embed_size = int(next(fp))
hidden_size = int(next(fp))
return EncoderDecoder(embed_size, hidden_size, s_vocab_size, t_vocab_size)
def forward(model, source_batch, target_batch, batch_size, is_train=True, t_EOS_id=0):
## initalize
source_len = len(source_batch[0])
target_len = len(target_batch[0])
output = [[] for _ in range(batch_size)]
model.reset_state(batch_size)
## encode
for index in reversed(range(source_len)):
x = my_array([source_batch[i][index] for i in range(batch_size)], xp.int32)
model.encode(x)
## decode
if is_train:
loss = my_zeros((), xp.float32)
x = my_array([t_EOS_id for _ in range(batch_size)], xp.int32)
for index in range(target_len):
y = model.decode(x)
t = my_array([target_batch[i][index] for i in range(batch_size)], xp.int32)
loss += F.softmax_cross_entropy(y, t)
predict_words = cuda.to_cpu(y.data.argmax(1))
x = t # 正解を入力として使う
for i in range(batch_size):
output[i].append(predict_words[i])
return loss, output
else:
loss = my_zeros((), xp.float32)
x = my_array([t_EOS_id for _ in range(batch_size)], xp.int32)
for index in range(target_len):
y = model.decode(x)
t = my_array([target_batch[i][index] for i in range(batch_size)], xp.int32)
loss += F.softmax_cross_entropy(y, t)
predict_words = cuda.to_cpu(y.data.argmax(1))
x = my_array(predict_words, xp.int32) # 違いは、入力として予測値を使うか正解を使うか
for i in range(batch_size):
output[i].append(predict_words[i])
return loss, output
""" 本当のテスト時(正解データが無い時用)
else:
x = my_array([t_EOS_id for _ in range(batch_size)], xp.int32)
for index in range(target_len):
y = model.decode(x)
predict_words = cuda.to_cpu(y.data.argmax(1))
x = my_array(predict_words, xp.int32)
for i in range(batch_size):
output[i].append(predict_words[i])
return output
"""
def epoch_loop(args, encdec, epochs, s_file, t_file, s_vocab, t_vocab, train=True):
""" 学習orテスト時のメイン動作部分 """
# 初期化
t_EOS_id = t_vocab.w2id('<EOS>')
# train時のみoptimaizerの準備
if train:
opt = optimizers.Adam()
opt.setup(encdec)
# GPU時の設定 (0番目のgpuを使用する、という意味)
if USE_GPU:
encdec.to_gpu(0)
for epoch in range(epochs):
trace('epoch:', epoch)
# 各epochでの初期化
trained = 0
sum_loss = 0
# generaterの準備
s_gen = load_input(s_file, s_vocab)
t_gen = load_input(t_file, t_vocab)
gen = generater(s_gen, t_gen, args.batch_size)
for s, t in gen: # s, tはバッチサイズ文のデータ
# EOSをtarget文の最後に追加
for i in range(len(t)):
t[i] = t[i] + [t_EOS_id]
# 長さの違う部分を-1で埋める(-1で埋めると、word embedが全て0になる、かつlossが計算されない)
s = fill_batch(s, token=-1)
t = fill_batch(t, token=-1)
cur_batch_size = len(s)
loss, outputs = forward(encdec, s, t, cur_batch_size, train, t_EOS_id)
sum_loss += loss.data*cur_batch_size
# 重みの更新
if train:
loss.backward()
opt.update()
trained += cur_batch_size
if args.mode == 'train':
show_outputs(trained - cur_batch_size, trained, s[:5], t[:5], outputs[:5], s_vocab, t_vocab, train)
else:
show_outputs(trained - cur_batch_size, trained, s, t, outputs, s_vocab, t_vocab, train)
# 後処理
if train:
trace('*** end_epoch:', epoch, ' ***')
trace('loss:', sum_loss)
trace('******')
else:
trace('*** end_test:', epoch, ' ***')
trace('test_loss:', sum_loss)
trace('******')
#if train:
if train and (epoch+1)%3==0:
trace('saving model ...')
prefix = args.model + '.%03.d' % (epoch + 1)
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
serializers.save_hdf5(prefix + '.opt', opt)
# テストをして汎化性能をチェック
epoch_loop(args, encdec, 1, args.s_test, args.t_test, s_vocab, t_vocab, train=False)
return
def main():
args = parse_args()
trace('load vocabulary.....')
source_vocab = load_vocab(args.s_vocab)
target_vocab = load_vocab(args.t_vocab)
if args.mode == 'train':
trace('make model.....')
encdec = EncoderDecoder(args.embed_size, args.hidden_size, source_vocab.size, target_vocab.size)
epoch_loop(args, encdec, args.epochs, args.source, args.target, source_vocab, target_vocab, train=True)
else:
trace('load model.....')
encdec = EncoderDecoder.load_spec(args.model + '.spec', source_vocab.size, target_vocab.size)
serializers.load_hdf5(args.model + '.weights', encdec)
epoch_loop(args, encdec, 1, args.s_test, args.t_test, source_vocab, target_vocab, train=False)
if __name__ == '__main__':
main()