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Hello Classification Python Sample

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.

The following Python API is used in the application:

Feature

API

Description

Basic Infer Flow

openvino.runtime.Core , openvino.runtime.Core.read_model , openvino.runtime.Core.compile_model

Common API to do inference

Synchronous Infer

openvino.runtime.CompiledModel.infer_new_request

Do synchronous inference

Model Operations

openvino.runtime.Model.inputs , openvino.runtime.Model.outputs

Managing of model

Preprocessing

openvino.preprocess.PrePostProcessor , openvino.preprocess.InputTensorInfo.set_element_type , openvino.preprocess.InputTensorInfo.set_layout , openvino.preprocess.InputTensorInfo.set_spatial_static_shape , openvino.preprocess.PreProcessSteps.resize , openvino.preprocess.InputModelInfo.set_layout , openvino.preprocess.OutputTensorInfo.set_element_type , openvino.preprocess.PrePostProcessor.build

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

Options

Values

Validated Models

alexnet , googlenet-v1

Model Format

OpenVINO Intermediate Representation (.xml + .bin), ONNX (.onnx)

Supported devices

All

Other language realization

C++ , C

How It Works

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.

For more information, refer to the explicit description of Integration Steps in the Integrate OpenVINO Runtime with Your Application.

Running

Before running the sample, specify a model and an image:

To run the sample, use the following script:

python hello_classification.py <path_to_model> <path_to_image> <device_name>

NOTES :

  • 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, you need 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 the Embedding Preprocessing Computation.

  • 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.

  • The sample accepts models in the ONNX format (.onnx) that do not require preprocessing.

Example

  1. Install the openvino-dev Python package to use Open Model Zoo Tools:

    python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]

  2. Download a pre-trained model:

    omz_downloader --name alexnet

  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:

    omz_converter --name alexnet

  4. Perform inference of the banana.jpg, using the alexnet model on a GPU, for example:

    python hello_classification.py alexnet.xml banana.jpg GPU

Sample Output

The sample application logs each step in a standard output stream and outputs top-10 inference results.

[ 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. Use the dedicated `benchmark_app` tool for any performance measurements.
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