mindspore.ops.MaxPoolWithArgmaxV2

class mindspore.ops.MaxPoolWithArgmaxV2(kernel_size, strides=None, pads=0, dilation=(1, 1), ceil_mode=False, argmax_type=mstype.int64)

Performs max pooling on the input Tensor and returns both max values and indices.

Typically the input is of shape $(N_{in},C_{in},H_{in},W_{in})$, MaxPool outputs regional maximum in the $(H_{in},W_{in})$-dimension. Given kernel size $(h_{ker},w_{ker})$ and stride $(s_0,s_1)$, 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

This is an experimental API that is subject to change or deletion.

Parameters

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

• strides (Union[int, tuple[int]], optional) – The distance of kernel moving, an int number that represents not only the height of movement but also the width of movement, or a tuple of two int numbers that represent height and width of movement respectively. Default: None , meaning that strides = kernel_size.

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

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

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

• argmax_type (mindspore.dtype, optional) – The dtype for argmax. Default: mstype.int64 . [Disabled in Ascend.]

Inputs:

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

Outputs:

Tuple of 2 Tensors, representing the maxpool result and where the max values are generated.

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

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

• argmax (Tensor) - Index corresponding to the maximum value. Data type is int32 or int64 in GPU and CPU, is uint16 in Ascend.

Raises

• TypeError – If x is not a Tensor.

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

• TypeError – If kernel_size , strides , pads or dilation is not int or tuple.

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

• ValueError – If pads is less than 0.

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

• ValueError – If argmax_type is not mindspore.int64 or mindspore.int32.

• TypeError – If ceil_mode is not bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.arange(20 * 16 * 50 * 32).reshape((20, 16, 50, 32)), mindspore.float32)
>>> maxpool_arg_v2_op = ops.MaxPoolWithArgmaxV2(kernel_size=(3, 2), strides=(2, 1))
>>> output_tensor, argmax = maxpool_arg_v2_op(x)
>>> print(output_tensor.shape)
(20, 16, 24, 31)
>>> print(argmax.shape)
(20, 16, 24, 31)