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.

Requirements

Options	Values
Validated Models	alexnet, googlenet-v1
Model Format	OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx)
Supported devices	All
Other language realization	C++, C

Python API

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

Sample Code

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import logging as log
import sys

import cv2
import numpy as np
from openvino.preprocess import PrePostProcessor, ResizeAlgorithm
from openvino.runtime import Core, Layout, Type


def main():
    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)

    # Parsing and validation of input arguments
    if len(sys.argv) != 4:
        log.info(f'Usage: {sys.argv[0]} <path_to_model> <path_to_image> <device_name>')
        return 1

    model_path = sys.argv[1]
    image_path = sys.argv[2]
    device_name = sys.argv[3]

# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
    log.info('Creating OpenVINO Runtime Core')
    core = Core()

# --------------------------- Step 2. Read a model --------------------------------------------------------------------
    log.info(f'Reading the model: {model_path}')
    # (.xml and .bin files) or (.onnx file)
    model = core.read_model(model_path)

    if len(model.inputs) != 1:
        log.error('Sample supports only single input topologies')
        return -1

    if len(model.outputs) != 1:
        log.error('Sample supports only single output topologies')
        return -1

# --------------------------- Step 3. Set up input --------------------------------------------------------------------
    # Read input image
    image = cv2.imread(image_path)
    # Add N dimension
    input_tensor = np.expand_dims(image, 0)

# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
    ppp = PrePostProcessor(model)

    _, h, w, _ = input_tensor.shape

    # 1) Set input tensor information:
    # - input() provides information about a single model input
    # - reuse precision and shape from already available `input_tensor`
    # - layout of data is 'NHWC'
    ppp.input().tensor() \
        .set_shape(input_tensor.shape) \
        .set_element_type(Type.u8) \
        .set_layout(Layout('NHWC'))  # noqa: ECE001, N400

    # 2) Adding explicit preprocessing steps:
    # - apply linear resize from tensor spatial dims to model spatial dims
    ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)

    # 3) Here we suppose model has 'NCHW' layout for input
    ppp.input().model().set_layout(Layout('NCHW'))

    # 4) Set output tensor information:
    # - precision of tensor is supposed to be 'f32'
    ppp.output().tensor().set_element_type(Type.f32)

    # 5) Apply preprocessing modifying the original 'model'
    model = ppp.build()

# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
    log.info('Loading the model to the plugin')
    compiled_model = core.compile_model(model, device_name)

# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
    log.info('Starting inference in synchronous mode')
    results = compiled_model.infer_new_request({0: input_tensor})

# --------------------------- Step 7. Process output ------------------------------------------------------------------
    predictions = next(iter(results.values()))

    # Change a shape of a numpy.ndarray with results to get another one with one dimension
    probs = predictions.reshape(-1)

    # Get an array of 10 class IDs in descending order of probability
    top_10 = np.argsort(probs)[-10:][::-1]

    header = 'class_id probability'

    log.info(f'Image path: {image_path}')
    log.info('Top 10 results: ')
    log.info(header)
    log.info('-' * len(header))

    for class_id in top_10:
        probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')

    log.info('')

# ----------------------------------------------------------------------------------------------------------------------
    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
    return 0


if __name__ == '__main__':
    sys.exit(main())

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.

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

Running¶

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

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

You can use public or Intel’s pre-trained models from the Open Model Zoo. The models can be downloaded using the Model Downloader.
You can use images from the media files collection available at the 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 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 intermediate representation (IR) format (*.xml + *.bin) using the model conversion API.
The sample accepts models in 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]
```
Download a pre-trained model:
```
omz_downloader --name alexnet
```
If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:
```
omz_converter --name alexnet
```
Perform inference of 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, for any performance measurements please use the dedicated benchmark_app tool

Hello Classification Python Sample¶

How It Works¶

Running¶

Example¶

Sample Output¶

See Also¶