Tengine Convert Tool supports converting multi framworks' models into tmfile that suitable for Tengine-Lite AI framework. Since this tool relys on protobuf to resolve proto file of Caffe, ONNX, TensorFlow, TFLite and so on, it can only run under x86 Linux system.
For loading caffe model or TensorFlow model.
sudo apt install libprotobuf-dev protobuf-compiler
If use the Fedora/CentOS ,use follow command instead.
sudo dnf install protobuf-devel
sudo dnf install boost-devel glog-devel
mkdir build && cd build
cmake ..
make -j`nproc` && make install
./build/install/bin/
named as convert_tool
.$ ./convert_tool -h
[Convert Tools Info]: optional arguments:
-h help show this help message and exit
-f input type path to input float32 tmfile
-p input structure path to the network structure of input model(*.prototxt, *.symbol, *.cfg)
-m input params path to the network params of input model(*.caffemodel, *.params, *.weight, *.pb, *.onnx, *.tflite)
-o output model path to output fp32 tmfile
To run the convert tool, running as following command, Note: The command examples are based on mobilenet
model:
Caffe
./install/bin/convert_tool -f caffe -p mobilenet_deploy.prototxt -m mobilenet.caffemodel -o mobilenet.tmfile
MxNet
./install/bin/convert_tool -f mxnet -p mobilenet1_0-symbol.json -m mobilene1_0-0000.params -o mobileent.tmfile
ONNX
./install/bin/convert_tool -f onnx -m mobilenet.onnx -o mobilenet.tmfile
TensorFlow
./install/bin/convert_tool -f tensorflow -m mobielenet_v1_1.0_224_frozen.pb -o mobilenet.tmfile
TFLITE
./install/bin/convert_tool -f tflite -m mobielenet.tflite -o mobilenet.tmfile
DarkNet: darknet only support for yolov3 model
./install/bin/convert_tool -f darknet -p yolov3.cfg -m yolov3.weights -o yolov3.tmfile
NCNN
./install/bin/convert_tool -f ncnn -p mobilenet.param -m mobilenet.bin -o mobilenet.tmfile
MegEngine
./install/bin/convert_tool -f megengine -m mobilenet.pkl -o mobilenet.tmfile
PaddlePaddle
./install/bin/convert_tool -f paddle -p inference.pdmodel -m inference.pdiparams -o mobilenetv2_paddle.tmfile
# clone MegEngine
git clone https://github.com/MegEngine/MegEngine.git
# prepare for building
cd MegEngine
./third_party/prepare.sh
./third_party/install-mkl.sh
mkdir build && cd build
# build MegEngine with DEBUG mode
cmake .. -DMGE_WITH_TEST=OFF -DMGE_BUILD_SDK=OFF -DCMAKE_BUILD_TYPE=Debug -DCMAKE_INSTALL_PREFIX={PREDEFINED_INSTALL_PATH}
make -j`nproc`
make install
make develop
# export environment
export PYTHONPATH=/path/to/MegEngine/python_module
# test with python
python3 -c "import megengine"
# clone Tengine convert tool
git clone https://github.com/OAID/Tengine-Convert-Tools
# build with MegEngine support
cmake -DBUILD_MEGENGINE_SERIALIZER=ON -DMEGENGINE_INSTALL_PATH={PREDEFINED_INSTALL_PATH} ..
make -j`nproc`
make install
# get a pre-trained resnet18 model from MegEngine Model Hub
import megengine.hub
resnet18 = megengine.hub.load("megengine/models", "resnet18", pretrained=True)
# use MegEngine trace to deal with downloaded model
from megengine.jit import trace
import megengine.functional as F
@trace(symbolic=True)
def pred_fun(data, *, net):
net.eval()
pred = net(data)
# if model has softmax
pred_normalized = F.softmax(pred)
return pred_normalized
# fill a random input for model
import numpy as np
data = np.random.random((1, 3, 224, 224)).astype(np.float32)
# trace and save the model
pred_fun.trace(data, net=resnet18)
pred_fun.dump('new_model.pkl')
A jupyter notebook was offered for users, check MegEngine.ipynb.
./install/bin/convert_tool -f megengine -m new_model.pkl -o resnet18.tmfile