Feature/azaytsev/merge to master (#2786)
* [IE CLDNN] Memory allocation optimizations (#2178) * [GNA] Safety fixes (#2193) * LSTMCell test [GNA] LSTMCell fix for GNA (#2216) * [GNA] fix scale factor calculation for unfused bias after fc (2021.1) (#2195) * [GNA] fix scale factor calculation for unfused bias after fc * change check * add test * apply requested changes * cpplint fix * apply test changes * modify model for test to match ::op:: * [LPT] Copy constant with several outputs before blob update (#2197) * [LPT] Copy constant implementation * [LPT] the same Constant ops as FQ interval boundaries * [Scripts] Fixing issue with exporting path-like env when it undef (#2164) * setupvars.sh: Added logic for exporting path env in case if it not defined * setupvars: Removed duplicated colon * Kept quotes where they were * setupvars: updated copyrights * FakeQuantize + Mul fusion (#2133) * FQ+Mul fusion transform skeleton * FQ+Mul fusion transform tests prep * Basic UT for the transform * Basic implementation of the transform * Parametrized UTs for FQMul transform * Parametrization of FQ+Mul UTs * Make sure that the shapes of constants match * Check if the mul constant matches FQ data * CentOs compilation error fix * PR feedback and adjusted tests * NHWC layout of the mul constant * UT: FQ output limits 4D * Redundant CF pass removed * Rewrite the graph in a different way * Shape checking infrastructure skeleton * Handle some negative cases * Check the rt info in the fusion test * Fuse all Mul nodes detected after FQ node * Dont cast the original FQ node * Dont throw if CF fails in new output range calculation * More UTs * Accept any type of input to FQ in the transformation * Test the fusion when all FQ inputs are non-const * Fusion test when only one output limit is const * Extend error message (#2174) * some nGraph KW fixes (#2176) * Removed redundant methods * Fixed KW for linux * Fix QueryNetwork for networks with KSO (#2202) * Added a test to reproduce QueryNetwork with KSO * Fixed QueryNetwork for networks with KSO * Added additional test * Fixed output names for case with redundant ops before result (#2209) * [IE][VPU]: Workaround to support parameter Beta for layer Swish (#2207) * Workaround to full support Swish layer. It is faster than native Swish for now. * [IE][VPU]: Remove the second call of ngraph::CommonOptimizations (#2221) * Remove the second call of ngraph::CommonOptimizations in myriad plugin * Reuse code with vpu ngraph transformations * Duplicate PR 2167 for release branch: GatherTree description was extended and outdated link fixed (#2235) * add more alrifications to description * move clarification to comment * pseudo code become more accurate * review changes * Add exposing function signatures via Cython (#2244) * [DOC] Reshape feature (#2194) * [IE][VPU][OpenCL] 2021.1 release compiler (#2189) * Statically analyzed issues. (#2261) * [IE][VPU]: Fix K propagation through Reshape (2021.1) (#2180) * Fix K propagation through Reshape * Add test cases * Revert "[IE TESTS] dynavic batch for mvn layer (#1010)" (#2256) This reverts commit2e3378c50f. * Fixed KW warning and review issues (#2262) * [IE][VPU]: update firmware 1381 (#2236) * Reverting devicePriorities to be vector and respect the order, as opposed to the incorrect (recent?) refactoring that introduced the unordered_map that effectively ignores the priorities (#2251) * update OpenCV version to 4.5.0 (#2260) * Add VPUX configuration to compile_tool (#2248) * [IE][TESTS] Fix compareRawBuffers and compareBlobData methods (#2246) Use `<=` comparison instead of `<` with thresholds. This allows to use `0` threshold for bit-exact comparison. * [IE][VPU]: KW fixes (#2186) * Some KW fixes * Fix printTo in vpu ngraph transformations * Fix for static PartialShape detection algorithm (#2177) * Fixes for Interpolate-4. (#2281) * Update get_ov_update_message.py (#2286) * Clone a specific tag for pybind11 (#2296) * [Scripts] Fix setting PYTHONPATH logic (#2305) * setupvars.sh: Added logic for exporting path env in case if it not defined * setupvars: Removed duplicated colon * install_openvino_dependencies: Updated copyrights setupvars.bat: Updated notification about incorrect Python version. Removed checking ICC2019 setupvars.sh: Removed logic with choosing higher version of installed Python. Added dynamic detecting python3 major and minor version for setting path. Add checking minimum required Python version(now 3.6) * Added python3-gi package and fixed libglib2.0-0 package location. (#2294) * [IE TESTS] CoreThreading_LoadNetwork tests were disabled for GPU plugin (#2245) (#2283) * setupvars: Updated notifications, fixed calling python in Windows case (#2318) * Updated operations specification documents (2021.1) (#2268) * Updated documentation structure and remove incorrect added files for Acosh-1, Asinh-1 and Atanh-1 * Fixed broken links * Fixed c samples build (#2278) (#2304) * Fixed c samples build fixed CVS-38816 - Failure to build samples in C * Fixed issue with gflags * Revert "[IE][VPU]: Fix K propagation through Reshape (2021.1) (#2180)" (#2322) This reverts commitd604a03ac0. * Added ONNX Resize-11 and ONNX