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model

Bases: Module, function

The base model class of the RPN model in the tinyBIG toolkit.

It inherits from the torch.nn.Module class, which also inherits the "state_dict" and "load_state_dict" methods from the base class.

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Attributes:

Name Type Description
name str, default = 'base_metric'

Name of the model.

Methods:

Name Description
__init__

It performs the initialization of the model
save_ckpt

It saves the model state as checkpoint to file.
load_ckpt

It loads the model state from a file.
__call__

It reimplementation the build-in callable method.
forward

The forward method of the model.
Source code in tinybig/module/base_model.py 
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class model(Module, function):
    """
    The base model class of the RPN model in the tinyBIG toolkit.

    It inherits from the torch.nn.Module class, which also inherits the
    "state_dict" and "load_state_dict" methods from the base class.

    ...

    Attributes
    ----------
    name: str, default = 'base_metric'
        Name of the model.

    Methods
    ----------
    __init__
        It performs the initialization of the model

    save_ckpt
        It saves the model state as checkpoint to file.

    load_ckpt
        It loads the model state from a file.

    __call__
        It reimplementation the build-in callable method.

    forward
        The forward method of the model.
    """
    def __init__(self, name: str = 'model_name', device: str = 'cpu', *args, **kwargs):
        """
        The initialization method of the base model class.

        It initializes a model object based on the provided model parameters.

        Parameters
        ----------
        name: str, default = 'model_name'
            The name of the model, with default value "model_name".

        Returns
        ----------
        object
            The initialized model object.
        """
        Module.__init__(self)
        function.__init__(self, name=name, device=device)

    def save_ckpt(self, cache_dir='./ckpt', checkpoint_file='checkpoint'):
        """
        The model state checkpoint saving method.

        It saves the current model state to a checkpoint file.

        Parameters
        ----------
        cache_dir: str, default = './ckpt'
            The cache directory of the model checkpoint file.
        checkpoint_file: str, default = 'checkpoint'
            The checkpoint file name.

        Returns
        -------
        None
            This method doesn't have return values.
        """
        create_directory_if_not_exists(f'{cache_dir}/{checkpoint_file}')
        torch.save(self.state_dict(), f'{cache_dir}/{checkpoint_file}')
        print("model checkpoint saving to {}/{}...".format(cache_dir, checkpoint_file))

    def load_ckpt(self, cache_dir: str = './ckpt', checkpoint_file: str = 'checkpoint', strict: bool = True):
        """
        The model state checkpoint loading method.

        It loads the model state from the provided checkpoint file.

        Parameters
        ----------
        cache_dir: str, default = './ckpt'
            The cache directory of the model checkpoint file.
        checkpoint_file: str, default = 'checkpoint'
            The checkpoint file name.
        strict: bool, default = True
            The boolean tag of whether the model state loading follows the strict configuration checking.

        Returns
        -------
        None
            This method doesn't have return values.
        """
        self.load_state_dict(torch.load(f'{cache_dir}/{checkpoint_file}'), strict=strict)
        print("model checkpoint loading from {}/{}...".format(cache_dir, checkpoint_file))

    @abstractmethod
    def to_config(self, *args, **kwargs):
        pass

    @abstractmethod
    def forward(self, *args, **kwargs):
        """
        The forward method of the model.

        It is declared to be an abstractmethod and needs to be implemented in the inherited RPN model classes.
        This callable method accepts the data instances as the input and generate the desired outputs.

        Returns
        ----------
        torch.Tensor
            The model generated outputs.
        """
        pass

__init__(name='model_name', device='cpu', *args, **kwargs)

The initialization method of the base model class.

It initializes a model object based on the provided model parameters.

Parameters:

Name Type Description Default
name str

The name of the model, with default value "model_name".

 'model_name'

Returns:

Type Description
object

The initialized model object.
Source code in tinybig/module/base_model.py 
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def __init__(self, name: str = 'model_name', device: str = 'cpu', *args, **kwargs):
    """
    The initialization method of the base model class.

    It initializes a model object based on the provided model parameters.

    Parameters
    ----------
    name: str, default = 'model_name'
        The name of the model, with default value "model_name".

    Returns
    ----------
    object
        The initialized model object.
    """
    Module.__init__(self)
    function.__init__(self, name=name, device=device)

forward(*args, **kwargs) abstractmethod

The forward method of the model.

It is declared to be an abstractmethod and needs to be implemented in the inherited RPN model classes. This callable method accepts the data instances as the input and generate the desired outputs.

Returns:

Type Description
Tensor

The model generated outputs.
Source code in tinybig/module/base_model.py 
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@abstractmethod
def forward(self, *args, **kwargs):
    """
    The forward method of the model.

    It is declared to be an abstractmethod and needs to be implemented in the inherited RPN model classes.
    This callable method accepts the data instances as the input and generate the desired outputs.

    Returns
    ----------
    torch.Tensor
        The model generated outputs.
    """
    pass

load_ckpt(cache_dir='./ckpt', checkpoint_file='checkpoint', strict=True)

The model state checkpoint loading method.

It loads the model state from the provided checkpoint file.

Parameters:

Name Type Description Default
cache_dir str

The cache directory of the model checkpoint file.

 './ckpt'
checkpoint_file str

The checkpoint file name.

 'checkpoint'
strict bool

The boolean tag of whether the model state loading follows the strict configuration checking.

 True

Returns:

Type Description
None

This method doesn't have return values.
Source code in tinybig/module/base_model.py 
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def load_ckpt(self, cache_dir: str = './ckpt', checkpoint_file: str = 'checkpoint', strict: bool = True):
    """
    The model state checkpoint loading method.

    It loads the model state from the provided checkpoint file.

    Parameters
    ----------
    cache_dir: str, default = './ckpt'
        The cache directory of the model checkpoint file.
    checkpoint_file: str, default = 'checkpoint'
        The checkpoint file name.
    strict: bool, default = True
        The boolean tag of whether the model state loading follows the strict configuration checking.

    Returns
    -------
    None
        This method doesn't have return values.
    """
    self.load_state_dict(torch.load(f'{cache_dir}/{checkpoint_file}'), strict=strict)
    print("model checkpoint loading from {}/{}...".format(cache_dir, checkpoint_file))

save_ckpt(cache_dir='./ckpt', checkpoint_file='checkpoint')

The model state checkpoint saving method.

It saves the current model state to a checkpoint file.

Parameters:

Name Type Description Default
cache_dir

The cache directory of the model checkpoint file.

 './ckpt'
checkpoint_file

The checkpoint file name.

 'checkpoint'

Returns:

Type Description
None

This method doesn't have return values.
Source code in tinybig/module/base_model.py 
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def save_ckpt(self, cache_dir='./ckpt', checkpoint_file='checkpoint'):
    """
    The model state checkpoint saving method.

    It saves the current model state to a checkpoint file.

    Parameters
    ----------
    cache_dir: str, default = './ckpt'
        The cache directory of the model checkpoint file.
    checkpoint_file: str, default = 'checkpoint'
        The checkpoint file name.

    Returns
    -------
    None
        This method doesn't have return values.
    """
    create_directory_if_not_exists(f'{cache_dir}/{checkpoint_file}')
    torch.save(self.state_dict(), f'{cache_dir}/{checkpoint_file}')
    print("model checkpoint saving to {}/{}...".format(cache_dir, checkpoint_file))