0df7dab345
* OV new package structure * Fixes * More fixes * Fixed code style in ngraph tests * Fixes * Paths to setupvars inside demo scripts * Fixed demo_security_barrier_camera.sh * Added setupvars.sh to old location as well * Fixed path * Fixed MO install path in .co * Fixed install of public headers * Fixed frontends installation * Updated DM config files * Keep opencv in the root * Improvements * Fixes for demo scripts * Added path to TBB * Fix for MO unit-tests * Fixed tests on Windows * Reverted arch * Removed arch * Reverted arch back: second attemp * System type * Fix for Windows * Resolve merge conflicts * Fixed path * Path for Windows * Added debug for Windows * Added requirements_dev.txt to install * Fixed wheel's setup.py * Fixed lin build * Fixes after merge * Fix 2 * Fixes * Frontends path * Fixed deployment manager * Fixed Windows * Added cldnn unit tests installation * Install samples * Fix samples * Fix path for samples * Proper path * Try to fix MO hardcodes * samples binary location * MO print * Added install for libopencv_c_wrapper.so * Added library destination * Fixed install rule for samples * Updated demo scripts readme.md * Samples * Keep source permissions for Python samples * Fixed python * Updated path to fast run scripts * Fixed C samples tests * Removed debug output * Small fixes * Try to unify prefix
65 lines
3.2 KiB
Plaintext
65 lines
3.2 KiB
Plaintext
=====================================================
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Scripts to build and run OpenVINO samples
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=====================================================
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These scripts simplify process of build samples, download and convert models and run samples to perform inference. They can used to quick validation of OpenVINO installation and proper environment initialization.
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Setting Up
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================
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If you are behind a proxy, set the following environment variables in the console session:
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On Linux* and Mac OS:
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export http_proxy=http://<proxyHost>:<proxyPort>
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export https_proxy=https://<proxyHost>:<proxyPort>
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On Windows* OS:
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set http_proxy=http://<proxyHost>:<proxyPort>
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set https_proxy=https://<proxyHost>:<proxyPort>
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Running Samples
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=============
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The "demo" folder contains two scripts:
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1. Classification sample using public SqueezeNet topology (run_sample_squeezenet.sh|bat)
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2. Benchmark sample using public SqueezeNet topology (run_sample_benchmark_app.sh|bat)
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To run the samples, invoke run_sample_squeezenet.sh or run_sample_benchmark_app.sh (*.bat on Windows) scripts from the console without parameters, for example:
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./run_sample_squeezenet.sh
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The script allows to specify the target device to infer on using -d <CPU|GPU|MYRIAD> option.
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Classification Sample Using SqueezeNet
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====================================
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The sample illustrates the general workflow of using the Intel(R) Deep Learning Deployment Toolkit and performs the following:
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- Downloads a public SqueezeNet model using the Model Downloader (extras\open_model_zoo\tools\downloader\downloader.py)
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- Installs all prerequisites required for running the Model Optimizer using the scripts from the "tools\model_optimizer\install_prerequisites" folder
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- Converts SqueezeNet to an IR using the Model Optimizer (tools\model_optimizer\mo.py) via the Model Converter (extras\open_model_zoo\tools\downloader\converter.py)
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- Builds the Inference Engine classification_sample (samples\cpp\classification_sample)
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- Runs the sample with the car.png picture located in the demo folder
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The sample application prints top-10 inference results for the picture.
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For more information about the Inference Engine classification sample, refer to the documentation available in the sample folder.
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Benchmark Sample Using SqueezeNet
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===============================
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The sample illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices.
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The sample script does the following:
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- Downloads a public SqueezeNet model using the Model Downloader (extras\open_model_zoo\tools\downloader\downloader.py)
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- Installs all prerequisites required for running the Model Optimizer using the scripts from the "tools\model_optimizer\install_prerequisites" folder
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- Converts SqueezeNet to an IR using the Model Optimizer (tools\model_optimizer\mo.py) via the Model Converter (extras\open_model_zoo\tools\downloader\converter.py)
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- Builds the Inference Engine benchmark tool (samples\benchmark_app)
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- Runs the tool with the car.png picture located in the demo folder
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The benchmark app prints performance counters, resulting latency, and throughput values.
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For more information about the Inference Engine benchmark app, refer to the documentation available in the sample folder.
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