Safe MMDeploy Rust wrapper.
In order to successfully build this repo, you are supposed to install some pre-packages.
The following guidance is tested on Ubuntu OS on x86 device.
Step 1. Install Clang required by Bindgen.
bash
apt install llvm-dev libclang-dev clang
Step 2. Download and install pre-built mmdeploy package. In this guidance, we choose a MMdepoloy prebuilt package target on ONNXRUNTIME-linux-x86.
bash
wget https://github.com/open-mmlab/mmdeploy/releases/download/v0.8.0/mmdeploy-0.8.0-linux-x86_64-onnxruntime1.8.1.tar.gz
tar -zxvf mmdeploy-0.8.0-linux-x86_64-onnxruntime1.8.1.tar.gz
cd mmdeploy-0.8.0-linux-x86_64-onnxruntime1.8.1.tar.gz
export $MMDEPLOY_DIR=$(pwd)
Step 3. Install OpenCV required by examples.
bash
apt install libopencv-dev
Please read the previous section to make sure the required packages have been installed before using this crate.
Update your Cargo.toml
toml
mmdeploy = "0.4.0"
Good news: Now, you can use Rust language to build your fantastic applications powered by MMDeploy! Take a look by running some examples!
Deploy image classification models converted by MMDeploy.
The example deploys a ResNet model converted by ONNXRUNTIME target on CPU device.
Before deploying, please follow the guidance from MMDeploy documentation to install it and convert an appropriate model in ../mmdeploy_model/resnet. An optional operation required to fetch MMClassification codebase into ../mmclassification/. In this example, we use demo-image from it.
bash
cargo run --example classifier cpu ../mmdeploy_model/resnet ../mmclassification/demo/dog.jpg
Deploy object detection models converted by MMDeploy.
The example deploys a FasterRCNN model converted by ONNXRUNTIME target on CPU device.
Before deploying, please follow the guidance from MMDeploy documentation to install it and convert an appropriate model in ../mmdeploy_model/faster-rcnn-ort. An optional operation required to fetch MMDetection codebase into ../mmdetection/. In this example, we use demo-image from it.
bash
cargo run --example detector cpu ../mmdeploy_model/faster-rcnn-ort ../mmdetection/demo/demo.jpg
A rendered result we can take a look located in the current directory and is named output_detection.png.
Deploy object segmentation models converted by MMDeploy.
The example deploys a DeepLabv3 model converted by ONNXRUNTIME target on CPU device.
Before deploying, please follow the guidance from MMDeploy documentation to install it and convert an appropriate model in ../mmdeploy_model/deeplabv3. An optional operation required to fetch MMSegmentation codebase into ../mmsegmentation/. In this example, we use demo-image from it.
bash
cargo run --example segmentor cpu ../mmdeploy_model/deeplabv3 ../mmsegmentation/demo/demo.png
A rendered result we can take a look located in the current directory and is named output_segmentation.png.
