def train(self):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.word_list(self.source), self.vocab)
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
encdec = EncoderDecoder(self.vocab, self.embed, self.hidden)
if self.word2vecFlag:
self.copy_model(self.word2vec, encdec.enc)
self.copy_model(self.word2vec, encdec.dec, dec_flag=True)
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
gen1 = gens.word_list(self.source)
gen2 = gens.word_list(self.target)
gen3 = gens.batch(
gens.sorted_parallel(gen1, gen2, 100 * self.minibatch),
self.minibatch)
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(encdec)
opt.add_hook(optimizer.GradientClipping(5))
random_number = random.randint(0, self.minibatch - 1)
for src_batch, trg_batch in gen3:
src_batch = fill_batch(src_batch)
trg_batch = fill_batch(trg_batch)
K = len(src_batch)
# If you use the ipython note book you hace to use the forward function
# hyp_batch, loss = self.forward(src_batch, trg_batch, src_vocab, trg_vocab, encdec, True, 0)
hyp_batch, loss = self.forward_implement(
src_batch, trg_batch, src_vocab, trg_vocab, encdec, True,
0)
loss.backward()
opt.update()
self.print_out(random_number, epoch, trained, src_batch,
trg_batch, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
src_vocab.save(prefix + '.srcvocab')
trg_vocab.save(prefix + '.trgvocab')
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
trace('finished.')
def train(self):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.word_list(self.source), self.vocab)
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
encdec = EncoderDecoder(self.vocab, self.embed, self.hidden)
if self.word2vecFlag:
self.copy_model(self.word2vec, encdec.enc)
self.copy_model(self.word2vec, encdec.dec, dec_flag=True)
else:
encdec = self.encdec
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
gen1 = gens.word_list(self.source)
gen2 = gens.word_list(self.target)
gen3 = gens.batch(gens.sorted_parallel(gen1, gen2, 100 * self.minibatch), self.minibatch)
opt = optimizers.AdaGrad(lr = 0.01)
opt.setup(encdec)
opt.add_hook(optimizer.GradientClipping(5))
random_number = random.randint(0, self.minibatch - 1)
for src_batch, trg_batch in gen3:
src_batch = fill_batch(src_batch)
trg_batch = fill_batch(trg_batch)
K = len(src_batch)
hyp_batch, loss = self.forward(src_batch, trg_batch, src_vocab, trg_vocab, encdec, True, 0)
loss.backward()
opt.update()
if trained == 0:
self.print_out(random_number, epoch, trained, src_batch, trg_batch, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
src_vocab.save(prefix + '.srcvocab')
trg_vocab.save(prefix + '.trgvocab')
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
trace('finished.')
class EncoderDecoderModel:
def __init__(self, parameter_dict):
self.parameter_dict = parameter_dict
self.id2image = parameter_dict["x"]
self.first_word = parameter_dict["first_word"]
self.target = parameter_dict["target"]
self.vocab = parameter_dict["vocab"]
self.embed = parameter_dict["embed"]
self.epoch = parameter_dict["epoch"]
self.hidden = parameter_dict["hidden"]
self.minibatch = parameter_dict["minibatch"]
self.generation_limit = parameter_dict["generation_limit"]
self.use_gpu = parameter_dict["use_gpu"]
self.gpu_id = parameter_dict["gpu_id"]
self.choose_model = parameter_dict["choose_model"]
self.common_function = CommonFunction()
self.model = "ChainerImageCaption"
self.trg_batch = []
self.trg_vocab = []
self.is_training = True
self.generation_limit = 0
self.encdec = EncoderDecoder(self.vocab, self.embed, self.hidden,
self.choose_model,
self.use_gpu, self.gpu_id)
if self.use_gpu:
self.encdec.to_gpu()
self.__set_gpu()
def __set_gpu(self):
XP.set_library(self.use_gpu, self.gpu_id)
def __forward_img(self):
x = XP.farray(self.id2image.data)
return self.encdec.encode(x)
def __forward_word(self, trg_batch, encdec, is_training, generation_limit):
trg_stoi = self.trg_vocab.stoi
trg_itos = self.trg_vocab.itos
t = XP.iarray([trg_stoi('<s>') for _ in range(self.batch_size)])
hyp_batch = [[] for _ in range(self.batch_size)]
trg_len = len(trg_batch[0]) if trg_batch else 0
if is_training:
loss = XP.fzeros(())
for l in range(trg_len):
y = encdec.decode(t)
t = XP.iarray([trg_stoi(trg_batch[k][l]) for k in range(self.batch_size)])
loss += functions.softmax_cross_entropy(y, t)
output = cuda.to_cpu(y.data.argmax(1))
for k in range(self.batch_size):
hyp_batch[k].append(trg_itos(output[k]))
return loss, hyp_batch
else:
while len(hyp_batch[0]) < generation_limit:
y = encdec.decode(t)
output = cuda.to_cpu(y.data.argmax(1))
t = self.common_function.my_array(output, np.int32)
for k in range(self.batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>' for k in range(self.batch_size)):
break
return hyp_batch
def train(self, epoch):
trace('making vocabularies ...')
