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RKNPU Execution Providers Reference 10

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.

Contents

{: .no_toc }

  • TOC placeholder {:toc}

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