Configuration

The base class PretrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's Hub).

PretrainedConfig

to_json_string: str property readonly

Serializes this instance to a JSON string.

Returns:

Type	Description
str	:obj:str: String containing all the attributes that make up this configuration instance in JSON format.

from_dict(config_dict, **kwargs) classmethod

Instantiates a :class:~super_image.PretrainedConfig from a Python dictionary of parameters.

Parameters:

Name	Type	Description	Default
config_dict	Dict[str, Any]	obj:Dict[str, Any]): Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the :func:~super_image.PretrainedConfig.get_config_dict method.	required
kwargs		obj:Dict[str, Any]): Additional parameters from which to initialize the configuration object.	{}

Returns:

Type	Description
Tuple[PretrainedConfig, Dict[str, Any]]	:class:PretrainedConfig: The configuration object instantiated from those parameters.

Source code in super_image\configuration_utils.py

@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> Tuple["PretrainedConfig", Dict[str, Any]]:
    """
    Instantiates a :class:`~super_image.PretrainedConfig` from a Python dictionary of parameters.
    Args:
        config_dict (:obj:`Dict[str, Any]`):
            Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
            retrieved from a pretrained checkpoint by leveraging the
            :func:`~super_image.PretrainedConfig.get_config_dict` method.
        kwargs (:obj:`Dict[str, Any]`):
            Additional parameters from which to initialize the configuration object.
    Returns:
        :class:`PretrainedConfig`: The configuration object instantiated from those parameters.
    """
    config = cls(**config_dict)

    # Update config with kwargs if needed
    to_remove = []
    for key, value in kwargs.items():
        if hasattr(config, key):
            setattr(config, key, value)
            to_remove.append(key)
    for key in to_remove:
        kwargs.pop(key, None)

    logger.info(f"Model config {config}")
    return config, kwargs

from_json_file(json_file) classmethod

Instantiates a :class:~super_image.PretrainedConfig from the path to a JSON file of parameters.

Parameters:

Name	Type	Description	Default
json_file	Union[str, os.PathLike]	obj:str or :obj:os.PathLike): Path to the JSON file containing the parameters.	required

Returns:

Type	Description
PretrainedConfig	:class:PretrainedConfig: The configuration object instantiated from that JSON file.

Source code in super_image\configuration_utils.py

@classmethod
def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
    """
    Instantiates a :class:`~super_image.PretrainedConfig` from the path to a JSON file of parameters.
    Args:
        json_file (:obj:`str` or :obj:`os.PathLike`):
            Path to the JSON file containing the parameters.
    Returns:
        :class:`PretrainedConfig`: The configuration object instantiated from that JSON file.
    """
    config_dict = cls._dict_from_json_file(json_file)
    return cls(**config_dict)

get_config_dict(pretrained_model_name_or_path, **kwargs) classmethod

From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating a :class:~super_image.PretrainedConfig using from_dict.

Parameters:

Name	Type	Description	Default
pretrained_model_name_or_path	Union[str, os.PathLike]	obj:str or :obj:os.PathLike): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.	required

Returns:

Type	Description
Tuple[Dict[str, Any], Dict[str, Any]]	:obj:Tuple[Dict, Dict]: The dictionary(ies) that will be used to instantiate the configuration object.

Source code in super_image\configuration_utils.py

@classmethod
def get_config_dict(
        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """
    From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
    :class:`~super_image.PretrainedConfig` using ``from_dict``.
    Parameters:
        pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
            The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
    Returns:
        :obj:`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
    """
    scale = kwargs.pop("scale", None)
    cache_dir = kwargs.pop("cache_dir", None)
    revision = kwargs.pop("revision", None)

    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
    if os.path.isdir(pretrained_model_name_or_path):
        config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
    elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
        config_file = pretrained_model_name_or_path
    else:
        config_file = get_model_url(
            pretrained_model_name_or_path, filename=CONFIG_NAME, revision=revision
        )

    try:
        # Load from URL or cache if already cached
        resolved_config_file = get_model_path(
            config_file,
            cache_dir=cache_dir
        )
        # Load config dict
        config_dict = cls._dict_from_json_file(resolved_config_file)
        if scale is not None:
            config_dict['scale'] = scale

    except EnvironmentError as err:
        logger.error(err)
        msg = (
            f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
            f"- '{pretrained_model_name_or_path}' is a correct model identifier \n\n"
            f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
        )
        raise EnvironmentError(msg)

    except json.JSONDecodeError:
        msg = (
            f"Couldn't reach server at '{config_file}' to download configuration file or "
            "configuration file is not a valid JSON file. "
            f"Please check network or file content here: {resolved_config_file}."
        )
        raise EnvironmentError(msg)

    if resolved_config_file == config_file:
        logger.info(f"loading configuration file {config_file}")
    else:
        logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}")

    return config_dict, kwargs

save_pretrained(self, save_directory)

Save a configuration object to the directory save_directory, so that it can be re-loaded using the :func:~super_image.PretrainedConfig.from_pretrained class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, os.PathLike]	obj:str or :obj:os.PathLike): Directory where the configuration JSON file will be saved (will be created if it does not exist).	required

Source code in super_image\configuration_utils.py

def save_pretrained(self, save_directory: Union[str, os.PathLike]):
    """
    Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
    :func:`~super_image.PretrainedConfig.from_pretrained` class method.
    Args:
        save_directory (:obj:`str` or :obj:`os.PathLike`):
            Directory where the configuration JSON file will be saved (will be created if it does not exist).
    """
    if os.path.isfile(save_directory):
        raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

    os.makedirs(save_directory, exist_ok=True)
    # If we save using the predefined names, we can load using `from_pretrained`
    output_config_file = os.path.join(save_directory, CONFIG_NAME)

    self.to_json_file(output_config_file)
    logger.info(f"Configuration saved in {output_config_file}")

to_dict(self)

Serializes this instance to a Python dictionary.

Returns:

Type	Description
Dict[str, Any]	:obj:Dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.

Source code in super_image\configuration_utils.py

def to_dict(self) -> Dict[str, Any]:
    """
    Serializes this instance to a Python dictionary.
    Returns:
        :obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
    """
    output = copy.deepcopy(self.__dict__)
    if hasattr(self.__class__, "model_type"):
        output["model_type"] = self.__class__.model_type

    return output

to_json_file(self, json_file_path)

Save this instance to a JSON file.

Parameters:

Name	Type	Description	Default
json_file_path	Union[str, os.PathLike]	obj:str or :obj:os.PathLike): Path to the JSON file in which this configuration instance's parameters will be saved.	required

Source code in super_image\configuration_utils.py

def to_json_file(self, json_file_path: Union[str, os.PathLike]):
    """
    Save this instance to a JSON file.
    Args:
        json_file_path (:obj:`str` or :obj:`os.PathLike`):
            Path to the JSON file in which this configuration instance's parameters will be saved.
    """
    with open(json_file_path, "w", encoding="utf-8") as writer:
        writer.write(self.to_json_string)