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

• mmdet>=2.3.0
• https://github.com/grimoire/torch2trt_dynamic
• https://github.com/grimoire/amirstan_plugin

Important!

Set 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(Note that TensorRT7.0 might have memory leak, better to upgrade to 7.1+)

# cuda10.2 tensorrt7.0 pytorch1.6
sudo docker build -t mmdet2trt_docker:v1.0 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)
torch.save(trt_model.state_dict(), save_path)

how to use the converted model

trt_model = init_detector(save_path)
num_detections, trt_bbox, trt_score, trt_cls = inference_detector(trt_model, image_path, cfg_path, "cuda:0")

how to save the tensorrt engine

with open(engine_path, mode='wb') as f:
    f.write(model_trt.state_dict()['engine'])

note that the bbox inference result did not divided by scale factor, divided by you self if needed.

play demo in demo/inference.py

getting_started.md for more detail

How does it works?

Most other project use pytorch=>ONNX=>tensorRT route, This repo convert pytorch=>tensorRT directly, avoid unnecessary ONNX IR. read https://github.com/NVIDIA-AI-IOT/torch2trt#how-does-it-work for detail.

Support Model/Module

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

Tested on:

• torch=1.6.0
• tensorrt=7.1.3.4
• mmdetection=2.10.0
• cuda=10.2
• cudnn=8.0.2.39

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

FAQ

read this page if you meet any problem.

Contact

This repo is maintained by @grimoire

Discuss group: QQ:1107959378