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third_party/paddle_ocr/src/ocr_rec.cpp

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+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "../include/ocr_rec.h"
+
+namespace PaddleOCR {
+
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
+                         std::vector<std::string> &rec_texts,
+                         std::vector<float> &rec_text_scores,
+                         std::vector<double> &times) {
+  std::chrono::duration<float> preprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+  int img_num = img_list.size();
+  std::vector<float> width_list;
+  for (int i = 0; i < img_num; i++) {
+    width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+  }
+  std::vector<int> indices = Utility::argsort(width_list);
+
+  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 = 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 = max(max_wh_ratio, wh_ratio);
+    }
+
+    int batch_width = imgW;
+    std::vector<cv::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;
+      this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
+                           this->use_tensorrt_, this->rec_image_shape_);
+      this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                              this->is_scale_);
+      norm_img_batch.push_back(resize_img);
+      batch_width = max(resize_img.cols, batch_width);
+    }
+
+    std::vector<float> input(batch_num * 3 * imgH * batch_width, 0.0f);
+    this->permute_op_.Run(norm_img_batch, input.data());
+    auto preprocess_end = std::chrono::steady_clock::now();
+    preprocess_diff += preprocess_end - preprocess_start;
+    // Inference.
+    auto input_names = this->predictor_->GetInputNames();
+    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());
+    this->predictor_->Run();
+
+    std::vector<float> 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<int>());
+    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 (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));
+}
+
+void CRNNRecognizer::LoadModel(const std::string &model_dir) {
+  //   AnalysisConfig config;
+  paddle_infer::Config config;
+  config.DisableGlogInfo();
+  config.SetModel(model_dir + "/inference.pdmodel",
+                  model_dir + "/inference.pdiparams");
+  std::cout << "In PP-OCRv3, default rec_img_h is 48,"
+            << "if you use other model, you should set the param rec_img_h=32"
+            << std::endl;
+  if (this->use_gpu_) {
+    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
+    if (this->use_tensorrt_) {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (this->precision_ == "fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      }
+      if (this->precision_ == "int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 15, precision, false, false);
+      int imgH = this->rec_image_shape_[1];
+      int imgW = this->rec_image_shape_[2];
+      std::map<std::string, std::vector<int>> min_input_shape = {
+          {"x", {1, 3, imgH, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}};
+      std::map<std::string, std::vector<int>> max_input_shape = {
+          {"x", {this->rec_batch_num_, 3, imgH, 2500}},
+          {"lstm_0.tmp_0", {1000, 1, 96}}};
+      std::map<std::string, std::vector<int>> opt_input_shape = {
+          {"x", {this->rec_batch_num_, 3, imgH, imgW}},
+          {"lstm_0.tmp_0", {25, 1, 96}}};
+
+      config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
+                                    opt_input_shape);
+    }
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+
+  // get pass_builder object
+  auto pass_builder = config.pass_builder();
+  // delete "matmul_transpose_reshape_fuse_pass"
+  pass_builder->DeletePass("matmul_transpose_reshape_fuse_pass");
+  config.SwitchUseFeedFetchOps(false);
+  // true for multiple input
+  config.SwitchSpecifyInputNames(true);
+
+  config.SwitchIrOptim(true);
+
+  config.EnableMemoryOptim();
+  //   config.DisableGlogInfo();
+
+  this->predictor_ = CreatePredictor(config);
+}
+
+} // namespace PaddleOCR