20220228_intel_deeplearning_day_hitnet_demo

https://www.intel.co.jp/content/www/jp/ja/now/iot-planet/deep-learning-day.html

1. Overview /

This is a demonstration of the steps to convert and infer HITNet, a stereo depth estimation model, using a custom build of OpenVINO. OpenVINOHITNet

2. Environment /

• Ubuntu 20.04 x86_64
• Docker 20.10.12, build e91ed57
• OpenVINO commit hash: e89db1c6de8eb551949330114d476a2a4be499ed
• ONNX

3. Overall flow /

In order to optimize the process as much as possible, the following processing flow is adopted. TensorFlow pb -> TensorFlow saved_model -> TensorFlow Lite tflite -> ONNX onnx -> OpenVINO IR xml/bin

• 4-1. Procurement of original model .pb / .pb .pb
• 4-2. Convert .pb to saved_model / .pbsaved_model
• 4-3. Convert saved_model to ONNX / saved_modelONNX
• 4-4. Building OpenVINO / OpenVINO
• 4-5. Convert ONNX to OpenVINO IR / ONNXOpenVINO IR
• 4-6. Download the Dataset / Dataset
• 4-7. HITNet's ONNX demo / HITNetONNX
• 4-8. HITNet's OpenVINO demo / HITNetOpenVINO
• 4-9. HITNet's TensorRT demo / HITNetTensorRT

4. Procedure /

4-1. Procurement of original model .pb / .pb

Download the official HITNet model published by Google Research here. The file to be downloaded is a Protocol Buffers format file. Google ResearchHITNetProtocol Buffers

$ git clone https://github.com/PINTO0309/20210228_intel_deeplearning_day_hitnet_demo
$ cd 20210228_intel_deeplearning_day_hitnet_demo

# [1, ?, ?, 2], Grayscale image x2
$ wget https://storage.googleapis.com/tensorflow-graphics/models/hitnet/default_models/eth3d.pb
or
# [1, ?, ?, 6], RGB image x2
$ wget https://storage.googleapis.com/tensorflow-graphics/models/hitnet/default_models/flyingthings_finalpass_xl.pb
or
# [1, ?, ?, 6], RGB image x2
$ wget https://storage.googleapis.com/tensorflow-graphics/models/hitnet/default_models/middlebury_d400.pb

Use Netron to check the structure of the model. In the case of eth3d, two grayscale images of one channel are used as input. The name of the input is input. Netron eth3d12 input The name of the output is reference_output_disparity. reference_output_disparity

For non-eth3d, the input is two 3-channel RGB images. eth3d3RGB2 Back to top

4-2. Convert .pb to saved_model / .pbsaved_model

Start a Docker container with all the latest versions of the various major frameworks such as OpenVINO, TensorFlow, PyTorch, ONNX, etc. Note that the Docker Image is quite large, 26GB, since all the huge frameworks such as CUDA and TensorRT are also installed. Also, in order to launch the demo with GUI from within the Docker container, many launch options are specified, such as xhost, --gpus, -v, -e, --net, --privileged, etc., but they do not need to be specified if you do not want to use the GUI. If you want to know what kind of framework is implemented in a Docker container, please click here. OpenVINOTensorFlowPyTorchONNXDockerCUDATensorRTDocker Image26GBDockerGUI**xhost**, --gpus, -v, -e, --net, --privileged GUIDocker

$ xhost +local: && \
docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
ghcr.io/pinto0309/openvino2tensorflow:latest

$ MODEL=eth3d
or
$ MODEL=flyingthings_finalpass_xl
or
$ MODEL=middlebury_d400

$ pb_to_saved_model \
--pb_file_path ${MODEL}.pb \
--inputs input:0 \
--outputs reference_output_disparity:0 \
--model_output_path ${MODEL}/saved_model

A sample without GUI is shown below. GUI

$ docker run -it --rm \
-v `pwd`:/home/user/workdir \
ghcr.io/pinto0309/openvino2tensorflow:latest

$ MODEL=eth3d
or
$ MODEL=flyingthings_finalpass_xl
or
$ MODEL=middlebury_d400

$ pb_to_saved_model \
--pb_file_path ${MODEL}.pb \
--inputs input:0 \
--outputs reference_output_disparity:0 \
--model_output_path ${MODEL}/saved_model

Let's check the shape of the generated saved_model, using the standard TensorFlow tool saved_model_cli.Of the input NHWC shape batch,height,width,channel, the height and width are undefined -1. saved_model TensorFlow saved_model_cli NHWC ,,, -1

