ReduceMean#

ReduceMean operation performs the reduction with finding the arithmetic mean on a given src data along dimensions specified by axes.

Take channel axis = 0 and keep_dims = True as an example:

$\dst_{0,\cdots,\cdots} = \frac{1}{channelNum} \cdot \sum\limits_{i}{\src_{i,\cdots,\cdots}} ,i \in [0,channelNum-1]$

Operation Attributes#

Attribute

Name

Description

Value Type

Supported

Values

Required or

Optional

axes

Specify indices of src tensor, along which the reduction is performed. If axes is a list, reduce over all of them. If axes is empty, corresponds to the identity operation. If axes contains all dimensions of src tensor, a single reduction value is calculated for the entire src tensor. Exactly one of attribute axes and the second input tensor axes should be available.

s64

A s64 list values which is in the range of [-r,r-1] where r = rank(src). Empty list(default)

Optional

keep_dims

If set to true it holds axes that are used for reduction. For each such axes, dst dimension is equal to 1.

bool

true, false (default)

Optional

Execution Arguments#

The inputs and outputs must be provided according to the below index order when constructing an operation.

Inputs#

Index

Argument Name

Required or Optional

0

src

Required

1

axes

Optional

@note axes is an 1-D tensor specifying the axis along which the reduction is performed. 1D tensor of unique elements. The range of elements is [-r, r-1], where r is the rank of src tensor. Exactly one of attribute axes and the second input tensor axes should be available.

Outputs#

Index

Argument Name

Required or Optional

0

dst

Required

@note The result of ReduceMean function applied to src tensor. shape[i] = shapeOf(data)[i] for all i that is not in the list of axes from the second input. For dimensions from axes, shape[i] == 1 if keep_dims == True, or i-th dimension is removed from the dst otherwise.

Supported Data Types#

ReduceMean operation supports the following data type combinations.

Source/Destination

Axes

f32

s32

bf16

s32

f16

s32