detectron2.checkpoint package

class detectron2.checkpoint.Checkpointer(model: torch.nn.modules.module.Module, save_dir: str = '', *, save_to_disk: bool = True, **checkpointables)[source]

Bases: object

A checkpointer that can save/load model as well as extra checkpointable objects.

__init__(model: torch.nn.modules.module.Module, save_dir: str = '', *, save_to_disk: bool = True, **checkpointables)[source]
  • model (nn.Module) – model.
  • save_dir (str) – a directory to save and find checkpoints.
  • save_to_disk (bool) – if True, save checkpoint to disk, otherwise disable saving for this checkpointer.
  • checkpointables (object) – any checkpointable objects, i.e., objects that have the state_dict() and load_state_dict() method. For example, it can be used like Checkpointer(model, “dir”, optimizer=optimizer).
save(name: str, **kwargs)[source]

Dump model and checkpointables to a file.

  • name (str) – name of the file.
  • kwargs (dict) – extra arbitrary data to save.
load(path: str)[source]

Load from the given checkpoint. When path points to network file, this function has to be called on all ranks.

Parameters:path (str) – path or url to the checkpoint. If empty, will not load anything.
Returns:dict – extra data loaded from the checkpoint that has not been processed. For example, those saved with save(**extra_data)().
Returns:bool – whether a checkpoint exists in the target directory.
Returns:str – The latest checkpoint file in target directory.
All available checkpoint files (.pth files) in target
resume_or_load(path: str, *, resume: bool = True)[source]

If resume is True, this method attempts to resume from the last checkpoint, if exists. Otherwise, load checkpoint from the given path. This is useful when restarting an interrupted training job.

  • path (str) – path to the checkpoint.
  • resume (bool) – if True, resume from the last checkpoint if it exists.

same as load().

tag_last_checkpoint(last_filename_basename: str)[source]

Tag the last checkpoint.

Parameters:last_filename_basename (str) – the basename of the last filename.
class detectron2.checkpoint.PeriodicCheckpointer(checkpointer: Any, period: int, max_iter: int = None)[source]

Bases: object

Save checkpoints periodically. When .step(iteration) is called, it will execute on the given checkpointer, if iteration is a multiple of period or if max_iter is reached.

__init__(checkpointer: Any, period: int, max_iter: int = None)[source]
  • checkpointer (Any) – the checkpointer object used to save
  • checkpoints.
  • period (int) – the period to save checkpoint.
  • max_iter (int) – maximum number of iterations. When it is reached, a checkpoint named “model_final” will be saved.
step(iteration: int, **kwargs)[source]

Perform the appropriate action at the given iteration.

  • iteration (int) – the current iteration, ranged in [0, max_iter-1].
  • kwargs (Any) – extra data to save, same as in
save(name: str, **kwargs)[source]

Same argument as Use this method to manually save checkpoints outside the schedule.

class detectron2.checkpoint.DetectionCheckpointer(model, save_dir='', *, save_to_disk=None, **checkpointables)[source]

Bases: fvcore.common.checkpoint.Checkpointer

Same as Checkpointer, but is able to handle models in detectron & detectron2 model zoo, and apply conversions for legacy models.