$ saved_model_cli show --dir middlebury_d400/saved_model/ --all

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, 6)
        name: input:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['reference_output_disparity'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, -1)
        name: reference_output_disparity:0
  Method name is: tensorflow/serving/predict

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4-3. Convert saved_model to ONNX / saved_modelONNX

The tool saved_model_to_tflite introduced in the Dokcer container is used to generate tflite from saved_model. The tool tensorflow-onnx can be used to generate onnx from saved_model immediately, but I will convert it once to tflite to make it as optimized as possible. The --input_shapes option can be used to fix undefined input shapes to a specified size. Dokcer saved_model_to_tflite saved_model tflite tensorflow-onnx saved_model onnx tflite --input_shapes

$ H=480
$ W=640
$ saved_model_to_tflite \
--saved_model_dir_path ${MODEL}/saved_model \
--input_shapes [1,${H},${W},6] \
--model_output_dir_path ${MODEL}/saved_model_${H}x${W} \
--output_no_quant_float32_tflite

Check the input and output structure of the generated TFLite. At this point, TensorFlowLite's optimizer has already removed a large number of unnecessary operations or merged multiple operations into a clean and simple structure. TFLiteTensorFlowLite Next, convert tflite to onnx. I will use tensorflow-onnx here. --inputs-as-nchw input is an option to convert the shape of the input from NHWC to NCHW. Note that the onnx opset to be generated must be 12. tflite onnx tensorflow-onnx --inputs-as-nchw input NHWC NCHW onnxopset 12

$ python -m tf2onnx.convert \
--opset 12 \
--inputs-as-nchw input \
--tflite ${MODEL}/saved_model_${H}x${W}/model_float32.tflite \
--output ${MODEL}/saved_model_${H}x${W}/model_float32.onnx

Redundant onnx files are generated with insufficient optimization and undefined input/output information for each operation. onnx Uses onnx-simplifier to further optimize onnx files. onnx-simplifier onnx

$ python -m onnxsim \
${MODEL}/saved_model_${H}x${W}/model_float32.onnx \
${MODEL}/saved_model_${H}x${W}/model_float32.onnx

The file size will increase, but the structure of the model will be optimized and inference performance will not be affected.

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4-4. Building OpenVINO / OpenVINO

Since there are some issues with the current latest version of the OpenVINO model optimizer, we will build OpenVINO itself from the source code of the commits that have already resolved the issues. OpenVINOOpenVINOIntel

$ git clone https://github.com/openvinotoolkit/openvino \
&& cd openvino \
&& git checkout e89db1c6de8eb551949330114d476a2a4be499ed \
&& git submodule update --init --recursive \
&& pip install pip --upgrade \
&& pip install Cython numpy setuptools wheel pafy youtube-dl \
&& chmod +x scripts/submodule_update_with_gitee.sh \
&& ./scripts/submodule_update_with_gitee.sh \
&& chmod +x install_build_dependencies.sh \
&& ./install_build_dependencies.sh \
&& mkdir build \
&& cd build \
&& cmake \
-DCMAKE_BUILD_TYPE=Release \
-DENABLE_PYTHON=ON \
-DPYTHON_EXECUTABLE=`which python3` \
-DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.8.so \
-DPYTHON_INCLUDE_DIR=/usr/include/python3.8 \
-DENABLE_CLDNN=ON \
-DENABLE_WHEEL=ON .. \
&& make -j$(nproc)

Build finished.

Check the generated Wheel files; two Wheel files have been generated. WheelWheel

$ ls -l wheels/*
-rw-r--r-- 1 user user 30777895 Feb 11 11:17 wheels/openvino-2022.1.0-000-cp38-cp38-manylinux_2_31_x86_64.whl
-rw-r--r-- 1 user user  6419721 Feb 11 11:06 wheels/openvino_dev-2022.1.0-000-py3-none-any.whl

Overwrite the OpenVINO installation. OpenVINO

$ sudo ${INTEL_OPENVINO_DIR}/openvino_toolkit_uninstaller/uninstall.sh --silent \
&& sudo pip install wheels/* && cd ../.. && rm -rf openvino

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4-5. Convert ONNX to OpenVINO IR / ONNXOpenVINO IR

Convert ONNX files to OpenVINO IR. ONNXOpenVINO IR

$ sudo python /usr/local/lib/python3.8/dist-packages/openvino/tools/mo/mo.py \
--input_model ${MODEL}/saved_model_${H}x${W}/model_float32.onnx \
--data_type FP32 \
--output_dir ${MODEL}/saved_model_${H}x${W}/openvino/FP32 \
--model_name ${MODEL}_${H}x${W} \
&& sudo chown -R user ${MODEL}

