def gen_barrage_wechcat(raw_input, model):
raw_input = pro_sentpiece(raw_input, hp.bpe_model)
bar_input = []
bar_input.append(raw_input)
test_batches = input_fn(bar_input, bar_input, hp.barrages_vocab, 1, shuffle=False)
iter = tf.data.Iterator.from_structure(test_batches.output_types, test_batches.output_shapes)
xs, ys = iter.get_next()
test_init_op = iter.make_initializer(test_batches)
y_hat, _, random_predict = model.eval_gen(xs, ys)
with tf.Session() as sess:
ckpt_ = tf.train.latest_checkpoint(hp.ckpt)
ckpt = hp.ckpt if ckpt_ is None else ckpt_ # None: ckpt is a file. otherwise dir.
saver = tf.train.Saver()
saver.restore(sess, ckpt)
sess.run(test_init_op)
logging.info("# get hypotheses")
hypotheses, yy = get_hypotheses(1, 1, sess, y_hat, model.idx2token)
logging.info("# write results")
logging.info(hypotheses)
logging.info("# Done")
return "".join(hypotheses)
def test(hp):
# Loading hyper params
load_hparams(hp, hp.ckpt)
logging.info("# Prepare test batches")
test_batches, num_test_batches, num_test_samples = get_batch(
hp.test1,
hp.test1,
100000,
100000,
hp.vocab,
hp.test_batch_size,
shuffle=False)
iter = tf.data.Iterator.from_structure(test_batches.output_types,
test_batches.output_shapes)
xs, ys = iter.get_next()
test_init_op = iter.make_initializer(test_batches)
logging.info("# Load model")
model = Transformer(hp)
logging.info("# Session")
with tf.Session() as sess:
ckpt_ = tf.train.latest_checkpoint(hp.ckpt)
ckpt = ckpt_ if ckpt_ else hp.ckpt
saver = tf.train.Saver()
saver.restore(sess, ckpt)
y_hat, mean_loss = model.eval(sess, test_init_op, xs, ys,
num_test_batches)
logging.info("# get hypotheses")
hypotheses = get_hypotheses(num_test_samples, y_hat, model.idx2token)
logging.info("# write results")
model_output = os.path.split(ckpt)[-1]
if not os.path.exists(hp.testdir):
os.makedirs(hp.testdir)
translation = os.path.join(hp.testdir, model_output)
with open(translation, 'w', encoding="utf-8") as fout:
fout.write("\n".join(hypotheses))
logging.info("# calc bleu score and append it to translation")
calc_bleu_nltk(hp.test2, translation)
sess.run(train_init_op)
for i in tqdm(range(_gs, total_steps+1)):
_, _gs, _summary = sess.run([train_op, global_step, train_summaries])
epoch = _gs // train_num_batches
summary_writer.add_summary(_summary, _gs)
if _gs and _gs % train_num_batches == 0:
logging.info("# Epoch {} is done".format(epoch))
_loss = sess.run(loss)
logging.info("# Test evaluation")
_, _eval_summary = sess.run([eval_init_op, eval_summaries])
summary_writer.add_summary(_eval_summary, epoch)
logging.info("# Get hypotheses")
hypotheses = get_hypotheses(num_eval_batches, num_eval_samples, sess, y_hat, m.idx2token)
logging.info("# Write results")
model_output = "iwslt2016_E%02dL%.2f" % (epoch, _loss)
if not os.path.exsits(hp.evaldir): os.makedirs(hp.evaldir)
translation = os.path.join(hp.evaldir, model_output)
with open(translation, "w") as f:
f.write("\n".join(hypotheses))
logging.info("# Calc bleu score and append it to translation")
calc_bleu(hp.eval3, translation)
logging.info("# Save model")
ckpt_name = os.ptah.join(hp.logdir, model_output)
saver.save(sess, ckpt_name, global_step=_gs)
logging.info("After training of {} epochs, {} has been saved.".format(epoch, ckpt_name))
def calculate_average(dict_accumulator, number_of_iterations):
for key in dict_accumulator:
dict_accumulator[key] = dict_accumulator[key] / number_of_iterations
return dict_accumulator
#enddef
if __name__ == "__main__":
final_dict_accumulator = {}
