Compute Library
 19.08
CLROIPoolingLayer Class Reference

Basic function to run CLROIPoolingLayerKernel. More...

#include <CLROIPoolingLayer.h>

Collaboration diagram for CLROIPoolingLayer:
[legend]

Public Member Functions

void configure (const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
 Set the input and output tensors. More...
 
- Public Member Functions inherited from ICLSimpleFunction
 ICLSimpleFunction ()
 Default constructor. More...
 
void run () override final
 Run the kernels contained in the function. More...
 
- Public Member Functions inherited from IFunction
virtual ~IFunction ()=default
 Destructor. More...
 
virtual void prepare ()
 Prepare the function for executing. More...
 

Detailed Description

Basic function to run CLROIPoolingLayerKernel.

This function calls the following OpenCL kernels:

  1. CLROIPoolingLayerKernel

Definition at line 42 of file CLROIPoolingLayer.h.

Member Function Documentation

◆ configure()

void configure ( const ICLTensor input,
const ICLTensor rois,
ICLTensor output,
const ROIPoolingLayerInfo pool_info 
)

Set the input and output tensors.

Parameters
[in]inputSource tensor. Data types supported: F16/F32.
[in]roisROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: U16
[out]outputDestination tensor. Data types supported: Same as input.
[in]pool_infoContains pooling operation information described in ROIPoolingLayerInfo.
Note
The x and y dimensions of output tensor must be the same as pool_info 's pooled width and pooled height.
The z dimensions of output tensor and input tensor must be the same.
The fourth dimension of output tensor must be the same as the number of elements in rois array.

Definition at line 33 of file CLROIPoolingLayer.cpp.

34 {
35  // Configure ROI pooling kernel
36  auto k = arm_compute::support::cpp14::make_unique<CLROIPoolingLayerKernel>();
37  k->configure(input, rois, output, pool_info);
38  _kernel = std::move(k);
39 }

The documentation for this class was generated from the following files: