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cpp_tm_classification.cpp
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* License); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*
* Copyright (c) 2020, OPEN AI LAB
* Author: qtang@openailab.com
*/
#include <iostream>
#include <string>
#ifdef _MSC_VER
#define NOMINMAX
#endif
#include <algorithm>
#include "common.h"
#include "tengine_cpp_api.h"
#include "tengine_operations.h"
#define DEFAULT_IMG_H 227
#define DEFAULT_IMG_W 227
#define DEFAULT_SCALE1 1.f
#define DEFAULT_SCALE2 1.f
#define DEFAULT_SCALE3 1.f
#define DEFAULT_MEAN1 104.007
#define DEFAULT_MEAN2 116.669
#define DEFAULT_MEAN3 122.679
#define DEFAULT_LOOP_COUNT 1
#define DEFAULT_THREAD_COUNT 1
using namespace std;
void show_usage()
{
std::cout << "[Usage]: [-h]\n"
<< " [-m model_file] [-l label_file] [-i image_file]\n"
<< " [-g img_h,img_w] [-s scale] [-w mean[0],mean[1],mean[2]] [-r repeat_count]\n";
std::cout << "\nmobilenet example: \n"
<< " ./classification -m /path/to/mobilenet.tmfile -l /path/to/labels.txt -i /path/to/img.jpg -g 224,224 -s 0.017 -w 104.007,116.669,122.679" << std::endl;
}
int main(int argc, char* argv[])
{
int loop_count = DEFAULT_LOOP_COUNT;
int num_thread = DEFAULT_THREAD_COUNT;
string model_file;
string image_file;
float img_hw[2] = {0.f};
int img_h = 0;
int img_w = 0;
float mean[3] = {-1.f, -1.f, -1.f};
float scale[3] = {0.f, 0.f, 0.f};
int res;
while ((res = getopt(argc, argv, "m:i:l:g:s:w:r:t:h")) != -1)
{
switch (res)
{
case 'm':
model_file = optarg;
break;
case 'i':
image_file = optarg;
break;
case 'g':
split(img_hw, optarg, ",");
img_h = (int)img_hw[0];
img_w = (int)img_hw[1];
break;
case 's':
split(scale, optarg, ",");
break;
case 'w':
split(mean, optarg, ",");
break;
case 'r':
loop_count = atoi(optarg);
break;
case 't':
num_thread = atoi(optarg);
break;
case 'h':
show_usage();
return 0;
default:
break;
}
}
if (model_file.empty())
{
std::cerr << "Error: Tengine model file not specified!" << std::endl;
show_usage();
return -1;
}
if (image_file.empty())
{
std::cerr << "Error: Image file not specified!" << std::endl;
show_usage();
return -1;
}
// check input files
if (!check_file_exist(model_file.c_str()) || !check_file_exist(image_file.c_str()))
return -1;
if (img_h == 0)
{
img_h = DEFAULT_IMG_H;
std::cout << "Image height not specified, use default [" << DEFAULT_IMG_H << "]" << std::endl;
}
if (img_w == 0)
{
img_w = DEFAULT_IMG_W;
std::cout << "Image width not specified, use default [" << DEFAULT_IMG_W << "]" << std::endl;
}
if (scale[0] == 0.f || scale[1] == 0.f || scale[2] == 0.f)
{
scale[0] = DEFAULT_SCALE1;
scale[1] = DEFAULT_SCALE2;
scale[2] = DEFAULT_SCALE3;
std::cout << "Scale value not specified, use default [" << scale[0] << ", " << scale[1] << ", " << scale[2] << "]" << std::endl;
}
if (mean[0] == -1.0 || mean[1] == -1.0 || mean[2] == -1.0)
{
mean[0] = DEFAULT_MEAN1;
mean[1] = DEFAULT_MEAN2;
mean[2] = DEFAULT_MEAN3;
std::cout << "Mean value not specified, use default [" << mean[0] << ", " << mean[1] << ", " << mean[2] << "]" << std::endl;
}
init_tengine();
{
tengine::Net somenet;
tengine::Tensor input_tensor;
tengine::Tensor output_tensor;
/* set runtime options of Net */
somenet.opt.num_thread = num_thread;
somenet.opt.cluster = TENGINE_CLUSTER_ALL;
somenet.opt.precision = TENGINE_MODE_FP32;
std::cout << "\ntengine model file : " << model_file << "\n"
<< "image file : " << image_file << "\n"
<< "img_h, imag_w, scale, mean[3] : " << img_h << " " << img_w << " " << scale[0] << " " << scale[1]
<< " " << scale[2] << " " << mean[0] << " " << mean[1] << " " << mean[2] << "\n";
/* load model */
somenet.load_model(nullptr, "tengine", model_file.c_str());
/* prepare input data */
input_tensor.create(1, 3, img_h, img_w);
get_input_data(image_file.c_str(), (float*)input_tensor.data, img_h, img_w, mean, scale);
/* forward */
somenet.input_tensor("data", input_tensor);
double min_time, max_time, total_time;
min_time = DBL_MAX;
max_time = DBL_MIN;
total_time = 0;
for (int i = 0; i < loop_count; i++)
{
double start_time = get_current_time();
somenet.run();
double end_time = get_current_time();
double cur_time = end_time - start_time;
total_time += cur_time;
max_time = std::max(max_time, cur_time);
min_time = std::min(min_time, cur_time);
}
printf("Repeat [%d] min %.3f ms, max %.3f ms, avg %.3f ms\n", loop_count, min_time, max_time,
total_time / loop_count);
/* get result */
somenet.extract_tensor("prob", output_tensor);
/* after process */
print_topk((float*)output_tensor.data, output_tensor.elem_num, 5);
std::cout << "--------------------------------------\n";
std::cout << "ALL TEST DONE\n";
}
release_tengine();
return 0;
}