Image Classification Async Python Sample¶
This sample demonstrates how to perform inference of image classification models using Asynchronous Inference Request API.
Models with only 1 input and output are supported.
The following Python API is used in the application:
Feature |
API |
Description |
---|---|---|
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 |
Basic OpenVINO™ Runtime API is described in Hello Classification Python Sample.
Options |
Values |
---|---|
Validated Models |
|
Model Format |
OpenVINO Intermediate Representation (.xml + .bin), ONNX (.onnx) |
Supported devices |
|
Other language realization |
How It Works¶
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.
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:
Use public or Intel’s pre-trained models from Open Model Zoo. The models can be downloaded by using the Model Downloader.
You may use images from the media files collection, available online in the test data storage.
Run the application with the -h
option to see the usage message:
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, MYRIAD, HDDL or HETERO: is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU.
NOTES :
By default, samples and demos in OpenVINO Toolkit expect input with
BGR
order of channels. If you trained your model to work withRGB
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.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¶
Install the
openvino-dev
Python package to use Open Model Zoo Tools:python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]
Download a pre-trained model:
omz_downloader --name alexnet
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
Perform inference on the
banana.jpg
and thecar.bmp
images, using thealexnet
model on aGPU
device, for example:python classification_sample_async.py -m alexnet.xml -i banana.jpg car.bmp -d 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: 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