mindspore.ops.avg_pool2d

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mindspore.ops.avg_pool2d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=0)[source]

Applies a 2D average pooling over an input Tensor which can be regarded as a composition of 2D input planes. Typically the input is of shape (Nin,Cin,Hin,Win), outputs regional average in the (Hin,Win)-dimension. Given kernel size (kh,kw) and strides , the operation is as follows.

output(Ni,Cj,h,w)=1khkwm=0kh1n=0kw1input(Ni,Cj,stride[0]×h+m,stride[1]×w+n)

Warning

kernel_size is in the range [1, 255]. stride is in the range [1, 63].

Parameters
  • input_x (Tensor) – Tensor of shape (N,Cin,Hin,Win).

  • kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the average value. It 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. Default: 1 .

  • stride (Union[int, tuple[int]]) – The distance of kernel moving, 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: 1 .

  • padding (Union(int, tuple[int])) – The pad value to be filled. Default: 0. If padding is an integer, the paddings of top, bottom, left and right are the same, equal to pad. If padding is a tuple of 4 integers, the padding of top, bottom, left and right equal to padding[0], padding[1], padding[2] and padding[3] correspondingly. Default: 0 .

  • ceil_mode (bool) – If True, apply ceil instead of floor to compute the output shape. Default: False.

  • count_include_pad (bool) – If True, include the zero-padding in the averaging calculation. Default: True .

  • divisor_override (int) – If specified, it will be used as divisor in the averaging calculation, otherwise kernel_size will be used. Default: 0, which means not specified.

Returns

Tensor, with shape (N,Cout,Hout,Wout).

Raises
  • TypeError – If input_x is not an Tensor.

  • TypeError – If kernel_size or stride is neither int nor tuple.

  • TypeError – If ceil_mode or count_include_pad is not a bool.

  • TypeError – If divisor_override is not an int.

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

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

  • ValueError – If kernel_size or stride is a tuple whose length is not equal to 2.

  • ValueError – If padding is not int nor a tuple whose length is equal to 4.

  • ValueError – If value(s) of 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(1 * 3 * 3 * 4).reshape(1, 3, 3, 4), mindspore.float32)
>>> output = ops.avg_pool2d(x, kernel_size=2, stride=1)
>>> print(output)
[[[[ 2.5   3.5   4.5]
   [ 6.5   7.5   8.5]]
  [[14.5  15.5  16.5]
   [18.5  19.5  20.5]]
  [[26.5  27.5  28.5]
   [30.5  31.5  32.5]]]]