def __init__(self, config, trainer):
"""
Attr:
config(mmf_typings.DictConfig): Config for the callback
trainer(Type[BaseTrainer]): Trainer object
"""
super().__init__(config, trainer)
self.total_timer = Timer()
self.log_interval = self.training_config.log_interval
self.evaluation_interval = self.training_config.evaluation_interval
self.checkpoint_interval = self.training_config.checkpoint_interval
# Total iterations for snapshot
self.snapshot_iterations = len(self.trainer.val_dataset)
self.snapshot_iterations //= self.training_config.batch_size
self.tb_writer = None
if self.training_config.tensorboard:
log_dir = setup_output_folder(folder_only=True)
env_tb_logdir = get_mmf_env(key="tensorboard_logdir")
if env_tb_logdir:
log_dir = env_tb_logdir
self.tb_writer = TensorboardLogger(log_dir, self.trainer.current_iteration)
def _load_loggers(self) -> None:
self.tb_writer = None
if self.training_config.tensorboard:
# TODO: @sash PL logger upgrade
log_dir = setup_output_folder(folder_only=True)
env_tb_logdir = get_mmf_env(key="tensorboard_logdir")
if env_tb_logdir:
log_dir = env_tb_logdir
self.tb_writer = TensorboardLogger(log_dir)
def __init__(self, config, trainer):
"""
Attr:
config(mmf_typings.DictConfig): Config for the callback
trainer(Type[BaseTrainer]): Trainer object
"""
super().__init__(config, trainer)
self.total_timer = Timer()
self.log_interval = self.training_config.log_interval
self.evaluation_interval = self.training_config.evaluation_interval
self.checkpoint_interval = self.training_config.checkpoint_interval
# Total iterations for snapshot
# len would be number of batches per GPU == max updates
self.snapshot_iterations = len(self.trainer.val_loader)
self.tb_writer = None
self.wandb_logger = None
if self.training_config.tensorboard:
log_dir = setup_output_folder(folder_only=True)
env_tb_logdir = get_mmf_env(key="tensorboard_logdir")
if env_tb_logdir:
log_dir = env_tb_logdir
self.tb_writer = TensorboardLogger(log_dir, self.trainer.current_iteration)
if self.training_config.wandb.enabled:
log_dir = setup_output_folder(folder_only=True)
env_wandb_logdir = get_mmf_env(key="wandb_logdir")
if env_wandb_logdir:
log_dir = env_wandb_logdir
self.wandb_logger = WandbLogger(
entity=config.training.wandb.entity,
config=config,
project=config.training.wandb.project,
)