Resize-13 to supported frameworks layers list. (#2325) * Implement `run_executable.py` to run `TimeTests` several times (#2125) (#2188) CI passed * install_NEO_OCL_driver: Updated exit codes, messages. Updated way to remove old driver on Ubuntu (#2333) * Bump cmake version to 3.13 (#2339) * install_NEO_OCL_driver: Added checking of installed packages before trying to remove them. Added quotes for echo. (#2350) * convert to doxygen comments * add doxygen doc build configurations (#2191) Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com> * [DOCS] Added an evaluate method for custom operation (#2272) * Added an evaluate method for custom operation * Fixed comments * Downgrade cmake for samples (#2372) * Downgrade cmake for samples Downgraded cmake version to default version for Ubuntu 18.04 * Updated supported python version The minimal python version in 2021.1 is 3.5 * Added notes about cmake requirements for samples and demo * Install dependency refactoring. (#2381) * Updated Transformation development doc (#2370) * Delete xfail for resolved known issue (#2385) * Fix layout links for dl streamer and c api (#2375) * fix layouts * change the dl-streamer link Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com> * Added link options for cross-compilation (#2397) * Added new GSG for macOS, made minor changes in Windows GSG (#2070) (#2405) * Added new GSG for macOS, made minor changes in Windows GSG * Update get_started_macos.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Fixed docs build on Windows (#2383) * layouts and code comments * Replace absolute links to docs.openvinotoolkit.org by relative ones (#2439) * Replaced direct links to docs.openvinotoolkit.org with relative links * Replaced direct links to docs.openvinotoolkit.org with relative links. Added GSGs for Win and macOS * Minor fixes in GSGs * Replaced direct links to docs.openvinotoolkit.org with relative links * Removed links to OpenVINO markdown files that contain anchor - they don't work in the current implementation of the doc process * Fixed Notes * Removed links to OpenVINO markdown files that contain anchor - they don't work in the current implementation of the doc process * fixed link to installing-openvino-linux.md * Update the menu to align with POT doc headers (#2433) * Update the menu to align with POT doc headers It changes the menu to align with Post-training Optimization Toolkit documentation titles. * Corrected one title Run Examples => How to Run Examples * Added closing braсket (#2466) Fixed syntax error (b4b03b1) * Remove the deprecation notice (#2314) * Removed deprecation notice * Removed the note from other files * [DOCS] Update Installation Guide - GPU steps (#2308) * Initial commit * fixing lists * Update installing-openvino-linux.md * Get rid of the note * Added the scrrenshot * Update installing-openvino-linux.md * fixes * separate layout * [Docs] Update MO What's new description (#2481) * Azure CI: Add separated pipelines for Windows, Linux, Mac * Feature/azaytsev/benchmarks 2021 1 (#2501) * Initial changes for 2021.1 * Inserted Graphtool scripts, updated configurations info * Updated FAQ and minor changes to performance_benchmarks.md * Updated for 2021.1 * Updated * incorporated review comments * incorporated review comments for FAQ * fixed link * Update build-instruction.md for MacOsX (#2457) * Update build-instruction.md for MacOsX * Removed call of install_dependencies.sh from the steps * Changed layouts * Feature/azaytsev/cvs-38240 (#2469) * Updated for 2020 version, replaced Ubuntu 16.04 with Ubuntu 20.04 * Updated the release package numbers * Removed FPGA from the documentation * Updated according to the comments in the ticket CVS-37827 (#2448) * Updated according to CVS-38225 * some changes * Update docs for speech libs and demos (#2518) * Made changes to benchmarks according to review comments * Remove `--collect_results_only` (#2523) * Remove `--collect_results_only` from MemCheckTests * Remove CLI keys from README * Added logo info to the Legal_Information, updated Ubuntu, CentOS supported versions * Updated supported Intel® Core™ processors list * Fixed table formatting * [Jenkinsfile] Bump infra (#2546) * [GNA] Documentation updates for 2021.1 (#2460) * [GNA] Documentation updates for 2021.1 * Take Mike's comments into account * More fixes according to review * Fix processor generation names * update api layouts * Added new index page with overview * Changed CMake and Python versions * Fixed links * some layout changes * some layout changes * nGraph Python API tutorial (#2500) * nGraph Python API tutorial * Tweaks * Code review comments * Code review comments * some layout changes * COnverted svg images to png * layouts * update layout * Added a label for nGraph_Python_API.md * fixed links * Fixed image * First draft of nGraph documentation (#2271) * First draft of nGraph documentation * updated according to review comments * Updated * Reviewed the nGraph Transformation section, added missing images * Update nGraph_dg.md * Delete python_api.md Removed since there is already the nGraph_Python_API.md document with a comprehensive overview. Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> Co-authored-by: CCR\avladimi <anastasiya.ageeva@intel.com> * Feature/azaytsev/docs 2021 1 (#2560) * Removed FPGA from the documentation * Updated according to CVS-38225 * Added logo info to the Legal_Information, updated Ubuntu, CentOS supported versions * Updated supported Intel® Core™ processors list * Added new index page with overview * Changed CMake and Python versions * Fixed links * COnverted svg images to png * Added a label for nGraph_Python_API.md * fixed links * Fixed image * Update SW requirements in build instructions and change latest release to 2021.1 (#2565) * removed links to ../IE_DG/Introduction.md * Removed links to tools overview page as removed * some changes * Remove link to Integrate_your_kernels_into_IE.md * remove openvino_docs_IE_DG_Graph_debug_capabilities from layout as it was removed * Fixed links to images (#2569) * update layouts * Added deprecation note for PassConfig class (#2593) * Post-release fixes and installation path changes * Added pip install documentation (#2465) * Added pip install documentation * Change references * tiny fixes of links * Update installing-openvino-pip.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Update OpenVino ONNX CI check (#2599) * Update OpenVino ONNX CI * Change parallel execution to single * Enlarge timeout * Remove timeout * Add timeout to test execution * Added PIP installation and Build from Source to the layout * Fixed formatting issue, removed broken link * Renamed section EXAMPLES to RESOURCES according to review comments * add mo faq navigation by url param * Skip hanging test case of OpenVino ONNX CI (#2608) * Update OpenVino ONNX CI * Change parallel execution to single * Enlarge timeout * Remove timeout * Add timeout to test execution * Skip hanging test * Add description to skip issue * Removed DLDT description * Replaced wrong links * MInor fix for path to the cpp samples * fixes * Update ops.py * Fix style * Improve pip installation guide (#2644) * Improve pip installation guide * Updated after comments * Feature/ntyukaev/separate layout (#2629) * convert to doxygen comments * layouts and code comments * separate layout * Changed layouts * Removed FPGA from the documentation * Updated according to CVS-38225 * some changes * Made changes to benchmarks according to review comments * Added logo info to the Legal_Information, updated Ubuntu, CentOS supported versions * Updated supported Intel® Core™ processors list * Fixed table formatting * update api layouts * Added new index page with overview * Changed CMake and Python versions * Fixed links * some layout changes * some layout changes * some layout changes * COnverted svg images to png * layouts * update layout * Added a label for nGraph_Python_API.md * fixed links * Fixed image * removed links to ../IE_DG/Introduction.md * Removed links to tools overview page as removed * some changes * Remove link to Integrate_your_kernels_into_IE.md * remove openvino_docs_IE_DG_Graph_debug_capabilities from layout as it was removed * update layouts * Post-release fixes and installation path changes * Added PIP installation and Build from Source to the layout * Fixed formatting issue, removed broken link * Renamed section EXAMPLES to RESOURCES according to review comments * add mo faq navigation by url param * Removed DLDT description * Replaced wrong links * MInor fix for path to the cpp samples * fixes * Update ops.py * Fix style Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com> Co-authored-by: Tyukaev <nikolay.tyukaev@intel.com> Co-authored-by: aalborov <alina.alborova@intel.com> Co-authored-by: Rafal Blaczkowski <rafal.blaczkowski@intel.com> Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com> * Fixed CVS-35316 (#2072) (#2670) Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * [install_dependencies.sh] install latest cmake if current version is lower 3.13 (#2695) (#2701) * [install_dependencies.sh] install latest cmake if current version is lower 3.13 * add shellcheck for Ubuntu * install python 2.7 for Ubuntu * Removed redundant file * Exclude files that we didn't changed from merging Co-authored-by: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com> Co-authored-by: Denis Orlov <denis.orlov@intel.com> Co-authored-by: Kamil Magierski <kamil.magierski@intel.com> Co-authored-by: Anna Alberska <anna.alberska@intel.com> Co-authored-by: Edward Shogulin <edward.shogulin@intel.com> Co-authored-by: Artyom Anokhov <artyom.anokhov@intel.com> Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com> Co-authored-by: Ilya Churaev <ilya.churaev@intel.com> Co-authored-by: Roman Vyunov (Intel) <roman.vyunov@intel.com> Co-authored-by: Maksim Doronin <maksim.doronin@intel.com> Co-authored-by: Svetlana Dolinina <svetlana.a.dolinina@intel.com> Co-authored-by: Evgeny Talanin <evgeny.talanin@intel.com> Co-authored-by: Evgenya Stepyreva <evgenya.stepyreva@intel.com> Co-authored-by: Maxim Kurin <maxim.kurin@intel.com> Co-authored-by: Nikolay Shchegolev <nikolay.shchegolev@intel.com> Co-authored-by: Andrew Bakalin <andrew.bakalin@intel.com> Co-authored-by: Gorokhov Dmitriy <dmitry.gorokhov@intel.com> Co-authored-by: Evgeny