mindspore.ops.AvgPool

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

Average pooling operation.

Refer to mindspore.ops.avg_pool2d() for more details.

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, optional) –

    The optional value for pad mode, is 'same' or 'valid' . Default: 'valid' .

    • 'same': The height and width of the output are the same as the input divided by ‘strides’ and rounded up.

    • 'valid': Returns the output of the valid calculation without filling. Redundant pixels that do not satisfy the calculation will be discarded.

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

Inputs:

  • x (Tensor) - Tensor of shape \((N, C_{in}, H_{in}, W_{in})\). Supported dtypes: float16, float32, float64.

Outputs:

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

Raises

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

  • TypeError – If dtype of x is not float16, float32 or float64.

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

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

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

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops, nn
>>> 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]]]]