number_of_iterations = 10
exp_iter = 0
#input all hypotheses
hypotheses = utils.get_hypotheses(hypotheses_file)
#get all examples
examples = utils.input_examples(ds)
# initial distribution
Q_0 = np.ones(len(hypotheses)) / len(hypotheses)
for iteration in range(0, number_of_iterations):
#Define deltas
deltas_for_noise_feature = np.arange(0, 0.201, 0.02)
teacher_type = "noise_feature" #"noise_feature" #limited_ground_truth
accumulator = {}
for delta in deltas_for_noise_feature:
delta = np.round(delta, 2)
teachers_examples = utils.get_teachers_examples(
logging.info("# Load model")
m = Transformer(hp)
y_hat, _, refs = m.eval(xs, ys)
logging.info("# Session")
with tf.Session() as sess:
ckpt = tf.train.latest_checkpoint(hp.modeldir)
saver = tf.train.Saver()
saver.restore(sess, ckpt)
sess.run(test_init_op)
logging.info("# get hypotheses")
hypotheses, refs_result = get_hypotheses(num_test_batches,
num_test_samples, sess, y_hat,
refs, m.idx2token)
# 将原始的结果写到本地
logging.info("write references")
result_output = "refs"
if not os.path.exists(hp.test_result): os.makedirs(hp.test_result)
ref_path = os.path.join(hp.test_result, result_output)
with open(ref_path, 'w', encoding='utf-8') as fout:
_refs = []
for r in refs_result:
words = r.decode('utf-8').split()
s = [word.replace("▁", " ")
for word in words] # remove bpe symbols
sent = ''.join(s)
_refs.append(sent.strip())
from utils import get_hypotheses
from collections import Counter
import nltk
from nltk.tag import pos_tag, map_tag
from nltk.corpus import wordnet as wn
import numpy, pickle
HYP_FILE = "./data/train-test.hyp1-hyp2-ref"
GOLD_FILE = "./data/train.gold"
OUTPUT_FILE = "./output-exp.pred"
TRADEOFF_PARAM = 0.35# between 0 and 1
[labeled_instances, unlabeled_instances] = get_hypotheses(HYP_FILE, GOLD_FILE)
def memoize(func):
memodict = {}
def inner(stuff):
if stuff not in memodict:
memodict[stuff] = func(stuff)
return memodict[stuff]
return inner
# nltk can't pos tag unicode
def preprocess(text):
return ''.join(character for character in text if ord(character)<128)
wntags = {'NOUN':'n','VERB':'v','ADJ':'a','ADV':'r'}
@memoize
def get_word_count_dict(sentence):
wcount_dict = Counter()
xs_output, ys_output, handle_output, _, _gs, _summary = sess.run(
[xs, ys, handle, train_op, global_step, train_summaries],
feed_dict={handle: training_handle})
summary_writer.add_summary(_summary, _gs)
if _gs % (hp.gpu_nums * 5000) == 0 and _gs != 0:
logging.info("steps {} is done".format(_gs))
logging.info("# test evaluation")
sess.run(val_iter.initializer) # initial val dataset
_eval_summaries = sess.run(eval_summaries,
feed_dict={handle: val_handle})
summary_writer.add_summary(_eval_summaries, _gs)
logging.info("# beam search")
hypotheses, all_targets = get_hypotheses(num_eval_batches,
num_eval_samples, sess, m,
bs, [xs[0], ys[2]],
handle, val_handle)
logging.info("# calc rouge score ")
if not os.path.exists(hp.evaldir): os.makedirs(hp.evaldir)
rouge_l = calc_rouge(rouge, all_targets, hypotheses, _gs,
hp.evaldir)
model_output = "trans_pointer%02dL%.2f" % (_gs, rouge_l)
logging.info('# write hypotheses')
with open(os.path.join(hp.evaldir, model_output),
'w',
encoding='utf-8') as f:
for target, hypothes in zip(all_targets, hypotheses):
f.write('{}-{} \n'.format(target, ' '.join(hypothes)))
logging.info("# Session")
saver = tf.train.Saver()
with tf.Session() as sess:
#ckpt_ = tf.train.latest_checkpoint(hp.ckpt)
#ckpt = hp.ckpt if ckpt_ is None else ckpt_ # None: ckpt is a file. otherwise dir.
