small c++ library to quickly deploy models using onnxruntime
If this work somehow makes your day, maybe you can consider :)
Thanks to cardboardcode, we have the documentation for this small library. Hope that they both are helpful for your work.
# onnxruntime needs newer cmake version to build
bash ./scripts/install_latest_cmake.bash
bash ./scripts/install_onnx_runtime.bash
# dependencies to build apps
bash ./scripts/install_apps_dependencies.bash
make default
# build examples
make apps
make gpu_default
make gpu_apps
# build
docker build -f ./dockerfiles/ubuntu2004.dockerfile -t onnx_runtime .
# run
docker run -it --rm -v `pwd`:/workspace onnx_runtime
# 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
# 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
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
# after make apps
./build/examples/tiny_yolo_v2 [path/to/tiny_yolov2/onnx/model] ./data/images/dog.jpg
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
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
# after make apps
./build/examples/ultra_light_face_detector ./data/version-RFB-640.onnx ./data/images/endgame.jpg
# 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
# after make apps
./build/examples/yolox ./data/yolox_l.onnx ./data/images/matrix.jpg
./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
git submodule update --init --recursive
python3 -m pip install -r scripts/superpoint/requirements.txt
python3 scripts/superpoint/convert_to_onnx.py
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
./build/examples/super_point /path/to/super_point.onnx data/VisionCS_0a.png data/VisionCS_0b.png
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
Download LoFTR weights indoords_new.ckpt from HERE. (LoFTR's latest commit seems to be only compatible with the new weights (Ref: https://github.com/zju3dv/LoFTR/issues/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