Visionworks OpenVX

OpenVX

heterogeneous computation framework

Spec

OpenVX 1.2源碼解析 — 目錄結構

除了官方的參考實作外,下方是不同廠商的實作,有些有開放原始碼有些則是包裝程動態函式庫.

  1. Intel Computer Vision SDK
  2. AMD OVX : https://github.com/GPUOpen-ProfessionalCompute-Libraries/amdovx-core -->
  3. TI OVX:
  4. Nvidia Vision Works:

以上是有通過conformance test的廠商,另外ARM 也有類似的SDK(compute library)而且初期開發時在架構上也是參考OpenVX。

  1. ARM compute library:

雖然一開始OpenVX是針對電腦視覺運算設計的軟體框架,但由於類神經網路的編程模式(programming model)跟熱門程度讓Khronos OpenVX工作小組也特別訂定了Neural Network Extension使得OpenVX也加入了深度學習的戰場。

VisionWorks

NVIDIA VisionWorks toolkit is a software development package for computer vision (CV) and image processing. VisionWorks™ implements and extends the Khronos OpenVX standard, and it is optimized for CUDA-capable GPUs and SOCs enabling developers to realize CV applications on a scalable and flexible platform.

VisionWorks includes the following primitives:

IMAGE ARITHMETIC

  • Absolute Difference
  • Accumulate Image
  • Accumulate Squared
  • Accumulate Weighted
  • Add / Subtract / Multiply +
  • Channel Combine
  • Channel Extract
  • Color Convert +
  • CopyImage
  • Convert Depth
  • Magnitude
  • MultiplyByScalar
  • Not / Or / And / Xor
  • Phase
  • Table Lookup
  • Threshold

FLOW & DEPTH

  • Median Flow
  • Optical Flow (LK) +
  • Semi-Global Matching
  • Stereo Block Matching
  • IME Create Motion Field
  • IME Refine Motion Field
  • IME Partition Motion Field

GEOMETRIC TRANSFORMS

  • Affine Warp +
  • Warp Perspective +
  • Flip Image
  • Remap
  • Scale Image +

FILTERS

  • BoxFilter
  • Convolution
  • Dilation Filter
  • Erosion Filter
  • Gaussian Filter
  • Gaussian Pyramid
  • Laplacian3x3
  • Median Filter
  • Scharr3x3
  • Sobel 3x3

FEATURES

  • Canny Edge Detector
  • FAST Corners +
  • FAST Track +
  • Harris Corners +
  • Harris Track
  • Hough Circles
  • Hough Lines

ANALYSIS

  • Histogram
  • Histogram Equalization
  • Integral Image
  • Mean Std Deviation
  • Min Max Locations

OpenVX for us

Requirements

  • [x] Support user defined processing
  • [ ] Support optimization of duplicate processing
  • [ ] Open source framework (if available)

User defined processing

Yes. user node, base it on the Advanced Tiling Extensions (see the Intel's Extensions to the OpenVX* API: Advanced Tiling chapter)

Support optimization of duplicate processing

ref:

optimization tips

  • Use virtual images whenever possible, as this unlocks many graph compiler optimizations.
  • Whenever possible, prefer standard nodes and/or extensions over user kernel nodes (which serve as memory and execution barriers, hindering performance). This gives the Pipeline Manager much more flexibility to optimize the graph execution.
  • If you still need to implement a user node, base it on the Advanced Tiling Extensions (see the Intel's Extensions to the OpenVX* API: Advanced Tiling chapter)
  • If the application has independent graphs, run these graphs in parallel using vxScheduleGraph API call.
  • Provide enough parallel slack to the scheduler- do not break work (for example, images) into too many tiny pieces. Consider kernel fusion.
  • For images, use smallest data type that fits the application accuracy needs (for example, 32->16->8 bits).
  • Consider heterogeneous execution (see the Heterogeneous Computing with OpenVINO™ toolkit chapter).
  • You can create an OpenVX image object that references a memory that was externally allocated (vxCreateImageFromHandle). To enable zero-copy with the GPU the externally allocated memory should be aligned. For more details, refer to https://software.intel.com/en-us/node/540453.
  • Beware of the (often prohibitive) vxVerifyGraph latency costs. For example, construct the graph in a way it would not require the verification upon the parameters updates. Notice that unlike Map/Unmap for the input images (see the Map/Unmap for OpenVX* Images section), setting new images with different meta-data (size, type, etc) almost certainly triggers the verification, potentially adding significant overhead.

