msb-public
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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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using module logger as default

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pull/578/head
Hui Zhang 5 years ago
parent 0d38a67065
commit 38ebec33e2
3 changed files with 36 additions and 114 deletions
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58
deepspeech/exps/deepspeech2/model.py
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@ -63,7 +63,7 @@ class DeepSpeech2Trainer(Trainer):
msg += "batch size: {}, ".format(self.config.data.batch_size)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_np.items())
self.logger.info(msg)
logger.info(msg)
if dist.get_rank() == 0 and self.visualizer:
for k, v in losses_np.items():
@ -74,8 +74,7 @@ class DeepSpeech2Trainer(Trainer):
@mp_tools.rank_zero_only
@paddle.no_grad()
def valid(self):
self.logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
self.model.eval()
valid_losses = defaultdict(list)
for i, batch in enumerate(self.valid_loader):
@ -92,7 +91,7 @@ class DeepSpeech2Trainer(Trainer):
msg += "step: {}, ".format(self.iteration)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_losses.items())
self.logger.info(msg)
logger.info(msg)
if self.visualizer:
for k, v in valid_losses.items():
@ -115,7 +114,7 @@ class DeepSpeech2Trainer(Trainer):
if self.parallel:
model = paddle.DataParallel(model)
layer_tools.print_params(model, self.logger.info)
layer_tools.print_params(model, logger.info)
grad_clip = ClipGradByGlobalNormWithLog(
config.training.global_grad_clip)
@ -133,7 +132,7 @@ class DeepSpeech2Trainer(Trainer):
self.model = model
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.logger.info("Setup model/optimizer/lr_scheduler!")
logger.info("Setup model/optimizer/lr_scheduler!")
def setup_dataloader(self):
config = self.config.clone()
@ -178,7 +177,7 @@ class DeepSpeech2Trainer(Trainer):
shuffle=False,
drop_last=False,
collate_fn=collate_fn)
self.logger.info("Setup train/valid Dataloader!")
logger.info("Setup train/valid Dataloader!")
class DeepSpeech2Tester(DeepSpeech2Trainer):
@ -221,11 +220,10 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
errors_sum += errors
len_refs += len_ref
num_ins += 1
self.logger.info(
"\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
self.logger.info("Current error rate [%s] = %f" % (
cfg.error_rate_type, error_rate_func(target, result)))
logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
logger.info("Current error rate [%s] = %f" %
(cfg.error_rate_type, error_rate_func(target, result)))
return dict(
errors_sum=errors_sum,
@ -237,8 +235,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
@mp_tools.rank_zero_only
@paddle.no_grad()
def test(self):
self.logger.info(
f"Test Total Examples: {len(self.test_loader.dataset)}")
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
self.model.eval()
cfg = self.config
error_rate_type = None
@ -250,8 +247,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
len_refs += metrics['len_refs']
num_ins += metrics['num_ins']
error_rate_type = metrics['error_rate_type']
self.logger.info("Error rate [%s] (%d/?) = %f" %
(error_rate_type, num_ins, errors_sum / len_refs))
logger.info("Error rate [%s] (%d/?) = %f" %
(error_rate_type, num_ins, errors_sum / len_refs))
# logging
msg = "Test: "
@ -259,7 +256,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
msg += "step: {}, ".format(self.iteration)
msg += ", Final error rate [%s] (%d/%d) = %f" % (
error_rate_type, num_ins, num_ins, errors_sum / len_refs)
self.logger.info(msg)
logger.info(msg)
def run_test(self):
self.resume_or_scratch()
@ -298,7 +295,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
self.setup_output_dir()
self.setup_checkpointer()
self.setup_logger()
self.setup_dataloader()
self.setup_model()
@ -317,7 +313,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
use_gru=config.model.use_gru,
share_rnn_weights=config.model.share_rnn_weights)
self.model = model
self.logger.info("Setup model!")
logger.info("Setup model!")
def setup_dataloader(self):
config = self.config.clone()
@ -335,7 +331,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
shuffle=False,
drop_last=False,
collate_fn=SpeechCollator(keep_transcription_text=True))
self.logger.info("Setup test Dataloader!")
logger.info("Setup test Dataloader!")
def setup_output_dir(self):
"""Create a directory used for output.
@ -350,25 +346,3 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
output_dir.mkdir(parents=True, exist_ok=True)
self.output_dir = output_dir
def setup_logger(self):
"""Initialize a text logger to log the experiment.
Each process has its own text logger. The logging message is write to
the standard output and a text file named ``worker_n.log`` in the
output directory, where ``n`` means the rank of the process.
"""
format = '[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s'
formatter = logging.Formatter(fmt=format, datefmt='%Y/%m/%d %H:%M:%S')
logger.setLevel("INFO")
# global logger
stdout = True
save_path = ""
logging.basicConfig(
level=logging.DEBUG if stdout else logging.INFO,
format=format,
datefmt='%Y/%m/%d %H:%M:%S',
filename=save_path if not stdout else None)
self.logger = logger

