pytorch_pfn_extras.training.ExtensionsManager

class pytorch_pfn_extras.training.ExtensionsManager(models: Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]], optimizers: Union[torch.optim.optimizer.Optimizer, Dict[str, torch.optim.optimizer.Optimizer]], max_epochs: int, *, iters_per_epoch: Optional[int], extensions: Optional[List[extension_module.ExtensionLike]] = None, out_dir: str = 'result', stop_trigger: trigger_module.TriggerLike = None, writer: Optional[pytorch_pfn_extras.writing.Writer] = None)

Manages the extensions and the current status.

Parameters

• models (dict or torch.nn.Module) – Map of string to Module or an actual Module

• optimizers (dict or torch.Optimizer) – Map of string to Optimizer or an actual Optimizer.

• max_epochs (int) – Number of epochs in the whole training loop. Ignored if stop_trigger is passed as a kwarg.

• iters_per_epoch (int) – Number of iterations in one epoch.

• extensions (list or None) – List of Extentions to be used.

• out_dir (str) – Output directory (default: result).

• stop_trigger (trigger object, optional) – to determine wether training has concluded. The default is an interval trigger set to max_epochs

• writer (writing.Writer object) – Writer that can be used by extensions to write data to custom filesystems.

__init__(models: Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]], optimizers: Union[torch.optim.optimizer.Optimizer, Dict[str, torch.optim.optimizer.Optimizer]], max_epochs: int, *, iters_per_epoch: Optional[int], extensions: Optional[List[extension_module.ExtensionLike]] = None, out_dir: str = 'result', stop_trigger: trigger_module.TriggerLike = None, writer: Optional[pytorch_pfn_extras.writing.Writer] = None) → None

Methods

__init__(models, optimizers, max_epochs, *, …)
extend(extension[, name, trigger, priority, …])	Registers an extension to the manager.
get_extension(name)	Returns the extension of a given name.
load_state_dict(to_load, *[, transform_models])	transform_models is a function that apply a transformation to a model before loading its state.
run_extensions()
run_iteration(*[, step_optimizers])	Context manager to run an iteration.
start_extensions()
state_dict(*[, transform_models])	transform_models is a function that apply a transformation to a model.

Attributes

elapsed_time
epoch
epoch_detail
is_before_training
iteration
models
optimizers
out
stop_trigger
updater

API Reference pytorch_pfn_extras.training.IgniteExtensionsManager