msb-public
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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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save best and test on tiny/s0

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pull/680/head
Haoxin Ma 4 years ago
parent 6487ca6022
commit 68bcc46940
3 changed files with 230 additions and 125 deletions
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14
deepspeech/training/trainer.py
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@ -18,7 +18,7 @@ import paddle
from paddle import distributed as dist
from tensorboardX import SummaryWriter
from deepspeech.utils import checkpoint
from deepspeech.utils.checkpoint import KBestCheckpoint
from deepspeech.utils import mp_tools
from deepspeech.utils.log import Log
@ -139,9 +139,12 @@ class Trainer():
"epoch": self.epoch,
"lr": self.optimizer.get_lr()
})
checkpoint.save_parameters(self.checkpoint_dir, self.iteration
self.checkpoint.add_checkpoint(self.checkpoint_dir, self.iteration
if tag is None else tag, self.model,
self.optimizer, infos)
# checkpoint.save_parameters(self.checkpoint_dir, self.iteration
# if tag is None else tag, self.model,
# self.optimizer, infos)
def resume_or_scratch(self):
"""Resume from latest checkpoint at checkpoints in the output
@ -151,7 +154,7 @@ class Trainer():
resume training.
"""
scratch = None
infos = checkpoint.load_parameters(
infos = self.checkpoint.load_parameters(
self.model,
self.optimizer,
checkpoint_dir=self.checkpoint_dir,
@ -180,7 +183,7 @@ class Trainer():
from_scratch = self.resume_or_scratch()
if from_scratch:
# save init model, i.e. 0 epoch
self.save(tag='init')
self.save(tag='init', infos=None)
self.lr_scheduler.step(self.iteration)
if self.parallel:
@ -263,6 +266,9 @@ class Trainer():
self.checkpoint_dir = checkpoint_dir
self.checkpoint = KBestCheckpoint(max_size=self.config.training.max_epoch,
last_size=self.config.training.last_epoch)
@mp_tools.rank_zero_only
def destory(self):
"""Close visualizer to avoid hanging after training"""

