MMDet to TensorRT

This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is experiment.

support:

• fp16
• int8(experiment)
• batched input
• dynamic input shape
• combination of different modules
• deepstream support

Any advices, bug reports and stars are welcome.

License

This project is released under the Apache 2.0 license.

Requirement

• install mmdetection:

  # mim is so cool!
  pip install openmim
  mim install mmdet==2.14.0

• install torch2trt_dynamic:

  git clone https://github.com/grimoire/torch2trt_dynamic.git torch2trt_dynamic
  cd torch2trt_dynamic
  python setup.py develop

• install amirstan_plugin:

  • Install tensorrt: TensorRT

  • clone repo and build plugin

    git clone --depth=1 https://github.com/grimoire/amirstan_plugin.git
    cd amirstan_plugin
    git submodule update --init --progress --depth=1
    mkdir build
    cd build
    cmake -DTENSORRT_DIR=${TENSORRT_DIR} ..
    make -j10

  • DON'T FORGET setting the envoirment variable(in ~/.bashrc):

    export AMIRSTAN_LIBRARY_PATH=${amirstan_plugin_root}/build/lib

Installation

Host

git clone https://github.com/grimoire/mmdetection-to-tensorrt.git
cd mmdetection-to-tensorrt
python setup.py develop

Docker

Build docker image

# cuda11.1 TensorRT7.2.2 pytorch1.8 cuda11.1
sudo docker build -t mmdet2trt_docker:v1.0 docker/

You can also specify CUDA, Pytorch and Torchvision versions with docker build args by:

# cuda11.1 tensorrt7.2.2 pytorch1.6 cuda10.2
sudo docker build -t mmdet2trt_docker:v1.0 --build-arg TORCH_VERSION=1.6.0 --build-arg TORCHVISION_VERSION=0.7.0 --build-arg CUDA=10.2 --docker/

Run (will show the help for the CLI entrypoint)

sudo docker run --gpus all -it --rm -v ${your_data_path}:${bind_path} mmdet2trt_docker:v1.0

Or if you want to open a terminal inside de container:

sudo docker run --gpus all -it --rm -v ${your_data_path}:${bind_path} --entrypoint bash mmdet2trt_docker:v1.0

Example conversion:

sudo docker run --gpus all -it --rm -v ${your_data_path}:${bind_path} mmdet2trt_docker:v1.0 ${bind_path}/config.py ${bind_path}/checkpoint.pth ${bind_path}/output.trt

Usage

how to create a TensorRT model from mmdet model (converting might take few minutes)(Might have some warning when converting.) detail can be found in getting_started.md

CLI

mmdet2trt ${CONFIG_PATH} ${CHECKPOINT_PATH} ${OUTPUT_PATH}

Run mmdet2trt -h for help on optional arguments.

Python

opt_shape_param=[
    [
        [1,3,320,320],      # min shape
        [1,3,800,1344],     # optimize shape
        [1,3,1344,1344],    # max shape
    ]
]
max_workspace_size=1<<30    # some module and tactic need large workspace.
trt_model = mmdet2trt(cfg_path, weight_path, opt_shape_param=opt_shape_param, fp16_mode=True, max_workspace_size=max_workspace_size)

# save converted model
torch.save(trt_model.state_dict(), save_model_path)

# save engine if you want to use it in c++ api
with open(save_engine_path, mode='wb') as f:
    f.write(trt_model.state_dict()['engine'])

Note:

• The input of the engine is the tensor after preprocess.
• The output of the engine is num_dets, bboxes, scores, class_ids. if you enable the enable_mask flag, there will be another output mask.
• The bboxes output of the engine did not divided by scale factor.

how to use the converted model

from mmdet.apis import inference_detector
from mmdet2trt.apis import create_wrap_detector

# create wrap detector
trt_detector = create_wrap_detector(trt_model, cfg_path, device_id)

# result share same format as mmdetection
result = inference_detector(trt_detector, image_path)

# visualize
trt_detector.show_result(
    image_path,
    result,
    score_thr=score_thr,
    win_name='mmdet2trt',
    show=True)

Try demo in demo/inference.py, or demo/cpp if you want to do inference with c++ api.

Read getting_started.md for more details.

How does it works?

Most other project use pytorch=>ONNX=>tensorRT route, This repo convert pytorch=>tensorRT directly, avoid unnecessary ONNX IR. Read how-does-it-work for detail.

Support Model/Module

• Faster R-CNN
• Cascade R-CNN
• Double-Head R-CNN
• Group Normalization
• Weight Standardization
• DCN
• SSD
• RetinaNet
• Libra R-CNN
• FCOS
• Fovea
• CARAFE
• FreeAnchor
• RepPoints
• NAS-FPN
• ATSS
• PAFPN
• FSAF
• GCNet
• Guided Anchoring
• Generalized Attention
• Dynamic R-CNN
• Hybrid Task Cascade
• DetectoRS
• Side-Aware Boundary Localization
• YOLOv3
• PAA
• CornerNet(WIP)
• Generalized Focal Loss
• Grid RCNN
• VFNet
• GROIE
• Mask R-CNN(experiment)
• Cascade Mask R-CNN(experiment)
• Cascade RPN
• DETR
• YOLOX

Tested on:

• torch=1.8.1
• tensorrt=8.0.1.6
• mmdetection=2.18.0
• cuda=11.1

If you find any error, please report it in the issue.

FAQ

read this page if you meet any problem.

Contact

This repo is maintained by @grimoire

Discuss group: QQ:1107959378

And send your resume to my e-mail if you want to join @OpenMMLab. Please read the JD for detail: link