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Get Started

MMDeploy provides useful tools for deploying OpenMMLab models to various platforms and devices.

With the help of them, you can not only do model deployment using our pre-defined pipelines but also customize your own deployment pipeline.

Introduction

In MMDeploy, the deployment pipeline can be illustrated by a sequential modules, i.e., Model Converter, MMDeploy Model and Inference SDK.

deploy-pipeline

Model Converter

Model Converter aims at converting training models from OpenMMLab into backend models that can be run on target devices. It is able to transform PyTorch model into IR model, i.e., ONNX, TorchScript, as well as convert IR model to backend model. By combining them together, we can achieve one-click end-to-end model deployment.

MMDeploy Model

MMDeploy Model is the result package exported by Model Converter. Beside the backend models, it also includes the model meta info, which will be used by Inference SDK.

Inference SDK

Inference SDK is developed by C/C++, wrapping the preprocessing, model forward and postprocessing modules in model inference. It supports FFI such as C, C++, Python, C#, Java and so on.

Prerequisites

In order to do an end-to-end model deployment, MMDeploy requires Python 3.6+ and PyTorch 1.8+.

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment and activate it.

conda create --name mmdeploy python=3.8 -y
conda activate mmdeploy

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch=={pytorch_version} torchvision=={torchvision_version} cudatoolkit={cudatoolkit_version} -c pytorch -c conda-forge

On CPU platforms:

conda install pytorch=={pytorch_version} torchvision=={torchvision_version} cpuonly -c pytorch
On GPU platform, please ensure that {cudatoolkit_version} matches your host CUDA toolkit version. Otherwise, it probably brings in conflicts when deploying model with TensorRT.

Installation

We recommend that users follow our best practices installing MMDeploy.

Step 0. Install MMCV.

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc2"

Step 1. Install MMDeploy and inference engine

We recommend using MMDeploy precompiled package as our best practice. Currently, we support model converter and sdk inference pypi package, and the sdk c/cpp library is provided here. You can download them according to your target platform and device.

The supported platform and device matrix is presented as following:

OS-Arch Device ONNX Runtime TensorRT
Linux-x86_64 CPU Y N/A
CUDA Y Y
Windows-x86_64 CPU Y N/A
CUDA Y Y

Note: if MMDeploy prebuilt package doesn't meet your target platforms or devices, please build MMDeploy from source

Take the latest precompiled package as example, you can install it as follows:

Linux-x86_64 
# 1. install MMDeploy model converter
pip install mmdeploy==1.3.1

# 2. install MMDeploy sdk inference
# you can install one to install according whether you need gpu inference
# 2.1 support onnxruntime
pip install mmdeploy-runtime==1.3.1
# 2.2 support onnxruntime-gpu, tensorrt
pip install mmdeploy-runtime-gpu==1.3.1

# 3. install inference engine
# 3.1 install TensorRT
# !!! If you want to convert a tensorrt model or inference with tensorrt,
# download TensorRT-8.2.3.0 CUDA 11.x tar package from NVIDIA, and extract it to the current directory
pip install TensorRT-8.2.3.0/python/tensorrt-8.2.3.0-cp38-none-linux_x86_64.whl
pip install pycuda
export TENSORRT_DIR=$(pwd)/TensorRT-8.2.3.0
export LD_LIBRARY_PATH=${TENSORRT_DIR}/lib:$LD_LIBRARY_PATH
# !!! Moreover, download cuDNN 8.2.1 CUDA 11.x tar package from NVIDIA, and extract it to the current directory
export CUDNN_DIR=$(pwd)/cuda
export LD_LIBRARY_PATH=$CUDNN_DIR/lib64:$LD_LIBRARY_PATH

# 3.2 install ONNX Runtime
# you can install one to install according whether you need gpu inference
# 3.2.1 onnxruntime
wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz
tar -zxvf onnxruntime-linux-x64-1.8.1.tgz
export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-1.8.1
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
# 3.2.2 onnxruntime-gpu
pip install onnxruntime-gpu==1.8.1
wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-gpu-1.8.1.tgz
tar -zxvf onnxruntime-linux-x64-gpu-1.8.1.tgz
export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-gpu-1.8.1
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
Windows-x86_64

Please learn its prebuilt package from this guide.

Convert Model

After the installation, you can enjoy the model deployment journey starting from converting PyTorch model to backend model by running tools/deploy.py.

Based on the above settings, we provide an example to convert the Faster R-CNN in MMDetection to TensorRT as below:

# clone mmdeploy to get the deployment config. `--recursive` is not necessary
git clone -b main https://github.com/open-mmlab/mmdeploy.git

# clone mmdetection repo. We have to use the config file to build PyTorch nn module
git clone -b 3.x https://github.com/open-mmlab/mmdetection.git
cd mmdetection
mim install -v -e .
cd ..

# download Faster R-CNN checkpoint
wget -P checkpoints https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

# run the command to start model conversion
python mmdeploy/tools/deploy.py \
    mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
    mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
    mmdetection/demo/demo.jpg \
    --work-dir mmdeploy_model/faster-rcnn \
    --device cuda \
    --dump-info

The converted model and its meta info will be found in the path specified by --work-dir. And they make up of MMDeploy Model that can be fed to MMDeploy SDK to do model inference.

