TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``indices`` is not a 2-D.
ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``indices`` is not a 2-D.
TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``crow``, ``cols`` and ``values`` is not a 2-D.
ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``crow``, ``cols`` and ``values`` is not a 2-D.
TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
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).
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'].
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): ${ceil_mode_comment}
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
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
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
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)
#[1, 2, 2, 2, 3]
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)
#[1, 2, 2, 2, 3]
"""
assertin_dynamic_mode(),"Currently, Sparse API only support dynamic mode"
assertx.is_sparse_coo(
assert(
x.is_sparse_coo()
),"Currently, sparse.relu only support the input of SparseCooTensor"
assertdata_format=='NDHWC',"Currently, sparse.max_pool3d only support data format of 'NDHWC'"
assert(
data_format=='NDHWC'
),"Currently, sparse.max_pool3d only support data format of 'NDHWC'"
The Sparse convolution3d layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are multidimensional SparseCooTensors with a shape of
Output(Output) are multidimensional SparseCooTensors with a shape of
:math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. If bias attribution is provided,
bias is added to the output of the convolution.
and W is the width of the feature. If bias attribution is provided,
bias is added to the output of the convolution.
For each input :math:`X`, the equation is:
.. math::
...
...
@@ -150,7 +163,7 @@ class Conv3D(_Conv3D):
stride_D = stride_H = stride_W = stride. The default value is 1.
padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
1. a string in ['valid', 'same'].
2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
...
...
@@ -208,63 +221,65 @@ class Conv3D(_Conv3D):
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
The Sparse submanifold convolution3d layer calculates the output based on the input, filter
**Submanifold Sparse Convlution3d Layer**
The submanifold sparse convolution3d layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are multidimensional SparseCooTensors with a shape of
Output(Output) are multidimensional SparseCooTensors with a shape of
:math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. If bias attribution is provided,
and W is the width of the feature. If bias attribution is provided,
bias is added to the output of the convolution.
For each input :math:`X`, the equation is:
.. math::
Out =(W \ast X + b
Out =W \ast X + b
In the above equation:
...
...
@@ -283,7 +298,7 @@ class SubmConv3D(_Conv3D):
stride_D = stride_H = stride_W = stride. The default value is 1.
padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
1. a string in ['valid', 'same'].
2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
...
...
@@ -297,7 +312,7 @@ class SubmConv3D(_Conv3D):
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. The default value is 1.
padding_mode(str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Currently only support ``'zeros'``.
key(str, optional): the key is used to save or use the same rulebook,
key(str, optional): the key is used to save or use the same rulebook,
the definition and role of rulebook refers to
https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
default value is None.
...
...
@@ -345,44 +360,46 @@ class SubmConv3D(_Conv3D):
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