Latkin <evgeny.latkin@intel.com> Co-authored-by: Maxim Shevtsov <maxim.y.shevtsov@intel.com> Co-authored-by: Alexey Suhov <alexey.suhov@intel.com> Co-authored-by: Alexander Novak <sasha-novak@yandex.ru> Co-authored-by: Vladislav Vinogradov <vlad.vinogradov@intel.com> Co-authored-by: Vladislav Volkov <vladislav.volkov@intel.com> Co-authored-by: Vladimir Gavrilov <vladimir.gavrilov@intel.com> Co-authored-by: Zoe Cayetano <zoe.cayetano@intel.com> Co-authored-by: Dmitrii Denisov <dmitrii.denisov@intel.com> Co-authored-by: Irina Efode <irina.efode@intel.com> Co-authored-by: Evgeny Lazarev <evgeny.lazarev@intel.com> Co-authored-by: Mikhail Ryzhov <mikhail.ryzhov@intel.com> Co-authored-by: Vitaliy Urusovskij <vitaliy.urusovskij@intel.com> Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com> Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com> Co-authored-by: Gleb Kazantaev <gleb.kazantaev@intel.com> Co-authored-by: Rafal Blaczkowski <rafal.blaczkowski@intel.com> Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Maksim Proshin <mvproshin@gmail.com> Co-authored-by: Alina Alborova <alina.alborova@intel.com> Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> Co-authored-by: azhogov <alexander.zhogov@intel.com> Co-authored-by: Alina Kladieva <alina.kladieva@intel.com> Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com> Co-authored-by: Anton Romanov <anton.romanov@intel.com>
This commit is contained in:
Andrey Zaytsev
GitHub
@@ -23,9 +23,15 @@ In addition, demo scripts, code samples and demo applications are provided to he
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## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
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This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Linux*](../install_guides/installing-openvino-linux.md).
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By default, the installation directory is `/opt/intel/openvino`, but the installation gave you the option to use the directory of your choice. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` with the directory in which you installed the software.
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By default, the Intel® Distribution of OpenVINO™ is installed to the following directory, referred to as `<INSTALL_DIR>`:
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* For root or administrator: `/opt/intel/openvino_<version>/`
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* For regular users: `/home/<USER>/intel/openvino_<version>/`
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The primary tools for deploying your models and applications are installed to the `/opt/intel/openvino/deployment_tools` directory.
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For simplicity, a symbolic link to the latest installation is also created: `/home/<user>/intel/openvino_2021/`
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If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` or `/home/<USER>/` with the directory in which you installed the software.
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The primary tools for deploying your models and applications are installed to the `/opt/intel/openvino_2021/deployment_tools` directory.
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<details>
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<summary><strong>Click for the Intel® Distribution of OpenVINO™ toolkit directory structure</strong></summary>
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@@ -57,7 +63,7 @@ The simplified OpenVINO™ workflow is:
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## Use the Demo Scripts to Learn the Workflow
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The demo scripts in `/opt/intel/openvino/deployment_tools/demo` give you a starting point to learn the OpenVINO workflow. These scripts automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios. The demo steps let you see how to:
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The demo scripts in `/opt/intel/openvino_2021/deployment_tools/demo` give you a starting point to learn the OpenVINO workflow. These scripts automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios. The demo steps let you see how to:
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* Compile several samples from the source files delivered as part of the OpenVINO toolkit.
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* Download trained models.
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* Perform pipeline steps and see the output on the console.
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@@ -194,10 +200,10 @@ Each demo and code sample is a separate application, but they use the same behav
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* [Demo Applications](@ref omz_demos_README) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
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Inputs you'll need to specify:
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- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resuiting in an IR, using the other inputs you provide.