For more details about model conversion, you can read how_to_convert_model. If you want to customize the conversion pipeline, you can edit the config file by following this tutorial.

You can convert the above model to onnx model and perform ONNX Runtime inference
just by changing 'detection_tensorrt_dynamic-320x320-1344x1344.py' to 'detection_onnxruntime_dynamic.py' and making '--device' as 'cpu'.

Inference Model

After model conversion, we can perform inference not only by Model Converter but also by Inference SDK.

Inference by Model Converter

Model Converter provides a unified API named as inference_model to do the job, making all inference backends API transparent to users. Take the previous converted Faster R-CNN tensorrt model for example,

from mmdeploy.apis import inference_model
result = inference_model(
  model_cfg='mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py',
  deploy_cfg='mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py',
  backend_files=['mmdeploy_model/faster-rcnn/end2end.engine'],
  img='mmdetection/demo/demo.jpg',
  device='cuda:0')
'backend_files' in this API refers to backend engine file path, which MUST be put in a list, since some inference engines like OpenVINO and ncnn separate the network structure and its weights into two files.

Inference by SDK

You can directly run MMDeploy demo programs in the precompiled package to get inference results.

wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.1/mmdeploy-1.3.1-linux-x86_64-cuda11.8.tar.gz
tar xf mmdeploy-1.3.1-linux-x86_64-cuda11.8
cd mmdeploy-1.3.1-linux-x86_64-cuda11.8
# run python demo
python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
# run C/C++ demo
# build the demo according to the README.md in the folder.
./bin/object_detection cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
In the above command, the input model is SDK Model path. It is NOT engine file path but actually the path passed to --work-dir. It not only includes engine files but also meta information like 'deploy.json' and 'pipeline.json'.

In the next section, we will provide examples of deploying the converted Faster R-CNN model talked above with SDK different FFI (Foreign Function Interface).

Python API

from mmdeploy_runtime import Detector
import cv2

img = cv2.imread('mmdetection/demo/demo.jpg')

# create a detector
detector = Detector(model_path='mmdeploy_models/faster-rcnn', device_name='cuda', device_id=0)
# run the inference
bboxes, labels, _ = detector(img)
# Filter the result according to threshold
indices = [i for i in range(len(bboxes))]
for index, bbox, label_id in zip(indices, bboxes, labels):
  [left, top, right, bottom], score = bbox[0:4].astype(int),  bbox[4]
  if score < 0.3:
      continue
  cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))

cv2.imwrite('output_detection.png', img)

You can find more examples from here.

C++ API

Using SDK C++ API should follow next pattern,

image

Now let's apply this procedure on the above Faster R-CNN model.

#include <cstdlib>
#include <opencv2/opencv.hpp>
#include "mmdeploy/detector.hpp"

int main() {
  const char* device_name = "cuda";
  int device_id = 0;
  std::string model_path = "mmdeploy_model/faster-rcnn";
  std::string image_path = "mmdetection/demo/demo.jpg";

  // 1. load model
  mmdeploy::Model model(model_path);
  // 2. create predictor
  mmdeploy::Detector detector(model, mmdeploy::Device{device_name, device_id});
  // 3. read image
  cv::Mat img = cv::imread(image_path);
  // 4. inference
  auto dets = detector.Apply(img);
  // 5. deal with the result. Here we choose to visualize it
  for (int i = 0; i < dets.size(); ++i) {
    const auto& box = dets[i].bbox;
    fprintf(stdout, "box %d, left=%.2f, top=%.2f, right=%.2f, bottom=%.2f, label=%d, score=%.4f\n",
            i, box.left, box.top, box.right, box.bottom, dets[i].label_id, dets[i].score);
    if (dets[i].score < 0.3) {
      continue;
    }
    cv::rectangle(img, cv::Point{(int)box.left, (int)box.top},
                  cv::Point{(int)box.right, (int)box.bottom}, cv::Scalar{0, 255, 0});
  }
  cv::imwrite("output_detection.png", img);
  return 0;
}

When you build this example, try to add MMDeploy package in your CMake project as following. Then pass -DMMDeploy_DIR to cmake, which indicates the path where MMDeployConfig.cmake locates. You can find it in the prebuilt package.

find_package(MMDeploy REQUIRED)
target_link_libraries(${name} PRIVATE mmdeploy ${OpenCV_LIBS})

For more SDK C++ API usages, please read these samples.

For the rest C, C# and Java API usages, please read C demos, C# demos and Java demos respectively. We'll talk about them more in our next release.

Accelerate preprocessing（Experimental）

If you want to fuse preprocess for acceleration，please refer to this doc

Evaluate Model

You can test the performance of deployed model using tool/test.py. For example,

python ${MMDEPLOY_DIR}/tools/test.py \
    ${MMDEPLOY_DIR}/configs/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
    ${MMDET_DIR}/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
    --model ${BACKEND_MODEL_FILES} \
    --metrics ${METRICS} \
    --device cuda:0
Regarding the --model option, it represents the converted engine files path when using Model Converter to do performance test. But when you try to test the metrics by Inference SDK, this option refers to the directory path of MMDeploy Model.

You can read how to evaluate a model for more details.