Open source framework (if available)

OpenVino

Requirements

Software Requirements

A Windows build environment needs these components:

Get the Software

Your license includes the full version of the product. To access the toolkit:

  1. Make sure your system meets the minimum requirements listed on this page.
  2. Complete the registration form.
  3. Download the product.

Register & Download

AMD OpenVX

Features

  • The code is highly optimized for both x86 CPU and OpenCL for GPU
  • Supported hardware spans the range from low power embedded APUs (like the new G series) to laptop, desktop and workstation graphics
  • Supports Windows, Linux, and OS X
  • Includes a “graph optimizer” that looks at the entire processing pipeline and removes/replaces/merges functions to improve performance and minimize bandwidth at runtime
  • Scripting support allows for rapid prototyping, without re-compiling at production performance levels

Pre-requisites

  • CPU: SSE4.1 or above CPU, 64-bit.

  • GPU: Radeon Professional Graphics Cards or Vega Family of Products (16GB required for vx_loomsl and vx_nn libraries)

    • Windows: install the latest drivers and OpenCL SDK download
    • Linux: install ROCm

  • OpenCV 3 (optional)

    download

    for RunVX

    • Set OpenCV_DIR environment variable to OpenCV/build folder

Build Instructions

Build this project to generate AMD OpenVX library and RunVX executable.

Build using Visual Studio Professional 2013 on 64-bit Windows 10/8.1/7

  • Install OpenCV 3 with contrib download for RunVX tool to support camera capture and image display (optional)
  • OpenCV_DIR environment variable should point to OpenCV/build folder
  • Use amdovx-core/amdovx.sln to build for x64 platform
  • If AMD GPU (or OpenCL) is not available, set build flag ENABLE_OPENCL=0 in openvx/openvx.vcxproj and runvx/runvx.vcxproj.

Test

Download to C:\Users\aeejshe\Downloads

  • C:\Users\aeejshe\Downloads\amdovx-core-0.9-beta2
  • C:\Users\aeejshe\Downloads\opencv

Build SW according to guidelines, especially

  • set ENABLE_OPENCL=0
  • modify lib to C:\Users\aeejshe\Downloads\opencv\build\x64\vc12\lib\opencv_world310d.lib

Demo

C:\Users\aeejshe\Downloads\amdovx-core-0.9-beta2\amdovx-core-0.9-beta2>runvx exa

mples\gdf\canny.gdf

***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 *****

runvx.exe 0.9.7

OK: using AMD OpenVX 0.9.7

OK: enabled graph scheduling in separate threads

csv,HEADER ,STATUS, COUNT,cur-ms,avg-ms,min-ms,clenqueue-ms,clwait-ms,clwrite-ms

,clread-ms

OK: capturing 480x360 image(s) into 480x360 RGB image buffer

csv,OVERALL,  PASS,     1,      ,  8.60,  8.60,  0.00,  0.00,  0.00,  0.00 (medi

an 8.598)

> total elapsed time:   0.11 sec

Abort: Press any key to exit...

canny.gdf

# create input and output images

data input  = image:480,360,RGB2

data output = image:480,360,U008

# specify input source for input image and request for displaying input and output images

read input  examples/images/face1.jpg

view input  inputWindow

view output edgesWindow

# compute luma image channel from input RGB image

data yuv  = image-virtual:0,0,IYUV

data luma = image-virtual:0,0,U008

node org.khronos.openvx.color_convert input yuv

node org.khronos.openvx.channel_extract yuv !CHANNEL_Y luma

# compute edges in luma image using Canny edge detector

data hyst = threshold:RANGE,UINT8:INIT,80,100

data gradient_size = scalar:INT32,3

node org.khronos.openvx.canny_edge_detector luma hyst gradient_size !NORM_L1 output
graph TB
input --> |color_convert| yuv
yuv --> |channel_extract| luma
luma --> |merge| merged
hyst --> merged
gradient_size --> merged
merged --> |canny_edge_detector| output

runvx

usage

C:\Users\aeejshe\Downloads\amdovx-core-0.9-beta2\amdovx-core-0.9-beta2>runvx

***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 *****

runvx.exe 0.9.7

Usage:

  runvx.exe [options] [file] <file.gdf> [argument(s)]

  runvx.exe [options] node <kernelName> [argument(s)]

  runvx.exe [options] shell [argument(s)]

The argument(s) are data objects created using <data-description> syntax.

