LpPool - version 2#

This page documents version 2 of operator LpPool. See LpPool for the latest version (since version 22).

  • Domain: ai.onnx

  • Since version: 2

LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Inputs

  • X (T): Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

  • Y (T): Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints

  • T: Constrain input and output types to float tensors. Allowed types: tensor(double), tensor(float), tensor(float16).

Differences with previous version (1)#

SchemaDiff: LpPool (domain 'ai.onnx')

  • old version: 1

  • new version: 2

  • breaking: no

Documentation:

  • line similarity: 0.83 (+1/-1 lines)

--- LpPool v1
+++ LpPool v2
@@ -1,5 +1,5 @@

- LpPool consumes an input tensor X and applies Lp pooling across the
+ LpPool consumes an input tensor X and applies Lp pooling across
  the tensor according to kernel sizes, stride sizes, and pad lengths.
  Lp pooling consisting of computing the Lp norm on all values of a subset
  of the input tensor according to the kernel size and downsampling the