def __init__(self, n_batches_per_epoch):
self.n_batches_per_epoch = n_batches_per_epoch
# Set up counter
# Set up evaluation/checkpointing unit (sample, batch, epoch)
self.counter_unit = Meta.config["logging_config"]["counter_unit"]
if self.counter_unit not in ["sample", "batch", "epoch"]:
raise ValueError(f"Unrecognized unit: {self.counter_unit}")
# Set up evaluation frequency
self.evaluation_freq = Meta.config["logging_config"]["evaluation_freq"]
logger.info(
f"Evaluating every {self.evaluation_freq} {self.counter_unit}.")
if Meta.config["logging_config"]["checkpointing"]:
self.checkpointing = True
# Set up checkpointing frequency
self.checkpointing_freq = int(
Meta.config["logging_config"]["checkpointer_config"]
["checkpoint_freq"])
logger.info(
f"Checkpointing every "
f"{self.checkpointing_freq * self.evaluation_freq} {self.counter_unit}."
)
# Set up checkpointer
self.checkpointer = Checkpointer()
else:
self.checkpointing = False
logger.info("No checkpointing.")
# Set up number of samples passed since last evaluation/checkpointing and
# total number of samples passed since learning process
self.sample_count = 0
self.sample_total = 0
# Set up number of batches passed since last evaluation/checkpointing and
# total number of batches passed since learning process
self.batch_count = 0
self.batch_total = 0
# Set up number of epochs passed since last evaluation/checkpointing and
# total number of epochs passed since learning process
self.epoch_count = 0
self.epoch_total = 0
# Set up number of unit passed since last evaluation/checkpointing and
# total number of unit passed since learning process
self.unit_count = 0
self.unit_total = 0
# Set up count that triggers the evaluation since last checkpointing
self.trigger_count = 0
# Set up log writer
writer_opt = Meta.config["logging_config"]["writer_config"]["writer"]
if writer_opt is None:
self.writer = None
elif writer_opt == "json":
self.writer = LogWriter()
elif writer_opt == "tensorboard":
self.writer = TensorBoardWriter()
else:
raise ValueError(f"Unrecognized writer option '{writer_opt}'")
class LoggingManager(object):
r"""A class to manage logging during training progress.
Args:
n_batches_per_epoch(int): Total number batches per epoch.
"""
def __init__(self, n_batches_per_epoch: int) -> None:
self.n_batches_per_epoch = n_batches_per_epoch
# Set up counter
# Set up evaluation/checkpointing unit (sample, batch, epoch)
self.counter_unit = Meta.config["logging_config"]["counter_unit"]
if self.counter_unit not in ["sample", "batch", "epoch"]:
raise ValueError(f"Unrecognized unit: {self.counter_unit}")
# Set up evaluation frequency
self.evaluation_freq = Meta.config["logging_config"]["evaluation_freq"]
if Meta.config["meta_config"]["verbose"]:
logger.info(f"Evaluating every {self.evaluation_freq} {self.counter_unit}.")
if Meta.config["logging_config"]["checkpointing"]:
self.checkpointing = True
# Set up checkpointing frequency
self.checkpointing_freq = int(
Meta.config["logging_config"]["checkpointer_config"]["checkpoint_freq"]
)
if Meta.config["meta_config"]["verbose"]:
logger.info(
f"Checkpointing every "
f"{self.checkpointing_freq * self.evaluation_freq} "
f"{self.counter_unit}."
)
# Set up checkpointer
self.checkpointer = Checkpointer()
else:
self.checkpointing = False
if Meta.config["meta_config"]["verbose"]:
logger.info("No checkpointing.")
# Set up number of samples passed since last evaluation/checkpointing and
# total number of samples passed since learning process
self.sample_count: int = 0
self.sample_total: int = 0
# Set up number of batches passed since last evaluation/checkpointing and
# total number of batches passed since learning process
self.batch_count: int = 0
self.batch_total: int = 0
# Set up number of epochs passed since last evaluation/checkpointing and
# total number of epochs passed since learning process
self.epoch_count: Union[float, int] = 0
self.epoch_total: Union[float, int] = 0
# Set up number of unit passed since last evaluation/checkpointing and
# total number of unit passed since learning process
self.unit_count: Union[float, int] = 0
self.unit_total: Union[float, int] = 0
# Set up count that triggers the evaluation since last checkpointing
self.trigger_count = 0
# Set up log writer
writer_opt = Meta.config["logging_config"]["writer_config"]["writer"]
if writer_opt is None:
self.writer = None
elif writer_opt == "json":
self.writer = LogWriter()
elif writer_opt == "tensorboard":
self.writer = TensorBoardWriter()
else:
raise ValueError(f"Unrecognized writer option '{writer_opt}'")
def update(self, batch_size: int) -> None:
r"""Update the counter.
