elif mode == "predict":
data_train, args = load_data(args)
is_batchnorm = args.config.getboolean('arch', 'is_batchnorm')
is_bucketing = args.config.getboolean('arch', 'is_bucketing')
# log current config
config_logger = ConfigLogger(log)
config_logger(args.config)
# load model
model_loaded, model_num_epoch = load_model(args, contexts, data_train)
# if mode is 'train', it trains the model
if mode == 'train':
if is_bucketing:
module = STTBucketingModule(
sym_gen=model_loaded,
default_bucket_key=data_train.default_bucket_key,
context=contexts)
else:
data_names = [x[0] for x in data_train.provide_data]
label_names = [x[0] for x in data_train.provide_label]
module = mx.mod.Module(model_loaded,
context=contexts,
data_names=data_names,
label_names=label_names)
do_training(args=args,
module=module,
data_train=data_train,
data_val=data_val)
# if mode is 'load', it loads model from the checkpoint and continues the training.
elif mode == 'load':
do_training(args=args,
def do_training(args, module, data_train, data_val, begin_epoch=0):
from distutils.dir_util import mkpath
from log_util import LogUtil
log = LogUtil().getlogger()
mkpath(os.path.dirname(get_checkpoint_path(args)))
#seq_len = args.config.get('arch', 'max_t_count')
batch_size = args.config.getint('common', 'batch_size')
save_checkpoint_every_n_epoch = args.config.getint(
'common', 'save_checkpoint_every_n_epoch')
save_checkpoint_every_n_batch = args.config.getint(
'common', 'save_checkpoint_every_n_batch')
enable_logging_train_metric = args.config.getboolean(
'train', 'enable_logging_train_metric')
enable_logging_validation_metric = args.config.getboolean(
'train', 'enable_logging_validation_metric')
contexts = parse_contexts(args)
num_gpu = len(contexts)
eval_metric = STTMetric(batch_size=batch_size,
num_gpu=num_gpu,
is_logging=enable_logging_validation_metric,
is_epoch_end=True)
# mxboard setting
loss_metric = STTMetric(batch_size=batch_size,
num_gpu=num_gpu,
is_logging=enable_logging_train_metric,
is_epoch_end=False)
optimizer = args.config.get('optimizer', 'optimizer')
learning_rate = args.config.getfloat('train', 'learning_rate')
learning_rate_annealing = args.config.getfloat('train',
'learning_rate_annealing')
mode = args.config.get('common', 'mode')
num_epoch = args.config.getint('train', 'num_epoch')
clip_gradient = args.config.getfloat('optimizer', 'clip_gradient')
weight_decay = args.config.getfloat('optimizer', 'weight_decay')
save_optimizer_states = args.config.getboolean('train',
'save_optimizer_states')
show_every = args.config.getint('train', 'show_every')
optimizer_params_dictionary = json.loads(
args.config.get('optimizer', 'optimizer_params_dictionary'))
kvstore_option = args.config.get('common', 'kvstore_option')
n_epoch = begin_epoch
is_bucketing = args.config.getboolean('arch', 'is_bucketing')
if clip_gradient == 0:
clip_gradient = None
if is_bucketing and mode == 'load':
model_file = args.config.get('common', 'model_file')
model_name = os.path.splitext(model_file)[0]
model_num_epoch = int(model_name[-4:])
model_path = 'checkpoints/' + str(model_name[:-5])
symbol, data_names, label_names = module(1600)
model = STTBucketingModule(
sym_gen=module,
default_bucket_key=data_train.default_bucket_key,
context=contexts)
data_train.reset()
model.bind(data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label,
for_training=True)
_, arg_params, aux_params = mx.model.load_checkpoint(
model_path, model_num_epoch)
model.set_params(arg_params, aux_params)
module = model
else:
module.bind(data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label,
for_training=True)
if begin_epoch == 0 and mode == 'train':
module.init_params(initializer=get_initializer(args))
lr_scheduler = SimpleLRScheduler(learning_rate=learning_rate)
def reset_optimizer(force_init=False):
optimizer_params = {
'lr_scheduler': lr_scheduler,
'clip_gradient': clip_gradient,
'wd': weight_decay
}
optimizer_params.update(optimizer_params_dictionary)
module.init_optimizer(kvstore=kvstore_option,
optimizer=optimizer,
optimizer_params=optimizer_params,
force_init=force_init)
if mode == "train":
reset_optimizer(force_init=True)
else:
