qaz734913414 / tengine

Tengine is a lite, high performance, modular inference engine for embedded device

Tengine Overview

Tengine, developed by OPEN AI LAB, is a lite, high-performance, and modular inference engine for embedded device.

Tengine is composed of five modules: core/operator/serializer/executor/driver.

• core provides the basic components and functionalities of the system.
• operator defines the schema of operators, such as convolution, relu, pooling, etc. al. Here is the current support operator list.
• serializer is to load the saved model. The serializer framework is extensible to support different format, including the customized one. Caffe/ONNX/Tensorflow/MXNet and Tengine models can be loaded directly by Tengine.
• executor implements the code to run graph and operators. Current version provides a highly optimized implementation for multi A72 cores.
• driver is the adapter of real H/W and provides service to device executor by HAL API. It is possible for single driver to create multiple devices.

This version can load and run Caffe model of mobilenet and squeezenet directly. For more details, please goto install.

NOTE: Old Caffe model has to be upgraded using upgrade_net_proto_binary/upgrade_net_proto_binary from Caffe's package.

Build and Install

please refer to the Linux build and Android build

Tengine examples and model zoo

please visit examples for demos on classification/detection and download models from Tengine model zoo (psw: hhgc)

tengine applications is a project for sharing android/linux applications powered by Tengine

Develop New Operator

It is easy to add new operator to Tengine. Here is the guide on new operator.

Support New Model Format

Tengine can be extended to support new serialization format, by building new serializer module.

How to build new serializer module

Communication && Tech Support

• Github issues
• QQ group: 829565581 (Question:Tengine Answer:openailab)
• Tengine Community: http://www.tengine.org.cn/

Release History

version 1.12.0 - 2020/02/29

New features

Support Arm Performance Op :

Conv,Deconv,Fc,Pooling,Absval,Batchnorm,Cast,Eltwise,Elu,Hardswish,Interp,Prelu,Relu,Scale,Selu,Sigmode,Softmax,Tanh

Support Arm Winograd

Others

Remove Makefile replaced with cmake

version 1.9.0 - 2019/09/25

Optimization

Optimize TF-LITE

Optimize NCHW Conv_DW

Optimize NCHW Deconv

New features

Support Online Report

Support NCHW Post trainging tool

More operator support

TF-LITE operator LayerNormLstm implementation

TF-LITE operator L2Normalization implementation

TF-LITE operator L2Pool implementation

TF-LITE operator ReluN1To1 implementation

Others

Remove Tensorflow/Caffe wrapper

Move caffe/tensorflow/tf-lite/onnx/mxnet/darknet serializers to tengime-module project

version 1.8.0 - 2019/08/27

New features

Support Alexnet Googlenet Inceptiov3 Inceptionv4 Resnet50 on ACL 19.02

Support Darknet models serializer (Yolov2, Yolov2-tiny, Yolov3, Yolov3-tiny)

version 1.7.1 - 2019/08/02

New features

GPU backend support with ACL 19.02

Static tool for convert other models to tengine

Tengine graph use origin layout to execute

Add CV implementation

version 1.3.2 - 2019/04/19

tengine model 2.0

New apis

get_graph_node_number() get_graph_node_by_idx()

New features

Separate CPU operator as a independent so: hclcpu.so

Add Reference Operator

Update Testcase & Update permute for mxnet

Update lstm grun mxnet serializer

Support MXNET serializer in CMakelist.txt

Support TFLITE serializer in CMakelist.txt

Support eltwise in TFLITE serializer

More operator support

RNN operator definition and blas implementation

LSTM operator definition and blas implementation

GRU operator definition and blas implementation

version 1.3.2 - 2019/04/19

tengine model 2.0

New apis

get_graph_node_number() get_graph_node_by_idx()

