o #j@sDddlmZmZddlmZddlmZgZ    d ddZdS) )_C_opsin_dynamic_mode)_update_padding_nd)convert_to_listNFNDHWCc CstsJd|sJd|dksJdt|dd}|dur$|}nt|dd}d }t|d||d \}}gd } t|||| |S) a Implements sparse max pooling 3d operation. See more details in :ref:`api_paddle_sparse_nn_MaxPool3D` . Args: x (Tensor): The input SparseCooTensor of pooling operator, which is a 5-D tensor with shape [N, D, H, W, C]. The format of input tensor `"NDHWC"`, where N represents batch size, C represents the number of channels, D, H and W represent the depth, height and width of the feature respectively. kernel_size (int|list|tuple): The pool kernel size. If the kernel size is a tuple or list, it must contain three integers, (kernel_size_Depth, kernel_size_Height, kernel_size_Width). Otherwise, the pool kernel size will be the cube of an int. stride (int|list|tuple, optional): The pool stride size. If pool stride size is a tuple or list, it must contain three integers, [stride_Depth, stride_Height, stride_Width). Otherwise, the pool stride size will be a cube of an int. padding (string|int|list|tuple, optional): The padding size. Padding could be in one of the following forms. 1. A string in ['valid', 'same']. 2. An int, which means the feature map is zero padded by size of `padding` on every sides. 3. A list[int] or tuple(int) whose length is 3, [pad_depth, pad_height, pad_weight] whose value means the padding size of each dimension. 4. A list[int] or tuple(int) whose length is 6. [pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side. 5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0). The default value is 0. ceil_mode (bool, optional): ${ceil_mode_comment} data_format (string, optional): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Currently only support `"NDHWC"` . name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: The output tensor of pooling result. The data type is same as input tensor. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.randn((1, 4, 4, 4, 3)) >>> sparse_x = dense_x.to_sparse_coo(4) >>> kernel_sizes = [3, 3, 3] >>> paddings = [0, 0, 0] >>> strides = [1, 1, 1] >>> out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings) >>> print(out.shape) [1, 2, 2, 2, 3] z/Currently, Sparse API only support dynamic modez@Currently, sparse.relu only support the input of SparseCooTensorrz@Currently, sparse.max_pool3d only support data format of 'NDHWC'Z pool_sizeNZ pool_strideT) channel_last ceil_mode)r r )rZ is_sparse_coorrrZsparse_maxpool) xZ kernel_sizeZstridepaddingr Z data_formatnamerZpadding_algorithmZdilationrd/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/sparse/nn/functional/pooling.py max_pool3ds$8    r)NrFrN) ZpaddlerrZpaddle.nn.functional.poolingrZ paddle.utilsr__all__rrrrrs