mindspore.ops.max_pool3d

mindspore.ops.max_pool3d(x, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

Performs a 3D max pooling on the input Tensor.

Typically the input is a Tensor with shape $(N_{in},C_{in},D_{in},H_{in},W_{in})$, outputs regional maximum in the $(D_{in},H_{in},W_{in})$-dimension. Given kernel_size $ks=(d_{ker},h_{ker},w_{ker})$ and stride $s=(s_0,s_1,s_2)$, 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

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

• kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum value and arg 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.

• stride (Union[int, tuple[int]]) – The distance of kernel moving, 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. Default: None , which indicates the moving step is kernel_size .

• padding (Union[int, tuple[int]]) – An int number that represents the depth, height and width of movement are both strides, or a tuple of three int numbers that represent depth, height and width of movement respectively. Default: 0 .

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

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

• return_indices (bool) – Whether to output the indices of max value. Default: False .

Returns

If return_indices is False, return a Tensor output, else return a tuple (output, argmax).

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

$$D_{out}=\lfloor \frac{D_{in}+2\times \text{padding}[0]-\text{dilation}[0]\times (\text{kernel\_size}[0]-1)-1}{\text{stride}[0]}+1\rfloor$$

$$H_{out}=\lfloor \frac{H_{in}+2\times \text{padding}[1]-\text{dilation}[1]\times (\text{kernel\_size}[1]-1)-1}{\text{stride}[1]}+1\rfloor$$

$$W_{out}=\lfloor \frac{W_{in}+2\times \text{padding}[2]-\text{dilation}[2]\times (\text{kernel\_size}[2]-1)-1}{\text{stride}[2]}+1\rfloor$$

• argmax (Tensor) - Index corresponding to the maximum value. Data type is int64. It will be returned only when return_indices is True .

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 not int or tuple.

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

• ValueError – If padding is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.arange(2 * 1 * 2 * 2 * 2).reshape((2, 1, 2, 2, 2)), mindspore.float32)
>>> output_tensor, argmax = ops.max_pool3d(x, kernel_size=2, stride=1, padding=1, return_indices=True)
>>> print(output_tensor.shape)
(2, 1, 3, 3, 3)
>>> print(argmax.shape)
(2, 1, 3, 3, 3)