These arguments can be accessed from inside GDF as $1, $2, etc.

The available command-line options are:

  -h

      Show full help.

  -v

      Turn on verbose logs.

  -root:<directory>

      Replace ~ in filenames with <directory> in the command-line and

      GDF file. The default value of '~' is current working directory.

  -frames:[<start>:]<end>|eof|live

      Run the graph/node for specified frames or until eof or just as live.

      Use live to indicate that input is live until aborted by user.

  -affinity:CPU|GPU[<device-index>]

      Set context affinity to CPU or GPU.

  -dump-profile

      Print performance profiling information after graph launch.

  -enable-profile

      use directive VX_DIRECTIVE_AMD_ENABLE_PROFILE_CAPTURE when graph is create

d

  -discard-compare-errors

      Continue graph processing even if compare mismatches occur.

  -disable-virtual

      Replace all virtual data types in GDF with non-virtual data types.

      Use of this flag (i.e. for debugging) can make a graph run slower.

dump profile

C:\Users\aeejshe\Downloads\amdovx-core-0.9-beta2\amdovx-core-0.9-beta2>runvx -du

mp-profile examples\gdf\canny.gdf

***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 *****

runvx.exe 0.9.7

OK: using AMD OpenVX 0.9.7

OK: enabled graph scheduling in separate threads

csv,HEADER ,STATUS, COUNT,cur-ms,avg-ms,min-ms,clenqueue-ms,clwait-ms,clwrite-ms

,clread-ms

OK: capturing 480x360 image(s) into 480x360 RGB image buffer

csv,OVERALL,  PASS,     1,      ,  8.62,  8.62,  0.00,  0.00,  0.00,  0.00 (medi

an 8.621)

> total elapsed time:   0.07 sec

> graph profile:

 COUNT,tmp(ms),avg(ms),min(ms),max(ms),DEV,KERNEL

     1,  8.621,  8.621,  8.621,  8.621,CPU,GRAPH

     1,  1.196,  1.196,  1.196,  1.196,CPU,com.amd.openvx.ColorConvert_Y_RGB

     1,  4.905,  4.905,  4.905,  4.905,CPU,com.amd.openvx.CannySobel_U16_U8_3x3_

L1NORM

     1,  2.305,  2.305,  2.305,  2.305,CPU,com.amd.openvx.CannySuppThreshold_U8X

Y_U16_3x3

     1,  0.208,  0.208,  0.208,  0.208,CPU,com.amd.openvx.CannyEdgeTrace_U8_U8XY

Abort: Press any key to exit...

Test if CSE works

input

# create input and output images

data input  = image:480,360,RGB2

data output = image:480,360,U008

data output2 = image:480,360,U008

# specify input source for input image and request for displaying input and output images

read input  examples/images/face1.jpg

view input  inputWindow

view output edgesWindow

# compute luma image channel from input RGB image

data yuv  = image-virtual:0,0,IYUV

data yuv2  = image-virtual:0,0,IYUV

data luma = image-virtual:0,0,U008

data luma2 = image-virtual:0,0,U008

node org.khronos.openvx.color_convert input yuv

node org.khronos.openvx.color_convert input yuv2

node org.khronos.openvx.channel_extract yuv !CHANNEL_Y luma

node org.khronos.openvx.channel_extract yuv2 !CHANNEL_Y luma2

# compute edges in luma image using Canny edge detector

data hyst = threshold:RANGE,UINT8:INIT,80,100

data gradient_size = scalar:INT32,3

node org.khronos.openvx.canny_edge_detector luma hyst gradient_size !NORM_L1 output

node org.khronos.openvx.canny_edge_detector luma2 hyst gradient_size !NORM_L1 output2

