基于python部署训练环境，训练的文字识别模型在C++源码部署加入该模型文件后不适用，报异常关于Paddle_inference的问题

[zhaocanglong]

请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem

• 系统环境/System Environment：Windows 11

• 版本号/Version：2.6 PaddleOCR：python训练的模型不能在C++源码中调用，编译器会引发异常 以下方法中根据输出判断是在std::cout << "predictor_ start:" << input_names.size() << std::endl;后就发生异常 void CRNNRecognizer::Run(std::vectorcv::Mat img_list, std::vectorstd::string &rec_texts, std::vector &rec_text_scores, std::vector &times) { std::chrono::duration preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); std::chrono::duration inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); std::chrono::duration postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();

  int img_num = img_list.size(); std::vector width_list; for (int i = 0; i < img_num; i++) { width_list.push_back(float(img_list[i].cols) / img_list[i].rows); } std::cout << "indices" << std::endl; std::vector indices = Utility::argsort(width_list); std::cout << "indices end:" << indices.size() << std::endl; for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) { auto preprocess_start = std::chrono::steady_clock::now(); int end_img_no = std::min(img_num, beg_img_no + this->rec_batch_num_); int batch_num = end_img_no - beg_img_no; int imgH = this->rec_image_shape_[1]; int imgW = this->rec_image_shape_[2]; float max_wh_ratio = imgW * 1.0 / imgH; for (int ino = beg_img_no; ino < end_img_no; ino++) { int h = img_list[indices[ino]].rows; int w = img_list[indices[ino]].cols; float wh_ratio = w * 1.0 / h; max_wh_ratio = std::max(max_wh_ratio, wh_ratio); }

  int batch_width = imgW; std::vectorcv::Mat norm_img_batch; for (int ino = beg_img_no; ino < end_img_no; ino++) { cv::Mat srcimg; img_list[indices[ino]].copyTo(srcimg); cv::Mat resize_img; std::cout << "resize_op_ start" << std::endl; this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_, this->rec_image_shape_); std::cout << "resize_op_ end" << std::endl; std::cout << "normalize_op_ start" << std::endl; this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_); std::cout << "normalize_op_ end" << std::endl; norm_img_batch.push_back(resize_img); batch_width = std::max(resize_img.cols, batch_width); }

  std::vector input(batch_num * 3 * imgH * batch_width, 0.0f); std::cout << "permute_op_ start" << std::endl; this->permute_op_.Run(norm_img_batch, input.data()); std::cout << "permute_op_ end" << std::endl; auto preprocess_end = std::chrono::steady_clock::now(); preprocess_diff += preprocess_end - preprocess_start; // Inference. auto input_names = this->predictor_->GetInputNames(); std::cout << "permute_op_ start" << input_names.size() << std::endl; auto input_t = this->predictor_->GetInputHandle(input_names[0]); input_t->Reshape({batch_num, 3, imgH, batch_width}); auto inference_start = std::chrono::steady_clock::now(); input_t->CopyFromCpu(input.data()); std::cout << "predictor_ start:" << input_names.size() << std::endl; this->predictor_->Run(); std::cout << "predictor_ end:" << input_names.size() << std::endl; std::vector predict_batch; auto output_names = this->predictor_->GetOutputNames(); auto output_t = this->predictor_->GetOutputHandle(output_names[0]); auto predict_shape = output_t->shape();

  int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1, std::multiplies()); predict_batch.resize(out_num); // predict_batch is the result of Last FC with softmax output_t->CopyToCpu(predict_batch.data()); auto inference_end = std::chrono::steady_clock::now(); inference_diff += inference_end - inference_start; // ctc decode auto postprocess_start = std::chrono::steady_clock::now(); for (int m = 0; m < predict_shape[0]; m++) { std::string str_res; int argmax_idx; int last_index = 0; float score = 0.f; int count = 0; float max_value = 0.0f;

  for (int n = 0; n < predict_shape[1]; n++) {
    // get idx
    argmax_idx = int(Utility::argmax(
        &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
    // get score
    max_value = float(*std::max_element(
        &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
  
    if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
      score += max_value;
      count += 1;
      str_res += label_list_[argmax_idx];
    }
    last_index = argmax_idx;
  }
  score /= count;
  if (std::isnan(score)) {
    continue;
  }
  rec_texts[indices[beg_img_no + m]] = str_res;
  rec_text_scores[indices[beg_img_no + m]] = score;
  

  } auto postprocess_end = std::chrono::steady_clock::now(); postprocess_diff += postprocess_end - postprocess_start; } times.push_back(double(preprocess_diff.count() * 1000)); times.push_back(double(inference_diff.count() * 1000)); times.push_back(double(postprocess_diff.count() * 1000)); }