fix bug in mem
WenmuZhou
parent
1a2c400750
commit
6fcc2e7150
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@ -15,108 +15,115 @@
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#include <include/ocr_rec.h>
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namespace PaddleOCR {
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void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
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std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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int img_num = img_list.size();
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std::vector<float> width_list;
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for (int i = 0; i < img_num; i++) {
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width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
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void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
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std::vector<double> *times) {
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std::chrono::duration<float> preprocess_diff =
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std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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std::chrono::duration<float> inference_diff =
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std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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std::chrono::duration<float> postprocess_diff =
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std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
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int img_num = img_list.size();
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std::vector<float> width_list;
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for (int i = 0; i < img_num; i++) {
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width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
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}
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std::vector<int> indices = Utility::argsort(width_list);
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for (int beg_img_no = 0; beg_img_no < img_num;
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beg_img_no += this->rec_batch_num_) {
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auto preprocess_start = std::chrono::steady_clock::now();
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int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
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float max_wh_ratio = 0;
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for (int ino = beg_img_no; ino < end_img_no; ino++) {
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int h = img_list[indices[ino]].rows;
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int w = img_list[indices[ino]].cols;
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float wh_ratio = w * 1.0 / h;
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max_wh_ratio = max(max_wh_ratio, wh_ratio);
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}
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std::vector<int> indices = Utility::argsort(width_list);
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for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
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auto preprocess_start = std::chrono::steady_clock::now();
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int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
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float max_wh_ratio = 0;
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for (int ino = beg_img_no; ino < end_img_no; ino ++) {
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int h = img_list[indices[ino]].rows;
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int w = img_list[indices[ino]].cols;
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float wh_ratio = w * 1.0 / h;
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max_wh_ratio = max(max_wh_ratio, wh_ratio);
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}
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std::vector<cv::Mat> norm_img_batch;
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for (int ino = beg_img_no; ino < end_img_no; ino ++) {
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cv::Mat srcimg;
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img_list[indices[ino]].copyTo(srcimg);
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cv::Mat resize_img;
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this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
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norm_img_batch.push_back(resize_img);
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}
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int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
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std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
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this->permute_op_.Run(norm_img_batch, input.data());
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auto preprocess_end = std::chrono::steady_clock::now();
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preprocess_diff += preprocess_end - preprocess_start;
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// Inference.
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
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auto inference_start = std::chrono::steady_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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std::vector<float> predict_batch;
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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auto predict_shape = output_t->shape();
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int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
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std::multiplies<int>());
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predict_batch.resize(out_num);
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output_t->CopyToCpu(predict_batch.data());
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auto inference_end = std::chrono::steady_clock::now();
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inference_diff += inference_end - inference_start;
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// ctc decode
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auto postprocess_start = std::chrono::steady_clock::now();
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for (int m = 0; m < predict_shape[0]; m++) {
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std::vector<std::string> str_res;
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int argmax_idx;
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int last_index = 0;
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float score = 0.f;
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int count = 0;
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float max_value = 0.0f;
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for (int n = 0; n < predict_shape[1]; n++) {
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argmax_idx =
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int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
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&predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
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max_value =
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float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
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&predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
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if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
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score += max_value;
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count += 1;
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str_res.push_back(label_list_[argmax_idx]);
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}
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last_index = argmax_idx;
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}
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score /= count;
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if (isnan(score))
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continue;
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for (int i = 0; i < str_res.size(); i++) {
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std::cout << str_res[i];
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}
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std::cout << "\tscore: " << score << std::endl;
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}
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auto postprocess_end = std::chrono::steady_clock::now();
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postprocess_diff += postprocess_end - postprocess_start;
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int batch_width = 0;
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std::vector<cv::Mat> norm_img_batch;
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for (int ino = beg_img_no; ino < end_img_no; ino++) {
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cv::Mat srcimg;
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img_list[indices[ino]].copyTo(srcimg);
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cv::Mat resize_img;
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this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
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this->use_tensorrt_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
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this->is_scale_);
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norm_img_batch.push_back(resize_img);
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batch_width = max(resize_img.cols, batch_width);
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}
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times->push_back(double(preprocess_diff.count() * 1000));
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times->push_back(double(inference_diff.count() * 1000));
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times->push_back(double(postprocess_diff.count() * 1000));
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std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
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this->permute_op_.Run(norm_img_batch, input.data());
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auto preprocess_end = std::chrono::steady_clock::now();
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preprocess_diff += preprocess_end - preprocess_start;
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// Inference.
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
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auto inference_start = std::chrono::steady_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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std::vector<float> predict_batch;
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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auto predict_shape = output_t->shape();
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int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
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std::multiplies<int>());
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predict_batch.resize(out_num);
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output_t->CopyToCpu(predict_batch.data());
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auto inference_end = std::chrono::steady_clock::now();
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inference_diff += inference_end - inference_start;
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// ctc decode
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auto postprocess_start = std::chrono::steady_clock::now();
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for (int m = 0; m < predict_shape[0]; m++) {
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std::vector<std::string> str_res;
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int argmax_idx;
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int last_index = 0;
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float score = 0.f;
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int count = 0;
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float max_value = 0.0f;
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for (int n = 0; n < predict_shape[1]; n++) {
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argmax_idx = int(Utility::argmax(
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&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
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&predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
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max_value = float(*std::max_element(
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&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
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&predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
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if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
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score += max_value;
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count += 1;
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str_res.push_back(label_list_[argmax_idx]);
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}
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last_index = argmax_idx;
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}
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score /= count;
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if (isnan(score))
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continue;
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for (int i = 0; i < str_res.size(); i++) {
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std::cout << str_res[i];
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}
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std::cout << "\tscore: " << score << std::endl;
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}
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auto postprocess_end = std::chrono::steady_clock::now();
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postprocess_diff += postprocess_end - postprocess_start;
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}
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times->push_back(double(preprocess_diff.count() * 1000));
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times->push_back(double(inference_diff.count() * 1000));
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times->push_back(double(postprocess_diff.count() * 1000));
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}
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void CRNNRecognizer::LoadModel(const std::string &model_dir) {
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// AnalysisConfig config;
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paddle_infer::Config config;
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@ -130,23 +137,17 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
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if (this->precision_ == "fp16") {
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precision = paddle_infer::Config::Precision::kHalf;
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}
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if (this->precision_ == "int8") {
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if (this->precision_ == "int8") {
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precision = paddle_infer::Config::Precision::kInt8;
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}
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config.EnableTensorRtEngine(
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1 << 20, 10, 3,
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precision,
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false, false);
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}
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config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
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std::map<std::string, std::vector<int>> min_input_shape = {
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{"x", {1, 3, 32, 10}},
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{"lstm_0.tmp_0", {10, 1, 96}}};
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{"x", {1, 3, 32, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}};
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std::map<std::string, std::vector<int>> max_input_shape = {
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{"x", {1, 3, 32, 2000}},
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{"lstm_0.tmp_0", {1000, 1, 96}}};
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{"x", {1, 3, 32, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}};
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std::map<std::string, std::vector<int>> opt_input_shape = {
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{"x", {1, 3, 32, 320}},
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{"lstm_0.tmp_0", {25, 1, 96}}};
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{"x", {1, 3, 32, 320}}, {"lstm_0.tmp_0", {25, 1, 96}}};
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config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
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opt_input_shape);
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@ -168,7 +169,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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// config.DisableGlogInfo();
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// config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
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