336
deepspeech/utils/checkpoint.py
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@ -23,130 +23,226 @@ from paddle.optimizer import Optimizer
from deepspeech.utils import mp_tools
from deepspeech.utils.log import Log
import glob
logger = Log(__name__).getlog()
__all__ = ["load_parameters", "save_parameters"]
class KBestCheckpoint(object):
def __init__(self,
max_size: int=5,
last_size: int=1):
self.best_records: Mapping[Path, float] = {}
self.last_records = []
self.max_size = max_size
self.last_size = last_size
self._save_all = (max_size == -1)
def should_save_best(self, metric: float) -> bool:
if not self.best_full():
return True
# already full
worst_record_path = max(self.best_records, key=self.best_records.get)
worst_metric = self.best_records[worst_record_path]
return metric < worst_metric
def best_full(self):
return (not self._save_all) and len(self.best_records) == self.max_size
def last_full(self):
return len(self.last_records) == self.last_size
def add_checkpoint(self,
checkpoint_dir, tag_or_iteration,
model, optimizer, infos):
if("val_loss" not in infos.keys()):
self.save_parameters(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
return
#save best
if self.should_save_best(infos["val_loss"]):
self.save_checkpoint_and_update(infos["val_loss"],
checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
#save last
self.save_last_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
if isinstance(tag_or_iteration, int):
self._save_record(checkpoint_dir, tag_or_iteration)
def save_checkpoint_and_update(self, metric,
checkpoint_dir, tag_or_iteration,
model, optimizer, infos):
# remove the worst
if self.best_full():
worst_record_path = max(self.best_records,
key=self.best_records.get)
self.best_records.pop(worst_record_path)
if(worst_record_path not in self.last_records):
print('----to remove (best)----')
print(worst_record_path)
self.del_checkpoint(checkpoint_dir, worst_record_path)
# add the new one
self.save_parameters(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
self.best_records[tag_or_iteration] = metric
def save_last_checkpoint_and_update(self, checkpoint_dir, tag_or_iteration,
model, optimizer, infos):
# remove the old
if self.last_full():
to_del_fn = self.last_records.pop(0)
if(to_del_fn not in self.best_records.keys()):
print('----to remove (last)----')
print(to_del_fn)
self.del_checkpoint(checkpoint_dir, to_del_fn)
self.last_records.append(tag_or_iteration)
self.save_parameters(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
# with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as handle:
# for iteration in self.best_records
# handle.write("model_checkpoint_path:{}\n".format(iteration))
def del_checkpoint(self, checkpoint_dir, tag_or_iteration):
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(tag_or_iteration))
for filename in glob.glob(checkpoint_path+".*"):
os.remove(filename)
print("delete file: "+filename)
def _load_latest_checkpoint(self, checkpoint_dir: str) -> int:
"""Get the iteration number corresponding to the latest saved checkpoint.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
Returns:
int: the latest iteration number. -1 for no checkpoint to load.
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint_last")
if not os.path.isfile(checkpoint_record):
return -1
# Fetch the latest checkpoint index.
with open(checkpoint_record, "rt") as handle:
latest_checkpoint = handle.readlines()[-1].strip()
iteration = int(latest_checkpoint.split(":")[-1])
return iteration
def _save_record(self, checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpoint record.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
Returns:
None
"""
checkpoint_record_last = os.path.join(checkpoint_dir, "checkpoint_last")
checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
# Update the latest checkpoint index.
# with open(checkpoint_record, "a+") as handle:
# handle.write("model_checkpoint_path:{}\n".format(iteration))
with open(checkpoint_record_best, "w") as handle:
for i in self.best_records.keys():
handle.write("model_checkpoint_path:{}\n".format(i))
with open(checkpoint_record_last, "w") as handle:
for i in self.last_records:
handle.write("model_checkpoint_path:{}\n".format(i))
def load_parameters(self, model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a specific model checkpoint from disk.
Args:
model (Layer): model to load parameters.
optimizer (Optimizer, optional): optimizer to load states if needed.
Defaults to None.
checkpoint_dir (str, optional): the directory where checkpoint is saved.
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
Returns:
configs (dict): epoch or step, lr and other meta info should be saved.
"""
configs = {}
if checkpoint_path is not None:
tag = os.path.basename(checkpoint_path).split(":")[-1]
elif checkpoint_dir is not None:
iteration = self._load_latest_checkpoint(checkpoint_dir)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
logger.info("Rank {}: loaded model from {}".format(rank, params_path))
def _load_latest_checkpoint(checkpoint_dir: str) -> int:
"""Get the iteration number corresponding to the latest saved checkpoint.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
Returns:
int: the latest iteration number. -1 for no checkpoint to load.
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
if not os.path.isfile(checkpoint_record):
return -1
# Fetch the latest checkpoint index.
with open(checkpoint_record, "rt") as handle:
latest_checkpoint = handle.readlines()[-1].strip()
iteration = int(latest_checkpoint.split(":")[-1])
return iteration
def _save_record(checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpoint record.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
Returns:
None
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Update the latest checkpoint index.
with open(checkpoint_record, "a+") as handle:
handle.write("model_checkpoint_path:{}\n".format(iteration))
def load_parameters(model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a specific model checkpoint from disk.
Args:
model (Layer): model to load parameters.
optimizer (Optimizer, optional): optimizer to load states if needed.
Defaults to None.
checkpoint_dir (str, optional): the directory where checkpoint is saved.
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
Returns:
configs (dict): epoch or step, lr and other meta info should be saved.
"""
configs = {}
if checkpoint_path is not None:
tag = os.path.basename(checkpoint_path).split(":")[-1]
elif checkpoint_dir is not None:
iteration = _load_latest_checkpoint(checkpoint_dir)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
logger.info("Rank {}: loaded model from {}".format(rank, params_path))
optimizer_path = checkpoint_path + ".pdopt"
if optimizer and os.path.isfile(optimizer_path):
optimizer_dict = paddle.load(optimizer_path)
optimizer.set_state_dict(optimizer_dict)
logger.info("Rank {}: loaded optimizer state from {}".format(
rank, optimizer_path))
info_path = re.sub('.pdparams$', '.json', params_path)
if os.path.exists(info_path):
with open(info_path, 'r') as fin:
configs = json.load(fin)
return configs
@mp_tools.rank_zero_only
def save_parameters(checkpoint_dir: str,
tag_or_iteration: Union[int, str],
model: paddle.nn.Layer,
optimizer: Optimizer=None,
infos: dict=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
tag_or_iteration (int or str): the latest iteration(step or epoch) number.
model (Layer): model to be checkpointed.
optimizer (Optimizer, optional): optimizer to be checkpointed.
Defaults to None.
infos (dict or None): any info you want to save.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(tag_or_iteration))
model_dict = model.state_dict()
params_path = checkpoint_path + ".pdparams"
paddle.save(model_dict, params_path)
logger.info("Saved model to {}".format(params_path))
if optimizer:
opt_dict = optimizer.state_dict()
optimizer_path = checkpoint_path + ".pdopt"
paddle.save(opt_dict, optimizer_path)
logger.info("Saved optimzier state to {}".format(optimizer_path))
info_path = re.sub('.pdparams$', '.json', params_path)
infos = {} if infos is None else infos
with open(info_path, 'w') as fout:
data = json.dumps(infos)
fout.write(data)
if optimizer and os.path.isfile(optimizer_path):
optimizer_dict = paddle.load(optimizer_path)
optimizer.set_state_dict(optimizer_dict)
logger.info("Rank {}: loaded optimizer state from {}".format(
rank, optimizer_path))
info_path = re.sub('.pdparams$', '.json', params_path)
if os.path.exists(info_path):
with open(info_path, 'r') as fin:
configs = json.load(fin)
return configs
@mp_tools.rank_zero_only
def save_parameters(self, checkpoint_dir: str,
tag_or_iteration: Union[int, str],
model: paddle.nn.Layer,
optimizer: Optimizer=None,
infos: dict=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
tag_or_iteration (int or str): the latest iteration(step or epoch) number.
model (Layer): model to be checkpointed.
optimizer (Optimizer, optional): optimizer to be checkpointed.
Defaults to None.
infos (dict or None): any info you want to save.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(tag_or_iteration))
model_dict = model.state_dict()
params_path = checkpoint_path + ".pdparams"
paddle.save(model_dict, params_path)
logger.info("Saved model to {}".format(params_path))
if optimizer:
opt_dict = optimizer.state_dict()
optimizer_path = checkpoint_path + ".pdopt"
paddle.save(opt_dict, optimizer_path)
logger.info("Saved optimzier state to {}".format(optimizer_path))
info_path = re.sub('.pdparams$', '.json', params_path)
infos = {} if infos is None else infos
with open(info_path, 'w') as fout:
data = json.dumps(infos)
fout.write(data)
if isinstance(tag_or_iteration, int):
_save_record(checkpoint_dir, tag_or_iteration)

5
examples/tiny/s0/conf/deepspeech2.yaml
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@ -43,12 +43,15 @@ model:
share_rnn_weights: True
training:
n_epoch: 24
n_epoch: 6
lr: 1e-5
lr_decay: 1.0
weight_decay: 1e-06
global_grad_clip: 5.0
log_interval: 1
max_epoch: 3
last_epoch: 2
decoding:
batch_size: 128

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