mindspore.mint.nn.functional.max_pool2d

mindspore.mint.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, *, ceil_mode=False, return_indices=False)

Performs a 2D max pooling on the input Tensor.

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

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

Warning

Only support on Atlas A2 training series.

Parameters

• input (Tensor) – Tensor of shape $(N_{in},C_{in},H_{in},W_{in})$ with data type of float32 in Ascend.

• 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 height and width of the kernel, or a tuple of two int numbers that represent height and width respectively.

• stride (Union[int, tuple[int], None]) – The distance of kernel moving, an int number that represents the height and width of movement are both stride, or a tuple of two int numbers that represent 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 height and width of movement are both strides, or a tuple of two int numbers that represent 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 $(N_{out},C_{out},H_{out},W_{out})$. It has the same data type as input.

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

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

• argmax (Tensor) - Index corresponding to the maximum value. In Ascend, data type is int32. It will be return only when return_indices is True.

Raises

• TypeError – If input is not a Tensor.

• ValueError – If length of shape of input is not equal to 4.

• TypeError – If kernel_size , stride , padding or dilation is not int or tuple.

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

• ValueError – If dilation is not all 1.

• ValueError – If padding is less than 0.

• ValueError – If padding is more than half of kernel_size.

• TypeError – If ceil_mode is not bool.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.arange(20 * 16 * 50 * 32).reshape((20, 16, 50, 32)), mindspore.float32)
>>> output_tensor, argmax = mint.nn.functional.max_pool2d(input, kernel_size=(3, 2), stride=(2, 1),
...                                               ceil_mode=False, return_indices=True)
>>> print(output_tensor.shape)
(20, 16, 24, 31)
>>> print(argmax.shape)
(20, 16, 24, 31)