ckpt = hp.ckpt
#saver = tf.train.import_meta_graph('/home/shuangzhao/transformer_1/mt_log/g213_2/NMT_E6_L2.444_lr7e-05_-73488.meta')
saver.restore(sess, ckpt)
sess.run(test_init_op)
logging.info("# get hypotheses")
t1 = time.time()
hypotheses = get_hypotheses(num_test_batches, num_test_samples, sess,
y_hat, m.en_idx2token)
t2 = time.time()
print("-" * 80)
print(f"Time for getting results: {t2 - t1}")
logging.info("# write results")
model_output = ckpt.split("/")[-1]
if not os.path.exists(hp.testdir): os.makedirs(hp.testdir)
translation = os.path.join(hp.testdir, model_output)
with open(translation, 'w') as fout:
fout.write("\n".join(hypotheses))
logging.info("# calc bleu score and append it to translation")
#calc_bleu(hp.test2, translation)
print(f"Time rest: {time.time() - t2}")
def train_(self, epochs):
train_batches, num_train_batches, num_train_samples = get_batch(
'../data/iwslt2016/segmented/train.de.bpe',
'../data/iwslt2016/segmented/train.en.bpe',
self.sequence_length,
self.sequence_length,
self.vocab_file,
self.batch_size,
shuffle=True)
eval_batches, num_eval_batches, num_eval_samples = get_batch(
'../data/iwslt2016/segmented/eval.de.bpe',
'../data/iwslt2016/segmented/eval.en.bpe',
100000,
100000,
self.vocab_file,
self.batch_size,
shuffle=False)
iter = tf.data.Iterator.from_structure(train_batches.output_types,
train_batches.output_shapes)
xs, ys = iter.get_next()
train_init_op = iter.make_initializer(train_batches)
eval_init_op = iter.make_initializer(eval_batches)
loss, train_op, global_step, train_summaries = self.model.train(xs, ys)
y_hat, eval_summaries = self.model.eval(xs, ys)
logging.info("# Session")
with tf.Session() as sess:
ckpt = tf.train.latest_checkpoint(self.model_dir)
if ckpt is None:
logging.info("Initializing from scratch")
sess.run(tf.global_variables_initializer())
save_variable_specs(os.path.join('../data/log/1', "specs"))
else:
self.saver.restore(sess, ckpt)
summary_writer = tf.summary.FileWriter(self.model_dir, sess.graph)
sess.run(train_init_op)
total_steps = epochs * num_train_batches
_gs = sess.run(global_step)
for i in tqdm(range(_gs, total_steps + 1)):
_, _gs, _summary = sess.run(
[train_op, global_step, train_summaries])
epoch = math.ceil(_gs / num_train_batches)
summary_writer.add_summary(_summary, _gs)
if _gs and _gs % num_train_batches == 0:
logging.info("epoch {} is done".format(epoch))
_loss = sess.run(loss) # train loss
logging.info("# test evaluation")
_, _eval_summaries = sess.run(
[eval_init_op, eval_summaries])
summary_writer.add_summary(_eval_summaries, _gs)
logging.info("# get hypotheses")
hypotheses = get_hypotheses(num_eval_batches,
num_eval_samples, sess, y_hat,
self.model.index_char)
logging.info("# write results")
model_output = "iwslt2016_E%02dL%.2f" % (epoch, _loss)
if not os.path.exists('data/eval/1'):
os.makedirs('../data/eval/1')
translation = os.path.join('../data/eval/1', model_output)
with open(translation, 'w') as fout:
fout.write("\n".join(hypotheses))
logging.info(
"# calc bleu score and append it to translation")
calc_bleu('../data/iwslt2016/prepro/eval.en', translation)
logging.info("# save models")
self.saver.save(sess,
os.path.join(self.model_dir,
'transformer.dat'),
global_step=_gs)
sess.run(train_init_op)
summary_writer.close()
logging.info("Done")
def train(hp):
save_hparams(hp, hp.checkpoints_dir)
# Data generator
logging.info("Prepare Train/Eval batches...")