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- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
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- **One or more models** in the Intermediate Representation format. Each model is trained for a specific task. Examples include pedestrian detection, face detection, vehicle detection, license plate recognition, head pose, and others. Different models are used for different applications. Models can be chained together to provide multiple features; for example vehicle + make/model + license plate recognition.
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- **One or more media files**. The media is typically a video file, but can be a still photo.
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- **One or more target device** on which you run inference. The target device can be the CPU, GPU, FPGA, or VPU accelerator.
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- **One or more target device** on which you run inference. The target device can be the CPU, GPU, or VPU accelerator.
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### Build the Code Samples and Demo Applications
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@@ -221,7 +227,7 @@ This guide uses the Model Downloader to get pre-trained models. You can use one
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* **List the models available in the downloader**:
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```sh
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cd /opt/intel/openvino/deployment_tools/tools/model_downloader/
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cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
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```
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```sh
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python3 info_dumper.py --print_all
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@@ -325,7 +331,7 @@ The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognit
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3. Run the Model Optimizer script:
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```sh
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cd /opt/intel/openvino/deployment_tools/model_optimizer
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cd /opt/intel/openvino_2021/deployment_tools/model_optimizer
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```
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```sh
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python3 ./mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>
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@@ -338,7 +344,7 @@ The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognit
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The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP16 IR and saves to the `~/models/public/squeezenet1.1/ir` output directory:
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```sh
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cd /opt/intel/openvino/deployment_tools/model_optimizer
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cd /opt/intel/openvino_2021/deployment_tools/model_optimizer
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```
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```sh
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python3 ./mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/models/public/squeezenet1.1/ir
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@@ -346,9 +352,9 @@ The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP
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After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `~/models/public/squeezenet1.1/ir` directory.
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Copy the `squeezenet1.1.labels` file from the `/opt/intel/openvino/deployment_tools/demo/` to `<ir_dir>`. This file contains the classes that ImageNet uses. Therefore, the inference results show text instead of classification numbers:
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Copy the `squeezenet1.1.labels` file from the `/opt/intel/openvino_2021/deployment_tools/demo/` to `<ir_dir>`. This file contains the classes that ImageNet uses. Therefore, the inference results show text instead of classification numbers:
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```sh
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cp /opt/intel/openvino/deployment_tools/demo/squeezenet1.1.labels <ir_dir>
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cp /opt/intel/openvino_2021/deployment_tools/demo/squeezenet1.1.labels <ir_dir>
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```
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</details>
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@@ -359,8 +365,8 @@ Many sources are available from which you can download video media to use the co
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- https://images.google.com
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As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes two sample images that you can use for running code samples and demo applications:
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* `/opt/intel/openvino/deployment_tools/demo/car.png`
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* `/opt/intel/openvino/deployment_tools/demo/car_1.bmp`
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* `/opt/intel/openvino_2021/deployment_tools/demo/car.png`
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* `/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp`
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### <a name="run-image-classification"></a>Step 4: Run the Image Classification Code Sample
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@@ -370,7 +376,7 @@ To run the **Image Classification** code sample with an input image on the IR:
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1. Set up the OpenVINO environment variables:
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```sh
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source /opt/intel/openvino/bin/setupvars.sh
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source /opt/intel/openvino_2021/bin/setupvars.sh
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```
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2. Go to the code samples build directory:
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```sh
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@@ -383,32 +389,32 @@ To run the **Image Classification** code sample with an input image on the IR:
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<details>
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<summary><strong>Click for examples of running the Image Classification code sample on different devices</strong></summary>
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The following commands run the Image Classification Code Sample using the `car.png` file from the `/opt/intel/openvino/deployment_tools/demo/` directory as an input image, the IR of your model from `~/models/public/squeezenet1.1/ir` and on different hardware devices:
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The following commands run the Image Classification Code Sample using the `car.png` file from the `/opt/intel/openvino_2021/deployment_tools/demo/` directory as an input image, the IR of your model from `~/models/public/squeezenet1.1/ir` and on different hardware devices:
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**CPU:**
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```sh
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./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
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./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
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```
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**GPU:**
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|
||||
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-linux.md).
|
||||
```sh
|
||||
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU
|
||||
./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU
|
||||
```
|
||||
|
||||
**MYRIAD:**
|
||||
|
||||
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-linux.md).
|
||||
```sh
|
||||
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
|
||||
./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
|
||||
```
|
||||
|
||||
**HDDL:**
|
||||
|
||||
> **NOTE**: Running inference on the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs device with the HDDL plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the [installation instructions](../install_guides/installing-openvino-linux.md).
