pinto0309 / 20220228_intel_deeplearning_day_hitnet_demo

Special Presentation Demo at Intel IoT Planet 2021 DeepLearning Day / インテル IoT プラネット 2021 DeepLearning Dayの特別講演の発表資料 https://www.intel.co.jp/content/www/jp/ja/now/iot-planet/deep-learning-day.html

20220228_intel_deeplearning_day_hitnet_demo

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

1. Overview / 概要

This is a demonstration of the steps to convert and infer HITNet, a stereo depth estimation model, using a custom build of OpenVINO.
OpenVINOをカスタムビルドしてステレオ深度推定モデルのHITNetを変換し、推論するまでの手順のデモです。

2. Environment / 環境

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

3. Overall flow / 全体の流れ

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 オリジナルモデル.pbの調達
• 4-2. Convert .pb to saved_model / .pbをsaved_modelに変換
• 4-3. Convert saved_model to ONNX / saved_modelをONNXに変換
• 4-4. Building OpenVINO / OpenVINOのビルド
• 4-5. Convert ONNX to OpenVINO IR / ONNXをOpenVINO IRへ変換
• 4-6. Download the Dataset / Datasetのダウンロード
• 4-7. HITNet's ONNX demo / HITNetのONNXデモ
• 4-8. HITNet's OpenVINO demo / HITNetのOpenVINOデモ
• 4-9. HITNet's TensorRT demo / HITNetのTensorRTデモ

4. Procedure / 手順

4-1. Procurement of original model .pb / オリジナルモデル.pbの調達

Download the official HITNet model published by Google Research here. The file to be downloaded is a Protocol Buffers format file.
こちら のGoogle Researchが公開しているHITNet公式モデルをダウンロードします。ダウンロードするファイルはProtocol Buffers形式のファイルです。

$ 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.
モデルの構造を確認するには、Netron を使用します。eth3dの場合、1チャンネルのグレースケール画像2枚を入力として使用します。入力の名前は input です。

The name of the output is reference_output_disparity.
出力の名前は reference_output_disparity です。

For non-eth3d, the input is two 3-channel RGB images.
eth3d以外のモデルの場合、入力は3チャンネルのRGB画像2枚です。

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4-2. Convert .pb to saved_model / .pbをsaved_modelに変換

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.
OpenVINOやTensorFlowやPyTorchやONNXなどの各種主要フレームワークの最新バージョンが全て導入されたDockerコンテナを起動します。CUDAやTensorRTなどの巨大なフレームワークも全てインストールされているため、Docker Imageは26GBとかなり大きいことに注意してください。また、Dockerコンテナの中からGUIを使用したデモを起動するため、xhost, --gpus, -v, -e, --net, --privileged などの多くの起動オプションを指定していますが、GUIを使用しない場合は指定不要です。どのようなフレームワークが導入されたDockerコンテナかを知りたい場合は こちら をご覧ください。

$ 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を使用しない場合のサンプルは下記のとおりです。

$ 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.
生成された saved_model の形状を確認してみます。TensorFlowの標準ツール saved_model_cli を使用します。入力のNHWC形状 バッチサイズ,高さ,幅,チャンネル のうち、高さと幅が未定義の -1 となっています。

$ 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_modelをONNXに変換

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コンテナに導入されている saved_model_to_tflite というツールを使用して saved_model から tflite を生成します。 公式の tensorflow-onnx というツールを使用すると saved_model から即座に onnx を生成することが可能ですが、なるべく最適化を行うためにあえて一度 tflite へ変換します。--input_shapes オプションを使用することで未定義の入力形状を指定のサイズへ固定することができます。

$ 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.
生成されたTFLiteの入力と出力の構造を確認します。この時点ですでにTensorFlowLiteのオプティマイザによって不要なオペレーションが大量に削除されたり、あるいは複数のオペレーションが融合して綺麗でシンプルな構造に変換されています。

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.
次に、tflite を onnx へ変換します。ここで tensorflow-onnx を使用します。--inputs-as-nchw input は入力の形状を NHWC から NCHW へ変換するためのオプションです。なお、生成するonnxのopsetは 12 を指定する必要があります。

$ 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.
最適化が不十分で、なおかつ各オペレーションの入出力情報が未定義の冗長なonnxファイルが生成されます。

Uses onnx-simplifier to further optimize onnx files.
onnx-simplifier を使用してonnxファイルをさらに最適化します。

$ 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.
ファイルサイズが肥大化しますが、モデルの構造は最適化されおり推論パフォーマンスに影響はありません。

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4-4. Building OpenVINO / OpenVINOのビルド

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.
OpenVINOモデルオプティマイザの現行最新バージョンには一部問題があるため、問題箇所を解消済みのコミットのソースコードからOpenVINOそのものをビルドします。Intelのエンジニアとやりとりして解消いただいた問題点の内容が気になる方は こちら をご覧ください。

$ 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.
ビルド終了。

Check the generated Wheel files; two Wheel files have been generated.
生成されたWheelファイルを確認します。Wheelファイルは２個生成されています。

$ 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を上書きインストールします。

$ 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 / ONNXをOpenVINO IRへ変換

Convert ONNX files to OpenVINO IR.
ONNXファイルをOpenVINO IRへ変換します。

$ 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のダウンロード

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.
テスト用のステレオドライビングデータセットをダウンロードします。見た目では分かりにくいですが、２個の左右のカメラからそれぞれ撮影した画像のペアのデータセットです。

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 / HITNetのONNXデモ

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.
ibaiGorordoさんのONNXデモをお借りして実行してみます。ONNXのCUDAプロバイダが有効になるように、プログラムを少しだけ調整します。

$ 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

Kazam_screencast_00008.mp4

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4-8. HITNet's OpenVINO demo / HITNetのOpenVINOデモ

Run a test inference program customized for OpenVINO: CPU inference.
OpenVINO用にカスタマイズしたテスト用推論プログラムを実行します。CPU推論です。

$ python drivingStereoTest_openvino.py

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4-9. HITNet's TensorRT demo / HITNetのTensorRTデモ

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.
iwatakeさんのTensorRTデモをお借りしてテストを実施します。こちらのリポジトリのチュートリアルに従って環境を構築して実行します。
https://github.com/iwatake2222/play_with_tensorrt/tree/master/pj_tensorrt_depth_stereo_hitnet

$ ./main stereo_movie.mp4

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5. Acknowledgements / 謝辞

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

    LICENSE

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

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

    LICENSE

    MIT License
    
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    SOFTWARE.
    

• iwatake2222:

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

    LICENSE

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

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

    LICENSE

    MIT License
    
    Copyright (c) 2019 drivingstereo-dataset
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
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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}
    }