What Is This

This is a C++ library to quickly use onnxruntime to deploy deep learning models.

Build

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CPU

make default

# build examples
make apps

GPU with CUDA

make gpu_default

make gpu_apps

Run with Docker

CPU

# build
docker build -f ./dockerfiles/ubuntu2004.dockerfile -t onnx_runtime .

# run
docker run -it --rm -v `pwd`:/workspace onnx_runtime

GPU with CUDA

# build
# change the cuda version to match your local cuda version before build the docker

docker build -f ./dockerfiles/ubuntu2004_gpu.dockerfile -t onnx_runtime_gpu .

# run
docker run -it --rm --gpus all -v `pwd`:/workspace onnx_runtime_gpu

• Onnxruntime will be built with TensorRT support if the environment has TensorRT. Check this memo for useful URLs related to building with TensorRT.
• Be careful to choose TensorRT version compatible with onnxruntime. A good guess can be inferred from HERE.
• Also it is not possible to use models whose input shapes are dynamic with TensorRT backend, according to this

Run

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Image Classification With Squeezenet

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Usage

# after make apps
./build/examples/TestImageClassification ./data/squeezenet1.1.onnx ./data/images/dog.jpg

the following result can be obtained

264 : Cardigan, Cardigan Welsh corgi : 0.391365
263 : Pembroke, Pembroke Welsh corgi : 0.376214
227 : kelpie : 0.0314975
158 : toy terrier : 0.0223435
230 : Shetland sheepdog, Shetland sheep dog, Shetland : 0.020529

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Object Detection With Tiny-Yolov2 trained on VOC dataset (with 20 classes)

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Usage

• Download model from onnx model zoo: HERE

• The shape of the output would be

    OUTPUT_FEATUREMAP_SIZE X OUTPUT_FEATUREMAP_SIZE * NUM_ANCHORS * (NUM_CLASSES + 4 + 1)
    where OUTPUT_FEATUREMAP_SIZE = 13; NUM_ANCHORS = 5; NUM_CLASSES = 20 for the tiny-yolov2 model from onnx model zoo

• Test tiny-yolov2 inference apps

# after make apps
./build/examples/tiny_yolo_v2 [path/to/tiny_yolov2/onnx/model] ./data/images/dog.jpg

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Object Instance Segmentation With MaskRCNN trained on MS CoCo Dataset (80 + 1(background) clasess)

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Usage

• Download model from onnx model zoo: HERE

• As also stated in the url above, there are four outputs: boxes(nboxes x 4), labels(nboxes), scores(nboxes), masks(nboxesx1x28x28)

• Test mask-rcnn inference apps

# after make apps
./build/examples/mask_rcnn [path/to/mask_rcnn/onnx/model] ./data/images/dogs.jpg
./build/examples/mask_rcnn [path/to/mask_rcnn/onnx/model] ./data/images/indoor.jpg

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Yolo V3 trained on Ms CoCo Dataset

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Usage

• Download model from onnx model zoo: HERE

• Test yolo-v3 inference apps

# after make apps
./build/examples/yolov3 [path/to/yolov3/onnx/model] ./data/images/no_way_home.jpg

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Ultra-Light-Fast-Generic-Face-Detector-1MB

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Usage

• App to use onnx model trained with famous light-weight Ultra-Light-Fast-Generic-Face-Detector-1MB
• Sample weight has been saved ./data/version-RFB-640.onnx
• Test inference apps

# after make apps
./build/examples/ultra_light_face_detector ./data/version-RFB-640.onnx ./data/images/endgame.jpg

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YoloX: high-performance anchor-free YOLO by Megvii

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Usage

• Download onnx model trained on COCO dataset from HERE

# this app tests yolox_l model but you can try with other yolox models also.
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_l.onnx -O ./data/yolox_l.onnx

• Test inference apps

# after make apps
./build/examples/yolox ./data/yolox_l.onnx ./data/images/matrix.jpg

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Semantic Segmentation Paddle Seg

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Usage

• Download PaddleSeg's bisenetv2 trained on cityscapes dataset that has been converted to onnx HERE and copy to ./data directory

You can also convert your own PaddleSeg with following procedures

• export PaddleSeg model
• convert exported model to onnx format with Paddle2ONNX

• Test inference apps

./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./data/images/sample_city_scapes.png
./build/examples/semantic_segmentation_paddleseg_bisenetv2 ./data/bisenetv2_cityscapes.onnx ./data/images/odaiba.jpg

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SuperPoint

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Usage

• Convert SuperPoint's pretrained weights to onnx format

git submodule update --init --recursive
python3 -m pip install -r scripts/superpoint/requirements.txt
python3 scripts/superpoint/convert_to_onnx.py

• Download test images from this dataset

wget https://raw.githubusercontent.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods/master/Multimodal_Image_Matching_Datasets/ComputerVision/CrossSeason/VisionCS_0a.png -P data

wget https://raw.githubusercontent.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods/master/Multimodal_Image_Matching_Datasets/ComputerVision/CrossSeason/VisionCS_0b.png -P data

• Test inference apps

./build/examples/super_point /path/to/super_point.onnx data/VisionCS_0a.png data/VisionCS_0b.png

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SuperGlue

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Usage

• Convert SuperPoint's pretrained weights to onnx format: Follow the above instruction

• Convert SuperGlue's pretrained weights to onnx format

git submodule update --init --recursive
python3 -m pip install -r scripts/superglue/requirements.txt
python3 -m pip install -r scripts/superglue/SuperGluePretrainedNetwork/requirements.txt
python3 scripts/superglue/convert_to_onnx.py

• Download test images from this dataset: Or prepare some pairs of your own images

• Test inference apps

./build/examples/super_glue /path/to/super_point.onnx /path/to/super_glue.onnx /path/to/1st/image /path/to/2nd/image

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LoFTR

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Usage

• Download LoFTR weights indoords_new.ckpt from HERE. (LoFTR's latest commit seems to be only compatible with the new weights (Ref: zju3dv/LoFTR#48). Hence, this onnx cpp application is only compatible with _indoor_ds_new.ckpt weights)

• Convert LoFTR's pretrained weights to onnx format

git submodule update --init --recursive
python3 -m pip install -r scripts/loftr/requirements.txt
python3 scripts/loftr/convert_to_onnx.py --model_path /path/to/indoor_ds_new.ckpt

• Download test images from this dataset: Or prepare some pairs of your own images

• Test inference apps

./build/examples/loftr /path/to/loftr.onnx /path/to/loftr.onnx /path/to/1st/image /path/to/2nd/image

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