60
deepspeech/exps/u2/model.py
Unescape View File

@ -109,7 +109,7 @@ class U2Trainer(Trainer):
msg += "accum: {}, ".format(train_conf.accum_grad)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_np.items())
self.logger.info(msg)
logger.info(msg)
def train(self):
"""The training process control by step."""
@ -129,8 +129,7 @@ class U2Trainer(Trainer):
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
self.logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch:
self.model.train()
try:
@ -145,7 +144,7 @@ class U2Trainer(Trainer):
self.train_batch(batch_index, batch, msg)
data_start_time = time.time()
except Exception as e:
self.logger.error(e)
logger.error(e)
raise e
valid_losses = self.valid()
@ -156,8 +155,7 @@ class U2Trainer(Trainer):
@paddle.no_grad()
def valid(self):
self.model.eval()
self.logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
valid_losses = defaultdict(list)
for i, batch in enumerate(self.valid_loader):
total_loss, attention_loss, ctc_loss = self.model(*batch)
@ -175,7 +173,7 @@ class U2Trainer(Trainer):
msg += "step: {}, ".format(self.iteration)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_losses.items())
self.logger.info(msg)
logger.info(msg)
if self.visualizer:
for k, v in valid_losses.items():
@ -239,7 +237,7 @@ class U2Trainer(Trainer):
shuffle=False,
drop_last=False,
collate_fn=SpeechCollator(keep_transcription_text=True))
self.logger.info("Setup train/valid/test Dataloader!")
logger.info("Setup train/valid/test Dataloader!")
def setup_model(self):
config = self.config
@ -253,7 +251,7 @@ class U2Trainer(Trainer):
if self.parallel:
model = paddle.DataParallel(model)
layer_tools.print_params(model, self.logger.info)
layer_tools.print_params(model, logger.info)
train_config = config.training
optim_type = train_config.optim
@ -289,7 +287,7 @@ class U2Trainer(Trainer):
self.model = model
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.logger.info("Setup model/optimizer/lr_scheduler!")
logger.info("Setup model/optimizer/lr_scheduler!")
class U2Tester(U2Trainer):
@ -367,11 +365,10 @@ class U2Tester(U2Trainer):
num_ins += 1
if fout:
fout.write(result + "\n")
self.logger.info(
"\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
self.logger.info("Current error rate [%s] = %f" % (
cfg.error_rate_type, error_rate_func(target, result)))
logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
logger.info("Current error rate [%s] = %f" %
(cfg.error_rate_type, error_rate_func(target, result)))
return dict(
errors_sum=errors_sum,
@ -385,8 +382,7 @@ class U2Tester(U2Trainer):
def test(self):
assert self.args.result_file
self.model.eval()
self.logger.info(
f"Test Total Examples: {len(self.test_loader.dataset)}")
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
error_rate_type = None
errors_sum, len_refs, num_ins = 0.0, 0, 0
@ -398,9 +394,8 @@ class U2Tester(U2Trainer):
len_refs += metrics['len_refs']
num_ins += metrics['num_ins']
error_rate_type = metrics['error_rate_type']
self.logger.info(
"Error rate [%s] (%d/?) = %f" %
(error_rate_type, num_ins, errors_sum / len_refs))
logger.info("Error rate [%s] (%d/?) = %f" %
(error_rate_type, num_ins, errors_sum / len_refs))
# logging
msg = "Test: "
@ -408,7 +403,7 @@ class U2Tester(U2Trainer):
msg += "step: {}, ".format(self.iteration)
msg += ", Final error rate [%s] (%d/%d) = %f" % (
error_rate_type, num_ins, num_ins, errors_sum / len_refs)
self.logger.info(msg)
logger.info(msg)
def run_test(self):
self.resume_or_scratch()
@ -459,7 +454,6 @@ class U2Tester(U2Trainer):
self.setup_output_dir()
self.setup_checkpointer()
self.setup_logger()
self.setup_dataloader()
self.setup_model()
@ -480,25 +474,3 @@ class U2Tester(U2Trainer):
output_dir.mkdir(parents=True, exist_ok=True)
self.output_dir = output_dir
def setup_logger(self):
"""Initialize a text logger to log the experiment.
Each process has its own text logger. The logging message is write to
the standard output and a text file named ``worker_n.log`` in the
output directory, where ``n`` means the rank of the process.
"""
format = '[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s'
formatter = logging.Formatter(fmt=format, datefmt='%Y/%m/%d %H:%M:%S')
logger.setLevel("INFO")
# global logger
stdout = True
save_path = ""
logging.basicConfig(
level=logging.DEBUG if stdout else logging.INFO,
format=format,
datefmt='%Y/%m/%d %H:%M:%S',
filename=save_path if not stdout else None)
self.logger = logger

