mindspore.mint.nn.functional.max_pool2d

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

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:

\[\text{output}(N_i, C_j, h, w) = \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1} \text{input}(N_i, C_j, s_0 \times h + m, s_1 \times w + n)\]

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.

\[H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor\]
\[W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor\]

  • 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)