def initialize(self, what: Tensor, config=None, configuration_key=None): """Initialize tensor with provided configuration. The initializers are taken from options "initialize" and "initialize_args". If set, config and configuration_key overwrite the default configuration used in this class. When both are set, self can be None. """ if config is None: config = self.config if configuration_key is None: configuration_key = self.configuration_key configurable = Configurable(config, configuration_key) initialize = configurable.get_option("initialize") try: initialize_args_key = "initialize_args." + initialize initialize_args = configurable.get_option(initialize_args_key) except KeyError: initialize_args_key = "initialize_args" initialize_args = configurable.get_option(initialize_args_key) # Automatically set arg a (lower bound) for uniform_ if not given if initialize == "uniform_" and "a" not in initialize_args: initialize_args["a"] = initialize_args["b"] * -1 KgeBase._initialize(what, initialize, initialize_args)
def _init_configuration(self, config: Config, configuration_key: Optional[str]): Configurable._init_configuration(self, config, configuration_key) if not hasattr(self, "model") or not self.model: if self.configuration_key: self.model: str = config.get(self.configuration_key + ".type") else: self.model: str = config.get("model") self.configuration_key = self.model
def __init__(self, config: Config, dataset: Dataset, configuration_key=None): Configurable.__init__(self, config, configuration_key) torch.nn.Module.__init__(self) self.dataset = dataset self.meta: Dict[str, Any] = dict() #: meta-data stored with this module
def __init__(self, config: Config, dataset: Dataset, configuration_key=None): Configurable.__init__(self, config, configuration_key) torch.nn.Module.__init__(self) self.dataset = dataset self.meta: Dict[str, Any] = dict() #: meta-data stored with this module self.backward_compatible_keys = { "_entity_embedder.embeddings.weight": "_entity_embedder._embeddings.weight", "_relation_embedder.embeddings.weight": "_relation_embedder._embeddings.weight", "_base_model._entity_embedder.embeddings.weight": "_base_model._entity_embedder._embeddings.weight", "_base_model._relation_embedder.embeddings.weight": "_base_model._relation_embedder._embeddings.weight", }