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.
Options |
Values |
|---|---|
Validated Models |
|
Model Format |
Inference Engine Intermediate Representation (*.xml + *.bin), ONNX (*.onnx) |
Validated images |
The sample uses OpenCV* to read input image (*.bmp, *.png) |
Supported devices |
|
Other language realization |
Hello Classification C sample application demonstrates how to use the C API from OpenVINO in applications.
Feature |
API |
Description |
|---|---|---|
OpenVINO Runtime Version |
|
Get Openvino API version |
Basic Infer Flow |
|
Common API to do inference: read and compile a model, create an infer request, configure input and output tensors |
Synchronous Infer |
|
Do synchronous inference |
Model Operations |
|
Get inputs and outputs of a model |
Tensor Operations |
|
Create a tensor shape |
Preprocessing |
|
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. |
// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <opencv_c_wrapper.h>
#include <stdbool.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "openvino/c/openvino.h"
/**
* @brief Struct to store infer results
*/
struct infer_result {
size_t class_id;
float probability;
};
/**
* @brief Sort result by probability
* @param struct with infer results to sort
* @param result_size of the struct
* @return none
*/
int compare(const void* a, const void* b) {
const struct infer_result* sa = (const struct infer_result*)a;
const struct infer_result* sb = (const struct infer_result*)b;
if (sa->probability < sb->probability) {
return 1;
} else if ((sa->probability == sb->probability) && (sa->class_id > sb->class_id)) {
return 1;
} else if (sa->probability > sb->probability) {
return -1;
}
return 0;
}
void infer_result_sort(struct infer_result* results, size_t result_size) {
qsort(results, result_size, sizeof(struct infer_result), compare);
}
/**
* @brief Convert output tensor to infer result struct for processing results
* @param tensor of output tensor
* @param result_size of the infer result
* @return struct infer_result
*/
struct infer_result* tensor_to_infer_result(ov_tensor_t* tensor, size_t* result_size) {
ov_shape_t output_shape = {0};
ov_status_e status = ov_tensor_get_shape(tensor, &output_shape);
if (status != OK)
return NULL;
*result_size = output_shape.dims[1];
struct infer_result* results = (struct infer_result*)malloc(sizeof(struct infer_result) * (*result_size));
if (!results)
return NULL;
void* data = NULL;
status = ov_tensor_data(tensor, &data);
if (status != OK) {
free(results);
return NULL;
}
float* float_data = (float*)(data);
size_t i;
for (i = 0; i < *result_size; ++i) {
results[i].class_id = i;
results[i].probability = float_data[i];
}
ov_shape_free(&output_shape);
return results;
}
/**
* @brief Print results of infer
* @param results of the infer results
* @param result_size of the struct of classification results
* @param img_path image path
* @return none
*/
void print_infer_result(struct infer_result* results, size_t result_size, const char* img_path) {
printf("\nImage %s\n", img_path);
printf("\nclassid probability\n");
printf("------- -----------\n");
size_t i;
for (i = 0; i < result_size; ++i) {
printf("%zu %f\n", results[i].class_id, results[i].probability);
}
}
void print_model_input_output_info(ov_model_t* model) {
char* friendly_name = NULL;
ov_model_get_friendly_name(model, &friendly_name);
printf("[INFO] model name: %s \n", friendly_name);
ov_free(friendly_name);
}
#define CHECK_STATUS(return_status) \
if (return_status != OK) { \
fprintf(stderr, "[ERROR] return status %d, line %d\n", return_status, __LINE__); \
goto err; \
}
int main(int argc, char** argv) {
// -------- Check input parameters --------
if (argc != 4) {
printf("Usage : ./hello_classification_c <path_to_model> <path_to_image> "
"<device_name>\n");
return EXIT_FAILURE;
}
ov_core_t* core = NULL;
ov_model_t* model = NULL;
ov_tensor_t* tensor = NULL;
ov_preprocess_prepostprocessor_t* preprocess = NULL;
ov_preprocess_input_info_t* input_info = NULL;
ov_model_t* new_model = NULL;
ov_preprocess_input_tensor_info_t* input_tensor_info = NULL;
ov_preprocess_preprocess_steps_t* input_process = NULL;
ov_preprocess_input_model_info_t* p_input_model = NULL;
ov_preprocess_output_info_t* output_info = NULL;
ov_preprocess_output_tensor_info_t* output_tensor_info = NULL;
ov_compiled_model_t* compiled_model = NULL;
ov_infer_request_t* infer_request = NULL;
ov_tensor_t* output_tensor = NULL;
struct infer_result* results = NULL;
ov_layout_t* input_layout = NULL;
ov_layout_t* model_layout = NULL;
ov_shape_t input_shape;
ov_output_const_port_t* output_port = NULL;
ov_output_const_port_t* input_port = NULL;
// -------- Get OpenVINO runtime version --------
ov_version_t version;
CHECK_STATUS(ov_get_openvino_version(&version));
printf("---- OpenVINO INFO----\n");
printf("Description : %s \n", version.description);
printf("Build number: %s \n", version.buildNumber);
ov_version_free(&version);
// -------- Parsing and validation of input arguments --------
const char* input_model = argv[1];
const char* input_image_path = argv[2];
