chore: import upstream snapshot with attribution

python/paddle/sparse/nn/functional/pooling.py

@@ -0,0 +1,117 @@
+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Literal
+
+from paddle import _C_ops
+from paddle.framework import in_dynamic_or_pir_mode
+from paddle.nn.functional.pooling import _update_padding_nd
+from paddle.utils import convert_to_list
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+    from paddle._typing import (
+        Size3,
+        Size6,
+    )
+    from paddle.nn.functional.common import _PaddingSizeMode
+
+__all__ = []
+
+
+def max_pool3d(
+    x: Tensor,
+    kernel_size: Size3,
+    stride: Size3 | None = None,
+    padding: _PaddingSizeMode | Size3 | Size6 = 0,
+    ceil_mode: bool = False,
+    data_format: Literal['NDHWC'] = "NDHWC",
+    name: str | None = None,
+) -> Tensor:
+    """
+    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])
+    """
+
+    assert in_dynamic_or_pir_mode(), (
+        "Currently, Sparse API only support dynamic mode or pir mode."
+    )
+    assert x.is_sparse_coo(), (
+        "Currently, sparse.relu only support the input of SparseCooTensor"
+    )
+    assert data_format == 'NDHWC', (
+        "Currently, sparse.max_pool3d only support data format of 'NDHWC'"
+    )
+
+    kernel_size = convert_to_list(kernel_size, 3, 'pool_size')
+    if stride is None:
+        stride = kernel_size
+    else:
+        stride = convert_to_list(stride, 3, 'pool_stride')
+
+    channel_last = True
+
+    padding, padding_algorithm = _update_padding_nd(
+        padding, 3, channel_last=channel_last, ceil_mode=ceil_mode
+    )
+
+    # TODO(zkh2016): remove the dependency on dilation from the backend
+    dilation = [1, 1, 1]
+
+    return _C_ops.sparse_maxpool(x, kernel_size, padding, dilation, stride)