pytorch_pfn_extras.training.IgniteExtensionsManager¶
- class pytorch_pfn_extras.training.IgniteExtensionsManager(engine: ignite.engine.Engine, 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, *, extensions: Optional[List[extension_module.ExtensionLike]] = None, out_dir: str = 'result', writer: Optional[pytorch_pfn_extras.writing.Writer] = None)¶
Manages extensions and the current status in Ignite training loop.
- Parameters
engine (ignite.engine.Engine) – Ignite trainer engine
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.
extensions (list or None) – List of Extentions to be used.
out_dir (str) – Output directory (default:
result
).writer (writing.Writer object) – Writer that can be used by extensions to write data to custom filesystems.
- __init__(engine: ignite.engine.Engine, 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, *, extensions: Optional[List[extension_module.ExtensionLike]] = None, out_dir: str = 'result', writer: Optional[pytorch_pfn_extras.writing.Writer] = None) None ¶
Methods
__init__
(engine, models, optimizers, …[, …])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
()set_ignite_handlers
()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