tbp.monty.frameworks.loggers

tbp.monty.frameworks.loggers.exp_logger

class BaseMontyLogger(handlers)

Bases: object

Basic logger that logs or saves information when logging is called.

__init__(handlers)

close(logger_args, output_dir, model)

flush()

post_episode(logger_args, output_dir, model)

post_epoch(logger_args, output_dir, model)

post_eval(logger_args, output_dir, model)

post_step(logger_args, output_dir, model)

post_train(logger_args, output_dir, model)

pre_episode(logger_args, output_dir, model)

pre_epoch(logger_args, output_dir, model)

pre_eval(logger_args, output_dir, model)

pre_step(logger_args, output_dir, model)

pre_train(logger_args, output_dir, model)

class LoggingCallbackHandler(loggers, model, output_dir)

Bases: object

Calls a list of loggers on an event (eg post_train).

Each logger receives: logger_args: dict with time stamps (steps, epochs, etc.) and

dataloader.primary_target which contains object id and pose

output_dir: Full path of the directory to store log files

Note

This logger handler is intended primarily for logging

__init__(loggers, model, output_dir)

call_event(event, logger_args)

close(logger_args)

post_episode(logger_args)

post_epoch(logger_args)

post_eval(logger_args)

post_step(logger_args)

post_train(logger_args)

pre_episode(logger_args)

pre_epoch(logger_args)

pre_eval(logger_args)

pre_step(logger_args)

pre_train(logger_args)

property logger_list

class TestLogger(handlers)

Bases: BaseMontyLogger

__init__(handlers)

close(logger_args, output_dir, model)

post_episode(logger_args, output_dir, model)

post_epoch(logger_args, output_dir, model)

post_eval(logger_args, output_dir, model)

post_train(logger_args, output_dir, model)

pre_episode(logger_args, output_dir, model)

pre_epoch(logger_args, output_dir, model)

pre_eval(logger_args, output_dir, model)

pre_train(logger_args, output_dir, model)

tbp.monty.frameworks.loggers.graph_matching_loggers

class BasicGraphMatchingLogger(handlers)

Bases: BaseMontyLogger

Basic logger that logs or saves information when logging is called.

__init__(handlers)

Initialize logger.

flush()

get_formatted_overall_stats(mode, episode)

log_episode(logger_args, output_dir, model)

maybe_log(logger_args, output_dir, model)

Left here in case we go back to size based logging.

Remove if not used after code has stabilized.

post_episode(logger_args, output_dir, model)

update_episode_data(logger_args, output_dir, model)

Run get_stats_per_lm and add to overall stats.

Store stats ~

1 (episode)

lm_0 (which lm)

stats

update_overall_stats(mode, episode, episode_steps, monty_matching_steps)

Update overall run stats for mode.

class DetailedGraphMatchingLogger(handlers)

Bases: BasicGraphMatchingLogger

Log detailed stats as .json file by saving data for each LM and SM.

__init__(handlers)

Initialize stats dicts.

log_episode(logger_args, output_dir, model)

post_episode(logger_args, output_dir, model)

update_episode_data(logger_args, output_dir, model)

Add episode data to overall buffer_data dict.

Store stats ~

1 (episode)

lm_0 (which lm)

stats

class SelectiveEvidenceLogger(handlers)

Bases: BasicGraphMatchingLogger

Log evidences as .json file by saving data for each LM and SM.

This is slimmed down to only log data needed for object similarity analysis. Data logged:

• evidences for each object and pose at the end of an episode

• first frame of the view finder

__init__(handlers)

Initialize stats dicts.

log_episode(logger_args, output_dir, model)

post_episode(logger_args, output_dir, model)

update_episode_data(logger_args, output_dir, model)

Add episode data to overall buffer_data dict.

tbp.monty.frameworks.loggers.monty_handlers

class BasicCSVStatsHandler

Bases: MontyHandler

Grab any logs at the BASIC level and append to train or eval CSV files.

__init__()

Initialize with empty dictionary to keep track of writes per file.

We only want to include the header the first time we write to a file. This keeps track of writes per file so we can format the file properly.

close()

classmethod log_level()

Handlers filter information from the data they receive.

This class method specifies the level they filter at.

move_columns_to_front(df, columns)

report_episode(data, output_dir, episode, mode='train', **kwargs)

class DetailedJSONHandler(detailed_episodes_to_save: Container[int] | Literal['all'] = 'all', detailed_save_per_episode: bool = False, episode_id_parallel: int | None = None) → None

Bases: MontyHandler

Grab any logs at the DETAILED level and append to a json file.

__init__(detailed_episodes_to_save: Container[int] | Literal['all'] = 'all', detailed_save_per_episode: bool = False, episode_id_parallel: int | None = None) → None

Initialize the DetailedJSONHandler.

Parameters:

• detailed_episodes_to_save (Container[int] | Literal[‘all’]) – Container of episodes to save or the string "all" (default) to include every episode.

• detailed_save_per_episode (bool) – Whether to save individual episode files or consolidate into a single detailed_run_stats.json file. Defaults to False.

• episode_id_parallel (int | None) – Episode id associated with current run, used to identify the episode when using run_parallel.

close()

get_detailed_stats(data, global_episode_id: int, local_episode: int, mode: Literal['train', 'eval']) → dict

Get detailed episode stats.

Returns:

Episode stats.

Return type:

stats

get_episode_id(local_episode, mode: Literal['train', 'eval'], **kwargs) → int

Get global episode id corresponding to a mode-local episode index.

This function is needed to determine correct episode id when using run_parallel.

Returns:

Combined train+eval episode id.

Return type:

global_episode_id

classmethod log_level()

Handlers filter information from the data they receive.

This class method specifies the level they filter at.

report_episode(data, output_dir, local_episode, mode='train', **kwargs)

Report episode data.

class MontyHandler

Bases: object

abstract close()

abstract classmethod log_level()

Handlers filter information from the data they receive.

This class method specifies the level they filter at.

abstract report_episode(**kwargs)

class ReproduceEpisodeHandler

Bases: MontyHandler

close()

classmethod log_level()

Handlers filter information from the data they receive.

This class method specifies the level they filter at.

report_episode(data, output_dir, episode, mode='train', **kwargs)

tbp.monty.frameworks.loggers.wandb_handlers