Hello Classification C Sample

This sample demonstrates how to execute an inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API and input auto-resize feature.

Hello Classification C sample application demonstrates how to use the C API from OpenVINO in applications.

Feature

API

Description

OpenVINO Runtime Version

ov_get_openvino_version

Get Openvino API version

Basic Infer Flow

ov_core_create , ov_core_read_model , ov_core_compile_model , ov_compiled_model_create_infer_request , ov_infer_request_set_input_tensor_by_index , ov_infer_request_get_output_tensor_by_index

Common API to do inference: read and compile a model, create an infer request, configure input and output tensors

Synchronous Infer

ov_infer_request_infer

Do synchronous inference

Model Operations

ov_model_const_input , ov_model_const_output

Get inputs and outputs of a model

Tensor Operations

ov_tensor_create_from_host_ptr

Create a tensor shape

Preprocessing

ov_preprocess_prepostprocessor_create , ov_preprocess_prepostprocessor_get_input_info_by_index , ov_preprocess_input_info_get_tensor_info , ov_preprocess_input_tensor_info_set_from , ov_preprocess_input_tensor_info_set_layout , ov_preprocess_input_info_get_preprocess_steps , ov_preprocess_preprocess_steps_resize , ov_preprocess_input_model_info_set_layout , ov_preprocess_output_set_element_type , ov_preprocess_prepostprocessor_build

Set image of the original size as input for a model with other input size. Resize and layout conversions are performed automatically by the corresponding plugin just before inference.

Options

Values

Validated Models

alexnet, googlenet-v1

Model Format

Inference Engine Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)

Validated images

The sample uses OpenCV* to read input image (*.bmp, *.png)

Supported devices

All

Other language realization

C++ , Python

How It Works

Upon the start-up, the sample application reads command line parameters, loads specified network and an image to the Inference Engine plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream.

You can see the explicit description of each sample step at Integration Steps section of “Integrate OpenVINO™ Runtime with Your Application” guide.

Building

To build the sample, please use instructions available at Build the Sample Applications section in Inference Engine Samples guide.

Running

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

NOTES :

  • 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 the Model Optimizer tool 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 the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

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

Example

  1. Download a pre-trained model using Model Downloader :

    python <path_to_omz_tools>/downloader.py --name alexnet

  2. If a model is not in the Inference Engine IR or ONNX format, it must be converted. You can do this using the model converter script:

    python <path_to_omz_tools>/converter.py --name alexnet

  3. Perform inference of car.bmp using alexnet model on a GPU, for example:

    <path_to_sample>/hello_classification_c <path_to_model>/alexnet.xml <path_to_image>/car.bmp GPU

Sample Output

The application outputs top-10 inference results.

Top 10 results:

Image /opt/intel/openvino/samples/scripts/car.png

classid probability
------- -----------
656       0.666479
654       0.112940
581       0.068487
874       0.033385
436       0.026132
817       0.016731
675       0.010980
511       0.010592
569       0.008178
717       0.006336

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool