mindspore.ops.MaxPool

class mindspore.ops.MaxPool(kernel_size=1, strides=1, pad_mode='valid', data_format='NCHW')

Max pooling operation.

Applies a 2D max pooling over an input Tensor which can be regarded as a composition of 2D planes.

Typically the input is of shape \((N_{in}, C_{in}, H_{in}, W_{in})\), MaxPool outputs regional maximum 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:

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

Parameters

• kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum 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 not only the height of movement but also the width of movement, 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) – The optional value for data format, is 'NHWC' or 'NCHW' . Default: 'NCHW' .

Inputs:

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

  • CPU: float16, float32, float64.

  • GPU/Ascend: float16, float32.

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 input is not equal to 4.

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)
>>> maxpool_op = ops.MaxPool(pad_mode="VALID", kernel_size=2, strides=1)
>>> output = maxpool_op(x)
>>> print(output)
[[[[ 5.  6.  7.]
   [ 9. 10. 11.]]
  [[17. 18. 19.]
   [21. 22. 23.]]
  [[29. 30. 31.]
   [33. 34. 35.]]]]