186 lines
7.2 KiB
C++
186 lines
7.2 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/ocr_det.h>
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namespace PaddleOCR {
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void DBDetector::LoadModel(const std::string &model_dir) {
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// AnalysisConfig config;
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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, 1, 20, precision, false, false);
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std::map<std::string, std::vector<int>> min_input_shape = {
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{"x", {1, 3, 50, 50}},
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{"conv2d_92.tmp_0", {1, 120, 20, 20}},
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{"conv2d_91.tmp_0", {1, 24, 10, 10}},
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{"conv2d_59.tmp_0", {1, 96, 20, 20}},
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{"nearest_interp_v2_1.tmp_0", {1, 256, 10, 10}},
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{"nearest_interp_v2_2.tmp_0", {1, 256, 20, 20}},
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{"conv2d_124.tmp_0", {1, 256, 20, 20}},
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{"nearest_interp_v2_3.tmp_0", {1, 64, 20, 20}},
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{"nearest_interp_v2_4.tmp_0", {1, 64, 20, 20}},
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{"nearest_interp_v2_5.tmp_0", {1, 64, 20, 20}},
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{"elementwise_add_7", {1, 56, 2, 2}},
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{"nearest_interp_v2_0.tmp_0", {1, 256, 2, 2}}};
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std::map<std::string, std::vector<int>> max_input_shape = {
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{"x", {1, 3, this->max_side_len_, this->max_side_len_}},
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{"conv2d_92.tmp_0", {1, 120, 400, 400}},
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{"conv2d_91.tmp_0", {1, 24, 200, 200}},
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{"conv2d_59.tmp_0", {1, 96, 400, 400}},
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{"nearest_interp_v2_1.tmp_0", {1, 256, 200, 200}},
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{"nearest_interp_v2_2.tmp_0", {1, 256, 400, 400}},
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{"conv2d_124.tmp_0", {1, 256, 400, 400}},
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{"nearest_interp_v2_3.tmp_0", {1, 64, 400, 400}},
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{"nearest_interp_v2_4.tmp_0", {1, 64, 400, 400}},
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{"nearest_interp_v2_5.tmp_0", {1, 64, 400, 400}},
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{"elementwise_add_7", {1, 56, 400, 400}},
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{"nearest_interp_v2_0.tmp_0", {1, 256, 400, 400}}};
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std::map<std::string, std::vector<int>> opt_input_shape = {
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{"x", {1, 3, 640, 640}},
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{"conv2d_92.tmp_0", {1, 120, 160, 160}},
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{"conv2d_91.tmp_0", {1, 24, 80, 80}},
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{"conv2d_59.tmp_0", {1, 96, 160, 160}},
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{"nearest_interp_v2_1.tmp_0", {1, 256, 80, 80}},
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{"nearest_interp_v2_2.tmp_0", {1, 256, 160, 160}},
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{"conv2d_124.tmp_0", {1, 256, 160, 160}},
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{"nearest_interp_v2_3.tmp_0", {1, 64, 160, 160}},
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{"nearest_interp_v2_4.tmp_0", {1, 64, 160, 160}},
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{"nearest_interp_v2_5.tmp_0", {1, 64, 160, 160}},
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{"elementwise_add_7", {1, 56, 40, 40}},
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{"nearest_interp_v2_0.tmp_0", {1, 256, 40, 40}}};
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config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
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opt_input_shape);
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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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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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// use zero_copy_run as default
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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_ = CreatePredictor(config);
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}
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void DBDetector::Run(cv::Mat &img,
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std::vector<std::vector<std::vector<int>>> &boxes,
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std::vector<double> ×) {
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float ratio_h{};
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float ratio_w{};
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cv::Mat srcimg;
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cv::Mat resize_img;
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img.copyTo(srcimg);
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auto preprocess_start = std::chrono::steady_clock::now();
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this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
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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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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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auto preprocess_end = std::chrono::steady_clock::now();
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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({1, 3, resize_img.rows, resize_img.cols});
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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> out_data;
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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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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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auto inference_end = std::chrono::steady_clock::now();
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auto postprocess_start = std::chrono::steady_clock::now();
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int n2 = output_shape[2];
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int n3 = output_shape[3];
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int n = n2 * n3;
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std::vector<float> pred(n, 0.0);
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std::vector<unsigned char> cbuf(n, ' ');
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for (int i = 0; i < n; i++) {
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pred[i] = float(out_data[i]);
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cbuf[i] = (unsigned char)((out_data[i]) * 255);
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}
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cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf.data());
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cv::Mat pred_map(n2, n3, CV_32F, (float *)pred.data());
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const double threshold = this->det_db_thresh_ * 255;
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const double maxvalue = 255;
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cv::Mat bit_map;
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cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
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if (this->use_dilation_) {
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cv::Mat dila_ele =
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cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
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cv::dilate(bit_map, bit_map, dila_ele);
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}
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boxes = post_processor_.BoxesFromBitmap(
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pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
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this->det_db_score_mode_);
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boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
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auto postprocess_end = std::chrono::steady_clock::now();
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std::chrono::duration<float> preprocess_diff =
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preprocess_end - preprocess_start;
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times.push_back(double(preprocess_diff.count() * 1000));
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std::chrono::duration<float> inference_diff = inference_end - inference_start;
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times.push_back(double(inference_diff.count() * 1000));
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std::chrono::duration<float> postprocess_diff =
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postprocess_end - postprocess_start;
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times.push_back(double(postprocess_diff.count() * 1000));
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}
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} // namespace PaddleOCR
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