mindspore.ops.lp_pool2d
- mindspore.ops.lp_pool2d(x, norm_type, kernel_size, stride=None, ceil_mode=False)[source]
Applying 2D LPPooling operation on an input Tensor can be regarded as forming a 2D input plane.
Typically the input is of shape \((N, C, H_{in}, W_{in})\), the output is of shape \((N, C, H_{in}, W_{in})\), with the same shape as input, the operation is as follows.
\[f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}\]- Parameters
x (Tensor) – Tensor of shape \((N, C, H_{in}, W_{in})\).
norm_type (Union[int, float]) –
Type of normalization, represents p in the formula, can not be 0,
if p = 1, the result obtained is the sum of elements in the pool nucleus(Proportional to average pooling).
if p = \(\infty\), the result is the result of maximum pooling.
kernel_size (Union[int, tuple[int]]) – The size of kernel window. The data type of kernel_size must be int and the value represents the height and width, or a tuple of two int numbers that represent height and width respectively.
stride (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:
None
, which indicates the moving step is kernel_size .ceil_mode (bool) – Whether to use ceil or floor to calculate output shape. Default:
False
.
- Returns
output (Tensor) - LPPool2d result, with shape \((N, C, H_{in}, W_{in})\), It has the same data type as x, where
\[H_{out} = \left\lfloor\frac{H_{in} - \text{kernel_size}[0]}{\text{stride}[0]} + 1\right\rfloor\]\[W_{out} = \left\lfloor\frac{W_{in} - \text{kernel_size}[1]}{\text{stride}[1]} + 1\right\rfloor\]
- Raises
TypeError – If x is not a Tensor.
TypeError – If kernel_size or stride is neither int nor tuple.
TypeError – If ceil_mode is not a bool.
TypeError – If norm_type is neither float nor int.
ValueError – If norm_type is equal to 0.
ValueError – If kernel_size or stride is less than 1.
ValueError – If kernel_size or stride is a tuple whose length is not equal to 2.
ValueError – If length of shape of x is not equal to 4.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> from mindspore import ops >>> from mindspore import Tensor >>> import numpy as np >>> x = Tensor(np.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5)), dtype=ms.float32) >>> out = ops.lp_pool2d(x, norm_type=1, kernel_size=3, stride=1, ceil_mode=False) >>> print(out) [[[[ 54. 63. 72.] [ 99. 108. 117.]] [[ 234. 243. 252.] [ 279. 288. 297.]] [[ 414. 423. 432.] [ 459. 468. 477.]]] [[[ 594. 603. 612.] [ 639. 648. 657.]] [[ 774. 783. 792.] [ 819. 828. 837.]] [[ 954. 963. 972.] [ 999. 1008. 1017.]]]]