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# Hello Classification Python Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_classification_README}
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@sphinxdirective
.. meta::
:description: Learn how to do inference of image classification
models using Synchronous Inference Request (Python) API.
This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API.
Models with only 1 input and output are supported.
.. tab-set::
.. tab-item:: Requirements
+-----------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Options | Values |
+===================================+===================================================================================================================================================================+
| Validated Models | :doc:`alexnet <omz_models_model_alexnet>`, :doc:`googlenet-v1 <omz_models_model_googlenet_v1>` |
+-----------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Model Format | OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx) |
+-----------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Supported devices | :doc:`All <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` |
+-----------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Other language realization | :doc:`C++ <openvino_inference_engine_samples_hello_classification_README>`, :doc:`C <openvino_inference_engine_ie_bridges_c_samples_hello_classification_README>` |
+-----------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
.. tab-item:: Python API
The following Python API is used in the application:
+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Feature | API | Description |
+=============================+===========================================================================================================================================================================================================================================+============================================================================================================================================================================================+
| Basic Infer Flow | `openvino.runtime.Core <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.Core.html>`__ , | |
| | `openvino.runtime.Core.read_model <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.Core.html#openvino.runtime.Core.read_model>`__ , | |
| | `openvino.runtime.Core.compile_model <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.Core.html#openvino.runtime.Core.compile_model>`__ | Common API to do inference |
+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Synchronous Infer | `openvino.runtime.CompiledModel.infer_new_request <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.CompiledModel.html#openvino.runtime.CompiledModel.infer_new_request>`__ | Do synchronous inference |
+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Model Operations | `openvino.runtime.Model.inputs <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.inputs>`__ , | Managing of model |
| | `openvino.runtime.Model.outputs <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.outputs>`__ | |
+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Preprocessing | `openvino.preprocess.PrePostProcessor <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.PrePostProcessor.html>`__ , | Set image of the original size as input for a model with other input size. Resize and layout conversions will be performed automatically by the corresponding plugin just before inference |
| | `openvino.preprocess.InputTensorInfo.set_element_type <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.InputTensorInfo.html#openvino.preprocess.InputTensorInfo.set_element_type>`__ , | |
| | `openvino.preprocess.InputTensorInfo.set_layout <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.InputTensorInfo.html#openvino.preprocess.InputTensorInfo.set_layout>`__ , | |
| | `openvino.preprocess.InputTensorInfo.set_spatial_static_shape <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.InputTensorInfo.html#openvino.preprocess.InputTensorInfo.set_spatial_static_shape>`__ , | |
| | `openvino.preprocess.PreProcessSteps.resize <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.PreProcessSteps.html#openvino.preprocess.PreProcessSteps.resize>`__ , | |
| | `openvino.preprocess.InputModelInfo.set_layout <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.InputModelInfo.html#openvino.preprocess.InputModelInfo.set_layout>`__ , | |
| | `openvino.preprocess.OutputTensorInfo.set_element_type <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.OutputTensorInfo.html#openvino.preprocess.OutputTensorInfo.set_element_type>`__ , | |
| | `openvino.preprocess.PrePostProcessor.build <https://docs.openvino.ai/2023.1/api/ie_python_api/_autosummary/openvino.preprocess.PrePostProcessor.html#openvino.preprocess.PrePostProcessor.build>`__ | |
+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
.. tab-item:: Sample Code
.. doxygensnippet:: samples/python/hello_classification/hello_classification.py
:language: python
How It Works
############
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[IE Python Sample] Update docs (#9807) * update hello_classification readme * update classification_async readme * update hello_query_device readme * Fix hello_classification launch line * Update hello_reshape_ssd readme * update speech sample docs * update ngraph sample docs * fix launch command * refactor py ngraph imports * Replace `network` with `model` * update example section with openvino-dev * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_reshape_ssd/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * Replace `Inference Engine` with `OpenVINO` * fix ngraph ref * Replace `Inference Engine` by `OpenVINO™ Runtime` * Fix IR mentions Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
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At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the OpenVINO™ Runtime plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
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You can see the explicit description of each sample step at :doc:`Integration Steps <openvino_docs_OV_UG_Integrate_OV_with_your_application>` section of "Integrate OpenVINO™ Runtime with Your Application" guide.