Output

C:\Users\aeejshe\Downloads\amdovx-core-0.9-beta2\amdovx-core-0.9-beta2>runvx -du

mp-profile examples\gdf\canny.gdf

***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 *****

runvx.exe 0.9.7

OK: using AMD OpenVX 0.9.7

OK: enabled graph scheduling in separate threads

csv,HEADER ,STATUS, COUNT,cur-ms,avg-ms,min-ms,clenqueue-ms,clwait-ms,clwrite-ms

,clread-ms

OK: capturing 480x360 image(s) into 480x360 RGB image buffer

csv,OVERALL,  PASS,     1,      , 17.13, 17.13,  0.00,  0.00,  0.00,  0.00 (medi

an 17.127)

> total elapsed time:   0.07 sec

> graph profile:

 COUNT,tmp(ms),avg(ms),min(ms),max(ms),DEV,KERNEL

     1, 17.127, 17.127, 17.127, 17.127,CPU,GRAPH

     1,  1.202,  1.202,  1.202,  1.202,CPU,com.amd.openvx.ColorConvert_Y_RGB

     1,  1.192,  1.192,  1.192,  1.192,CPU,com.amd.openvx.ColorConvert_Y_RGB

     1,  4.857,  4.857,  4.857,  4.857,CPU,com.amd.openvx.CannySobel_U16_U8_3x3_

L1NORM

     1,  4.838,  4.838,  4.838,  4.838,CPU,com.amd.openvx.CannySobel_U16_U8_3x3_

L1NORM

     1,  2.312,  2.312,  2.312,  2.312,CPU,com.amd.openvx.CannySuppThreshold_U8X

Y_U16_3x3

     1,  2.302,  2.302,  2.302,  2.302,CPU,com.amd.openvx.CannySuppThreshold_U8X

Y_U16_3x3

     1,  0.209,  0.209,  0.209,  0.209,CPU,com.amd.openvx.CannyEdgeTrace_U8_U8XY

     1,  0.207,  0.207,  0.207,  0.207,CPU,com.amd.openvx.CannyEdgeTrace_U8_U8XY

Abort: Press any key to exit...

Q: Why CSE not work?

TODO:

API

//vx_api.h

VX_API_ENTRY vx_graph VX_API_CALL vxCreateGraph(vx_context context);

VX_API_ENTRY vx_status VX_API_CALL vxVerifyGraph(vx_graph graph);

VX_API_ENTRY vx_status VX_API_CALL vxProcessGraph(vx_graph graph);

VX_API_ENTRY vx_image VX_API_CALL vxCreateVirtualImage(vx_graph graph, vx_uint32 width, vx_uint32 height, vx_df_image color);

//vx_node.h

VX_API_ENTRY vx_node VX_API_CALL vxColorConvertNode(vx_graph graph, vx_image input, vx_image output);

OpenCV G-API

Intro

[G-API Intro](file:///C:/Users/aeejshe/Downloads/2018-12-24-GAPI_Overview.pdf)

Features

API

//core.hpp

GAPI_EXPORTS GMat resize(const GMat& src, const Size& dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);

//GComputation.hpp

class GComputation{

    ...

    GComputation(GProtoInputArgs &&ins,

                 GProtoOutputArgs &&outs);             // Arg-to-arg overload

	void apply(GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args = {});

...

}

implementation

of G-API apply function

GComputation -> GComputation2: apply

GComputation2 -> GCompiler: compile

GCompiler -> Graph: build graph

Graph --> GComputation2: return ade::Graph

GComputation2 -> Graph: exec the graph

ref:

Vision grab post processing

Study if OpenVINO or OpenCV supports

  • CSE（common-subexpression elimination）
  • feed partially inputs
Lib CSE partially inputs
OpenVINO x x
AMDOVX x x
OpenCV G-API x x
Intel TBB x v
behavior: the ready nodes are called then exit
Code: C:\jshe\codes\lua\src\tbbtest\test_tbb_behavior.cpp
Tensorflow v

TODO

Test if can be called multiples like following

while true

    modify input

    vxProcessGraph()

ref: http://projects.eees.dei.unibo.it/adrenaline/tutorial-02-execute-openvx-examples/

