mmcv/runner/runner.py
def init_optimizer(self, optimizer):
        """Init the optimizer.

        Args:
            optimizer (dict or :obj:`~torch.optim.Optimizer`): Either an
                optimizer object or a dict used for constructing the optimizer.

        Returns:
            :obj:`~torch.optim.Optimizer`: An optimizer object.

        Examples:
            >>> optimizer = dict(type='SGD', lr=0.01, momentum=0.9)
            >>> type(runner.init_optimizer(optimizer))
            <class 'torch.optim.sgd.SGD'>
        """
        if isinstance(optimizer, dict):
            optimizer = obj_from_dict(
                optimizer, torch.optim, dict(params=self.model.parameters()))
        elif not isinstance(optimizer, torch.optim.Optimizer):
            raise TypeError(
                'optimizer must be either an Optimizer object or a dict, '
                'but got {}'.format(type(optimizer)))
        return optimizer
def init_optimizer(self, optimizer):
        if isinstance(optimizer, dict):
            optimizer = obj_from_dict(
                optimizer, torch.optim, dict(params=self.model.parameters()))
        elif not isinstance(optimizer, torch.optim.Optimizer):
            raise TypeError(
                'optimizer must be either an Optimizer object or a dict, '
                'but got {}'.format(type(optimizer)))
        return optimizer
def init_optimizer ( self , optimizer ) : if isinstance ( optimizer , dict ) : optimizer = obj_from_dict ( optimizer , torch . optim , dict ( params = self . model . parameters ( ) ) ) elif not isinstance ( optimizer , torch . optim . Optimizer ) : raise TypeError ( 'optimizer must be either an Optimizer object or a dict, ' 'but got {}' . format ( type ( optimizer ) ) ) return optimizer
Init the optimizer.

        Args:
            optimizer (dict or :obj:`~torch.optim.Optimizer`): Either an
                optimizer object or a dict used for constructing the optimizer.

        Returns:
            :obj:`~torch.optim.Optimizer`: An optimizer object.

        Examples:
            >>> optimizer = dict(type='SGD', lr=0.01, momentum=0.9)
            >>> type(runner.init_optimizer(optimizer))
            <class 'torch.optim.sgd.SGD'>
Init the optimizer .