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Running
#######
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.. code-block:: console
python hello_classification.py <path_to_model> <path_to_image> <device_name>
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Add benchmark samples (#13388) * Add sync_bnehcmark * Fix Unix comilation * niter->time * Explain main loop * samples: factor out common * Code style * clang-format -i * return 0; -> return EXIT_SUCCESS;, +x * Update throughput_benchmark * Add READMEs * Fix READMEs refs * Add sync_benchmark.py * Add niter, infer_new_request, -pc * from datetime import timedelta * Fix niter and seconds_to_run * Add disclaimer about benchmark_app performance * Update samples/cpp/benchmark/sync_benchmark/README.md * Add dynamic_shape_bert_benhcmark * Add dynamic_shape_detection_benchmark * Adopt for detr-resnet50 * Remove sync_benchmark2, throughput_benchmark2, perf counters * clang-format -i * Fix flake8 * Add README.md * Add links to sample_dynamic_shape_bert_benchmark * Add softmax * nameless LatencyMetrics * parent.parent -> parents[2] * Add bert_benhcmark sample * Code style * Add bert_benhcmark/README.md * rm -r samples/python/benchmark/dynamic_shape_bert_benhcmark/ * rm -r samples/cpp/benchmark/dynamic_shape_detection_benchmark/ * bert_benhcmark/README.md: remove dynamic shape * Remove add_subdirectory(dynamic_shape_detection_benchmark) * flake8 * samples: Add a note about CUMULATIVE_THROUGHPUT, don’t expect get_property() to throw, don’t introduce json dependency for samples/cpp/common * / namespace * Add article * namespace -> static * Update README, seconds_ro_run 10, niter 10, no inter alinment * percentile->median * benchmark samples: use generate(), align logs, update READMEs * benchmakr samples: remove percentile() * samples/python/benchmark/bert_benhcmark/bert_benhcmark.py: report average sequence length and processing time * Python samples: move requirements.txt to every sample * Remove numpy from requirements.txt * Remove Building section from Python samples, install only required extras from openvino-dev, set up environment for bert_benhcmark, report duration for bert_benhcmark * Install openvino-dev for Hello Reshape SSD C++ Sample
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To run the sample, you need to specify a model and image:
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- You can use :doc:`public <omz_models_group_public>` or :doc:`Intel's <omz_models_group_intel>` pre-trained models from the Open Model Zoo. The models can be downloaded using the :doc:`Model Downloader <omz_tools_downloader>`.
- You can use images from the media files collection available at `the storage <https://storage.openvinotoolkit.org/data/test_data>`__.
.. note::
- By default, OpenVINO™ Toolkit Samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using model conversion API with ``reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
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Example
+++++++
[IE Python Sample] Update docs (#9807) * update hello_classification readme * update classification_async readme * update hello_query_device readme * Fix hello_classification launch line * Update hello_reshape_ssd readme * update speech sample docs * update ngraph sample docs * fix launch command * refactor py ngraph imports * Replace `network` with `model` * update example section with openvino-dev * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_reshape_ssd/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * Replace `Inference Engine` with `OpenVINO` * fix ngraph ref * Replace `Inference Engine` by `OpenVINO™ Runtime` * Fix IR mentions Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
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1. Install the ``openvino-dev`` Python package to use Open Model Zoo Tools:
.. code-block:: console
python -m pip install openvino-dev[caffe]
[IE Python Sample] Update docs (#9807) * update hello_classification readme * update classification_async readme * update hello_query_device readme * Fix hello_classification launch line * Update hello_reshape_ssd readme * update speech sample docs * update ngraph sample docs * fix launch command * refactor py ngraph imports * Replace `network` with `model` * update example section with openvino-dev * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_reshape_ssd/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * Replace `Inference Engine` with `OpenVINO` * fix ngraph ref * Replace `Inference Engine` by `OpenVINO™ Runtime` * Fix IR mentions Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
2022-02-14 19:03:45 +03:00
2. Download a pre-trained model:
.. code-block:: console
omz_downloader --name alexnet
[IE Python Sample] Update docs (#9807) * update hello_classification readme * update classification_async readme * update hello_query_device readme * Fix hello_classification launch line * Update hello_reshape_ssd readme * update speech sample docs * update ngraph sample docs * fix launch command * refactor py ngraph imports * Replace `network` with `model` * update example section with openvino-dev * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/classification_sample_async/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_classification/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/hello_reshape_ssd/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update samples/python/ngraph_function_creation_sample/README.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * Replace `Inference Engine` with `OpenVINO` * fix ngraph ref * Replace `Inference Engine` by `OpenVINO™ Runtime` * Fix IR mentions Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
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3. If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:
.. code-block:: console
omz_converter --name alexnet
4. Perform inference of ``banana.jpg`` using the ``alexnet`` model on a ``GPU``, for example:
.. code-block:: console
python hello_classification.py alexnet.xml banana.jpg GPU
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Sample Output
#############
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The sample application logs each step in a standard output stream and outputs top-10 inference results.
.. code-block:: console
[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: /models/alexnet/alexnet.xml
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Image path: /images/banana.jpg
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 954 0.9703885
[ INFO ] 666 0.0219518
[ INFO ] 659 0.0033120
[ INFO ] 435 0.0008246
[ INFO ] 809 0.0004433
[ INFO ] 502 0.0003852
[ INFO ] 618 0.0002906
[ INFO ] 910 0.0002848
[ INFO ] 951 0.0002427
[ INFO ] 961 0.0002213
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
See Also
########
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective