mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-06-03 21:53:39 +08:00
163 lines
6.9 KiB
C++
163 lines
6.9 KiB
C++
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <include/structure_table.h>
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namespace PaddleOCR {
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void StructureTableRecognizer::Run(
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std::vector<cv::Mat> img_list,
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std::vector<std::vector<std::string>> &structure_html_tags,
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std::vector<float> &structure_scores,
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std::vector<std::vector<std::vector<int>>> &structure_boxes,
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std::vector<double> ×) {
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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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for (int beg_img_no = 0; beg_img_no < img_num;
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beg_img_no += this->table_batch_num_) {
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// preprocess
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auto preprocess_start = std::chrono::steady_clock::now();
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int end_img_no = std::min(img_num, beg_img_no + this->table_batch_num_);
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int batch_num = end_img_no - beg_img_no;
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std::vector<cv::Mat> norm_img_batch;
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std::vector<int> width_list;
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std::vector<int> height_list;
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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[ino].copyTo(srcimg);
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cv::Mat resize_img;
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cv::Mat pad_img;
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this->resize_op_.Run(srcimg, resize_img, this->table_max_len_);
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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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this->pad_op_.Run(resize_img, pad_img, this->table_max_len_);
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norm_img_batch.push_back(pad_img);
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width_list.push_back(srcimg.cols);
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height_list.push_back(srcimg.rows);
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}
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std::vector<float> input(
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batch_num * 3 * this->table_max_len_ * this->table_max_len_, 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(
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{batch_num, 3, this->table_max_len_, this->table_max_len_});
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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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auto output_names = this->predictor_->GetOutputNames();
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auto output_tensor0 = this->predictor_->GetOutputHandle(output_names[0]);
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auto output_tensor1 = this->predictor_->GetOutputHandle(output_names[1]);
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std::vector<int> predict_shape0 = output_tensor0->shape();
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std::vector<int> predict_shape1 = output_tensor1->shape();
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int out_num0 = std::accumulate(predict_shape0.begin(), predict_shape0.end(),
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1, std::multiplies<int>());
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int out_num1 = std::accumulate(predict_shape1.begin(), predict_shape1.end(),
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1, std::multiplies<int>());
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std::vector<float> loc_preds;
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std::vector<float> structure_probs;
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loc_preds.resize(out_num0);
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structure_probs.resize(out_num1);
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output_tensor0->CopyToCpu(loc_preds.data());
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output_tensor1->CopyToCpu(structure_probs.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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// postprocess
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auto postprocess_start = std::chrono::steady_clock::now();
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std::vector<std::vector<std::string>> structure_html_tag_batch;
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std::vector<float> structure_score_batch;
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std::vector<std::vector<std::vector<int>>> structure_boxes_batch;
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this->post_processor_.Run(loc_preds, structure_probs, structure_score_batch,
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predict_shape0, predict_shape1,
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structure_html_tag_batch, structure_boxes_batch,
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width_list, height_list);
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for (int m = 0; m < predict_shape0[0]; m++) {
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structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
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"<table>");
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structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
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"<body>");
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structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
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"<html>");
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structure_html_tag_batch[m].push_back("</table>");
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structure_html_tag_batch[m].push_back("</body>");
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structure_html_tag_batch[m].push_back("</html>");
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structure_html_tags.push_back(structure_html_tag_batch[m]);
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structure_scores.push_back(structure_score_batch[m]);
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structure_boxes.push_back(structure_boxes_batch[m]);
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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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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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}
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void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
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paddle_infer::Config config;
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config.SetModel(model_dir + "/inference.pdmodel",
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model_dir + "/inference.pdiparams");
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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auto precision = paddle_infer::Config::Precision::kFloat32;
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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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precision = paddle_infer::Config::Precision::kInt8;
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}
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config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
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if (!Utility::PathExists("./trt_table_shape.txt")) {
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config.CollectShapeRangeInfo("./trt_table_shape.txt");
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} else {
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config.EnableTunedTensorRtDynamicShape("./trt_table_shape.txt", true);
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}
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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// false for zero copy tensor
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = paddle_infer::CreatePredictor(config);
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}
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} // namespace PaddleOCR
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