reset_optimizer(force_init=False)
data_train.reset()
data_train.is_first_epoch = True
#mxboard setting
mxlog_dir = args.config.get('common', 'mxboard_log_dir')
summary_writer = SummaryWriter(mxlog_dir)
while True:
if n_epoch >= num_epoch:
break
loss_metric.reset()
log.info('---------train---------')
for nbatch, data_batch in enumerate(data_train):
module.forward_backward(data_batch)
module.update()
# mxboard setting
if (nbatch + 1) % show_every == 0:
module.update_metric(loss_metric, data_batch.label)
#summary_writer.add_scalar('loss batch', loss_metric.get_batch_loss(), nbatch)
if (nbatch + 1) % save_checkpoint_every_n_batch == 0:
log.info('Epoch[%d] Batch[%d] SAVE CHECKPOINT', n_epoch,
nbatch)
module.save_checkpoint(
prefix=get_checkpoint_path(args) + "n_epoch" +
str(n_epoch) + "n_batch",
epoch=(int(
(nbatch + 1) / save_checkpoint_every_n_batch) - 1),
save_optimizer_states=save_optimizer_states)
# commented for Libri_sample data set to see only train cer
log.info('---------validation---------')
data_val.reset()
eval_metric.reset()
for nbatch, data_batch in enumerate(data_val):
# when is_train = False it leads to high cer when batch_norm
module.forward(data_batch, is_train=True)
module.update_metric(eval_metric, data_batch.label)
# mxboard setting
val_cer, val_n_label, val_l_dist, _ = eval_metric.get_name_value()
log.info("Epoch[%d] val cer=%f (%d / %d)", n_epoch, val_cer,
int(val_n_label - val_l_dist), val_n_label)
curr_acc = val_cer
summary_writer.add_scalar('CER validation', val_cer, n_epoch)
assert curr_acc is not None, 'cannot find Acc_exclude_padding in eval metric'
data_train.reset()
data_train.is_first_epoch = False
# mxboard setting
train_cer, train_n_label, train_l_dist, train_ctc_loss = loss_metric.get_name_value(
)
summary_writer.add_scalar('loss epoch', train_ctc_loss, n_epoch)
summary_writer.add_scalar('CER train', train_cer, n_epoch)
# save checkpoints
if n_epoch % save_checkpoint_every_n_epoch == 0:
log.info('Epoch[%d] SAVE CHECKPOINT', n_epoch)
module.save_checkpoint(prefix=get_checkpoint_path(args),
epoch=n_epoch,
save_optimizer_states=save_optimizer_states)
n_epoch += 1
lr_scheduler.learning_rate = learning_rate / learning_rate_annealing
log.info('FINISH')
def do_training(args, module, data_train, data_val, begin_epoch=0):
from distutils.dir_util import mkpath
from log_util import LogUtil
log = LogUtil.getInstance().getlogger()
mkpath(os.path.dirname(get_checkpoint_path(args)))
#seq_len = args.config.get('arch', 'max_t_count')
batch_size = args.config.getint('common', 'batch_size')
save_checkpoint_every_n_epoch = args.config.getint('common', 'save_checkpoint_every_n_epoch')
save_checkpoint_every_n_batch = args.config.getint('common', 'save_checkpoint_every_n_batch')
enable_logging_train_metric = args.config.getboolean('train', 'enable_logging_train_metric')
enable_logging_validation_metric = args.config.getboolean('train', 'enable_logging_validation_metric')
contexts = parse_contexts(args)
num_gpu = len(contexts)
eval_metric = STTMetric(batch_size=batch_size, num_gpu=num_gpu, is_logging=enable_logging_validation_metric,is_epoch_end=True)
# mxboard setting
loss_metric = STTMetric(batch_size=batch_size, num_gpu=num_gpu, is_logging=enable_logging_train_metric,is_epoch_end=False)
optimizer = args.config.get('optimizer', 'optimizer')
learning_rate = args.config.getfloat('train', 'learning_rate')
learning_rate_annealing = args.config.getfloat('train', 'learning_rate_annealing')
mode = args.config.get('common', 'mode')
num_epoch = args.config.getint('train', 'num_epoch')
clip_gradient = args.config.getfloat('optimizer', 'clip_gradient')
weight_decay = args.config.getfloat('optimizer', 'weight_decay')
save_optimizer_states = args.config.getboolean('train', 'save_optimizer_states')
show_every = args.config.getint('train', 'show_every')
optimizer_params_dictionary = json.loads(args.config.get('optimizer', 'optimizer_params_dictionary'))
kvstore_option = args.config.get('common', 'kvstore_option')
n_epoch=begin_epoch
is_bucketing = args.config.getboolean('arch', 'is_bucketing')
if clip_gradient == 0:
clip_gradient = None
if is_bucketing and mode == 'load':