|
||||
```sh
|
||||
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d HDDL
|
||||
./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d HDDL
|
||||
```
|
||||
|
||||
When the Sample Application completes, you see the label and confidence for the top-10 categories on the display. Below is a sample output with inference results on CPU:
|
||||
@@ -449,7 +455,7 @@ To run the **Security Barrier Camera Demo Application** using an input image on
|
||||
|
||||
1. Set up the OpenVINO environment variables:
|
||||
```sh
|
||||
source /opt/intel/openvino/bin/setupvars.sh
|
||||
source /opt/intel/openvino_2021/bin/setupvars.sh
|
||||
```
|
||||
2. Go to the demo application build directory:
|
||||
```sh
|
||||
@@ -466,14 +472,14 @@ To run the **Security Barrier Camera Demo Application** using an input image on
|
||||
**CPU:**
|
||||
|
||||
```sh
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_va /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml -m_lpr /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -d CPU
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp -m /home/username/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_va /home/username/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml -m_lpr /home/username/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -d CPU
|
||||
```
|
||||
|
||||
**GPU:**
|
||||
|
||||
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-linux.md).
|
||||
```sh
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
|
||||
```
|
||||
|
||||
**MYRIAD:**
|
||||
@@ -498,7 +504,7 @@ Following are some basic guidelines for executing the OpenVINO™ workflow using
|
||||
|
||||
1. Before using the OpenVINO™ samples, always set up the environment:
|
||||
```sh
|
||||
source /opt/intel/openvino/bin/setupvars.sh
|
||||
source /opt/intel/openvino_2021/bin/setupvars.sh
|
||||
```
|
||||
2. Have the directory path for the following:
|
||||
- Code Sample binaries located in `~/inference_engine_cpp_samples_build/intel64/Release`
|
||||
@@ -559,7 +565,7 @@ You can see all the sample application’s parameters by adding the `-h` or `--h
|
||||
Use these resources to learn more about the OpenVINO™ toolkit:
|
||||
|
||||
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
|
||||
* [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
|
||||
* [OpenVINO™ Toolkit Overview](../index.md)
|
||||
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
|
||||
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
|
||||
|
||||
@@ -24,10 +24,12 @@ In addition, demo scripts, code samples and demo applications are provided to he
|
||||
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../install_guides/installing-openvino-macos.md).
|
||||
|
||||
By default, the Intel® Distribution of OpenVINO™ is installed to the following directory, referred to as `<INSTALL_DIR>`:
|
||||
* For root or administrator: `/opt/intel/openvino/`
|
||||
* For regular users: `/home/<USER>/intel/openvino/`
|
||||
* For root or administrator: `/opt/intel/openvino_<version>/`
|
||||
* For regular users: `/home/<USER>/intel/openvino_<version>/`
|
||||
|
||||
If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` or `/home/<USER>/` with the directory in which you installed the software.
|
||||
For simplicity, a symbolic link to the latest installation is also created: `/home/<user>/intel/openvino_2021/`.
|
||||
|
||||
If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` or `/home/<USER>/` with the directory in which you installed the software.
|
||||
|
||||
The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>/deployment_tools` directory.
|
||||
<details>
|
||||
@@ -105,7 +107,7 @@ When the script completes, you see the label and confidence for the top-10 categ
|
||||
|
||||
Top 10 results:
|
||||
|
||||
Image /opt/intel/openvino/deployment_tools/demo/car.png
|
||||
Image /opt/intel/openvino_2021/deployment_tools/demo/car.png
|
||||
|
||||
classid probability label
|
||||
------- ----------- -----
|
||||
@@ -192,7 +194,7 @@ Inputs you need to specify when using a code sample or demo application:
|
||||
- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
|
||||
- **One or more models** in the IR format. Each model is trained for a specific task. Examples include pedestrian detection, face detection, vehicle detection, license plate recognition, head pose, and others. Different models are used for different applications. Models can be chained together to provide multiple features; for example, vehicle + make/model + license plate recognition.
|
||||
- **One or more media files**. The media is typically a video file, but can be a still photo.
|
||||
- **One or more target device** on which you run inference. The target device can be the CPU, FPGA, or VPU accelerator.
|
||||
- **One or more target device** on which you run inference. The target device can be the CPU, or VPU accelerator.
