def run(cls, command=None):
cls.configure()
command, command_args, common_args \
= cls._parse_args(command)
try:
def handler(signum, frame):
raise SystemExit("Signal(%d) received: "
"The program %s will be closed"
% (signum, __file__))
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
os.umask(0)
self = cls._get_instance()
self._initialize(command, command_args, common_args)
self._preprocess()
self._process()
self._postprocess()
self._finalize()
except Exception:
logging.e("Exception occurred during execution:",
exc_info=True, stack_info=cls.debug)
except SystemExit as e:
logging.w(e)
finally:
logging.getLogger().finalize()
def test(model_file, test_file, device=-1):
context = utils.Saver.load_context(model_file)
if context.seed is not None:
utils.set_random_seed(context.seed, device)
test_dataset = context.loader.load(test_file, train=False, bucketing=True)
kwargs = dict(context)
if context.model_config is not None:
kwargs.update(context.model_config)
model = _build_parser(**dict(kwargs))
chainer.serializers.load_npz(model_file, model)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
pbar = training.listeners.ProgressBar(lambda n: tqdm(total=n))
pbar.init(len(test_dataset))
evaluator = Evaluator(model, context.loader.rel_map, test_file,
logging.getLogger())
utils.chainer_train_off()
for batch in test_dataset.batch(context.batch_size,
colwise=True,
shuffle=False):
xs, ts = batch[:-1], batch[-1]
ys = model.forward(*xs)
evaluator.on_batch_end({'train': False, 'xs': xs, 'ys': ys, 'ts': ts})
pbar.update(len(ts))
evaluator.on_epoch_validate_end({})
def test(model_file, test_file, device=-1):
context = utils.Saver.load_context(model_file)
if context.seed is not None:
utils.set_random_seed(context.seed, device)
test_dataset = context.loader.load(test_file, train=False, bucketing=True)
model = _build_parser(**dict(context))
chainer.serializers.load_npz(model_file, model)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
pbar = training.listeners.ProgressBar(lambda n: tqdm(total=n))
pbar.init(len(test_dataset))
evaluator = Evaluator(model, context.loader.rel_map, test_file,
Log.getLogger())
utils.chainer_train_off()
for batch in test_dataset.batch(context.batch_size,
colwise=True,
shuffle=False):
xs, ts = batch[:-1], batch[-1]
parsed = model.parse(*xs)
evaluator.append([tokens[1:] for tokens in xs[-1]], parsed)
pbar.update(len(ts))
evaluator.report(show_details=False)
def test(model_file, test_file, filter_type=True, limit=-1, device=-1):
context = utils.Saver.load_context(model_file)
logger = logging.getLogger()
logger.trace('# context: {}'.format(context))
if context.seed is not None:
utils.set_random_seed(context.seed, device)
loader = context.builder.loader
loader.filter_coord = filter_type
encoder_input = context.encoder_input
cont_embed_file_ext = _get_cont_embed_file_ext(encoder_input)
use_cont_embed = cont_embed_file_ext is not None
test_dataset = loader.load_with_external_resources(
test_file, train=False, bucketing=False,
size=None if limit < 0 else limit,
use_external_postags=True,
use_contextualized_embed=use_cont_embed,
contextualized_embed_file_ext=cont_embed_file_ext,
logger=logger)
logger.info('{} samples loaded for test'.format(len(test_dataset)))
model = context.builder.build()
chainer.serializers.load_npz(model_file, model)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
parser = parsers.build_parser(loader, model)
evaluator = eval_module.Evaluator(
parser, logger=logger, report_details=True)
reporter = training.listeners.Reporter(logger)
logger.info('Start decoding')
utils.chainer_train_off()
evaluator.on_epoch_validate_begin({'epoch': -1})
pbar = tqdm(total=len(test_dataset))
for batch in test_dataset.batch(
context.batch_size, colwise=True, shuffle=False):
xs, ts = batch[:-1], batch[-1]
ys = model.forward(*xs)
loss = model.compute_loss(ys, ts)
with reporter:
values = dict(loss=float(chainer.cuda.to_cpu(loss.data)))
model.compute_accuracy(ys, ts)
for k, v in model.result.items():
if 'loss' in k:
values[k] = float(chainer.cuda.to_cpu(v.data))
elif 'accuracy' in k:
values[k] = v
reporter.report(values)
evaluator.on_batch_end({'train': False, 'xs': xs, 'ts': ts})
pbar.update(len(ts))
pbar.close()
