"""Import optimizer class dynamically."""
import argparse
from espnet.utils.dynamic_import import dynamic_import
from espnet.utils.fill_missing_args import fill_missing_args
[docs]class OptimizerFactoryInterface:
"""Optimizer adaptor."""
[docs] @staticmethod
def from_args(target, args: argparse.Namespace):
"""Initialize optimizer from argparse Namespace.
Args:
target: for pytorch `model.parameters()`,
for chainer `model`
args (argparse.Namespace): parsed command-line args
"""
raise NotImplementedError()
[docs] @staticmethod
def add_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
"""Register args."""
return parser
[docs] @classmethod
def build(cls, target, **kwargs):
"""Initialize optimizer with python-level args.
Args:
target: for pytorch `model.parameters()`,
for chainer `model`
Returns:
new Optimizer
"""
args = argparse.Namespace(**kwargs)
args = fill_missing_args(args, cls.add_arguments)
return cls.from_args(target, args)
[docs]def dynamic_import_optimizer(name: str, backend: str) -> OptimizerFactoryInterface:
"""Import optimizer class dynamically.
Args:
name (str): alias name or dynamic import syntax `module:class`
backend (str): backend name e.g., chainer or pytorch
Returns:
OptimizerFactoryInterface or FunctionalOptimizerAdaptor
"""
if backend == "pytorch":
from espnet.optimizer.pytorch import OPTIMIZER_FACTORY_DICT
return OPTIMIZER_FACTORY_DICT[name]
elif backend == "chainer":
from espnet.optimizer.chainer import OPTIMIZER_FACTORY_DICT
return OPTIMIZER_FACTORY_DICT[name]
else:
raise NotImplementedError(f"unsupported backend: {backend}")
factory_class = dynamic_import(name)
assert issubclass(factory_class, OptimizerFactoryInterface)
return factory_class