msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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fix bug

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pull/680/head
Haoxin Ma 4 years ago
parent 91e70a2857
commit 16210c0587
2 changed files with 63 additions and 60 deletions
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2
deepspeech/training/trainer.py
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@ -64,7 +64,7 @@ class Trainer():
The parsed command line arguments.
Examples
--------
>>> def p(config, args):
>>> def main_sp(config, args):
>>> exp = Trainer(config, args)
>>> exp.setup()
>>> exp.run()

121
deepspeech/utils/checkpoint.py
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@ -11,9 +11,11 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import json
import os
import re
from pathlib import Path
from typing import Union
import paddle
@ -22,25 +24,21 @@ from paddle.optimizer import Optimizer
from deepspeech.utils import mp_tools
from deepspeech.utils.log import Log
import glob
# import operator
from pathlib import Path
logger = Log(__name__).getlog()
__all__ = ["Checkpoint"]
class Checkpoint(object):
def __init__(self,
kbest_n: int=5,
latest_n: int=1):
def __init__(self, kbest_n: int=5, latest_n: int=1):
self.best_records: Mapping[Path, float] = {}
self.latest_records = []
self.kbest_n = kbest_n
self.latest_n = latest_n
self._save_all = (kbest_n == -1)
def should_save_best(self, metric: float) -> bool:
if not self.best_full():
return True
@ -53,68 +51,72 @@ class Checkpoint(object):
def best_full(self):
return (not self._save_all) and len(self.best_records) == self.kbest_n
def latest_full(self):
return len(self.latest_records) == self.latest_n
def add_checkpoint(self, checkpoint_dir, tag_or_iteration,
model, optimizer, infos, metric_type = "val_loss"):
if(metric_type not in infos.keys()):
self.save_parameters(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
def add_checkpoint(self,
checkpoint_dir,
tag_or_iteration,
model,
optimizer,
infos,
metric_type="val_loss"):
if (metric_type not in infos.keys()):
self.save_parameters(checkpoint_dir, tag_or_iteration, model,
optimizer, infos)
return
#save best
if self.should_save_best(infos[metric_type]):
self.save_best_checkpoint_and_update(infos[metric_type],
checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
self.save_best_checkpoint_and_update(
infos[metric_type], checkpoint_dir, tag_or_iteration, model,
optimizer, infos)
#save latest
self.save_latest_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
model, optimizer, infos)
if isinstance(tag_or_iteration, int):
self.save_checkpoint_record(checkpoint_dir, tag_or_iteration)
def save_best_checkpoint_and_update(self, metric,
checkpoint_dir, tag_or_iteration,
model, optimizer, infos):
def save_best_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.latest_records):
logger.info("remove the worst checkpoint: {}".format(worst_record_path))
if (worst_record_path not in self.latest_records):
logger.info(
"remove the worst checkpoint: {}".format(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.save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
infos)
self.best_records[tag_or_iteration] = metric
def save_latest_checkpoint_and_update(self, checkpoint_dir, tag_or_iteration,
model, optimizer, infos):
def save_latest_checkpoint_and_update(
self, checkpoint_dir, tag_or_iteration, model, optimizer, infos):
# remove the old
if self.latest_full():
to_del_fn = self.latest_records.pop(0)
if(to_del_fn not in self.best_records.keys()):
logger.info("remove the latest checkpoint: {}".format(to_del_fn))
if (to_del_fn not in self.best_records.keys()):
logger.info(
"remove the latest checkpoint: {}".format(to_del_fn))
self.del_checkpoint(checkpoint_dir, to_del_fn)
self.latest_records.append(tag_or_iteration)
self.save_parameters(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
self.save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
infos)
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+".*"):
"{}".format(tag_or_iteration))
for filename in glob.glob(checkpoint_path + ".*"):
os.remove(filename)
logger.info("delete file: {}".format(filename))
def load_checkpoint_idx(self, checkpoint_record: str) -> int:
"""Get the iteration number corresponding to the latest saved checkpoint.
@ -131,7 +133,6 @@ class Checkpoint(object):
latest_checkpoint = handle.readlines()[-1].strip()
iteration = int(latest_checkpoint.split(":")[-1])
return iteration
def save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpoint record.
@ -141,9 +142,10 @@ class Checkpoint(object):
Returns:
None
"""
checkpoint_record_latest = os.path.join(checkpoint_dir, "checkpoint_latest")
checkpoint_record_latest = os.path.join(checkpoint_dir,
"checkpoint_latest")
checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
with open(checkpoint_record_best, "w") as handle:
for i in self.best_records.keys():
handle.write("model_checkpoint_path:{}\n".format(i))
@ -151,11 +153,11 @@ class Checkpoint(object):
for i in self.latest_records:
handle.write("model_checkpoint_path:{}\n".format(i))
def load_last_parameters(self, model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
def load_last_parameters(self,
model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a last model checkpoint from disk.
Args:
model (Layer): model to load parameters.
@ -173,11 +175,13 @@ class Checkpoint(object):
if checkpoint_path is not None:
tag = os.path.basename(checkpoint_path).split(":")[-1]
elif checkpoint_dir is not None:
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint_latest")
checkpoint_record = os.path.join(checkpoint_dir,
"checkpoint_latest")
iteration = self.load_checkpoint_idx(checkpoint_record)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
@ -203,11 +207,11 @@ class Checkpoint(object):
configs = json.load(fin)
return configs
def load_best_parameters(self, model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
def load_best_parameters(self,
model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a last model checkpoint from disk.
Args:
model (Layer): model to load parameters.
@ -229,7 +233,8 @@ class Checkpoint(object):
iteration = self.load_checkpoint_idx(checkpoint_record)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
@ -255,10 +260,9 @@ class Checkpoint(object):
configs = json.load(fin)
return configs
@mp_tools.rank_zero_only
def save_parameters(self, checkpoint_dir: str,
def save_parameters(self,
checkpoint_dir: str,
tag_or_iteration: Union[int, str],
model: paddle.nn.Layer,
optimizer: Optimizer=None,
@ -275,7 +279,7 @@ class Checkpoint(object):
None
"""
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(tag_or_iteration))
"{}".format(tag_or_iteration))
model_dict = model.state_dict()
params_path = checkpoint_path + ".pdparams"
@ -293,4 +297,3 @@ class Checkpoint(object):
with open(info_path, 'w') as fout:
data = json.dumps(infos)
fout.write(data)

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