self.trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
for trg_batch in gen:
self.batch_size = len(trg_batch)
self.trg_batch = fill_batch(trg_batch)
if len(trg_batch) != self.minibatch:
break
self.encdec.clear(self.batch_size)
self.__forward_img()
self.encdec.reset(self.batch_size)
loss, hyp_batch = self.__forward_word(self.trg_batch, self.encdec, True, 0)
loss.backward()
opt.update()
K = len(self.trg_batch) - 2
self.print_out(K, hyp_batch, epoch)
def save_model(self):
trace('saving model ...')
prefix = self.model
self.trg_vocab.save("model/" + prefix + '.trgvocab')
self.encdec.save_spec("model/" + prefix + '.spec')
serializers.save_hdf5("model/" + prefix + '.weights', self.encdec)
trace('finished.')
def test(self):
trace('loading model ...')
self.trg_vocab = Vocabulary.load("model/" + self.model + '.trgvocab')
self.batch_size = len(trg_batch)
encdec = EncoderDecoder.load_spec("model/" + self.model + '.spec')
serializers.load_hdf5("model/" + self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
self.__forward_img()
trace('sample %8d ...' % (generated + 1))
hyp_batch = self.__forward_word(self.trg_batch, encdec, False, self.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print('hyp : ' +''.join(hyp))
print(' '.join(hyp), file=fp)
trace('finished.')
def print_out(self, K, hyp_batch, epoch):
if len(self.trg_batch) - 2 < K:
K = len(self.trg_batch) - 2
if len(hyp_batch) - 2 < K:
K = len(hyp_batch) - 2
trace('epoch %3d/%3d, sample %8d' % (epoch, self.epoch, K + 1))
# trace('epoch %3d/%3d, sample %8d' % (i_epoch + 1, self.epoch, trained + 1))
trace(' trg = ' + ' '.join([x if x != '</s>' else '*' for x in self.trg_batch[K]]))
trace(' hyp = ' + ' '.join([x if x != '</s>' else '*' for x in hyp_batch[K]]))
class EncoderDecoderModel:
def __init__(self, parameter_dict):
self.parameter_dict = parameter_dict
self.id2image = parameter_dict["x"]
self.first_word = parameter_dict["first_word"]
self.target = parameter_dict["target"]
self.vocab = parameter_dict["vocab"]
self.embed = parameter_dict["embed"]
self.epoch = parameter_dict["epoch"]
self.hidden = parameter_dict["hidden"]
self.minibatch = parameter_dict["minibatch"]
self.generation_limit = parameter_dict["generation_limit"]
self.use_gpu = parameter_dict["use_gpu"]
self.gpu_id = parameter_dict["gpu_id"]
self.choose_model = parameter_dict["choose_model"]
self.common_function = CommonFunction()
self.model = "ChainerImageCaption"
self.trg_batch = []
self.trg_vocab = []
self.is_training = True
self.generation_limit = 0
self.encdec = EncoderDecoder(self.vocab, self.embed, self.hidden,
self.choose_model, self.use_gpu,
self.gpu_id)
if self.use_gpu:
self.encdec.to_gpu()
self.__set_gpu()
def __set_gpu(self):
XP.set_library(self.use_gpu, self.gpu_id)
def __forward_img(self):
x = XP.farray(self.id2image.data)
return self.encdec.encode(x)
