marca711/rknn-toolkit2

Description

RKNN software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:

In order to use RKNPU, users need to first run the RKNN-Toolkit2 tool on the computer, convert the trained model into an RKNN format model, and then inference on the development board using the RKNN C API or Python API.

• RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms.

• RKNN-Toolkit-Lite2 provides Python programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.

• RKNN Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications.

• RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code.

Support Platform

• RK3566/RK3568 Series
• RK3588 Series
• RK3562 Series
• RV1103/RV1106

Note:

​ For RK1808/RV1109/RV1126/RK3399Pro, please refer to :

​ https://github.com/airockchip/rknn-toolkit

​ https://github.com/airockchip/rknpu

​ https://github.com/airockchip/RK3399Pro_npu

Download

• You can also download all packages, docker image, examples, docs and platform-tools from RKNPU2_SDK, fetch code: rknn
• You can get more examples from rknn mode zoo

Notes

• RKNN-Toolkit2 is not compatible with RKNN-Toolkit
• Currently only support on:
  • Ubuntu 18.04 python 3.6/3.7
  • Ubuntu 20.04 python 3.8/3.9
  • Ubuntu 22.04 python 3.10/3.11
• Latest version:1.6.0(Release version)

CHANGELOG

1.6.0

• Support ONNX model of OPSET 12~19
• Support custom operators (including CPU and GPU)
• Optimization operators support such as dynamic weighted convolution, Layernorm, RoiAlign, Softmax, ReduceL2, Gelu, GLU, etc.
• Added support for python3.7/3.9/3.11
• Add rknn_convert function
• Optimize transformer support
• Optimize the MatMul API, such as increasing the K limit length, RK3588 adding int4 * int4 -> int16 support, etc.
• Optimize RV1106 rknn_init initialization time, memory consumption, etc.
• RV1106 adds int16 support for some operators
• Fixed the problem that the convolution operator of RV1106 platform may make random errors in some cases.
• Optimize user manual
• Reconstruct the rknn model zoo and add support for multiple models such as detection, segmentation, OCR, and license plate recognition.

for older version, please refer CHANGELOG

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