/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:122: PkgResourcesDeprecationWarning: 0.1.9-nvc is an invalid version and will not be supported in a future release
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:122: PkgResourcesDeprecationWarning: 0.1.9-nvc is an invalid version and will not be supported in a future release
  warnings.warn(
Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	/home/user/workdir/middlebury_d400/saved_model_480x640/model_float32.onnx
	- Path for generated IR: 	/home/user/workdir/middlebury_d400/saved_model_480x640/openvino/FP32
	- IR output name: 	middlebury_d400_480x640
	- Log level: 	ERROR
	- Batch: 	Not specified, inherited from the model
	- Input layers: 	Not specified, inherited from the model
	- Output layers: 	Not specified, inherited from the model
	- Input shapes: 	Not specified, inherited from the model
	- Source layout: 	Not specified
	- Target layout: 	Not specified
	- Layout: 	Not specified
	- Mean values: 	Not specified
	- Scale values: 	Not specified
	- Scale factor: 	Not specified
	- Precision of IR: 	FP32
	- Enable fusing: 	True
	- Enable grouped convolutions fusing: 	True
	- Move mean values to preprocess section: 	None
	- Reverse input channels: 	False
	- Use legacy API for model processing: 	False
	- Use the transformations config file: 	None
ONNX specific parameters:
/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:122: PkgResourcesDeprecationWarning: 0.1.9-nvc is an invalid version and will not be supported in a future release
  warnings.warn(
	- OpenVINO runtime found in: 	/usr/local/lib/python3.8/dist-packages/openvino
OpenVINO runtime version: 	2022.1.custom_HEAD_e89db1c6de8eb551949330114d476a2a4be499ed
Model Optimizer version: 	custom_HEAD_e89db1c6de8eb551949330114d476a2a4be499ed
[ WARNING ] Model Optimizer and OpenVINO runtime versions do no match.
[ WARNING ] Consider building the OpenVINO Python API from sources or reinstall OpenVINO (TM) toolkit using "pip install openvino" (may be incompatible with the current Model Optimizer version)
[ WARNING ]  
Detected not satisfied dependencies:
	numpy: installed: 1.22.2, required: < 1.20
	fastjsonschema: not installed, required: ~= 2.15.1

Please install required versions of components or use install_prerequisites script
/usr/local/lib/python3.8/dist-packages/openvino/tools/mo/install_prerequisites/install_prerequisites_onnx.sh
Note that install_prerequisites scripts may install additional components.
/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py:122: PkgResourcesDeprecationWarning: 0.1.9-nvc is an invalid version and will not be supported in a future release
  warnings.warn(
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /home/user/workdir/middlebury_d400/saved_model_480x640/openvino/FP32/middlebury_d400_480x640.xml
[ SUCCESS ] BIN file: /home/user/workdir/middlebury_d400/saved_model_480x640/openvino/FP32/middlebury_d400_480x640.bin
[ SUCCESS ] Total execution time: 50.17 seconds. 
[ SUCCESS ] Memory consumed: 410 MB.

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4-6. Download the Dataset / Dataset

Download a stereo driving dataset for testing. It is hard to see, but it is a dataset of pairs of images taken from each of the two left and right cameras.

Left	Right

$ mkdir -p "DrivingStereo images/left" \
&& mkdir -p "DrivingStereo images/right" \
&& mkdir -p "DrivingStereo images/depth" \
&& wget https://github.com/PINTO0309/20210228_intel_deeplearning_day_hitnet_demo/releases/download/v1.0/2018-07-11-14-48-52_left.zip \
&& unzip -d "DrivingStereo images/left" -q 2018-07-11-14-48-52_left.zip \
&& rm 2018-07-11-14-48-52_left.zip \
&& wget https://github.com/PINTO0309/20210228_intel_deeplearning_day_hitnet_demo/releases/download/v1.0/2018-07-11-14-48-52_right.zip \
&& unzip -d "DrivingStereo images/right" -q 2018-07-11-14-48-52_right.zip \
&& rm 2018-07-11-14-48-52_right.zip \
&& wget https://github.com/PINTO0309/20210228_intel_deeplearning_day_hitnet_demo/releases/download/v1.0/2018-07-11-14-48-52_depth.zip \
&& unzip -d "DrivingStereo images/depth" -q 2018-07-11-14-48-52_depth.zip \
&& rm 2018-07-11-14-48-52_depth.zip \
&& wget https://github.com/PINTO0309/20210228_intel_deeplearning_day_hitnet_demo/releases/download/v1.0/stereo_movie.mp4