OpenVX讀書筆記

summary

high level low level
ovx strong typed
eg VX_API_ENTRY vx_node VX_API_CALL vxColorConvertNode(vx_graph graph, vx_image input, vx_image output);		 weak typed, eg
OpenVX.dll!agoCreateNode(_vx_graph * graph, int kernel_id)
tbb strong typed
make_edge(tbb::flow::output_port<1>(gpu_slm_split_n), tbb::flow::input_port<1>(gpu_slm_mat_mult_n))
tbb::flow::function_node< validation_args_type > mat_validation_n(g, tbb::flow::unlimited, [](const validation_args_type& result) {
// Get references to matrixes
const tbb::flow::gfx_buffer& GPU_SLM_MAT = std::get<0>(result);
const tbb::flow::gfx_buffer& CPU_SLM_MAT = std::get<1>(result);
const tbb::flow::gfx_buffer& CPU_NAIVE_MAT = std::get<2>(result);

// Verify results
// Check that slm algorithm produces correct results on CPU:
validate_mat("matrix multiply: 'SLM' CPU vs. CPU", SIZE_Y, SIZE_X, CPU_SLM_MAT.data(), CPU_NAIVE_MAT.data());
// Verify Gen results:
validate_mat("matrix multiply: SLM Gen vs. CPU", SIZE_Y, SIZE_X, GPU_SLM_MAT.data(), CPU_NAIVE_MAT.data());
});

 Not sure
G-API strong typed TODO

// ovx: \vis_bep_12\C\Users\aeejshe\Downloads\amdovx-core-0.9-beta2\amdovx-core-0.9-beta2

// tbb: C:\Users\aeejshe\Downloads\tbb2017_20170604oss_win\tbb2017_20170604oss

How to register Kernel

Define a enum

VX_KERNEL_COLOR_CONVERT = VX_KERNEL_BASE(VX_ID_KHRONOS, VX_LIBRARY_KHR_BASE) + 0x1,

Registrtion

OVX_KERNEL_ENTRY( VX_KERNEL_COLOR_CONVERT         , ColorConvert, "color_convert",             AIN_AOUT,             ATYPE_II           , false ),

the parameters meaning

#define OVX_KERNEL_ENTRY(kernel_id,name,kname,argCfg,argType,validRectReset) \


#define ATYPE_II                               { VX_TYPE_IMAGE, VX_TYPE_IMAGE }
  • AIN_AOUT: 1 in, 1 out
  • ATYPE_II: 2 image types

Implement "DramaDivideNode" operation, it is used to select the best suited for this PC architecture

int agoDramaDivideNode(AgoNodeList * nodeList, AgoNode * anode)

{

	// save parameter list

	vx_uint32 paramCount = anode->paramCount;

	AgoData * paramList[AGO_MAX_PARAMS]; memcpy(paramList, anode->paramList, sizeof(paramList));

	// divide the node depending on the type

	int status = -1;

	switch (anode->akernel->id)

	{

		case VX_KERNEL_COLOR_CONVERT:

			status = agoDramaDivideColorConvertNode(nodeList, anode);

			break;

the function is called by optimize function

>	OpenVX.dll!agoCreateNode(_vx_graph * graph, int kernel_id) Line 2699	C++

 	OpenVX.dll!agoDramaDivideAppend(AgoNodeList * nodeList, _vx_node * anode, int new_kernel_id, _vx_reference * * paramList, unsigned int paramCount) Line 37	C++

 	OpenVX.dll!agoDramaDivideAppend(AgoNodeList * nodeList, _vx_node * anode, int new_kernel_id) Line 56	C++

 	OpenVX.dll!agoDramaDivideColorConvertNode(AgoNodeList * nodeList, _vx_node * anode) Line 244	C++

 	OpenVX.dll!agoDramaDivideNode(AgoNodeList * nodeList, _vx_node * anode) Line 1818	C++

 	OpenVX.dll!agoOptimizeDramaDivide(_vx_graph * agraph) Line 1962	C++

 	OpenVX.dll!agoOptimizeDrama(_vx_graph * agraph) Line 522	C++

 	OpenVX.dll!agoOptimizeGraph(_vx_graph * agraph) Line 209	C++

 	OpenVX.dll!vxVerifyGraph(_vx_graph * graph) Line 2450	C++

 	runvx.exe!CVxEngine::ProcessGraph(std::vector<char const *,std::allocator<char const *> > * graphNameList, unsigned __int64 beginIndex) Line 285	C++

How to schedule graph?

What optimization is done in optimize()?

Choose the best

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