32
deepspeech/training/trainer.py
Unescape View File

@ -92,7 +92,7 @@ class Trainer():
self.visualizer = None
self.output_dir = None
self.checkpoint_dir = None
self.logger = None
logger = None
self.iteration = 0
self.epoch = 0
@ -106,7 +106,6 @@ class Trainer():
self.setup_output_dir()
self.dump_config()
self.setup_visualizer()
self.setup_logger()
self.setup_checkpointer()
self.setup_dataloader()
@ -182,8 +181,7 @@ class Trainer():
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
self.logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch:
self.model.train()
try:
@ -198,7 +196,7 @@ class Trainer():
self.train_batch(batch_index, batch, msg)
data_start_time = time.time()
except Exception as e:
self.logger.error(e)
logger.error(e)
raise e
valid_losses = self.valid()
@ -217,7 +215,7 @@ class Trainer():
exit(-1)
finally:
self.destory()
self.logger.info("Training Done.")
logger.info("Training Done.")
def setup_output_dir(self):
"""Create a directory used for output.
@ -262,28 +260,6 @@ class Trainer():
self.visualizer = visualizer
def setup_logger(self):
"""Initialize a text logger to log the experiment.
Each process has its own text logger. The logging message is write to
the standard output and a text file named ``worker_n.log`` in the
output directory, where ``n`` means the rank of the process.
when - how to split the log file by time interval
'S' : Seconds
'M' : Minutes
'H' : Hours
'D' : Days
'W' : Week day
default value: 'D'
format - format of the log
default format:
%(levelname)s: %(asctime)s: %(filename)s:%(lineno)d * %(thread)d %(message)s
INFO: 12-09 18:02:42: log.py:40 * 139814749787872 HELLO WORLD
backup - how many backup file to keep
default value: 7
"""
self.logger = logger
@mp_tools.rank_zero_only
def dump_config(self):
"""Save the configuration used for this experiment.

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