add cviruntime

commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20
Author: sophgo-forum-service <forum_service@sophgo.com>
Date:   Mon May 13 13:44:23 2024 +0800

    [feat] cviruntime opensource for cv18xx soc.

    - a4b6a3, add cumsum and gatherelements_pt.

cviruntime/samples/classifier/classifier.cpp

@@ -0,0 +1,127 @@
+#include <stdio.h>
+#include <fstream>
+#include <string>
+#include <numeric>
+#include <cviruntime.h>
+#include <opencv2/opencv.hpp>
+
+#define IMG_RESIZE_DIMS 256,256
+#define BGR_MEAN        103.94,116.78,123.68
+#define INPUT_SCALE     0.017
+
+static void usage(char **argv) {
+  printf("Usage:\n");
+  printf("   %s cvimodel image.jpg label_file\n", argv[0]);
+}
+
+int main(int argc, char **argv) {
+  if (argc != 4) {
+    usage(argv);
+    exit(-1);
+  }
+
+  // load model file
+  const char *model_file = argv[1];
+  CVI_MODEL_HANDLE model = nullptr;
+  int ret = CVI_NN_RegisterModel(model_file, &model);
+  if (CVI_RC_SUCCESS != ret) {
+    printf("CVI_NN_RegisterModel failed, err %d\n", ret);
+    exit(1);
+  }
+  printf("CVI_NN_RegisterModel succeeded\n");
+
+  // get input output tensors
+  CVI_TENSOR *input_tensors;
+  CVI_TENSOR *output_tensors;
+  int32_t input_num;
+  int32_t output_num;
+  CVI_NN_GetInputOutputTensors(model, &input_tensors, &input_num, &output_tensors,
+                               &output_num);
+  CVI_TENSOR *input = CVI_NN_GetTensorByName(CVI_NN_DEFAULT_TENSOR, input_tensors, input_num);
+  assert(input);
+  printf("input, name:%s\n", input->name);
+  CVI_TENSOR *output = CVI_NN_GetTensorByName(CVI_NN_DEFAULT_TENSOR, output_tensors, output_num);
+  assert(output);
+
+  float qscale = CVI_NN_TensorQuantScale(input);
+  printf("qscale:%f\n", qscale);
+  CVI_SHAPE shape = CVI_NN_TensorShape(input);
+
+  // nchw
+  int32_t height = shape.dim[2];
+  int32_t width = shape.dim[3];
+
+  // imread
+  cv::Mat image;
+  image = cv::imread(argv[2]);
+  if (!image.data) {
+    printf("Could not open or find the image\n");
+    return -1;
+  }
+
+  // resize
+  cv::resize(image, image, cv::Size(IMG_RESIZE_DIMS)); // linear is default
+  // crop
+  cv::Size size = cv::Size(height, width);
+  cv::Rect crop(cv::Point(0.5 * (image.cols - size.width),
+                          0.5 * (image.rows - size.height)), size);
+  image = image(crop);
+  // split
+  cv::Mat channels[3];
+  for (int i = 0; i < 3; i++) {
+    channels[i] = cv::Mat(height, width, CV_8SC1);
+  }
+  cv::split(image, channels);
+  // normalize
+  float mean[] = {BGR_MEAN};
+  for (int i = 0; i < 3; ++i) {
+    channels[i].convertTo(channels[i], CV_8SC1, INPUT_SCALE * qscale,
+                          -1 * mean[i] * INPUT_SCALE * qscale);
+  }
+
+  // fill to input tensor
+  int8_t *ptr = (int8_t *)CVI_NN_TensorPtr(input);
+  int channel_size = height * width;
+  for (int i = 0; i < 3; ++i) {
+    memcpy(ptr + i * channel_size, channels[i].data, channel_size);
+  }
+
+  // run inference
+  CVI_NN_Forward(model, input_tensors, input_num, output_tensors, output_num);
+  printf("CVI_NN_Forward succeeded\n");
+
+  // output result
+  std::vector<std::string> labels;
+  std::ifstream file(argv[3]);
+  if (!file) {
+    printf("Didn't find synset_words file\n");
+    exit(1);
+  } else {
+    std::string line;
+    while (std::getline(file, line)) {
+      labels.push_back(std::string(line));
+    }
+  }
+
+  int32_t top_num = 5;
+  float *prob = (float *)CVI_NN_TensorPtr(output);
+  int32_t count = CVI_NN_TensorCount(output);
+
+  // find top-k prob and cls
+  std::vector<size_t> idx(count);
+  std::iota(idx.begin(), idx.end(), 0);
+  std::sort(idx.begin(), idx.end(), [&prob](size_t idx_0, size_t idx_1) {return prob[idx_0] > prob[idx_1];});
+  // show results.
+  printf("------\n");
+  for (size_t i = 0; i < top_num; i++) {
+    int top_k_idx = idx[i];
+    printf("  %f, idx %d", prob[top_k_idx], top_k_idx);
+    if (!labels.empty())
+      printf(", %s", labels[top_k_idx].c_str());
+    printf("\n");
+  }
+  printf("------\n");
+  CVI_NN_CleanupModel(model);
+  printf("CVI_NN_CleanupModel succeeded\n");
+  return 0;
+}