146 lines
7.8 KiB
Markdown
146 lines
7.8 KiB
Markdown
# Image Classification Async Python Sample {#openvino_inference_engine_ie_bridges_python_sample_classification_sample_async_README}
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This sample demonstrates how to perform inference of image classification models using Asynchronous Inference Request API.
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Models with only 1 input and output are supported.
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The following Python API is used in the application:
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| Feature | API | Description |
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| :----------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :------------------------ |
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| Asynchronous Infer | [openvino.runtime.AsyncInferQueue], [openvino.runtime.AsyncInferQueue.set_callback], [openvino.runtime.AsyncInferQueue.start_async], [openvino.runtime.AsyncInferQueue.wait_all], [openvino.runtime.InferRequest.results] | Do asynchronous inference |
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Basic OpenVINO™ Runtime API is described in [Hello Classification Python Sample](../hello_classification/README.md).
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| Options | Values |
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| :------------------------- | :----------------------------------------------------------------------- |
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| Validated Models | [alexnet](@ref omz_models_model_alexnet) |
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| Model Format | OpenVINO Intermediate Representation (.xml + .bin), ONNX (.onnx) |
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| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
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| Other language realization | [C++](../../../samples/cpp/classification_sample_async/README.md) |
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## How It Works
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In the beginning, the sample application reads command-line parameters, prepares input data, loads a specified model and image(s) to the OpenVINO™ Runtime plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
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For more information, refer to the explicit description of
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**Integration Steps** in the [Integrate OpenVINO Runtime with Your Application](../../../docs/OV_Runtime_UG/integrate_with_your_application.md).
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## Running
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Before running the sample, specify a model and an image:
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- Use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from Open Model Zoo. The models can be downloaded by using the [Model Downloader](@ref omz_tools_downloader).
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- You may use images from the media files collection, available online in the [test data storage](https://storage.openvinotoolkit.org/data/test_data).
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Run the application with the `-h` option to see the usage message:
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```
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python classification_sample_async.py -h
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```
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Usage message:
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```
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usage: classification_sample_async.py [-h] -m MODEL -i INPUT [INPUT ...]
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[-d DEVICE]
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Options:
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-h, --help Show this help message and exit.
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-m MODEL, --model MODEL
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Required. Path to an .xml or .onnx file with a trained
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model.
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-i INPUT [INPUT ...], --input INPUT [INPUT ...]
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Required. Path to an image file(s).
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-d DEVICE, --device DEVICE
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Optional. Specify the target device to infer on; CPU,
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GPU, MYRIAD, HDDL or HETERO: is acceptable. The sample
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will look for a suitable plugin for device specified.
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Default value is CPU.
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```
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> **NOTES**:
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>
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> - By default, samples and demos in OpenVINO Toolkit expect input with `BGR` order of channels. If you trained your model to work with `RGB` order, it is required to manually rearrange the default order of channels in the sample or demo application, or reconvert your model, using Model Optimizer with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Embedding Preprocessing Computation](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
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>
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> - Before running the sample with a trained model, make sure that the model is converted to the OpenVINO Intermediate Representation (OpenVINO IR) format (\*.xml + \*.bin) by using [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
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>
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> - The sample accepts models in the ONNX format (.onnx) that do not require preprocessing.
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### Example
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1. Install the `openvino-dev` Python package to use Open Model Zoo Tools:
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```
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python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]
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```
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2. Download a pre-trained model:
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```
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omz_downloader --name alexnet
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```
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3. If a model is not in the OpenVINO IR or ONNX format, it must be converted. You can do this using the model converter:
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```
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omz_converter --name alexnet
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```
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4. Perform inference on the `banana.jpg` and the `car.bmp` images, using the `alexnet` model on a `GPU` device, for example:
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```
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python classification_sample_async.py -m alexnet.xml -i banana.jpg car.bmp -d GPU
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```
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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.
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```
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[ INFO ] Creating OpenVINO Runtime Core
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[ INFO ] Reading the model: C:/test_data/models/alexnet.xml
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[ INFO ] Loading the model to the plugin
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[ INFO ] Starting inference in asynchronous mode
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[ INFO ] Image path: /test_data/images/banana.jpg
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[ INFO ] Top 10 results:
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[ INFO ] class_id probability
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[ INFO ] --------------------
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[ INFO ] 954 0.9707602
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[ INFO ] 666 0.0216788
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[ INFO ] 659 0.0032558
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[ INFO ] 435 0.0008082
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[ INFO ] 809 0.0004359
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[ INFO ] 502 0.0003860
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[ INFO ] 618 0.0002867
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[ INFO ] 910 0.0002866
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[ INFO ] 951 0.0002410
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[ INFO ] 961 0.0002193
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[ INFO ]
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[ INFO ] Image path: /test_data/images/car.bmp
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[ INFO ] Top 10 results:
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[ INFO ] class_id probability
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[ INFO ] --------------------
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[ INFO ] 656 0.5120340
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[ INFO ] 874 0.1142275
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[ INFO ] 654 0.0697167
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[ INFO ] 436 0.0615163
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[ INFO ] 581 0.0552262
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[ INFO ] 705 0.0304179
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[ INFO ] 675 0.0151660
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[ INFO ] 734 0.0151582
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[ INFO ] 627 0.0148493
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[ INFO ] 757 0.0120964
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[ INFO ]
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[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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```
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## Additional Resources
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- [Integrate the OpenVINO Runtime with Your Application](../../../docs/OV_Runtime_UG/integrate_with_your_application.md)
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- [Using OpenVINO Toolkit Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
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- [Model Downloader](@ref omz_tools_downloader)
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- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
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- [OpenVINO Toolkit Test Data Storage](https://storage.openvinotoolkit.org/data/test_data).
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[openvino.runtime.AsyncInferQueue]:https://docs.openvino.ai/2022.2/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html
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[openvino.runtime.AsyncInferQueue.set_callback]:https://docs.openvino.ai/2022.2/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.set_callback
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[openvino.runtime.AsyncInferQueue.start_async]:https://docs.openvino.ai/2022.2/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.start_async
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[openvino.runtime.AsyncInferQueue.wait_all]:https://docs.openvino.ai/2022.2/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.wait_all
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[openvino.runtime.InferRequest.results]:https://docs.openvino.ai/2022.2/api/ie_python_api/_autosummary/openvino.runtime.InferRequest.html#openvino.runtime.InferRequest.results
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