|
||||
|
||||
### Build the Code Samples and Demo Applications
|
||||
|
||||
@@ -216,7 +218,7 @@ This guide uses the Model Downloader to get pre-trained models. You can use one
|
||||
|
||||
* **List the models available in the downloader**:
|
||||
```sh
|
||||
cd /opt/intel/openvino/deployment_tools/tools/model_downloader/
|
||||
cd /opt/intel/openvino_2021/deployment_tools/tools/model_downloader/
|
||||
```
|
||||
```sh
|
||||
python3 info_dumper.py --print_all
|
||||
@@ -321,7 +323,7 @@ The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognit
|
||||
|
||||
3. Run the Model Optimizer script:
|
||||
```sh
|
||||
cd /opt/intel/openvino/deployment_tools/model_optimizer
|
||||
cd /opt/intel/openvino_2021/deployment_tools/model_optimizer
|
||||
```
|
||||
```sh
|
||||
python3 ./mo.py --input_model <model_dir>/<model_file> --data_type <model_precision> --output_dir <ir_dir>
|
||||
@@ -334,7 +336,7 @@ The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognit
|
||||
The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP16 IR and saves to the `~/models/public/squeezenet1.1/ir` output directory:
|
||||
|
||||
```sh
|
||||
cd /opt/intel/openvino/deployment_tools/model_optimizer
|
||||
cd /opt/intel/openvino_2021/deployment_tools/model_optimizer
|
||||
```
|
||||
```sh
|
||||
python3 ./mo.py --input_model ~/models/public/squeezenet1.1/squeezenet1.1.caffemodel --data_type FP16 --output_dir ~/models/public/squeezenet1.1/ir
|
||||
@@ -342,9 +344,9 @@ The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP
|
||||
|
||||
After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `~/models/public/squeezenet1.1/ir` directory.
|
||||
|
||||
Copy the `squeezenet1.1.labels` file from the `/opt/intel/openvino/deployment_tools/demo/` to `<ir_dir>`. This file contains the classes that ImageNet uses. Therefore, the inference results show text instead of classification numbers:
|
||||
Copy the `squeezenet1.1.labels` file from the `/opt/intel/openvino_2021/deployment_tools/demo/` to `<ir_dir>`. This file contains the classes that ImageNet uses. Therefore, the inference results show text instead of classification numbers:
|
||||
```sh
|
||||
cp /opt/intel/openvino/deployment_tools/demo/squeezenet1.1.labels <ir_dir>
|
||||
cp /opt/intel/openvino_2021/deployment_tools/demo/squeezenet1.1.labels <ir_dir>
|
||||
```
|
||||
</details>
|
||||
|
||||
@@ -355,8 +357,8 @@ Many sources are available from which you can download video media to use the co
|
||||
- https://images.google.com
|
||||
|
||||
As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes two sample images that you can use for running code samples and demo applications:
|
||||
* `/opt/intel/openvino/deployment_tools/demo/car.png`
|
||||
* `/opt/intel/openvino/deployment_tools/demo/car_1.bmp`
|
||||
* `/opt/intel/openvino_2021/deployment_tools/demo/car.png`
|
||||
* `/opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp`
|
||||
|
||||
### <a name="run-image-classification"></a>Step 4: Run the Image Classification Code Sample
|
||||
|
||||
@@ -366,7 +368,7 @@ To run the **Image Classification** code sample with an input image on the IR:
|
||||
|
||||
1. Set up the OpenVINO environment variables:
|
||||
```sh
|
||||
source /opt/intel/openvino/bin/setupvars.sh
|
||||
source /opt/intel/openvino_2021/bin/setupvars.sh
|
||||
```
|
||||
2. Go to the code samples build directory:
|
||||
```sh
|
||||
@@ -379,11 +381,11 @@ To run the **Image Classification** code sample with an input image on the IR:
|
||||
<details>
|
||||
<summary><strong>Click for examples of running the Image Classification code sample on different devices</strong></summary>
|
||||
|
||||
The following commands run the Image Classification Code Sample using the `car.png` file from the `/opt/intel/openvino/deployment_tools/demo/` directory as an input image, the IR of your model from `~/models/public/squeezenet1.1/ir` and on different hardware devices:
|
||||
The following commands run the Image Classification Code Sample using the `car.png` file from the `/opt/intel/openvino_2021/deployment_tools/demo/` directory as an input image, the IR of your model from `~/models/public/squeezenet1.1/ir` and on different hardware devices:
|
||||
|
||||
**CPU:**
|
||||
```sh
|
||||
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
|
||||
./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d CPU
|
||||
```
|
||||
|
||||
|
||||
@@ -391,14 +393,14 @@ The following commands run the Image Classification Code Sample using the `car.p
|
||||
|
||||
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-macos.md).