def __forward_word(self, trg_batch, encdec, is_training, generation_limit):
trg_stoi = self.trg_vocab.stoi
trg_itos = self.trg_vocab.itos
t = XP.iarray([trg_stoi('<s>') for _ in range(self.batch_size)])
hyp_batch = [[] for _ in range(self.batch_size)]
trg_len = len(trg_batch[0]) if trg_batch else 0
if is_training:
loss = XP.fzeros(())
for l in range(trg_len):
y = encdec.decode(t)
t = XP.iarray([
trg_stoi(trg_batch[k][l]) for k in range(self.batch_size)
])
loss += functions.softmax_cross_entropy(y, t)
output = cuda.to_cpu(y.data.argmax(1))
for k in range(self.batch_size):
hyp_batch[k].append(trg_itos(output[k]))
return loss, hyp_batch
else:
while len(hyp_batch[0]) < generation_limit:
y = encdec.decode(t)
output = cuda.to_cpu(y.data.argmax(1))
t = self.common_function.my_array(output, np.int32)
for k in range(self.batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>'
for k in range(self.batch_size)):
break
return hyp_batch
def train(self, epoch):
trace('making vocabularies ...')
self.trg_vocab = Vocabulary.new(gens.word_list(self.target),
self.vocab)
trace('making model ...')
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(self.encdec)
opt.add_hook(optimizer.GradientClipping(5))
gen1 = gens.word_list(self.target)
gen = gens.batch(gen1, self.minibatch)
for trg_batch in gen:
self.batch_size = len(trg_batch)
self.trg_batch = fill_batch(trg_batch)
if len(trg_batch) != self.minibatch:
break
self.encdec.clear(self.batch_size)
self.__forward_img()
self.encdec.reset(self.batch_size)
loss, hyp_batch = self.__forward_word(self.trg_batch, self.encdec,
True, 0)
loss.backward()
opt.update()
K = len(self.trg_batch) - 2
self.print_out(K, hyp_batch, epoch)
def save_model(self):
trace('saving model ...')
prefix = self.model
self.trg_vocab.save("model/" + prefix + '.trgvocab')
self.encdec.save_spec("model/" + prefix + '.spec')
serializers.save_hdf5("model/" + prefix + '.weights', self.encdec)
trace('finished.')
def test(self):
trace('loading model ...')
self.trg_vocab = Vocabulary.load("model/" + self.model + '.trgvocab')
self.batch_size = len(trg_batch)
encdec = EncoderDecoder.load_spec("model/" + self.model + '.spec')
serializers.load_hdf5("model/" + self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
self.__forward_img()
trace('sample %8d ...' % (generated + 1))
hyp_batch = self.__forward_word(self.trg_batch, encdec, False,
self.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
trace('finished.')
def print_out(self, K, hyp_batch, epoch):
if len(self.trg_batch) - 2 < K:
K = len(self.trg_batch) - 2
if len(hyp_batch) - 2 < K:
K = len(hyp_batch) - 2
trace('epoch %3d/%3d, sample %8d' % (epoch, self.epoch, K + 1))
# trace('epoch %3d/%3d, sample %8d' % (i_epoch + 1, self.epoch, trained + 1))
trace(' trg = ' +
' '.join([x if x != '</s>' else '*' for x in self.trg_batch[K]]))
trace(' hyp = ' +
' '.join([x if x != '</s>' else '*' for x in hyp_batch[K]]))