New features

Separate CPU operator as a independent so: hclcpu.so

Add Reference Operator

Update Testcase & Update permute for mxnet

Update lstm grun mxnet serializer

Support MXNET serializer in CMakelist.txt

Support TFLITE serializer in CMakelist.txt

Support eltwise in TFLITE serializer

More operator support

RNN operator definition and blas implementation

LSTM operator definition and blas implementation

GRU operator definition and blas implementation

version 1.0.0 - 2018/12/31

tengine API 2.0

New API set for NN inference

Simplify graph create process: just create_graph() instead of load_model() and create_runtime_graph()

Support perf stat and tensor dump

Support log redirect

Support to build Android NN Driver with new Tengine API

By setting CONFIG_LEGACY_API=y in makefile.config, tengine API 1.0 still works

more tensorflow models support

Support inceptionv3/v4, resnet_v2_101, mobilenet v1/v2 models from tensorflow

version 0.8.0 - 2018/11/15

Support GPU/CPU Heterogeneous Computing

By calling set_graph_device(graph,"acl_opencl"), operators that GPU supports will be scheduled to GPU, while left operators will be scheduled on CPU automatically.

Here is the guide to run a MSSD example with GPU FP16

Using c++_shared for Android build

As NDK toolchains will drop gun_stl finally, this version switches to c++_shared

Please download the pre-built libraries with c++_shared from Tengine Android Build Libraries (password: wtcz).

Support ACL in Android

Update the cmake system to support ACL in Android build. please refer to Android build guide

Bugfix

The issue to load tengine model converted from MXNet

version 0.7.2 - 2018/10/15

Serializer:

update ONNX module with new onnx proto version

version 0.7.0 - 2018/9/15

New features

Serializer: support saving model as c files

ACL GPU: add FP16 support

NN: mobilenet v2 support in examples

Accuracy tools: yolov2 accuracy test

Build:

   support cross-building arm32 library 

   support building on raspberry pi 3b

   automatically clean the build directory when makfile.config changed

Bug fix

A few memory leakage issues in library and examples

A race condition issue between front thread and the background working thread

Tensorflow serializer issue: fail to load inception_v3 model

version 0.6.0 - 2018/7/02

Support Tengine model file. protobuf is optional now.

Please refer to tengine_model exmaples

version 0.5.0 - 2018/6/15

New features

Support GPU: using ACL (Arm computing library) as a backend graph device

Support blas operator implementation: Tengine can run on x86 without caffe now

Support new NN: Inception-v3/vgg16/faster-rcnn/ssd/yolo-v2

Support Android build: includes 32bit and 64bit

Support cross-compile on x86 (experimental): debian example contributed by mcharleb and Mani-Sadhasivam @ Linaro

Support Tensorflow serializer: load inception-v3 and mobilenet TF model directly

Support Tensorflow wrapper: label_image.cpp from tensorflow repo

Others

Single so file now and remove the etc/config according to feedback from field.

Tengine will automatically probe the CPU arch/part settings, and there is just one CPU driver now.

To assign cpu manually when necessary:

 export TENGINE_CPU_LIST=1,2 

Besides probing CPU, a few CPUs are defined in cpu_predefined.cpp, including rk3399/a63/kirin960/apq8096. To use the predefined CPU, refers to below:

const struct cpu_info * p_info=get_predefined_cpu("rk3399");
create_cpu_device("rk3399",p_info);

version 0.3.0 - 2018/2/6

Introduce the driver/device model to support MT(Multi-Thread)

Support new NN: Inception-v4

Caffe Wrapper examples: squeezenet/mobilenet/mtcnn

MXNet model load examples: squeezenet/mobilenet

version 0.2.0 - 2018/1/24

Support new operator: Eltwise, PReLU, Slice

Support new NN: mtcnn, resnet and lighten_cnn

Experimental caffe API wrapper: caffe based application just needs to recompile to use Tengine

version 0.1.2 - 2017/12/30

Update documents, as well a few fixes.

version 0.1.0 - 2017/12/29

Initial release of single A72 support