LpPool - version 18#

This page documents version 18 of operator LpPool. See LpPool for the latest version (since version 22).

  • Domain: ai.onnx

  • Since version: 18

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. The output spatial shape will be following:

output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

or

output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled pad_shape[i] is the sum of pads along axis i.

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:

VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

And pad shape will be following if SAME_UPPER or SAME_LOWER:

pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]

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 (11)#

SchemaDiff: LpPool (domain 'ai.onnx')

  • old version: 11

  • new version: 18

  • breaking: no

Documentation:

  • line similarity: 0.33 (+19/-1 lines)

--- LpPool v11
+++ LpPool v18
@@ -3,4 +3,22 @@
  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.
+ data into the output tensor Y for further processing. The output spatial shape will be following:
+ ```
+ output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ or
+ ```
+ output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)
+ ```
+ if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`.
+
+ `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
+ ```
+ VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i])
+ SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
+ ```
+ And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
+ ```
+ pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
+ ```