def load()

in mico/model/mico.py [0:0]

• 18 lines of code
• 7 McCabe index (conditional complexity)

    def load(self, suffix="", subprocess_index=0):
        """Load the existing model (previously saved on disk).
        If there is optimizer infomation (e.g., for Adam), we also load it for resuming training.

        Parameters
        ----------
        suffix : string
            The model path for loading will be `self.hparams.model_path + suffix`.
            Different models will be loaded if you set different `suffix`.

        """

        checkpoint = torch.load(self.hparams.model_path + suffix,
                                map_location=torch.device(subprocess_index)) \
            if self.hparams.cuda \
            else torch.load(self.hparams.model_path + suffix,
                            map_location=torch.device('cpu'))
        if checkpoint['hparams'].cuda and not self.hparams.cuda:
            checkpoint['hparams'].cuda = False
        if not self.hparams.not_resume_hparams:
            self.__init__(checkpoint['hparams'])
        self.resume_epoch = checkpoint['epoch']
        self.load_state_dict(checkpoint['state_dict'])
        if 'optimizer' in checkpoint:
            self.optimizers = checkpoint['optimizer']
        if 'current_iteration_number' in checkpoint:
            self.resume_iteration = checkpoint['current_iteration_number']
        else:
            self.resume_iteration = 0