mindspore.ops.AvgPool

class mindspore.ops.AvgPool(kernel_size=1, strides=1, pad_mode="valid", data_format="NCHW")[source]

Average pooling operation.

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 \((N_{in}, C_{in}, H_{in}, W_{in})\), AvgPool outputs regional average 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.

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

Warning

  • Only single input and single output are supported.

  • Global pooling is supported.

  • The height of “kernel_size” and the weight of “kernel_size” are positive integers within the range [1, 255]. ksize_h * ksize_w < 256.

  • Due to instruction restrictions, the values of “strides_h” and “strides_w” are positive integers within the range [1, 63].

Parameters

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

  • strides (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.

  • pad_mode (str) –

    The optional value for pad mode, is “same” or “valid”, not case sensitive. Default: “valid”.

    • same: Adopts the way of completion. The height and width of the output will be the same as the input. The total number of padding will be calculated in horizontal and vertical directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the last extra padding will be done from the bottom and the right side.

    • valid: Adopts the way of discarding. The possible largest height and width of output will be returned without padding. Extra pixels will be discarded.

  • data_format (str) – The format of input and output data. It should be ‘NHWC’ or ‘NCHW’. Default: ‘NCHW’.

Inputs:

  • x (Tensor) - Tensor of shape \((N, C_{in}, H_{in}, W_{in})\).

Outputs:

Tensor, with shape \((N, C_{out}, H_{out}, W_{out})\).

Raises

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

  • ValueError – If pad_mode is neither ‘valid’ nor ‘same’ with not case sensitive.

  • ValueError – If data_format is neither ‘NCHW’ nor ‘NHWC’.

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

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.avgpool_op = ops.AvgPool(pad_mode="VALID", kernel_size=2, strides=1)
...
...     def construct(self, x):
...         result = self.avgpool_op(x)
...         return result
...
>>> x = Tensor(np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4), mindspore.float32)
>>> net = Net()
>>> output = net(x)
>>> 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]]]]