model_file = args.config.get('common', 'model_file')
model_name = os.path.splitext(model_file)[0]
model_num_epoch = int(model_name[-4:])
model_path = 'checkpoints/' + str(model_name[:-5])
symbol, data_names, label_names = module(1600)
model = STTBucketingModule(
sym_gen=module,
default_bucket_key=data_train.default_bucket_key,
context=contexts)
data_train.reset()
model.bind(data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label,
for_training=True)
_, arg_params, aux_params = mx.model.load_checkpoint(model_path, model_num_epoch)
model.set_params(arg_params, aux_params)
module = model
else:
module.bind(data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label,
for_training=True)
if begin_epoch == 0 and mode == 'train':
module.init_params(initializer=get_initializer(args))
lr_scheduler = SimpleLRScheduler(learning_rate=learning_rate)
def reset_optimizer(force_init=False):
optimizer_params = {'lr_scheduler': lr_scheduler,
'clip_gradient': clip_gradient,
'wd': weight_decay}
optimizer_params.update(optimizer_params_dictionary)
module.init_optimizer(kvstore=kvstore_option,
optimizer=optimizer,
optimizer_params=optimizer_params,
force_init=force_init)
if mode == "train":
reset_optimizer(force_init=True)
else:
reset_optimizer(force_init=False)
data_train.reset()
data_train.is_first_epoch = True
#mxboard setting
mxlog_dir = args.config.get('common', 'mxboard_log_dir')
summary_writer = SummaryWriter(mxlog_dir)
while True:
if n_epoch >= num_epoch:
break
loss_metric.reset()
log.info('---------train---------')
for nbatch, data_batch in enumerate(data_train):
module.forward_backward(data_batch)
module.update()
# mxboard setting
if (nbatch + 1) % show_every == 0:
module.update_metric(loss_metric, data_batch.label)
#summary_writer.add_scalar('loss batch', loss_metric.get_batch_loss(), nbatch)
if (nbatch+1) % save_checkpoint_every_n_batch == 0:
log.info('Epoch[%d] Batch[%d] SAVE CHECKPOINT', n_epoch, nbatch)
module.save_checkpoint(prefix=get_checkpoint_path(args)+"n_epoch"+str(n_epoch)+"n_batch", epoch=(int((nbatch+1)/save_checkpoint_every_n_batch)-1), save_optimizer_states=save_optimizer_states)
# commented for Libri_sample data set to see only train cer
log.info('---------validation---------')
data_val.reset()
eval_metric.reset()
for nbatch, data_batch in enumerate(data_val):
# when is_train = False it leads to high cer when batch_norm
module.forward(data_batch, is_train=True)
module.update_metric(eval_metric, data_batch.label)
# mxboard setting
val_cer, val_n_label, val_l_dist, _ = eval_metric.get_name_value()
log.info("Epoch[%d] val cer=%f (%d / %d)", n_epoch, val_cer, int(val_n_label - val_l_dist), val_n_label)
curr_acc = val_cer
summary_writer.add_scalar('CER validation', val_cer, n_epoch)
assert curr_acc is not None, 'cannot find Acc_exclude_padding in eval metric'
data_train.reset()
data_train.is_first_epoch = False
# mxboard setting
train_cer, train_n_label, train_l_dist, train_ctc_loss = loss_metric.get_name_value()
summary_writer.add_scalar('loss epoch', train_ctc_loss, n_epoch)
summary_writer.add_scalar('CER train', train_cer, n_epoch)
# save checkpoints
if n_epoch % save_checkpoint_every_n_epoch == 0:
log.info('Epoch[%d] SAVE CHECKPOINT', n_epoch)
module.save_checkpoint(prefix=get_checkpoint_path(args), epoch=n_epoch, save_optimizer_states=save_optimizer_states)
n_epoch += 1
lr_scheduler.learning_rate=learning_rate/learning_rate_annealing
log.info('FINISH')
do_training(args=args, module=model_loaded, data_train=data_train, data_val=data_val,
begin_epoch=model_num_epoch + 1)
# if mode is 'predict', it predict label from the input by the input model
elif mode == 'predict':
# predict through data
if is_bucketing:
max_t_count = args.config.getint('arch', 'max_t_count')
load_optimizer_states = args.config.getboolean('load', 'load_optimizer_states')
model_file = args.config.get('common', 'model_file')
model_name = os.path.splitext(model_file)[0]
model_num_epoch = int(model_name[-4:])
model_path = 'checkpoints/' + str(model_name[:-5])
model = STTBucketingModule(
sym_gen=model_loaded,
default_bucket_key=data_train.default_bucket_key,
context=contexts
)
model.bind(data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label,
