ёiSSKJr SSKJrJr SSKJr SSKJr SSK J r SSK J r \(aSSKJ r SSKJrJr SS KJr /rS S S jjrg )) annotations) TYPE_CHECKINGLiteral)_C_ops)in_dynamic_or_pir_mode)_update_padding_nd)convert_to_list)Tensor)Size3Size6)_PaddingSizeModeNc[5(dS5eUR5(dS5eUS:XdS5e[USS5nUcUnO [USS5nSn[USXtS 9up8/S Qn [R "XX9U5$) 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): ${ceil_mode_comment} data_format (string, optional): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. Currently only support `"NDHWC"` . name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Returns: Tensor: The output tensor of pooling result. The data type is same as input tensor. Examples: .. code-block:: pycon >>> import paddle >>> dense_x = paddle.randn((1, 4, 4, 4, 3)) >>> sparse_x = dense_x.to_sparse_coo(4) >>> kernel_sizes = [3, 3, 3] >>> paddings = [0, 0, 0] >>> strides = [1, 1, 1] >>> out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings) >>> print(out.shape) paddle.Size([1, 2, 2, 2, 3]) zK ~ M:L!3"G H  F KK)NrFrN)rr rr rz Size3 | Nonerz _PaddingSizeMode | Size3 | Size6rboolrzLiteral['NDHWC']rz str | Nonereturnr ) __future__rtypingrrpaddlerpaddle.frameworkrpaddle.nn.functional.poolingr paddle.utilsr r paddle._typingr r paddle.nn.functional.commonr __all__r!r"r r/s #)3;(=   01$+QL QLQL QL. QL  QL " QL QL QLr"