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) –
Specifies the padding mode with a padding value of 0. It can be set to: "same" or "valid" . Default: "valid" .
- "same": Pad the input around its edges so that the shape of input and output are the same when stride is set to 1. The amount of padding to is calculated by the operator internally, If the amount is even, it is uniformly distributed around the input, if it is odd, the excess amount goes to the right/bottom side.
- "valid": No padding is applied to the input, and the output returns the maximum possible height and width. Extra pixels that could not complete a full stride 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]]]]