mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-05-14 20:48:00 +00:00

* Initial update of readme

* Readme updates

* Review of consolidated README (#3930)

* Proposed updates for readme (#3953)

I found some of the information was duplicated within the doc, so attempted to streamline

* Fix links

* More updates

- fix build instructions
- nodejs doc reorganization
- roadmap update
- version fixes

* Update ORT Server build instructions

* More doc cleanup

* fix python dev notes name

* Update nodejs and some links

* sync eigen version back to master

* Minor fixes

* add nodsjs to sample table of content

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* address PR feedback

* address PR feedback

* nodejs build instruction

* Update Java instructions to include gradle

* Roadmap refresh

Reformat some data, fix link, minor rewording

* Clarify Visual C++ runtime req

Co-authored-by: Nat Kershaw (MSFT) <nakersha@microsoft.com>
Co-authored-by: Prasanth Pulavarthi <prasantp@microsoft.com>
Co-authored-by: manashgoswami <magoswam@microsoft.com>

2020-05-18 20:08:36 -07:00

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RKNPU Execution Provider (preview)

RKNPU DDK is an advanced interface to access Rockchip NPU. RKNPU Execution Provider enables deep learning inference on Rockchip NPU via RKNPU DDK.

Supported platforms

RK1808 Linux

Note: RK3399Pro platform is not supported.

Build

For build instructions, please see the BUILD page.

Usage

C/C++

To use RKNPU as execution provider for inferencing, please register it as below.

string log_id = "Foo";
auto logging_manager = std::make_unique<LoggingManager>
(std::unique_ptr<ISink>{new CLogSink{}},
                                  static_cast<Severity>(lm_info.default_warning_level),
                                  false,
                                  LoggingManager::InstanceType::Default,
                                  &log_id)
Environment::Create(std::move(logging_manager), env)
InferenceSession session_object{so,env};
session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::RknpuExecutionProvider>());
status = session_object.Load(model_file_name);

The C API details are here.

Supported Operators

The table below shows the ONNX Ops supported using RKNPU Execution Provider and the mapping between ONNX Ops and RKNPU Ops.

ONNX Ops	RKNPU Ops
Add	ADD
Mul	MULTIPLY
Conv	CONV2D
QLinearConv	CONV2D
Gemm	FULLCONNECT
Softmax	SOFTMAX
AveragePool	POOL
GlobalAveragePool	POOL
MaxPool	POOL
GlobalMaxPool	POOL
LeakyRelu	LEAKY_RELU
Concat	CONCAT
BatchNormalization	BATCH_NORM
Reshape	RESHAPE
Flatten	RESHAPE
Squeeze	RESHAPE
Unsqueeze	RESHAPE
Transpose	PERMUTE
Relu	RELU
Sub	SUBTRACT
Clip(0~6)	RELU6
DequantizeLinear	DATACONVERT
Clip	CLIP

Supported Models

Below Models are supported from ONNX open model zoo using RKNPU Execution Provider

Image Classification

squeezenet
mobilenetv2-1.0
resnet50v1
resnet50v2
inception_v2

Object Detection

ssd
yolov3

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