4-7. HITNet's ONNX demo / HITNetONNX

4-7-1. ONNX+CUDA

I'll borrow ibaiGorordo's ONNX demo to run it. Adjust the program slightly so that ONNX's CUDA provider is enabled. ibaiGorordoONNXONNXCUDA

$ rm -rf ONNX-HITNET-Stereo-Depth-estimation \
&& git clone https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation.git \
&& cd ONNX-HITNET-Stereo-Depth-estimation \
&& git checkout 20471bfe2a23c34681141a9c0401eeff45680330 \
&& cd .. \
&& sed -i 's/models\///g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/cv2.WINDOW_NORMAL/cv2.WINDOW_AUTOSIZE/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/max_distance = 30/max_distance = 80/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/np.hstack((left_img,color_real_depth, color_depth))/np.hstack((left_img, color_depth))/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i '31i \\t\tsession_option = onnxruntime.SessionOptions()' ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py \
&& sed -i '32i \\t\tmodel_file_name = model_path.split(".")[0]' ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py \
&& sed -i '33i \\t\tsession_option.optimized_model_filepath = f"{model_file_name}_cudaopt.onnx"'  ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py \
&& sed -i '34i \\t\tsession_option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_EXTENDED' ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py \
&& sed -i "s/onnxruntime.InferenceSession(model_path/onnxruntime.InferenceSession(model_path, session_option, providers=[\'CUDAExecutionProvider\']/g" ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py

$ python ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py

4-7-2. ONNX+TensorRT

$ rm -rf ONNX-HITNET-Stereo-Depth-estimation \
&& git clone https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation.git \
&& cd ONNX-HITNET-Stereo-Depth-estimation \
&& git checkout 20471bfe2a23c34681141a9c0401eeff45680330 \
&& cd .. \
&& sed -i 's/models\///g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/cv2.WINDOW_NORMAL/cv2.WINDOW_AUTOSIZE/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/max_distance = 30/max_distance = 80/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i 's/np.hstack((left_img,color_real_depth, color_depth))/np.hstack((left_img, color_depth))/g' ONNX-HITNET-Stereo-Depth-estimation/drivingStereoTest.py \
&& sed -i "s/onnxruntime.InferenceSession(model_path/onnxruntime.InferenceSession(model_path, providers=[\'TensorrtExecutionProvider', 'CUDAExecutionProvider']/g" ONNX-HITNET-Stereo-Depth-estimation/hitnet/hitnet.py

https://user-images.githubusercontent.com/33194443/156153624-4a94754e-bfaf-470f-a830-2c9483efb474.mp4

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4-8. HITNet's OpenVINO demo / HITNetOpenVINO

Run a test inference program customized for OpenVINO: CPU inference. OpenVINOCPU

$ python drivingStereoTest_openvino.py

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4-9. HITNet's TensorRT demo / HITNetTensorRT

I will be borrowing iwatake's TensorRT demo to run the test. Follow the tutorial in this repository to set up and run the environment. iwatakeTensorRT https://github.com/iwatake2222/play_with_tensorrt/tree/master/pj_tensorrt_depth_stereo_hitnet

$ ./main stereo_movie.mp4

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5. Acknowledgements /

Thanks!!!

• Intel Team:

  • GatherND shape conversion from ONNX is inaccurate #7379
  • Const data got different desc and content byte sizes (24 and 96 respectively)" error when converting ConvolutionBackpropData using compile_tools #9517

• openvinotoolkit:

  • https://github.com/openvinotoolkit/openvino

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• NobuoTsukamoto:

  • https://github.com/NobuoTsukamoto/benchmarks

    MIT License
    
    Copyright (c) 2021 Nobuo Tsukamoto
    
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• ibaiGorordo:

  • https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation

    MIT License
    
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• iwatake2222:

  • https://github.com/iwatake2222/play_with_tensorrt

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• A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios:

  • https://drivingstereo-dataset.github.io/

    MIT License
    
    Copyright (c) 2019 drivingstereo-dataset
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
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    @inproceedings{yang2019drivingstereo,
        title={DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios},
        author={Yang, Guorun and Song, Xiao and Huang, Chaoqin and Deng, Zhidong and Shi, Jianping and Zhou, Bolei},
        booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
        year={2019}
    }