def run(self) -> Tuple[tf.keras.Model, Mapping[str, Any]]:
"""Run experiments by mode.
Returns:
A 2-tuple of (model, eval_logs).
model: `tf.keras.Model` instance.
eval_logs: returns eval metrics logs when run_post_eval is set to True,
otherwise, returns {}.
"""
mode = self._mode
params = self.params
logging.info('Starts to execute mode: %s', mode)
with self.strategy.scope():
if mode == 'train' or mode == 'train_and_post_eval':
self.controller.train(steps=params.trainer.train_steps)
elif mode == 'train_and_eval':
self.controller.train_and_evaluate(
train_steps=params.trainer.train_steps,
eval_steps=params.trainer.validation_steps,
eval_interval=params.trainer.validation_interval)
elif mode == 'eval':
self.controller.evaluate(steps=params.trainer.validation_steps)
elif mode == 'continuous_eval':
def timeout_fn():
if self.trainer.global_step.numpy(
) >= params.trainer.train_steps:
return True
return False
self.controller.evaluate_continuously(
steps=params.trainer.validation_steps,
timeout=params.trainer.continuous_eval_timeout,
timeout_fn=timeout_fn)
else:
raise NotImplementedError('The mode is not implemented: %s' %
mode)
num_params = train_utils.try_count_params(self.trainer.model)
if num_params is not None:
logging.info('Number of trainable params in model: %f Millions.',
num_params / 10.**6)
flops = train_utils.try_count_flops(self.trainer.model)
if flops is not None:
logging.info('FLOPs (multi-adds) in model: %f Billions.',
flops / 10.**9 / 2)
if self._run_post_eval or mode == 'train_and_post_eval':
with self.strategy.scope():
return self.trainer.model, self.controller.evaluate(
steps=params.trainer.validation_steps)
else:
return self.trainer.model, {}
def run_experiment(
distribution_strategy: tf.distribute.Strategy,
task: base_task.Task,
mode: str,
params: config_definitions.ExperimentConfig,
model_dir: str,
run_post_eval: bool = False,
save_summary: bool = True,
trainer: Optional[base_trainer.Trainer] = None,
controller_cls=orbit.Controller
) -> Tuple[tf.keras.Model, Mapping[str, Any]]:
"""Runs train/eval configured by the experiment params.
Args:
distribution_strategy: A distribution distribution_strategy.
task: A Task instance.
mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
or 'continuous_eval'.
params: ExperimentConfig instance.
model_dir: A 'str', a path to store model checkpoints and summaries.
run_post_eval: Whether to run post eval once after training, metrics logs
are returned.
save_summary: Whether to save train and validation summary.
trainer: the base_trainer.Trainer instance. It should be created within the
strategy.scope().
controller_cls: The controller class to manage the train and eval process.
Must be a orbit.Controller subclass.
Returns:
A 2-tuple of (model, eval_logs).
model: `tf.keras.Model` instance.
eval_logs: returns eval metrics logs when run_post_eval is set to True,
otherwise, returns {}.
"""
with distribution_strategy.scope():
if not trainer:
trainer = train_utils.create_trainer(
params,
task,
train='train' in mode,
evaluate=('eval' in mode) or run_post_eval,
checkpoint_exporter=maybe_create_best_ckpt_exporter(
params, model_dir))
if trainer.checkpoint:
if model_dir is None:
raise ValueError('model_dir must be specified, but got None')
checkpoint_manager = tf.train.CheckpointManager(
trainer.checkpoint,
directory=model_dir,
max_to_keep=params.trainer.max_to_keep,
step_counter=trainer.global_step,
checkpoint_interval=params.trainer.checkpoint_interval,
init_fn=trainer.initialize)
else:
checkpoint_manager = None
controller = controller_cls(
strategy=distribution_strategy,
trainer=trainer if 'train' in mode else None,
evaluator=trainer,
global_step=trainer.global_step,
steps_per_loop=params.trainer.steps_per_loop,
checkpoint_manager=checkpoint_manager,
summary_dir=os.path.join(model_dir, 'train') if
(save_summary) else None,
eval_summary_dir=os.path.join(
model_dir, params.trainer.validation_summary_subdir) if
(save_summary) else None,
summary_interval=params.trainer.summary_interval if
(save_summary) else None,
train_actions=actions.get_train_actions(
params, trainer, model_dir, checkpoint_manager=checkpoint_manager),
eval_actions=actions.get_eval_actions(params, trainer, model_dir))
logging.info('Starts to execute mode: %s', mode)
with distribution_strategy.scope():
if mode == 'train':
controller.train(steps=params.trainer.train_steps)
elif mode == 'train_and_eval':
controller.train_and_evaluate(
train_steps=params.trainer.train_steps,
eval_steps=params.trainer.validation_steps,
eval_interval=params.trainer.validation_interval)
elif mode == 'eval':
controller.evaluate(steps=params.trainer.validation_steps)
elif mode == 'continuous_eval':
def timeout_fn():
if trainer.global_step.numpy() >= params.trainer.train_steps:
return True
return False
controller.evaluate_continuously(
steps=params.trainer.validation_steps,
timeout=params.trainer.continuous_eval_timeout,
timeout_fn=timeout_fn)
else:
raise NotImplementedError('The mode is not implemented: %s' % mode)
num_params = train_utils.try_count_params(trainer.model)
if num_params is not None:
logging.info('Number of trainable params in model: %f Millions.',
num_params / 10.**6)
flops = train_utils.try_count_flops(trainer.model)
if flops is not None:
logging.info('FLOPs (multi-adds) in model: %f Billions.',
flops / 10.**9 / 2)
if run_post_eval:
with distribution_strategy.scope():
return trainer.model, trainer.evaluate(
tf.convert_to_tensor(params.trainer.validation_steps))
else:
return trainer.model, {}
def run_experiment(
distribution_strategy: tf.distribute.Strategy,
task: base_task.Task,
mode: str,
params: config_definitions.ExperimentConfig,
model_dir: str,
run_post_eval: bool = False,
save_summary: bool = True,
trainer: Optional[base_trainer.Trainer] = None
) -> Tuple[tf.keras.Model, Mapping[str, Any]]:
"""Runs train/eval configured by the experiment params.
