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101 行
3.9 KiB
101 行
3.9 KiB
# # Unity ML-Agents Toolkit
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from typing import Dict, Any, Optional, List
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import os
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import attr
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from mlagents.trainers.training_status import GlobalTrainingStatus, StatusType
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from mlagents_envs.logging_util import get_logger
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logger = get_logger(__name__)
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@attr.s(auto_attribs=True)
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class ModelCheckpoint:
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steps: int
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file_path: str
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reward: Optional[float]
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creation_time: float
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auxillary_file_paths: List[str] = attr.ib(factory=list)
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class ModelCheckpointManager:
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@staticmethod
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def get_checkpoints(behavior_name: str) -> List[Dict[str, Any]]:
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checkpoint_list = GlobalTrainingStatus.get_parameter_state(
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behavior_name, StatusType.CHECKPOINTS
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)
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if not checkpoint_list:
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checkpoint_list = []
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GlobalTrainingStatus.set_parameter_state(
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behavior_name, StatusType.CHECKPOINTS, checkpoint_list
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)
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return checkpoint_list
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@staticmethod
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def remove_checkpoint(checkpoint: Dict[str, Any]) -> None:
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"""
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Removes a checkpoint stored in checkpoint_list.
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If checkpoint cannot be found, no action is done.
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:param checkpoint: A checkpoint stored in checkpoint_list
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"""
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file_paths: List[str] = [checkpoint["file_path"]]
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file_paths.extend(checkpoint["auxillary_file_paths"])
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for file_path in file_paths:
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if os.path.exists(file_path):
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os.remove(file_path)
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logger.debug(f"Removed checkpoint model {file_path}.")
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else:
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logger.debug(f"Checkpoint at {file_path} could not be found.")
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return
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@classmethod
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def _cleanup_extra_checkpoints(
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cls, checkpoints: List[Dict], keep_checkpoints: int
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) -> List[Dict]:
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"""
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Ensures that the number of checkpoints stored are within the number
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of checkpoints the user defines. If the limit is hit, checkpoints are
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removed to create room for the next checkpoint to be inserted.
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:param behavior_name: The behavior name whose checkpoints we will mange.
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:param keep_checkpoints: Number of checkpoints to record (user-defined).
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"""
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while len(checkpoints) > keep_checkpoints:
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if keep_checkpoints <= 0 or len(checkpoints) == 0:
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break
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ModelCheckpointManager.remove_checkpoint(checkpoints.pop(0))
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return checkpoints
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@classmethod
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def add_checkpoint(
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cls, behavior_name: str, new_checkpoint: ModelCheckpoint, keep_checkpoints: int
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) -> None:
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"""
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Make room for new checkpoint if needed and insert new checkpoint information.
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:param behavior_name: Behavior name for the checkpoint.
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:param new_checkpoint: The new checkpoint to be recorded.
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:param keep_checkpoints: Number of checkpoints to record (user-defined).
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"""
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new_checkpoint_dict = attr.asdict(new_checkpoint)
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checkpoints = cls.get_checkpoints(behavior_name)
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checkpoints.append(new_checkpoint_dict)
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cls._cleanup_extra_checkpoints(checkpoints, keep_checkpoints)
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GlobalTrainingStatus.set_parameter_state(
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behavior_name, StatusType.CHECKPOINTS, checkpoints
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)
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@classmethod
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def track_final_checkpoint(
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cls, behavior_name: str, final_checkpoint: ModelCheckpoint
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) -> None:
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"""
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Ensures number of checkpoints stored is within the max number of checkpoints
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defined by the user and finally stores the information about the final
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model (or intermediate model if training is interrupted).
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:param behavior_name: Behavior name of the model.
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:param final_checkpoint: Checkpoint information for the final model.
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"""
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final_model_dict = attr.asdict(final_checkpoint)
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GlobalTrainingStatus.set_parameter_state(
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behavior_name, StatusType.FINAL_CHECKPOINT, final_model_dict
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)
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