reporter._output_log("testing", reporter.get_summary(),
{'epoch': -1, 'size': len(test_dataset)})
evaluator.on_epoch_validate_end({'epoch': -1})
def check_grammar(test_file, limit=-1, grammar_type=1):
logger = logging.getLogger()
loader = dataset.DataLoader(filter_coord=True)
test_dataset = loader.load(test_file, train=True, bucketing=False,
size=None if limit < 0 else limit)
word_vocab = loader.get_processor('word').vocab
from models.gold import GoldModel
model = GoldModel()
if grammar_type == 1:
cfg = parsers.Grammar.CFG_COORD_1 + parsers.Grammar.CFG
elif grammar_type == 2:
cfg = parsers.Grammar.CFG_COORD_2 + parsers.Grammar.CFG
else:
raise ValueError("Invalid grammar type: {}".format(grammar_type))
grammar = parsers.Grammar(word_vocab, cfg)
parser = parsers.CkyParser(model, grammar)
evaluator = eval_module.Evaluator(
parser, logger=logger, report_details=False)
n_corrects = 0
pbar = tqdm(total=len(test_dataset))
for batch in test_dataset.batch(size=20, colwise=True, shuffle=False):
xs, ts = batch[:-1], batch[-1]
true_coords_batch = ts
model.set_gold(true_coords_batch)
pred_coords_batch = evaluator._parser.parse(*xs, n_best=1)
for i, (pred_coord_entries, true_coords) in \
enumerate(zip(pred_coords_batch, true_coords_batch)):
pred_coords, _score = pred_coord_entries[0]
true_coords = {ckey: coord for ckey, coord
in true_coords.items() if coord is not None}
for k, v in tuple(pred_coords.items()):
if v is None:
del pred_coords[k]
if pred_coords == true_coords:
n_corrects += 1
else:
sentence = ' '.join(
[word_vocab.lookup(word_id) for word_id in xs[0][i]])
print("SENTENCE: {}\nPRED: {}\nTRUE: {}\n-"
.format(sentence, pred_coords, true_coords))
evaluator.add(pred_coords, true_coords)
pbar.update(len(ts))
pbar.close()
evaluator.report()
logger.info("Number of correct tree: {}/{}"
.format(n_corrects, len(test_dataset)))
def fit(self, data, valid_data=None, epochs=10, batch_size=32,
valid_batch_size=None):
if isinstance(data, Dataset):
train_dataset = data
elif isinstance(data, (tuple, list)) and len(data) == 2:
train_dataset = Dataset(*data)
else:
raise ValueError('invalid data: {}'.format(type(data)))
if valid_data:
do_validation = True
if isinstance(valid_data, Dataset):
val_dataset = valid_data
elif isinstance(valid_data, (tuple, list)) \
and len(valid_data) == 2:
val_dataset = Dataset(*valid_data)
else:
raise ValueError('When passing valid_data, '
'it must be dataset or contain '
'two (x_val, y_val) items: {}'
.format(type(valid_data)))
if valid_batch_size is None:
valid_batch_size = batch_size
else:
do_validation = False
self._reporter = listeners.Reporter(logging.getLogger())
self.add_listener(self._reporter, priority=110)
if self._acc_func is not None:
def _report_accuracy(data):
listeners.report(
{"accuracy": self._acc_func(data['ys'], data['ts'])})
self.add_hook(TrainEvent.BATCH_END, _report_accuracy, priority=120)
forward = self._forward
if not callable(forward):
if hasattr(self._forward, 'forward'):
forward = self._forward.forward
else:
raise RuntimeError('`forward` is not callable')
lossfun = self._loss_func
convert = (self._converter if callable(self._converter)
else lambda x: x)
history = []
self.notify(TrainEvent.TRAIN_BEGIN)
def main_loop():
for epoch in range(1, epochs + 1):
epoch_logs = PseudoImmutableMap(
epoch=epoch,
size=train_dataset.size,
)
self.notify(TrainEvent.EPOCH_BEGIN, epoch_logs)
self._process(forward, train_dataset, lossfun, convert,
batch_size, epoch_logs.copy(), train=True)
if do_validation:
self._process(
forward, val_dataset, lossfun, convert,
valid_batch_size, epoch_logs.copy(), train=False)
self.notify(TrainEvent.EPOCH_END, epoch_logs)
if self._reporter is not None:
with self._reporter:
main_loop()
else:
main_loop()
self.notify(TrainEvent.TRAIN_END)
return history
def train(train_file,
test_file=None,
format='tree',
embed_file=None,
n_epoch=20,
batch_size=20,
lr=0.001,
limit=-1,
l2_lambda=0.0,
grad_clip=5.0,
encoder_input=('char', 'postag'),
model_config=None,
device=-1,
save_dir=None,
seed=None,
cache_dir='',
refresh_cache=False,
bert_model=0,
bert_dir=''):
if seed is not None:
utils.set_random_seed(seed, device)
logger = logging.getLogger()
# logger.configure(filename='log.txt', logdir=save_dir)
assert isinstance(logger, logging.AppLogger)
