o
    + i                     @  s   d dl mZ d dlmZ d dlmZ ddlmZ er d dl	m
Z
 g ZG dd deZG d	d
 d
eZG dd deZG dd deZdS )    )annotations)TYPE_CHECKING)Layer   )
functional)Tensorc                      8   e Zd ZdZdd fddZdddZdddZ  ZS )ReLUa~  

    Sparse ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        ReLU(x) = max(x, 0)

    Parameters:
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 1.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> relu = paddle.sparse.nn.ReLU()
            >>> out = relu(sparse_x)
            >>> print(out)
            Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 2]],
                   values=[0., 1.])
    Nname
str | NonereturnNonec                      t    || _d S Nsuper__init___nameselfr
   	__class__ m/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/sparse/nn/layer/activation.pyr   =      

zReLU.__init__xr   c                 C     t || jS r   )FZrelur   r   r   r   r   r   forwardA      zReLU.forwardstrc                 C     | j rd| j  }|S d}|S Nzname= r   r   Zname_strr   r   r   
extra_reprD      zReLU.extra_reprr   r
   r   r   r   r   r   r   r   r   r!   __name__
__module____qualname____doc__r   r   r'   __classcell__r   r   r   r   r	      s
    
r	   c                      s8   e Zd ZdZdd fd
dZdddZdddZ  ZS )SoftmaxaU  

    Sparse Softmax Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    Note:
        Only support axis=-1 for SparseCsrTensor, which is faster when read data
        by row (axis=-1).

    Transform x to dense matrix, and :math:`i` is row index, :math:`j` is column index.
    If axis=-1, We have:

    .. math::

        softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))}

    Parameters:
        axis (int, optional): The axis along which to perform softmax calculations. Only support -1 for SparseCsrTensor.
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: SparseCooTensor / SparseCsrTensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle
            >>> paddle.seed(2022)

            >>> mask = paddle.rand((3, 4)) < 0.7
            >>> x = paddle.rand((3, 4)) * mask.astype('float32')
            >>> print(x)
            Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0.88156885, 0.14463395, 0.17831714, 0.43818203],
             [0.07617740, 0.75576496, 0.        , 0.61921930],
             [0.        , 0.        , 0.42460245, 0.03001321]])

            >>> csr = x.to_sparse_csr()
            >>> print(csr)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   crows=[0, 4, 7, 9],
                   cols=[0, 1, 2, 3, 0, 1, 3, 2, 3],
                   values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740,
                           0.75576496, 0.61921930, 0.42460245, 0.03001321])

            >>> softmax = paddle.sparse.nn.Softmax()
            >>> out = softmax(csr)
            >>> print(out)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   crows=[0, 4, 7, 9],
                   cols=[0, 1, 2, 3, 0, 1, 3, 2, 3],
                   values=[0.38234913, 0.18298410, 0.18925257, 0.24541418, 0.21302439,
                           0.42031071, 0.36666498, 0.59738696, 0.40261301])

            >>> coo = x.to_sparse_coo(sparse_dim=2)
            >>> print(coo)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2],
                            [0, 1, 2, 3, 0, 1, 3, 2, 3]],
                   values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740,
                           0.75576496, 0.61921930, 0.42460245, 0.03001321])

            >>> out = softmax(coo)
            >>> print(out)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2],
                            [0, 1, 2, 3, 0, 1, 3, 2, 3]],
                   values=[0.38234913, 0.18298411, 0.18925257, 0.24541420, 0.21302438,
                           0.42031071, 0.36666498, 0.59738696, 0.40261301])
    Naxisintr
   r   r   r   c                      t    || _|| _d S r   )r   r   _axisr   )r   r4   r
   r   r   r   r      s   

zSoftmax.__init__r   r   c                 C     t || j| jS r   )r   Zsoftmaxr7   r   r   r   r   r   r         zSoftmax.forwardr!   c                 C  r"   r#   r%   r&   r   r   r   r'      r(   zSoftmax.extra_repr)r3   N)r4   r5   r
   r   r   r   r*   r+   r,   r   r   r   r   r2   I   s
    H
r2   c                      r   )ReLU6a  

    Sparse ReLU6 Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        ReLU6(x) = min(max(0,x), 6)

    Parameters:
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 8.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> relu6 = paddle.sparse.nn.ReLU6()
            >>> out = relu6(sparse_x)
    Nr
   r   r   r   c                   r   r   r   r   r   r   r   r      r   zReLU6.__init__r   r   c                 C  r   r   )r   Zrelu6r   r   r   r   r   r      r    zReLU6.forwardr!   c                 C  r"   r#   r%   r&   r   r   r   r'      r(   zReLU6.extra_reprr   r)   r*   r+   r,   r   r   r   r   r:      s
    
r:   c                      s:   e Zd ZdZ	dd fd
dZdddZdddZ  ZS )	LeakyReLUa  

    Sparse Leaky ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        LeakyReLU(x)=
            \left\{
                \begin{array}{rcl}
                    x, & & if \ x >= 0 \\
                    negative\_slope * x, & & otherwise \\
                \end{array}
            \right.

    Parameters:
        negative_slope (float, optional): Slope of the activation function at
            :math:`x < 0` . Default is 0.01.
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 5.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> leaky_relu = paddle.sparse.nn.LeakyReLU(0.5)
            >>> out = leaky_relu(sparse_x)

    {Gz?Nnegative_slopefloatr
   r   r   r   c                   r6   r   )r   r   _negative_sloper   )r   r=   r
   r   r   r   r      s   

zLeakyReLU.__init__r   r   c                 C  r8   r   )r   Z
leaky_relur?   r   r   r   r   r   r      r9   zLeakyReLU.forwardr!   c                 C  r"   r#   r%   r&   r   r   r   r'      r(   zLeakyReLU.extra_repr)r<   N)r=   r>   r
   r   r   r   r*   r+   r,   r   r   r   r   r;      s    %
r;   N)
__future__r   typingr   Z	paddle.nnr   r$   r   r   Zpaddler   __all__r	   r2   r:   r;   r   r   r   r   <module>   s   ,V(