MMDet to TensorRT

[!NOTE]

The main branch is used to support model conversion of MMDetection>=3.0.
If you want to convert model on older MMDetection, Please switch to branch:

• mmdet2trt=v0.5.0
• torch2trt_dynamic=v0.5.0
• amirstan_plugin=v0.5.0.

News

• 2024.02: Support MMDetection>=3.0

Introduction

This project aims to support End2End deployment of models in MMDetection with TensorRT.

Mask support is experiment.

Features:

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

Requirement

• install MMDetection:

    pip install openmim
    mim install mmdet==3.3.0

• install torch2trt_dynamic:

    git clone https://github.com/grimoire/torch2trt_dynamic.git torch2trt_dynamic
    cd torch2trt_dynamic
    pip install -e .

• 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

    [!NOTE]

    DON’T FORGET setting the environment 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
pip install -e .

Docker

Build docker image

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

Create a TensorRT model from mmdet model.
detail can be found in getting_started.md

CLI

# conversion might take few minutes.
mmdet2trt ${CONFIG_PATH} ${CHECKPOINT_PATH} ${OUTPUT_PATH}

Run mmdet2trt -h for help on optional arguments.

Python

shape_ranges=dict(
    x=dict(
        min=[1,3,320,320],
        opt=[1,3,800,1344],
        max=[1,3,1344,1344],
    )
)
trt_model = mmdet2trt(cfg_path,
                      weight_path,
                      shape_ranges=shape_ranges,
                      fp16_mode=True)
# 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 perform inference with 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)

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

[!NOTE]

Some models have only been tested on MMDet<3.0. If you found any failed model,
Please report in the issue.

• 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=2.2.0
• tensorrt=8.6.1
• mmdetection=3.3.0
• cuda=11.7

FAQ

read this page if you meet any problem.

License

This project is released under the Apache 2.0 license.