if model_config is None:
model_config = {}
model_config['bert_model'] = bert_model
model_config['bert_dir'] = bert_dir
os.makedirs(save_dir, exist_ok=True)
read_genia = format == 'genia'
loader = dataset.DataLoader.build(
postag_embed_size=model_config.get('postag_embed_size', 50),
char_embed_size=model_config.get('char_embed_size', 10),
word_embed_file=embed_file,
filter_coord=(not read_genia),
refresh_cache=refresh_cache,
format=format,
cache_options=dict(dir=cache_dir, mkdir=True, logger=logger),
extra_ids=(git.hash(), ))
use_external_postags = not read_genia
cont_embed_file_ext = _get_cont_embed_file_ext(encoder_input)
use_cont_embed = cont_embed_file_ext is not None
train_dataset = loader.load_with_external_resources(
train_file,
train=True,
bucketing=False,
size=None if limit < 0 else limit,
refresh_cache=refresh_cache,
use_external_postags=use_external_postags,
use_contextualized_embed=use_cont_embed,
contextualized_embed_file_ext=cont_embed_file_ext)
logging.info('{} samples loaded for training'.format(len(train_dataset)))
test_dataset = None
if test_file is not None:
test_dataset = loader.load_with_external_resources(
test_file,
train=False,
bucketing=False,
size=None if limit < 0 else limit // 10,
refresh_cache=refresh_cache,
use_external_postags=use_external_postags,
use_contextualized_embed=use_cont_embed,
contextualized_embed_file_ext=cont_embed_file_ext)
logging.info('{} samples loaded for validation'.format(
len(test_dataset)))
builder = models.CoordSolverBuilder(loader,
inputs=encoder_input,
**model_config)
logger.info("{}".format(builder))
model = builder.build()
logger.trace("Model: {}".format(model))
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
if bert_model == 1:
optimizer = chainer.optimizers.AdamW(alpha=lr)
optimizer.setup(model)
# optimizer.add_hook(chainer.optimizer.GradientClipping(1.))
else:
optimizer = chainer.optimizers.AdamW(alpha=lr,
beta1=0.9,
beta2=0.999,
eps=1e-08)
optimizer.setup(model)
if l2_lambda > 0.0:
optimizer.add_hook(chainer.optimizer.WeightDecay(l2_lambda))
if grad_clip > 0.0:
optimizer.add_hook(chainer.optimizer.GradientClipping(grad_clip))
def _report(y, t):
values = {}
model.compute_accuracy(y, t)
for k, v in model.result.items():
if 'loss' in k:
values[k] = float(chainer.cuda.to_cpu(v.data))
elif 'accuracy' in k:
values[k] = v
training.report(values)
trainer = training.Trainer(optimizer, model, loss_func=model.compute_loss)
trainer.configure(utils.training_config)
trainer.add_listener(
training.listeners.ProgressBar(lambda n: tqdm(total=n)), priority=200)
trainer.add_hook(training.BATCH_END,
lambda data: _report(data['ys'], data['ts']))
if test_dataset:
parser = parsers.build_parser(loader, model)
evaluator = eval_module.Evaluator(parser,
logger=logging,
report_details=False)
trainer.add_listener(evaluator)
if bert_model == 2:
num_train_steps = 20000 * 5 / 20
num_warmup_steps = 10000 / 20
learning_rate = 2e-5
# learning rate (eta) scheduling in Adam
lr_decay_init = learning_rate * \
(num_train_steps - num_warmup_steps) / num_train_steps
trainer.add_hook(
training.BATCH_END,
extensions.LinearShift( # decay
'eta', (lr_decay_init, 0.),
(num_warmup_steps, num_train_steps),
optimizer=optimizer))
trainer.add_hook(
training.BATCH_END,
extensions.WarmupShift( # warmup
'eta',
0.,
num_warmup_steps,
learning_rate,
optimizer=optimizer))
if save_dir is not None:
accessid = logging.getLogger().accessid
date = logging.getLogger().accesstime.strftime('%Y%m%d')
# metric = 'whole' if isinstance(model, models.Teranishi17) else 'inner'
metric = 'exact'
trainer.add_listener(
utils.Saver(
model,
basename="{}-{}".format(date, accessid),
context=dict(App.context, builder=builder),
directory=save_dir,
logger=logger,
save_best=True,
evaluate=(lambda _: evaluator.get_overall_score(metric))))
trainer.fit(train_dataset, test_dataset, n_epoch, batch_size)
def parse(model_file, target_file, contextualized_embed_file=None,
n_best=1, device=-1):
context = utils.Saver.load_context(model_file)
logger = logging.getLogger()
logger.trace('# context: {}'.format(context))
if context.seed is not None:
utils.set_random_seed(context.seed, device)
loader = context.builder.loader
encoder_input = context.encoder_input
