detectron2.checkpoint

class detectron2.checkpoint.Checkpointer(model: torch.nn.Module, save_dir: str = '', *, save_to_disk: bool = True, **checkpointables: Any)

Bases: object

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

__init__(model: torch.nn.Module, save_dir: str = '', *, save_to_disk: bool = True, **checkpointables: Any) → None

Parameters

• 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).

add_checkpointable(key: str, checkpointable: Any) → None

Add checkpointable object for this checkpointer to track.

Parameters

• key (str) – the key used to save the object

• checkpointable – any object with state_dict() and load_state_dict() method

save(name: str, **kwargs: Any) → None

Dump model and checkpointables to a file.

Parameters

• name (str) – name of the file.

• kwargs (dict) – extra arbitrary data to save.

load(path: str, checkpointables: Optional[List[str]] = None) → Dict[str, Any]

Load from the given checkpoint.

Parameters

• path (str) – path or url to the checkpoint. If empty, will not load anything.

• checkpointables (list) – List of checkpointable names to load. If not specified (None), will load all the possible checkpointables.

Returns

dict – extra data loaded from the checkpoint that has not been processed. For example, those saved with save(**extra_data)().

has_checkpoint() → bool

Returns

bool – whether a checkpoint exists in the target directory.

get_checkpoint_file() → str

Returns

str – The latest checkpoint file in target directory.

get_all_checkpoint_files() → List[str]

Returns

list –

All available checkpoint files (.pth files) in target

directory.

resume_or_load(path: str, *, resume: bool = True) → Dict[str, Any]

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.

Parameters

• path (str) – path to the checkpoint.

• resume (bool) – if True, resume from the last checkpoint if it exists and load the model together with all the checkpointables. Otherwise only load the model without loading any checkpointables.

Returns

same as load().

tag_last_checkpoint(last_filename_basename: str) → None

Tag the last checkpoint.

Parameters

last_filename_basename (str) – the basename of the last filename.

class detectron2.checkpoint.PeriodicCheckpointer(checkpointer: fvcore.common.checkpoint.Checkpointer, period: int, max_iter: Optional[int] = None, max_to_keep: Optional[int] = None, file_prefix: str = 'model')

Bases: object

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

checkpointer

the underlying checkpointer object

Type

Checkpointer

__init__(checkpointer: fvcore.common.checkpoint.Checkpointer, period: int, max_iter: Optional[int] = None, max_to_keep: Optional[int] = None, file_prefix: str = 'model') → None

Parameters

• checkpointer – 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 “{file_prefix}_final” will be saved.

• max_to_keep (int) – maximum number of most current checkpoints to keep, previous checkpoints will be deleted

• file_prefix (str) – the prefix of checkpoint’s filename

step(iteration: int, **kwargs: Any) → None

Perform the appropriate action at the given iteration.

Parameters

• iteration (int) – the current iteration, ranged in [0, max_iter-1].

• kwargs (Any) – extra data to save, same as in Checkpointer.save().

save(name: str, **kwargs: Any) → None

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

Parameters

• name (str) – file name.

• kwargs (Any) – extra data to save, same as in Checkpointer.save().

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

Bases: fvcore.common.checkpoint.Checkpointer

Same as Checkpointer, but is able to: 1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models. 2. correctly load checkpoints that are only available on the master worker

load(path, *args, **kwargs)