for_training=True)
_, arg_params, aux_params = mx.model.load_checkpoint(model_path, model_num_epoch)
model.set_params(arg_params, aux_params)
model_loaded = model
else:
model_loaded.bind(for_training=False, data_shapes=data_train.provide_data,
label_shapes=data_train.provide_label)
max_t_count = args.config.getint('arch', 'max_t_count')
eval_metric = STTMetric(batch_size=batch_size, num_gpu=num_gpu)
if is_batchnorm:
def __init__(self):
if len(sys.argv) <= 1:
raise Exception('cfg file path must be provided. ' +
'ex)python main.py --configfile examplecfg.cfg')
self.args = parse_args(sys.argv[1])
# set parameters from cfg file
# give random seed
self.random_seed = self.args.config.getint('common', 'random_seed')
self.mx_random_seed = self.args.config.getint('common',
'mx_random_seed')
# random seed for shuffling data list
if self.random_seed != -1:
np.random.seed(self.random_seed)
# set mx.random.seed to give seed for parameter initialization
if self.mx_random_seed != -1:
mx.random.seed(self.mx_random_seed)
else:
mx.random.seed(hash(datetime.now()))
# set log file name
self.log_filename = self.args.config.get('common', 'log_filename')
self.log = LogUtil(filename=self.log_filename).getlogger()
# set parameters from data section(common)
self.mode = self.args.config.get('common', 'mode')
# get meta file where character to number conversions are defined
self.contexts = parse_contexts(self.args)
self.num_gpu = len(self.contexts)
self.batch_size = self.args.config.getint('common', 'batch_size')
# check the number of gpus is positive divisor of the batch size for data parallel
self.is_batchnorm = self.args.config.getboolean('arch', 'is_batchnorm')
self.is_bucketing = self.args.config.getboolean('arch', 'is_bucketing')
# log current config
self.config_logger = ConfigLogger(self.log)
self.config_logger(self.args.config)
default_bucket_key = 1600
self.args.config.set('arch', 'max_t_count', str(default_bucket_key))
self.args.config.set('arch', 'max_label_length', str(100))
self.labelUtil = LabelUtil()
is_bi_graphemes = self.args.config.getboolean('common',
'is_bi_graphemes')
load_labelutil(self.labelUtil, is_bi_graphemes, language="zh")
self.args.config.set('arch', 'n_classes',
str(self.labelUtil.get_count()))
self.max_t_count = self.args.config.getint('arch', 'max_t_count')
# self.load_optimizer_states = self.args.config.getboolean('load', 'load_optimizer_states')
# load model
self.model_loaded, self.model_num_epoch, self.model_path = load_model(
self.args)
self.model = STTBucketingModule(sym_gen=self.model_loaded,
default_bucket_key=default_bucket_key,
context=self.contexts)
from importlib import import_module
prepare_data_template = import_module(
self.args.config.get('arch', 'arch_file'))
init_states = prepare_data_template.prepare_data(self.args)
width = self.args.config.getint('data', 'width')
height = self.args.config.getint('data', 'height')
self.model.bind(data_shapes=[
('data', (self.batch_size, default_bucket_key, width * height))
] + init_states,
label_shapes=[
('label',
(self.batch_size,
self.args.config.getint('arch',
'max_label_length')))
],
for_training=True)
_, self.arg_params, self.aux_params = mx.model.load_checkpoint(
self.model_path, self.model_num_epoch)
self.model.set_params(self.arg_params,
self.aux_params,
allow_extra=True,
allow_missing=True)
try:
from swig_wrapper import Scorer
vocab_list = [
chars.encode("utf-8") for chars in self.labelUtil.byList
]
self.log.info("vacab_list len is %d" % len(vocab_list))
_ext_scorer = Scorer(0.26, 0.1,
self.args.config.get('common', 'kenlm'),
vocab_list)
lm_char_based = _ext_scorer.is_character_based()
lm_max_order = _ext_scorer.get_max_order()
lm_dict_size = _ext_scorer.get_dict_size()
self.log.info("language model: "
"is_character_based = %d," % lm_char_based +
" max_order = %d," % lm_max_order +
" dict_size = %d" % lm_dict_size)
self.scorer = _ext_scorer
# self.eval_metric = EvalSTTMetric(batch_size=self.batch_size, num_gpu=self.num_gpu, is_logging=True,
# scorer=_ext_scorer)
except ImportError:
import kenlm
km = kenlm.Model(self.args.config.get('common', 'kenlm'))
self.scorer = km.score