use_cont_embed = _get_cont_embed_file_ext(encoder_input) is not None
if use_cont_embed and contextualized_embed_file is None:
raise ValueError(
"contextualized_embed_file must be specified when using "
"a model trained with contextualized embeddings")
elif not use_cont_embed and contextualized_embed_file is not None:
raise ValueError(
"contextualized_embed_file must not be specified when using "
"a model trained without contextualized embeddings")
if target_file.endswith('.txt'):
loader.init_reader(format='default')
loader.set_contextualized_embed_file(contextualized_embed_file)
target_dataset = loader.load_with_external_resources(
target_file, mode='parse', use_external_postags=True, logger=logger)
logger.info('{} samples loaded for parsing'.format(len(target_dataset)))
model = context.builder.build()
chainer.serializers.load_npz(model_file, model)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
parser = parsers.build_parser(loader, model)
logger.info('Start parsing')
utils.chainer_train_off()
pbar = tqdm(total=len(target_dataset))
for batch in target_dataset.batch(
context.batch_size, colwise=True, shuffle=False):
xs, (words, indices, sentence_id) = batch[:-3], batch[-3:]
parsed = parser.parse(*xs, n_best)
for results, words_i, indices_i, sentence_id_i \
in zip(parsed, words, indices, sentence_id):
raw_sentence = ' '.join(words_i)
for best_k, (coords, score) in enumerate(results):
output = [
"#!RAW: {}".format(raw_sentence),
"SENTENCE: {}".format(sentence_id_i),
"CANDIDATE: #{}".format(best_k),
"SCORE: {}".format(score),
]
if indices_i is not None:
coords = dataset.postprocess(coords, indices_i)
for cc, coord in sorted(coords.items()):
output.append("CC: {} {}".format(cc, words_i[cc]))
if coord is not None:
b, e = coord.conjuncts[0][0], coord.conjuncts[-1][1]
output.append("COORD: {} {} {}".format(
b, e, ' '.join(words_i[b:e + 1])))
for (b, e) in coord.conjuncts:
output.append("CONJ: {} {} {}".format(
b, e, ' '.join(words_i[b:e + 1])))
else:
output.append("COORD: None")
print('\n'.join(output) + '\n')
pbar.update(len(sentence_id))
pbar.close()
def train(train_file,
test_file=None,
embed_file=None,
n_epoch=20,
batch_size=5000,
lr=2e-3,
model_config=None,
device=-1,
save_dir=None,
seed=None,
cache_dir='',
refresh_cache=False):
if seed is not None:
utils.set_random_seed(seed, device)
logger = logging.getLogger()
assert isinstance(logger, logging.AppLogger)
if model_config is None:
model_config = {}
loader = dataset.DataLoader.build(input_file=train_file,
word_embed_file=embed_file,
refresh_cache=refresh_cache,
extra_ids=(git.hash(), ),
cache_options=dict(dir=cache_dir,
mkdir=True,
logger=logger))
train_dataset = loader.load(train_file,
train=True,
bucketing=True,
refresh_cache=refresh_cache)
test_dataset = None
if test_file is not None:
test_dataset = loader.load(test_file,
train=False,
bucketing=True,
refresh_cache=refresh_cache)
model = _build_parser(loader, **model_config)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
optimizer = chainer.optimizers.Adam(alpha=lr,
beta1=0.9,
beta2=0.9,
eps=1e-12)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(5.0))
optimizer.add_hook(
optimizers.ExponentialDecayAnnealing(initial_lr=lr,
decay_rate=0.75,
decay_step=5000,
lr_key='alpha'))
def _report(y, t):
arc_accuracy, rel_accuracy = model.compute_accuracy(y, t)
training.report({
'arc_accuracy': arc_accuracy,
'rel_accuracy': rel_accuracy
})
trainer = training.Trainer(optimizer, model, loss_func=model.compute_loss)
trainer.configure(utils.training_config)
trainer.add_listener(
training.listeners.ProgressBar(lambda n: tqdm(total=n)), priority=200)
trainer.add_hook(training.BATCH_END,
lambda data: _report(data['ys'], data['ts']))
if test_dataset:
evaluator = Evaluator(model, loader.rel_map, test_file, logger)
trainer.add_listener(evaluator, priority=128)
if save_dir is not None:
accessid = logger.accessid
date = logger.accesstime.strftime('%Y%m%d')
trainer.add_listener(
utils.Saver(model,
basename="{}-{}".format(date, accessid),
context=dict(App.context, loader=loader),
directory=save_dir,
logger=logger,
save_best=True,
evaluate=(lambda _: evaluator._parsed['UAS'])))
trainer.fit(train_dataset, test_dataset, n_epoch, batch_size)
