Image Classification Async Sample — OpenVINO™ documentation (original) (raw)

This sample demonstrates how to do inference of image classification models using Asynchronous Inference Request API. Before using the sample, refer to the following requirements:

Models with only one input and output are supported.
The sample accepts any file format supported by core.read_model.
To build the sample, use instructions available at Build the Sample Applicationssection in “Get Started with Samples” guide.

How It Works#

At startup, the sample application reads command-line parameters, prepares input data, and loads a specified model and an image to the OpenVINO™ Runtime plugin. The batch size of the model is set according to the number of read images. The batch mode is an independent attribute on the asynchronous mode. The asynchronous mode works efficiently with any batch size.

Then, the sample creates an inference request object and assigns completion callback for it. In scope of the completion callback handling, the inference request is executed again.

After that, the application starts inference for the first infer request and waits until 10th inference request execution has been completed. The asynchronous mode might increase the throughput of the pictures.

When inference is done, the application outputs data to the standard output stream. You can place labels in .labels file near the model to get pretty output.

You can see the explicit description of each sample step atIntegration Stepssection of “Integrate OpenVINO™ Runtime with Your Application” guide.

Running#

Run the application with the -h option to see the usage message:

Python

python classification_sample_async.py -h

Usage message:

usage: classification_sample_async.py [-h] -m MODEL -i INPUT [INPUT ...] [-d DEVICE]

Options: -h, --help Show this help message and exit. -m MODEL, --model MODEL Required. Path to an .xml or .onnx file with a trained model. -i INPUT [INPUT ...], --input INPUT [INPUT ...] Required. Path to an image file(s). -d DEVICE, --device DEVICE Optional. Specify the target device to infer on; CPU, GPU or HETERO: is acceptable. The sample will look for a suitable plugin for device specified. Default value is CPU.

C++

classification_sample_async -h

Usage instructions:

[ INFO ] OpenVINO Runtime version ......... [ INFO ] Build ...........

classification_sample_async [OPTION] Options:

-h                      Print usage instructions.
-m "<path>"             Required. Path to an .xml file with a trained model.
-i "<path>"             Required. Path to a folder with images or path to image files: a .ubyte file for LeNet and a .bmp file for other models.
-d "<device>"           Optional. Specify the target device to infer on (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma_separated_devices_list>" format to specify the HETERO plugin. Sample will look for a suitable plugin for the device specified.

Available target devices:

To run the sample, you need to specify a model and an image:

You can get a model specific for your inference task from one of model repositories, such as TensorFlow Zoo, HuggingFace, or TensorFlow Hub.
You can use images from the media files collection available atthe storage.

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 the Color Conversion section of Preprocessing API.
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using model conversion API.
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
The sample supports NCHW model layout only.
When you specify single options multiple times, only the last value will be used. For example, the -m flag:
Python
python classification_sample_async.py -m model.xml -m model2.xml
C++
./classification_sample_async -m model.xml -m model2.xml

Example#

Download a pre-trained model:
You can convert it by using:
Python
import openvino as ov
ov_model = ov.convert_model('./models/alexnet')

or, when model is a Python model object

ov_model = ov.convert_model(alexnet)
CLI

Perform inference of image files, using a model on a GPU, for example:
Python
python classification_sample_async.py -m ./models/alexnet.xml -i ./test_data/images/banana.jpg ./test_data/images/car.bmp -d GPU
C++
classification_sample_async -m ./models/googlenet-v1.xml -i ./images/dog.bmp -d GPU

Sample Output#

Python

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: C:/test_data/models/alexnet.xml [ INFO ] Loading the model to the plugin [ INFO ] Starting inference in asynchronous mode [ INFO ] Image path: /test_data/images/banana.jpg [ INFO ] Top 10 results: [ INFO ] class_id probability [ INFO ] -------------------- [ INFO ] 954 0.9707602 [ INFO ] 666 0.0216788 [ INFO ] 659 0.0032558 [ INFO ] 435 0.0008082 [ INFO ] 809 0.0004359 [ INFO ] 502 0.0003860 [ INFO ] 618 0.0002867 [ INFO ] 910 0.0002866 [ INFO ] 951 0.0002410 [ INFO ] 961 0.0002193 [ INFO ] [ INFO ] Image path: /test_data/images/car.bmp [ INFO ] Top 10 results: [ INFO ] class_id probability [ INFO ] -------------------- [ INFO ] 656 0.5120340 [ INFO ] 874 0.1142275 [ INFO ] 654 0.0697167 [ INFO ] 436 0.0615163 [ INFO ] 581 0.0552262 [ INFO ] 705 0.0304179 [ INFO ] 675 0.0151660 [ INFO ] 734 0.0151582 [ INFO ] 627 0.0148493 [ INFO ] 757 0.0120964 [ INFO ] [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

C++

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

[ INFO ] OpenVINO Runtime version ......... [ INFO ] Build ........... [ INFO ] [ INFO ] Parsing input parameters [ INFO ] Files were added: 1 [ INFO ] /images/dog.bmp [ INFO ] Loading model files: [ INFO ] /models/googlenet-v1.xml [ INFO ] model name: GoogleNet [ INFO ] inputs [ INFO ] input name: data [ INFO ] input type: f32 [ INFO ] input shape: {1, 3, 224, 224} [ INFO ] outputs [ INFO ] output name: prob [ INFO ] output type: f32 [ INFO ] output shape: {1, 1000} [ INFO ] Read input images [ INFO ] Set batch size 1 [ INFO ] model name: GoogleNet [ INFO ] inputs [ INFO ] input name: data [ INFO ] input type: u8 [ INFO ] input shape: {1, 224, 224, 3} [ INFO ] outputs [ INFO ] output name: prob [ INFO ] output type: f32 [ INFO ] output shape: {1, 1000} [ INFO ] Loading model to the device GPU [ INFO ] Create infer request [ INFO ] Start inference (asynchronous executions) [ INFO ] Completed 1 async request execution [ INFO ] Completed 2 async request execution [ INFO ] Completed 3 async request execution [ INFO ] Completed 4 async request execution [ INFO ] Completed 5 async request execution [ INFO ] Completed 6 async request execution [ INFO ] Completed 7 async request execution [ INFO ] Completed 8 async request execution [ INFO ] Completed 9 async request execution [ INFO ] Completed 10 async request execution [ INFO ] Completed async requests execution

Top 10 results:

Image /images/dog.bmp

classid probability