pytorch_pfn_extras.engine.create_evaluator¶
- pytorch_pfn_extras.engine.create_evaluator(models, *, progress_bar=False, device='cpu', metrics=None, logic=None, handler_class=None, options=None, runtime_options=None)¶
Creates an evaluator object. The return value of this function is expected to be fed to ppe.engine.create_trainer as an argument.
- Parameters
models (Union[torch.nn.modules.module.Module, Mapping[str, torch.nn.modules.module.Module]]) – Map of string to
torch.nn.Moduleor an actual Module. In most cases, this arugment is the same as the model arguemnt of ppe.engine.create_trainer.progress_bar (bool) – If True, a progress bar is enabled in evaluation.
device (str or torch.device) – Device name used for selecting a corresponding runtime class.
metrics (list of metrics) – List of metrics, which computes various quantities and update output for the reporting.
logic (Optional[pytorch_pfn_extras.handler._logic.Logic]) – A logic object. If None is given, an logic object is instantiated from the default logic class.
handler_class (Optional[Type[pytorch_pfn_extras.handler._handler.Handler]]) – A handler class that instantiates a handler object. If None is given, ppe.handler.Handler is used as a default handler class.
options (Optional[Dict[str, Any]]) – Options that are set to the handler and logic object. See the documentation of ppe.handler.Handler and ppe.handler.Logic for details.
runtime_options (Optional[Mapping[str, Any]]) – Options that are set to the runtime object. See the documentation of ppe.handler.Handler for details.
- Return type