Class InputSpec

python/paddle/static/input.py:227–453

InputSpec describes the signature information of the model input, such as ``shape`` , ``dtype`` , ``name`` . This interface is often used to specify input tensor information of models in high-level API. It's also used to specify the tensor information for each input parameter of the fo

class InputSpec:
    """
    InputSpec describes the signature information of the model input, such as ``shape`` , ``dtype`` , ``name`` .

    This interface is often used to specify input tensor information of models in high-level API.
    It's also used to specify the tensor information for each input parameter of the forward function
    decorated by `@paddle.jit.to_static`.

    Args:
        shape (tuple(integers)|list[integers]): List|Tuple of integers
            declaring the shape. You can set "None" or -1 at a dimension
            to indicate the dimension can be of any size. For example,
            it is useful to set changeable batch size as "None" or -1.
        dtype (np.dtype|str, optional): The type of the data. Supported
            dtype: bool, float16, float32, float64, int8, int16, int32, int64,
            uint8. Default: float32.
        name (str): The name/alias of the variable, see :ref:`api_guide_Name`
            for more details.
        stop_gradient (bool, optional): A boolean that mentions whether gradient should flow. Default is False, means don't stop calculate gradients.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> from paddle.static import InputSpec

            >>> input = InputSpec([None, 784], 'float32', 'x')
            >>> label = InputSpec([None, 1], 'int64', 'label')

            >>> print(input)
            InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x, stop_gradient=False)

            >>> print(label)
            InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label, stop_gradient=False)
    """

    def __init__(
        self,
        shape: ShapeLike,
        dtype: DTypeLike = 'float32',
        name: str | None = None,
        stop_gradient: bool = False,
    ) -> None:
        # replace `None` in shape  with -1
        self.shape = self._verify(shape)
        # convert dtype into united representation
        if dtype is not None:
            if isinstance(dtype, (np.dtype, str)):
                dtype = convert_np_dtype_to_dtype_(dtype)

        self.dtype = dtype
        self.name = name
        self.stop_gradient = stop_gradient

    def _create_feed_layer(self):
        return data(self.name, shape=self.shape, dtype=self.dtype)

    def __repr__(self) -> str: