101 lines
5.0 KiB
Plaintext
101 lines
5.0 KiB
Plaintext
=====================================================
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Demo Scripts for Model Optimizer and Inference Engine
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=====================================================
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The demo scripts illustrate Intel(R) Deep Learning Deployment Toolkit usage to convert and optimize pre-trained models and perform inference.
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Setting Up Demos
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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 Demos
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=============
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The "demo" folder contains three scripts:
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1. Classification demo using public SqueezeNet topology (demo_squeezenet_download_convert_run.sh|bat)
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2. Security barrier camera demo that showcases three models coming with the product (demo_squeezenet_download_convert_run.sh|bat)
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3. Benchmark demo using public SqueezeNet topology (demo_benchmark_app.sh|bat)
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4. Speech recognition demo utilizing models trained on open LibriSpeech dataset
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To run the demos, run demo_squeezenet_download_convert_run.sh or demo_security_barrier_camera.sh or demo_benchmark_app.sh or demo_speech_recognition.sh (*.bat on Windows) scripts from the console without parameters, for example:
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./demo_squeezenet_download_convert_run.sh
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The script allows to specify the target device to infer on using -d <CPU|GPU|MYRIAD|FPGA> option.
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Classification Demo Using SqueezeNet
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====================================
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The demo 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 (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 "model_optimizer\install_prerequisites" folder
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- Converts SqueezeNet to an IR using the Model Optimizer (model_optimizer\mo.py) via the Model Converter (open_model_zoo\tools\downloader\converter.py)
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- Builds the Inference Engine classification_sample (inference_engine\samples\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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Security Barrier Camera Demo
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============================
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The demo illustrates using the Inference Engine with pre-trained models to perform vehicle detection, vehicle attributes and license-plate recognition tasks.
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As the sample produces visual output, it should be run in GUI mode.
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The demo script does the following:
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- Builds the Inference Engine security barrier camera sample (inference_engine\samples\security_barrier_camera_sample)
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- Runs the sample with the car_1.bmp located in the demo folder
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The sample application displays the resulting frame with detections rendered as bounding boxes and text.
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For more information about the Inference Engine security barrier camera sample, refer to the documentation available in the sample folder.
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Benchmark Demo Using SqueezeNet
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===============================
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The demo illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices.
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The demo script does the following:
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- Downloads a public SqueezeNet model using the Model Downloader (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 "model_optimizer\install_prerequisites" folder
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- Converts SqueezeNet to an IR using the Model Optimizer (model_optimizer\mo.py) via the Model Converter (open_model_zoo\tools\downloader\converter.py)
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- Builds the Inference Engine benchmark tool (inference_engine\samples\demo_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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Speech Recognition Demo Using LibriSpeech models
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================================================
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The demo illustrates live speech recognition - transcribing speech from microphone or offline (from wave file).
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The demo is also capable of live close captioning of an audio clip or movie, where signal is intercepted from the speaker.
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The demo script does the following:
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- Downloads US English models trained on LibriSpeech dataset prepared for direct usage by the Inference Engine
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- Installs the required components
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- Runs the command line offline demo
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- As a final step, runs live speech recognition application with graphical interface
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The GUI application prints the speech transcribed from input signal in window. Up to two channels can be transcribed in parallel: microphone & speakers streams.
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