const char* device_name = argv[3];
// -------- Step 1. Initialize OpenVINO Runtime Core --------
CHECK_STATUS(ov_core_create(&core));
// -------- Step 2. Read a model --------
printf("[INFO] Loading model files: %s\n", input_model);
CHECK_STATUS(ov_core_read_model(core, input_model, NULL, &model));
print_model_input_output_info(model);
CHECK_STATUS(ov_model_const_output(model, &output_port));
if (!output_port) {
fprintf(stderr, "[ERROR] Sample supports models with 1 output only %d\n", __LINE__);
goto err;
}
CHECK_STATUS(ov_model_const_input(model, &input_port));
if (!input_port) {
fprintf(stderr, "[ERROR] Sample supports models with 1 input only %d\n", __LINE__);
goto err;
}
// -------- Step 3. Set up input
c_mat_t img;
image_read(input_image_path, &img);
ov_element_type_e input_type = U8;
int64_t dims[4] = {1, (size_t)img.mat_height, (size_t)img.mat_width, 3};
ov_shape_create(4, dims, &input_shape);
CHECK_STATUS(ov_tensor_create_from_host_ptr(input_type, input_shape, img.mat_data, &tensor));
// -------- Step 4. Configure preprocessing --------
CHECK_STATUS(ov_preprocess_prepostprocessor_create(model, &preprocess));
CHECK_STATUS(ov_preprocess_prepostprocessor_get_input_info_by_index(preprocess, 0, &input_info));
CHECK_STATUS(ov_preprocess_input_info_get_tensor_info(input_info, &input_tensor_info));
CHECK_STATUS(ov_preprocess_input_tensor_info_set_from(input_tensor_info, tensor));
const char* input_layout_desc = "NHWC";
CHECK_STATUS(ov_layout_create(input_layout_desc, &input_layout));
CHECK_STATUS(ov_preprocess_input_tensor_info_set_layout(input_tensor_info, input_layout));
CHECK_STATUS(ov_preprocess_input_info_get_preprocess_steps(input_info, &input_process));
CHECK_STATUS(ov_preprocess_preprocess_steps_resize(input_process, RESIZE_LINEAR));
CHECK_STATUS(ov_preprocess_input_info_get_model_info(input_info, &p_input_model));
const char* model_layout_desc = "NCHW";
CHECK_STATUS(ov_layout_create(model_layout_desc, &model_layout));
CHECK_STATUS(ov_preprocess_input_model_info_set_layout(p_input_model, model_layout));
CHECK_STATUS(ov_preprocess_prepostprocessor_get_output_info_by_index(preprocess, 0, &output_info));
CHECK_STATUS(ov_preprocess_output_info_get_tensor_info(output_info, &output_tensor_info));
CHECK_STATUS(ov_preprocess_output_set_element_type(output_tensor_info, F32));
CHECK_STATUS(ov_preprocess_prepostprocessor_build(preprocess, &new_model));
// -------- Step 5. Loading a model to the device --------
CHECK_STATUS(ov_core_compile_model(core, new_model, device_name, 0, &compiled_model));
// -------- Step 6. Create an infer request --------
CHECK_STATUS(ov_compiled_model_create_infer_request(compiled_model, &infer_request));
// -------- Step 7. Prepare input --------
CHECK_STATUS(ov_infer_request_set_input_tensor_by_index(infer_request, 0, tensor));
// -------- Step 8. Do inference synchronously --------
CHECK_STATUS(ov_infer_request_infer(infer_request));
// -------- Step 9. Process output
CHECK_STATUS(ov_infer_request_get_output_tensor_by_index(infer_request, 0, &output_tensor));
// Print classification results
size_t results_num;
results = tensor_to_infer_result(output_tensor, &results_num);
infer_result_sort(results, results_num);
size_t top = 10;
if (top > results_num) {
top = results_num;
}
printf("\nTop %zu results:\n", top);
print_infer_result(results, top, input_image_path);
// -------- free allocated resources --------
err:
free(results);
image_free(&img);
ov_shape_free(&input_shape);
ov_output_const_port_free(output_port);
ov_output_const_port_free(input_port);
if (output_tensor)
ov_tensor_free(output_tensor);
if (infer_request)
ov_infer_request_free(infer_request);
if (compiled_model)
ov_compiled_model_free(compiled_model);
if (input_layout)
ov_layout_free(input_layout);
if (model_layout)
ov_layout_free(model_layout);
if (output_tensor_info)
ov_preprocess_output_tensor_info_free(output_tensor_info);
if (output_info)
ov_preprocess_output_info_free(output_info);
if (p_input_model)
ov_preprocess_input_model_info_free(p_input_model);
if (input_process)
ov_preprocess_preprocess_steps_free(input_process);
if (input_tensor_info)
ov_preprocess_input_tensor_info_free(input_tensor_info);
if (input_info)
ov_preprocess_input_info_free(input_info);
if (preprocess)
ov_preprocess_prepostprocessor_free(preprocess);
if (new_model)
ov_model_free(new_model);
if (tensor)
ov_tensor_free(tensor);
if (model)
ov_model_free(model);
if (core)
ov_core_free(core);
return EXIT_SUCCESS;
}
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:
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
mowith 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 conversion API.
The sample accepts models in ONNX format (*.onnx) that do not require preprocessing.
Example¶
Download a pre-trained model using [Model Downloader](@ref omz_tools_downloader):
python <path_to_omz_tools>/downloader.py --name alexnetIf 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 alexnetPerform inference of
car.bmpusingalexnetmodel on aGPU, 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