Args:
distribution_strategy: A distribution distribution_strategy.
task: A Task instance.
mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
or 'continuous_eval'.
params: ExperimentConfig instance.
model_dir: A 'str', a path to store model checkpoints and summaries.
run_post_eval: Whether to run post eval once after training, metrics logs
are returned.
save_summary: Whether to save train and validation summary.
trainer: the base_trainer.Trainer instance. It should be created within the
strategy.scope().
Returns:
A 2-tuple of (model, eval_logs).
model: `tf.keras.Model` instance.
eval_logs: returns eval metrics logs when run_post_eval is set to True,
otherwise, returns {}.
"""
with distribution_strategy.scope():
if not trainer:
trainer = train_utils.create_trainer(
params,
task,
train='train' in mode,
evaluate=('eval' in mode) or run_post_eval,
checkpoint_exporter=maybe_create_best_ckpt_exporter(
params, model_dir))
if trainer.checkpoint:
checkpoint_manager = tf.train.CheckpointManager(
trainer.checkpoint,
directory=model_dir,
max_to_keep=params.trainer.max_to_keep,
step_counter=trainer.global_step,
checkpoint_interval=params.trainer.checkpoint_interval,
init_fn=trainer.initialize)
# Adds recovery handling.
trainer.add_recovery(params.trainer,
checkpoint_manager=checkpoint_manager)
else:
checkpoint_manager = None
#Create logs matching tensorboard log parser format
#see tensorboard_for_parser.md
hparams = {
"batch_size": params.task.train_data.global_batch_size,
"precision": params.runtime.mixed_precision_dtype
}
controller = orbit.Controller(
strategy=distribution_strategy,
trainer=trainer if 'train' in mode else None,
evaluator=trainer,
global_step=trainer.global_step,
steps_per_loop=params.trainer.steps_per_loop,
checkpoint_manager=checkpoint_manager,
summary_dir=model_dir if (save_summary) else None,
eval_summary_dir=os.path.join(
model_dir, params.trainer.validation_summary_subdir) if
(save_summary) else None,
summary_interval=params.trainer.summary_interval if
(save_summary) else None,
hparams=hparams if (save_summary) else None,
train_actions=None,
eval_actions=actions.get_eval_actions(params, trainer, model_dir))
logging.info('Starts to execute mode: %s', mode)
with distribution_strategy.scope():
if (params.runtime.dump_config):
from TensorFlow.common.debug import dump_callback
with dump_callback(
params.runtime.dump_config
) if params.runtime.dump_config else contextlib.ExitStack():
if mode == 'train':
controller.train(steps=params.trainer.train_steps)
elif mode == 'train_and_eval':
controller.train_and_evaluate(
train_steps=params.trainer.train_steps,
eval_steps=params.trainer.validation_steps,
eval_interval=params.trainer.validation_interval)
elif mode == 'eval':
controller.evaluate(steps=params.trainer.validation_steps)
elif mode == 'continuous_eval':
def timeout_fn():
if trainer.global_step.numpy(
) >= params.trainer.train_steps:
return True
return False
controller.evaluate_continuously(
steps=params.trainer.validation_steps,
timeout=params.trainer.continuous_eval_timeout,
timeout_fn=timeout_fn)
else:
raise NotImplementedError('The mode is not implemented: %s' %
mode)
num_params = train_utils.try_count_params(trainer.model)
if num_params is not None:
logging.info('Number of trainable params in model: %f Millions.',
num_params / 10.**6)
if run_post_eval:
with distribution_strategy.scope():
return trainer.model, trainer.evaluate(
tf.convert_to_tensor(params.trainer.validation_steps))
else:
return trainer.model, {}