text: /*
   * 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 <stdlib.h>
  #include <stdio.h>
  
  #include "common.h"
  #include "tengine/c_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
  #define DEFAULT_CPU_AFFINITY 255
  
  void get_input_uint8_data(const char* image_file, uint8_t* input_data, int img_h, int img_w, float* mean, float* scale,
                            float input_scale, int zero_point)
  {
      image img = imread_process(image_file, img_w, img_h, mean, scale);
  
      float* image_data = ( float* )img.data;
  
      for (int i = 0; i < img_w * img_h * 3; i++)
      {
          int udata = (round)(image_data[i] / input_scale + (float)zero_point);
          if (udata > 255)
              udata = 255;
          else if (udata < 0)
              udata = 0;
  
          input_data[i] = udata;
      }
  
      free_image(img);
  }
  
  int tengine_classify(const char* model_file, const char* image_file, int img_h, int img_w, float* mean, float* scale,
  					  int loop_count, int num_thread, int affinity)
  {
      /* set runtime options */
      struct options opt;
      opt.num_thread = num_thread;
      opt.cluster = TENGINE_CLUSTER_ALL;
      opt.precision = TENGINE_MODE_UINT8;
      opt.affinity = affinity;
  
      /* inital tengine */
      if (init_tengine() != 0)
      {
          fprintf(stderr, "Initial tengine failed.\n");
          return -1;
      }
      fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
  
      /* create VeriSilicon TIM-VX backend */
      context_t timvx_context = create_context("timvx", 1);
      int rtt = set_context_device(timvx_context, "TIMVX", NULL, 0);
      if (0 > rtt)
      {
          fprintf(stderr, " add_context_device VSI DEVICE failed.\n");
          return -1;
      }
  
      /* create graph, load tengine model xxx.tmfile */
      graph_t graph = create_graph(timvx_context, "tengine", model_file);
      if (NULL == graph)
      {
          fprintf(stderr, "Create graph failed.\n");
          return -1;
      }
  
      /* set the input shape to initial the graph, and prerun graph to infer shape */
      int img_size = img_h * img_w * 3;
      int dims[] = {1, 3, img_h, img_w};    // nchw
      uint8_t* input_data = ( uint8_t* )malloc(img_size);
  
      tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
      if (input_tensor == NULL)
      {
          fprintf(stderr, "Get input tensor failed\n");
          return -1;
      }
  
      if (set_tensor_shape(input_tensor, dims, 4) < 0)
      {
          fprintf(stderr, "Set input tensor shape failed\n");
          return -1;
      }
  
      if (set_tensor_buffer(input_tensor, input_data, img_size) < 0)
      {
          fprintf(stderr, "Set input tensor buffer failed\n");
          return -1;
      }    
  
      /* prerun graph, set work options(num_thread, cluster, precision) */
      if (prerun_graph_multithread(graph, opt) < 0)
      {
          fprintf(stderr, "Prerun multithread graph failed.\n");
          return -1;
      }
  
      /* prepare process input data, set the data mem to input tensor */
      float input_scale = 0.f;
      int input_zero_point = 0;
      get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
      get_input_uint8_data(image_file, input_data, img_h, img_w, mean, scale, input_scale, input_zero_point);
  
      /* run graph */
      double min_time = DBL_MAX;
      double max_time = DBL_MIN;
      double total_time = 0.;
      for (int i = 0; i < loop_count; i++)
      {
          double start = get_current_time();
          if (run_graph(graph, 1) < 0)
          {
              fprintf(stderr, "Run graph failed\n");
              return -1;
          }
          double end = get_current_time();
          double cur = end - start;
          total_time += cur;
          if (min_time > cur)
              min_time = cur;
          if (max_time < cur)
              max_time = cur;
      }
      fprintf(stderr, "\nmodel file : %s\n", model_file);
      fprintf(stderr, "image file : %s\n", image_file);
      fprintf(stderr, "img_h, img_w, scale[3], mean[3] : %d %d , %.3f %.3f %.3f, %.1f %.1f %.1f\n", img_h, img_w,
              scale[0], scale[1], scale[2], mean[0], mean[1], mean[2]);
      fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", loop_count,
              num_thread, total_time / loop_count, max_time, min_time);
      fprintf(stderr, "--------------------------------------\n");
  
      /* get the result of classification */
      tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0);
      uint8_t* output_u8 = ( uint8_t* )get_tensor_buffer(output_tensor);
      int output_size = get_tensor_buffer_size(output_tensor);
  
      /* dequant */
      float output_scale = 0.f;
      int output_zero_point = 0;
      get_tensor_quant_param(output_tensor, &output_scale, &output_zero_point, 1);
      float* output_data = ( float* )malloc(output_size * sizeof(float));
      for (int i = 0; i < output_size; i++)
          output_data[i] = (( float )output_u8[i] - ( float )output_zero_point) * output_scale;
  
      print_topk(output_data, output_size, 5);
      fprintf(stderr, "--------------------------------------\n");
  
      /* release tengine */
      free(input_data);
      free(output_data);
      postrun_graph(graph);
      destroy_graph(graph);
      release_tengine();
  
      return 0;
  }
  
  void show_usage()
  {
      fprintf(
          stderr,
          "[Usage]:  [-h]\n    [-m model_file] [-i image_file]\n [-g img_h,img_w] [-s scale[0],scale[1],scale[2]] [-w "
          "mean[0],mean[1],mean[2]] [-r loop_count] [-t thread_count] [-a cpu_affinity]\n");
      fprintf(
          stderr,
          "\nmobilenet example: \n    ./classification -m /path/to/mobilenet.tmfile -i /path/to/img.jpg -g 224,224 -s "
          "0.017,0.017,0.017 -w 104.007,116.669,122.679\n");
  }
  
  int main(int argc, char* argv[])
  {
      int loop_count = DEFAULT_LOOP_COUNT;
      int num_thread = DEFAULT_THREAD_COUNT;
      int cpu_affinity = DEFAULT_CPU_AFFINITY;
      char* model_file = NULL;
      char* image_file = NULL;
      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:a: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 'a':
                  cpu_affinity = atoi(optarg);
                  break;
              case 'h':
                  show_usage();
                  return 0;
              default:
                  break;
          }
      }
  
      /* check files */
      if (model_file == NULL)
      {
          fprintf(stderr, "Error: Tengine model file not specified!\n");
          show_usage();
          return -1;
      }
  
      if (image_file == NULL)
      {
          fprintf(stderr, "Error: Image file not specified!\n");
          show_usage();
          return -1;
      }
  
      if (!check_file_exist(model_file) || !check_file_exist(image_file))
          return -1;
  
      if (img_h == 0)
      {
          img_h = DEFAULT_IMG_H;
          fprintf(stderr, "Image height not specified, use default %d\n", img_h);
      }
  
      if (img_w == 0)
      {
          img_w = DEFAULT_IMG_W;
          fprintf(stderr, "Image width not specified, use default  %d\n", img_w);
      }
  
      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;
          fprintf(stderr, "Scale value not specified, use default  %.1f, %.1f, %.1f\n", scale[0], scale[1], scale[2]);
      }
  
      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;
          fprintf(stderr, "Mean value not specified, use default   %.1f, %.1f, %.1f\n", mean[0], mean[1], mean[2]);
      }
  
      if (tengine_classify(model_file, image_file, img_h, img_w, mean, scale, loop_count, num_thread, cpu_affinity) < 0)
          return -1;
  
      return 0;
  }
  