|
||||
```sh
|
||||
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
|
||||
./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
|
||||
```
|
||||
|
||||
When the Sample Application completes, you see the label and confidence for the top-10 categories on the display. Below is a sample output with inference results on CPU:
|
||||
```sh
|
||||
Top 10 results:
|
||||
|
||||
Image /opt/intel/openvino/deployment_tools/demo/car.png
|
||||
Image /opt/intel/openvino_2021/deployment_tools/demo/car.png
|
||||
|
||||
classid probability label
|
||||
------- ----------- -----
|
||||
@@ -426,7 +428,7 @@ To run the **Security Barrier Camera Demo Application** using an input image on
|
||||
|
||||
1. Set up the OpenVINO environment variables:
|
||||
```sh
|
||||
source /opt/intel/openvino/bin/setupvars.sh
|
||||
source /opt/intel/openvino_2021/bin/setupvars.sh
|
||||
```
|
||||
2. Go to the demo application build directory:
|
||||
```sh
|
||||
@@ -443,7 +445,7 @@ To run the **Security Barrier Camera Demo Application** using an input image on
|
||||
**CPU:**
|
||||
|
||||
```sh
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m ~/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_va ~/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml -m_lpr ~/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -d CPU
|
||||
./security_barrier_camera_demo -i /opt/intel/openvino_2021/deployment_tools/demo/car_1.bmp -m ~/models/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_va ~/models/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml -m_lpr ~/models/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -d CPU
|
||||
```
|
||||
|
||||
**MYRIAD:**
|
||||
@@ -461,7 +463,7 @@ Following are some basic guidelines for executing the OpenVINO™ workflow using
|
||||
|
||||
1. Before using the OpenVINO™ samples, always set up the environment:
|
||||
```sh
|
||||
source /opt/intel/openvino/bin/setupvars.sh
|
||||
source /opt/intel/openvino_2021/bin/setupvars.sh
|
||||
```
|
||||
2. Have the directory path for the following:
|
||||
- Code Sample binaries located in `~/inference_engine_cpp_samples_build/intel64/Release`
|
||||
@@ -522,7 +524,7 @@ You can see all the sample application’s parameters by adding the `-h` or `--h
|
||||
Use these resources to learn more about the OpenVINO™ toolkit:
|
||||
|
||||
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
|
||||
* [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
|
||||
* [OpenVINO™ Toolkit Overview](../index.md)
|
||||
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
|
||||
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
|
||||
|
||||
@@ -24,7 +24,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
|
||||
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
|
||||
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Windows*](../install_guides/installing-openvino-windows.md).
|
||||
|
||||
By default, the installation directory is `C:\Program Files (x86)\IntelSWTools\openvino`, referred to as `<INSTALL_DIR>`. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `C:\Program Files (x86)\IntelSWTools` with the directory in which you installed the software.
|
||||
By default, the installation directory is `C:\Program Files (x86)\Intel\openvino_<version>`, referred to as `<INSTALL_DIR>`. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `C:\Program Files (x86)\Intel` with the directory in which you installed the software. For simplicity, a shortcut to the latest installation is also created: `C:\Program Files (x86)\Intel\openvino_2021`.
|
||||
|
||||
The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>\deployment_tools` directory.
|
||||
<details>
|
||||
@@ -106,7 +106,7 @@ When the script completes, you see the label and confidence for the top-10 categ
|
||||
|
||||
Top 10 results:
|
||||
|
||||
Image C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png
|
||||
Image C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo\car.png
|
||||
|
||||
classid probability label
|
||||
------- ----------- -----
|
||||
@@ -193,7 +193,7 @@ Inputs you need to specify when using a code sample or demo application:
|
||||
- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
|
||||
- **One or more models** in the IR format. Each model is trained for a specific task. Examples include pedestrian detection, face detection, vehicle detection, license plate recognition, head pose, and others. Different models are used for different applications. Models can be chained together to provide multiple features; for example, vehicle + make/model + license plate recognition.
|
||||
- **One or more media files**. The media is typically a video file, but can be a still photo.
|
||||
- **One or more target device** on which you run inference. The target device can be the CPU, GPU, FPGA, or VPU accelerator.
|
||||
- **One or more target device** on which you run inference. The target device can be the CPU, GPU, or VPU accelerator.
|
||||
|
||||
### Build the Code Samples and Demo Applications
|
||||
|
||||
@@ -403,7 +403,7 @@ When the Sample Application completes, you see the label and confidence for the
|
||||
```bat
|
||||
Top 10 results:
|
||||
|
||||
Image C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png
|
||||
Image C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\demo\car.png
|
||||
|
||||
classid probability label
|
||||
------- ----------- -----
|
||||
@@ -533,7 +533,7 @@ You can see all the sample application’s parameters by adding the `-h` or `--h
|
||||
Use these resources to learn more about the OpenVINO™ toolkit:
|
||||
|
||||
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
|
||||
* [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
|
||||
* [OpenVINO™ Toolkit Overview](../index.md)
|
||||
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
|
||||
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
|
||||
|
||||
Reference in New Issue
Block a user