e25f20f7a3
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.
127 lines
3.6 KiB
C++
127 lines
3.6 KiB
C++
#include <stdio.h>
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#include <fstream>
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#include <string>
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#include <numeric>
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#include <cviruntime.h>
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#include <opencv2/opencv.hpp>
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#define IMG_RESIZE_DIMS 256,256
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#define BGR_MEAN 103.94,116.78,123.68
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#define INPUT_SCALE 0.017
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static void usage(char **argv) {
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printf("Usage:\n");
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printf(" %s cvimodel image.jpg label_file\n", argv[0]);
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}
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int main(int argc, char **argv) {
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if (argc != 4) {
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usage(argv);
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exit(-1);
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}
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// load model file
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const char *model_file = argv[1];
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CVI_MODEL_HANDLE model = nullptr;
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int ret = CVI_NN_RegisterModel(model_file, &model);
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if (CVI_RC_SUCCESS != ret) {
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printf("CVI_NN_RegisterModel failed, err %d\n", ret);
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exit(1);
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}
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printf("CVI_NN_RegisterModel succeeded\n");
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// get input output tensors
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CVI_TENSOR *input_tensors;
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CVI_TENSOR *output_tensors;
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int32_t input_num;
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int32_t output_num;
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CVI_NN_GetInputOutputTensors(model, &input_tensors, &input_num, &output_tensors,
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&output_num);
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CVI_TENSOR *input = CVI_NN_GetTensorByName(CVI_NN_DEFAULT_TENSOR, input_tensors, input_num);
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assert(input);
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printf("input, name:%s\n", input->name);
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CVI_TENSOR *output = CVI_NN_GetTensorByName(CVI_NN_DEFAULT_TENSOR, output_tensors, output_num);
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assert(output);
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float qscale = CVI_NN_TensorQuantScale(input);
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printf("qscale:%f\n", qscale);
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CVI_SHAPE shape = CVI_NN_TensorShape(input);
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// nchw
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int32_t height = shape.dim[2];
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int32_t width = shape.dim[3];
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// imread
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cv::Mat image;
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image = cv::imread(argv[2]);
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if (!image.data) {
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printf("Could not open or find the image\n");
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return -1;
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}
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// resize
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cv::resize(image, image, cv::Size(IMG_RESIZE_DIMS)); // linear is default
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// crop
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cv::Size size = cv::Size(height, width);
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cv::Rect crop(cv::Point(0.5 * (image.cols - size.width),
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0.5 * (image.rows - size.height)), size);
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image = image(crop);
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// split
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cv::Mat channels[3];
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for (int i = 0; i < 3; i++) {
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channels[i] = cv::Mat(height, width, CV_8SC1);
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}
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cv::split(image, channels);
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// normalize
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float mean[] = {BGR_MEAN};
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for (int i = 0; i < 3; ++i) {
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channels[i].convertTo(channels[i], CV_8SC1, INPUT_SCALE * qscale,
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-1 * mean[i] * INPUT_SCALE * qscale);
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}
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// fill to input tensor
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int8_t *ptr = (int8_t *)CVI_NN_TensorPtr(input);
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int channel_size = height * width;
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for (int i = 0; i < 3; ++i) {
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memcpy(ptr + i * channel_size, channels[i].data, channel_size);
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}
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// run inference
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CVI_NN_Forward(model, input_tensors, input_num, output_tensors, output_num);
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printf("CVI_NN_Forward succeeded\n");
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// output result
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std::vector<std::string> labels;
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std::ifstream file(argv[3]);
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if (!file) {
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printf("Didn't find synset_words file\n");
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exit(1);
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} else {
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std::string line;
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while (std::getline(file, line)) {
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labels.push_back(std::string(line));
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}
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}
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int32_t top_num = 5;
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float *prob = (float *)CVI_NN_TensorPtr(output);
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int32_t count = CVI_NN_TensorCount(output);
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// find top-k prob and cls
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std::vector<size_t> idx(count);
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std::iota(idx.begin(), idx.end(), 0);
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std::sort(idx.begin(), idx.end(), [&prob](size_t idx_0, size_t idx_1) {return prob[idx_0] > prob[idx_1];});
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// show results.
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printf("------\n");
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for (size_t i = 0; i < top_num; i++) {
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int top_k_idx = idx[i];
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printf(" %f, idx %d", prob[top_k_idx], top_k_idx);
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if (!labels.empty())
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printf(", %s", labels[top_k_idx].c_str());
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printf("\n");
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}
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printf("------\n");
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CVI_NN_CleanupModel(model);
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printf("CVI_NN_CleanupModel succeeded\n");
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return 0;
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} |