Args:
batch_size(int): The number of the samples in the batch.
"""
# Update number of samples
self.sample_count += batch_size
self.sample_total += batch_size
# Update number of batches
self.batch_count += 1
self.batch_total += 1
# Update number of epochs
self.epoch_count = self.batch_count / self.n_batches_per_epoch
self.epoch_total = self.batch_total / self.n_batches_per_epoch
# Update number of units
if self.counter_unit == "sample":
self.unit_count = self.sample_count
self.unit_total = self.sample_total
if self.counter_unit == "batch":
self.unit_count = self.batch_count
self.unit_total = self.batch_total
elif self.counter_unit == "epoch":
self.unit_count = self.epoch_count
self.unit_total = self.epoch_total
def trigger_evaluation(self) -> bool:
r"""Check if triggers the evaluation."""
satisfied = self.unit_count >= self.evaluation_freq
if satisfied:
self.trigger_count += 1
self.reset()
return satisfied
def trigger_checkpointing(self) -> bool:
r"""Check if triggers the checkpointing."""
if not self.checkpointing:
return False
satisfied = self.trigger_count >= self.checkpointing_freq
if satisfied:
self.trigger_count = 0
return satisfied
def reset(self) -> None:
r"""Reset the counter."""
self.sample_count = 0
self.batch_count = 0
self.epoch_count = 0
self.unit_count = 0
def write_log(self, metric_dict: Dict[str, float]) -> None:
r"""Write the metrics to the log.
Args:
metric_dict(dict): The metric dict.
"""
for metric_name, metric_value in metric_dict.items():
self.writer.add_scalar(metric_name, metric_value, self.unit_total)
def checkpoint_model(
self,
model: EmmentalModel,
optimizer: Optimizer,
lr_scheduler: _LRScheduler,
metric_dict: Dict[str, float],
) -> None:
r"""Checkpoint the model.
Args:
model(EmmentalModel): The model to checkpoint.
optimizer(Optimizer): The optimizer used during training process.
lr_scheduler(_LRScheduler): Learning rate scheduler.
metric_dict(dict): the metric dict.
"""
self.checkpointer.checkpoint(
self.unit_total, model, optimizer, lr_scheduler, metric_dict
)
def close(self, model: EmmentalModel) -> EmmentalModel:
r"""Close the checkpointer and reload the model if necessary.
Args:
model(EmmentalModel): The trained model.
Returns:
EmmentalModel: The reloaded model if necessary
"""
self.writer.close()
if self.checkpointing:
model = self.checkpointer.load_best_model(model)
self.checkpointer.clear()
return model
def test_checkpointer_clear_condition(caplog):
"""Unit test of checkpointer (clear condition)"""
caplog.set_level(logging.INFO)
emmental.Meta.reset()
emmental.init()
emmental.Meta.update_config(
config={
"logging_config": {
"counter_unit": "sample",
"evaluation_freq": 10,
"checkpointing": True,
"checkpointer_config": {
"clear_intermediate_checkpoints": True,
"clear_all_checkpoints": True,
},
}
})
checkpointer = Checkpointer()
checkpointer.clear()
emmental.Meta.reset()
emmental.init()
emmental.Meta.update_config(
config={
"logging_config": {
"counter_unit": "sample",
"evaluation_freq": 10,
"checkpointing": True,
"checkpointer_config": {
"clear_intermediate_checkpoints": False,
"clear_all_checkpoints": True,
},
}
})
checkpointer = Checkpointer()
checkpointer.clear()
emmental.Meta.reset()
emmental.init()
emmental.Meta.update_config(
config={
"logging_config": {
"counter_unit": "sample",
"evaluation_freq": 10,
"checkpointing": True,
"checkpointer_config": {
"clear_intermediate_checkpoints": True,
"clear_all_checkpoints": False,
},
}
})
checkpointer = Checkpointer()
checkpointer.clear()
emmental.Meta.reset()
emmental.init()
emmental.Meta.update_config(
config={
"logging_config": {
"counter_unit": "sample",
"evaluation_freq": 10,
"checkpointing": True,
"checkpointer_config": {
"clear_intermediate_checkpoints": False,
"clear_all_checkpoints": False,
},
}
})
checkpointer = Checkpointer()
checkpointer.clear()