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mindspore.nn.MaxPool3d

class mindspore.nn.MaxPool3d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

3D max pooling operation.

Applies a 3D max pooling over an input Tensor which can be regarded as a composition of 3D planes.

Typically the input is of shape $(N_{in},C_{in},D_{in},H_{in},W_{in})$, MaxPool outputs regional maximum in the $(D_{in},H_{in},W_{in})$-dimension. Given kernel size is $ks=(d_{ker},h_{ker},w_{ker})$ and stride is $s=(s_0,s_1,s_2)$, the operation is as follows.

$$\text{output}(N_i,C_j,d,h,w)=\underset{l=0,\dots ,d_{ker}-1}{max}\underset{m=0,\dots ,h_{ker}-1}{max}\underset{n=0,\dots ,w_{ker}-1}{max}\text{input}(N_i,C_j,s_0\times d+l,s_1\times h+m,s_2\times w+n)$$

Parameters

• kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum value, is an int number that represents depth, height and width of the kernel, or a tuple of three int numbers that represent depth, height and width respectively. The value must be a positive integer.

• stride (Union[int, tuple[int]]) – The moving stride of pooling operation, an int number that represents the moving stride of pooling kernel in the directions of depth, height and the width, or a tuple of three int numbers that represent depth, height and width of movement respectively. The value must be a positive integer. If the value is None, the default value kernel_size is used.

• padding (Union[int, tuple[int]]) – Pooling padding length. An int number that represents the depth, height and width of movement are both stride, or a tuple of three int numbers that represent depth, height and width of movement respectively. The value cannot be negative. Default: 0.

• dilation (Union[int, tuple[int]]) – Control the spacing of elements in the pooling kernel. Default: 1.

• return_indices (bool) – If True, output is a Tuple of 2 Tensors, representing the maxpool result and where the max values are generated. Otherwise, only the maxpool result is returned. Default: False.

• ceil_mode (bool) – Whether to use ceil or floor to calculate output shape. Default: False.

Inputs:

• x (Tensor) - Tensor of shape $(N_{in},C_{in},D_{in},H_{in},W_{in})$ or $(C_{in},D_{in},H_{in},W_{in})$ with data type of int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32 or float64.

Outputs:

If return_indices is False, output is a Tensor, with shape $(N,C,D_{out},H_{out},W_{out})$, or $(C_{out},D_{out},H_{out},W_{out})$. It has the same data type as x.

If return_indices is True, output is a Tuple of 2 Tensors, representing the maxpool result and where the max values are generated.

• output (Tensor) - Maxpooling result, with shape $(N_{out},C_{out},D_{out},H_{out},W_{out})$ or $(C_{out},D_{out},H_{out},W_{out})$. It has the same data type as x.

• argmax (Tensor) - Index corresponding to the maximum value. Data type is int64.

Raises

• TypeError – If x is not a Tensor.

• ValueError – If length of shape of x is not equal to 5.

• TypeError – If kernel_size , stride , padding or dilation is neither an int nor a tuple.

• ValueError – If kernel_size or stride is less than 1.

• ValueError – If padding is less than 0.

Supported Platforms:

GPU

Examples

>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> import numpy as np
>>> pool1 = nn.MaxPool3d(kernel_size=3, stride=1, padding=1)
>>> pool2 = nn.MaxPool3d(kernel_size=3, stride=1, padding=1, return_indices=True)
>>> x = ms.Tensor(np.random.randint(0, 10, [1, 2, 2, 2, 2]), ms.float32)
>>> output1 = pool1(x)
>>> print(output1)
[[[[[8. 8.]
    [8. 8.]]
   [[8. 8.]
    [8. 8.]]]
  [[[9. 9.]
    [9. 9.]]
   [[9. 9.]
    [9. 9.]]]]]
>>> output2 = pool2(x)
>>> print(output2)
(Tensor(shape=[1, 2, 2, 2, 2], dtype=Float32, value=
[[[[[8.00000000e+000, 8.00000000e+000],
    [8.00000000e+000, 8.00000000e+000]],
   [[8.00000000e+000, 8.00000000e+000],
    [8.00000000e+000, 8.00000000e+000]]],
  [[[9.00000000e+000, 9.00000000e+000],
    [9.00000000e+000, 9.00000000e+000]],
   [[9.00000000e+000, 9.00000000e+000],
    [9.00000000e+000, 9.00000000e+000]]]]]), Tensor(shape=[1, 2, 2, 2, 2], dtype=Int64, value=
[[[[[7, 7],
    [7, 7]],
   [[7, 7],
    [7, 7]]],
  [[[2, 2],
    [2, 2]],
   [[2, 2],
    [2, 2]]]]]))