def process(self,
corpora,
output_path,
test_data_path=None,
dev_data_path=None,
log=None):
if log is None:
log = osutils.DEVNULL
args = [
'-s', self._source_lang, '-t', self._target_lang, '--output',
output_path, '--input'
]
for root in set([corpus.get_folder() for corpus in corpora]):
args.append(root)
if dev_data_path is not None:
args.append('--dev')
args.append(dev_data_path)
if test_data_path is not None:
args.append('--test')
args.append(test_data_path)
command = mmt_javamain(self._java_main, args)
osutils.shell_exec(command, stdout=log, stderr=log)
return BilingualCorpus.list(self._source_lang, self._target_lang,
output_path)
def build(self, corpora, log=None):
if log is None:
log = osutils.DEVNULL
shutil.rmtree(self._model, ignore_errors=True)
osutils.makedirs(self._model, exist_ok=True)
source_path = set([corpus.get_folder() for corpus in corpora])
assert len(source_path) == 1
source_path = source_path.pop()
command = [
self._build_bin, '-s', self._source_lang, '-t', self._target_lang,
'-i', source_path, '-m', self._model, '-I', '4'
]
osutils.shell_exec(command, stdout=log, stderr=log)
def calculate(self, document, reference):
script = os.path.join(cli.PYOPT_DIR, 'charcut.py')
command = ['python', script, '-c', '/dev/stdin', '-r', reference]
with open(document) as input_stream:
stdout, _ = osutils.shell_exec(command, stdin=input_stream)
return 1.0 - float(stdout)
def calculate(self, document, reference):
script = os.path.join(cli.PYOPT_DIR, 'mmt-bleu.perl')
command = ['perl', script, reference]
with open(document) as input_stream:
stdout, _ = osutils.shell_exec(command, stdin=input_stream)
return float(stdout)
def train_model(self,
train_dir,
output_dir,
batch_size=1024,
n_train_steps=None,
n_eval_steps=1000,
hparams='transformer_base',
log=None,
fromModel=None):
if log is None:
log = osutils.DEVNULL
if not os.path.isdir(output_dir):
osutils.makedirs(output_dir)
# if an existing checkpoint is loaded for starting the training (i.e fromModel != None)
# copy the checkpoint files into the right location
if fromModel is not None:
self._copy_and_fix_model(fromModel, output_dir, gpus=self._gpus)
data_dir = os.path.join(train_dir, 'data')
src_model_vocab = os.path.join(data_dir, 'model.vcb')
tgt_model_vocab = os.path.join(output_dir, 'model.vcb')
if not os.path.isfile(tgt_model_vocab):
os.symlink(src_model_vocab, tgt_model_vocab)
env = self._get_env()
hparams_p = 'batch_size=%d' % batch_size
command = [
't2t-trainer', '--t2t_usr_dir', self._t2t_dir,
'--data_dir=%s' % data_dir, '--problem=translate_mmt',
'--model=transformer',
'--hparams_set=%s' % hparams,
'--output_dir=%s' % output_dir,
'--local_eval_frequency=%d' % n_eval_steps,
'--train_steps=%d' %
(n_train_steps if n_train_steps is not None else 100000000),
'--worker_gpu=%d' % len(self._gpus), '--hparams', hparams_p
]
process = osutils.shell_exec(command,
stdout=log,
stderr=log,
env=env,
background=True)
try:
return_code = process.wait()
if return_code != 0:
raise ShellError(' '.join(command), return_code, None)
except KeyboardInterrupt:
process.kill()
def process_file(self, input_path, output_path, lang):
if lang == self._source_lang:
args = ['-s', self._source_lang, '-t', self._target_lang]
elif lang == self._target_lang:
args = ['-s', self._target_lang, '-t', self._source_lang]
else:
raise ValueError('Unsupported language "%s"' % lang)
if not self._print_tags:
args.append('--no-tags')
if self._print_placeholders:
args.append('--print-placeholders')
command = mmt_javamain(self._java_main, args=args)
with open(input_path) as input_stream:
with open(output_path, 'w') as output_stream:
osutils.shell_exec(command,
stdin=input_stream,
stdout=output_stream)
def prepare_data(self,
train_corpora,
eval_corpora,
output_path,
log=None,
bpe_symbols=2**15,
fromModel=None):
if log is None:
log = osutils.DEVNULL
data_dir = os.path.join(output_path, 'data')
tmp_dir = os.path.join(output_path, 'tmp')
train_path = self._get_common_root(train_corpora)
eval_path = self._get_common_root(eval_corpora)
shutil.rmtree(data_dir, ignore_errors=True)
osutils.makedirs(data_dir)
# if an existing checkpoint is loaded for starting the training (i.e fromModel!=None)
# copy the subtoken vocabulary associated to the existing checkpoint into the right location,
# so that the subtoken vocabulary is not re-created from the new training data,
# and so that it is only exploited to bpe-fy the new data
# it assumes that the vocabulary is called "model.vcb" and is located in the same directory of the checkpoint
if fromModel is not None:
shutil.copyfile(os.path.join(fromModel, 'model.vcb'),
os.path.join(data_dir, 'model.vcb'))
if not os.path.isdir(tmp_dir):
osutils.makedirs(tmp_dir)
env = self._get_env(train_path, eval_path, bpe=bpe_symbols)
command = [
't2t-datagen', '--t2t_usr_dir', self._t2t_dir,
'--data_dir=%s' % data_dir,
'--tmp_dir=%s' % tmp_dir, '--problem=translate_mmt'
]
osutils.shell_exec(command, stdout=log, stderr=log, env=env)
def clean(self, corpora, output_path, log=None):
if log is None:
log = osutils.DEVNULL
args = [
'-s', self._source_lang, '-t', self._target_lang, '--output',
output_path, '--input'
]
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
extended_heap_mb = int(osutils.mem_size() * 90 / 100)
command = mmt_javamain(self._java_main,
args=args,
max_heap_mb=extended_heap_mb)
osutils.shell_exec(command, stdout=log, stderr=log)
return BilingualCorpus.list(self._source_lang, self._target_lang,
output_path)
