mindspore.ops.avg_pool3d

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mindspore.ops.avg_pool3d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=0)[source]

Applies a 3D average pooling over an input Tensor which can be regarded as a composition of 3D input planes. Typically the input is of shape \((N, C, D_{in}, H_{in}, W_{in})\), avg_pool3d outputs regional average 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.

\[ \begin{align}\begin{aligned}\text{output}(N_i, C_j, d, h, w) = \frac{1}{d_{ker} * h_{ker} * w_{ker}} \sum_{l=0}^{d_{ker}-1} \sum_{m=0}^{h_{ker}-1} \sum_{n=0}^{w_{ker}-1}\\\text{input}(N_i, C_j, s_0 \times d + l, s_1 \times h + m, s_2 \times w + n)\end{aligned}\end{align} \]

Warning

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

Note

This interface currently does not support Atlas A2 training series products.

Parameters

  • input_x (Tensor) – Tensor of shape \((N, C, D_{in}, H_{in}, W_{in})\). Currently support float16 and float32 data type.

  • kernel_size (Union[int, tuple[int]], optional) – The size of kernel used to take the average value, is an int number that represents depth, height and width are both kernel_size, or a tuple of three int numbers that represent depth, height and width respectively. Default: 1 .

  • stride (Union[int, tuple[int]], optional) – 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: 1 .

  • padding (Union(int, tuple[int]), optional) – The pad value to be filled. If padding is an integer, the addings of head, tail, top, bottom, left and right are the same, equal to pad. If padding is a tuple of six integers, the padding of head, tail, top, bottom, left and right equal to padding[0], padding[1], padding[2], padding[3], padding[4] and padding[5] correspondingly. Default: 0 .

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

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

  • divisor_override (int, optional) – 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 \((N, C, D_{out}, H_{out}, W_{out})\). Has the same data type with input_x.

Raises

  • TypeError – If input_x is not a Tensor.

  • TypeError – If kernel_size, stride or padding is neither an int not a 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 5.

  • ValueError – If numbers in kernel_size or stride are not positive.

  • ValueError – If kernel_size or stride is a tuple whose length is not equal to 3.

  • ValueError – If padding is a tuple whose length is not equal to 6.

  • ValueError – If element of padding is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.arange(1 * 2 * 2 * 2 * 3).reshape((1, 2, 2, 2, 3)), mindspore.float16)
>>> output = ops.avg_pool3d(input_x, kernel_size=2, stride=1)
>>> print(output)
[[[[[ 5.  6.]]]
  [[[17. 18.]]]]]