train_batches, num_train_batches, num_train_samples = get_batch(
hp.train1,
hp.train2,
hp.maxlen1,
hp.maxlen2,
hp.vocab,
hp.batch_size,
shuffle=True)
eval_batches, num_eval_batches, num_eval_samples = get_batch(hp.eval1,
hp.eval2,
10000,
10000,
hp.vocab,
hp.batch_size,
shuffle=False)
# Batch iterator
iter = tf.data.Iterator.from_structure(train_batches.output_types,
train_batches.output_shapes)
xs, ys = iter.get_next()
train_init_op = iter.make_initializer(train_batches)
eval_init_op = iter.make_initializer(eval_batches)
# Build model
logging.info("Build model...")
model = Transformer(hp)
logging.info("Model is built!")
# Session
logging.info("Session initialize")
saver = tf.train.Saver(max_to_keep=5)
with tf.Session() as sess:
# Check & Load latest version model checkpoint
ckpt = tf.train.latest_checkpoint(hp.checkpoints_dir)
if ckpt is None:
logging.info("Initializing from scratch")
sess.run(tf.global_variables_initializer())
save_variable_specs(os.path.join(hp.checkpoints_dir, "specs"))
else:
saver.restore(sess, ckpt)
summary_writer = tf.summary.FileWriter(hp.checkpoints_dir, sess.graph)
sess.run(train_init_op)
total_steps = hp.num_epochs * num_train_batches
_gs = sess.run(model.global_step)
k = 5
min_dev_loss = 0
stop_alpha = 20.0
eval_losses = []
# Start training
for i in tqdm(range(_gs, total_steps + 1)):
_input_x, _decoder_input, _target = sess.run([xs[0], ys[0], ys[1]])
_, _gs, _summary = sess.run(
[model.train_op, model.global_step, model.summaries],
feed_dict={
model.input_x: _input_x,
model.decoder_input: _decoder_input,
model.target: _target,
model.is_training: True
})
epoch = math.ceil(_gs / num_train_batches)
summary_writer.add_summary(_summary, _gs)
# Evaluation
if _gs and _gs % num_train_batches == 0:
logging.info("Epoch {} is done".format(epoch))
_loss = sess.run(model.loss,
feed_dict={
model.input_x: _input_x,
model.decoder_input: _decoder_input,
model.target: _target,
model.is_training: False
})
# evaluation
y_hat, mean_loss = model.eval(sess, eval_init_op, xs, ys,
num_eval_batches)
# id to token
logging.info("# Get hypotheses")
hypotheses = get_hypotheses(num_eval_samples, y_hat,
model.idx2token)
# save translation results
if not os.path.exists(hp.evaldir):
os.makedirs(hp.evaldir)
logging.info("# Write results")
model_output = "translation_E{:02d}L{:.2f}EL{:.2f}".format(
epoch, _loss, mean_loss)
translation = os.path.join(hp.evaldir, model_output)
with open(translation, 'w', encoding="utf-8") as fout:
fout.write("\n".join(hypotheses))
logging.info(
"# Calculate bleu score and append it to translation")
# bleu
calc_bleu_nltk(hp.eval2, translation)
# save model
logging.info("# Save models")
ckpt_name = os.path.join(hp.checkpoints_dir, model_output)
saver.save(sess, ckpt_name, global_step=_gs)
logging.info(
"After training of {} epochs, {} has been saved.".format(
epoch, ckpt_name))
# claculate early stop
if len(eval_losses) == 0:
min_dev_loss = mean_loss
eval_losses.append(mean_loss)
gl, p_k, pq_alpha = calculate_earlystop_baseline(
mean_loss, min_dev_loss, eval_losses, k)
min_dev_loss = mean_loss if mean_loss < min_dev_loss else min_dev_loss
eval_losses = eval_losses[-k:]
logging.info(
"GL(t): {:.4f}, P_k: {:.4f}, PQ_alpha: {:.4f}".format(
gl, p_k, pq_alpha))
if gl > stop_alpha:
logging.info(
"No optimization for a long time, auto-stopping...")
break
# change data iterator back to train iterator
sess.run(train_init_op)
summary_writer.close()
logging.info("Done")
