Otherwise, the pool kernel size will be the cube of an int.
Otherwise, the pool kernel size will be the cube of an int.
stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
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).
it must contain three integers, [stride_Depth, stride_Height, stride_Width).
Otherwise, the pool stride size will be a cube of an int.
Otherwise, the pool stride size will be a cube of an int.
padding (string|int|list|tuple): The padding size. Padding could be in one of the following forms.
padding (string|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
1. A string in ['valid', 'same'].
1. A string in ['valid', 'same'].
2. An int, which means the feature map is zero padded by size of `padding` on every sides.
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.
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.
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).
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.
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
ceil_mode (bool, optional): ${ceil_mode_comment}
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
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:
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"` .
`[batch_size, input_channels, input_depth, input_height, input_width]`. Currently only support `"NDHWC"` .
name(str, optional): For detailed information, please refer
name(str, optional): For detailed information, please refer
...
@@ -64,16 +64,14 @@ def max_pool3d(
...
@@ -64,16 +64,14 @@ def max_pool3d(
.. code-block:: python
.. code-block:: python
import paddle
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.randn((1, 4, 4, 4, 3))
dense_x = paddle.randn((1, 4, 4, 4, 3))
sparse_x = dense_x.to_sparse_coo(4)
sparse_x = dense_x.to_sparse_coo(4)
kernel_sizes = [3, 3, 3]
kernel_sizes = [3, 3, 3]
paddings = [0, 0, 0]
paddings = [0, 0, 0]
strides = [1, 1, 1]
strides = [1, 1, 1]
out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
#[1, 2, 2, 2, 3]
#[1, 2, 2, 2, 3]
"""
"""
assertin_dynamic_mode(),"Currently, Sparse API only support dynamic mode"
assertin_dynamic_mode(),"Currently, Sparse API only support dynamic mode"
