Merge remote-tracking branch 'origin/dygraph' into dygraph

configs/det/ch_PP-OCRv2/ch_PP-OCR_det_distill.yml

@@ -90,7 +90,7 @@ Optimizer:
 
 PostProcess:
  name: DistillationDBPostProcess
- model_name: ["Student", "Student2"]
+ model_name: ["Student"]
  key: head_out
  thresh: 0.3
  box_thresh: 0.6

deploy/cpp_infer/include/ocr_rec.h

@@ -44,14 +44,16 @@ class CRNNRecognizer {
  const int &gpu_id, const int &gpu_mem,
  const int &cpu_math_library_num_threads,
  const bool &use_mkldnn, const string &label_path,
- const bool &use_tensorrt, const std::string &precision) {
+ const bool &use_tensorrt, const std::string &precision,
+ const int &rec_batch_num) {
  this->use_gpu_ = use_gpu;
  this->gpu_id_ = gpu_id;
  this->gpu_mem_ = gpu_mem;
  this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
  this->use_mkldnn_ = use_mkldnn;
  this->use_tensorrt_ = use_tensorrt;
  this->precision_ = precision;
+ this->rec_batch_num_ = rec_batch_num;
 
  this->label_list_ = Utility::ReadDict(label_path);
  this->label_list_.insert(this->label_list_.begin(),
@@ -64,7 +66,7 @@ class CRNNRecognizer {
  // Load Paddle inference model
  void LoadModel(const std::string &model_dir);
 
- void Run(cv::Mat &img, std::vector<double> *times);
+ void Run(std::vector<cv::Mat> img_list, std::vector<double> *times);
 
 private:
  std::shared_ptr<Predictor> predictor_;
@@ -82,10 +84,12 @@ class CRNNRecognizer {
  bool is_scale_ = true;
  bool use_tensorrt_ = false;
  std::string precision_ = "fp32";
+ int rec_batch_num_ = 6;
+
  // pre-process
  CrnnResizeImg resize_op_;
  Normalize normalize_op_;
- Permute permute_op_;
+ PermuteBatch permute_op_;
 
  // post-process
  PostProcessor post_processor_;

deploy/cpp_infer/include/preprocess_op.h

@@ -44,6 +44,11 @@ class Permute {
  virtual void Run(const cv::Mat *im, float *data);
 };
 
+class PermuteBatch {
+public:
+ virtual void Run(const std::vector<cv::Mat> imgs, float *data);
+};
+
 class ResizeImgType0 {
 public:
  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,

deploy/cpp_infer/include/utility.h

@@ -50,6 +50,9 @@ class Utility {
 
  static cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
  std::vector<std::vector<int>> box);
+
+ static std::vector<int> argsort(const std::vector<float>& array);
+
 };
 
 } // namespace PaddleOCR

deploy/cpp_infer/src/main.cpp

@@ -61,7 +61,7 @@ DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
 DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
 // recognition related
 DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
-DEFINE_int32(rec_batch_num, 1, "rec_batch_num.");
+DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
 DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
 
 
@@ -146,8 +146,9 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
  FLAGS_gpu_mem, FLAGS_cpu_threads,
  FLAGS_enable_mkldnn, char_list_file,
- FLAGS_use_tensorrt, FLAGS_precision);
+ FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
 
+ std::vector<cv::Mat> img_list;
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
  LOG(INFO) << "The predict img: " << cv_all_img_names[i];
 
@@ -156,22 +157,21 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
  std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
  exit(1);
  }
-
- std::vector<double> rec_times;
- rec.Run(srcimg, &rec_times);
-
- time_info[0] += rec_times[0];
- time_info[1] += rec_times[1];
- time_info[2] += rec_times[2];
+ img_list.push_back(srcimg);
  }
-
+ std::vector<double> rec_times;
+ rec.Run(img_list, &rec_times);
+ time_info[0] += rec_times[0];
+ time_info[1] += rec_times[1];
+ time_info[2] += rec_times[2];
+
  if (FLAGS_benchmark) {
  AutoLogger autolog("ocr_rec", 
  FLAGS_use_gpu,
  FLAGS_use_tensorrt,
  FLAGS_enable_mkldnn,
  FLAGS_cpu_threads,
- 1, 
+ FLAGS_rec_batch_num, 
  "dynamic", 
  FLAGS_precision, 
  time_info, 
@@ -209,7 +209,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
  FLAGS_gpu_mem, FLAGS_cpu_threads,
  FLAGS_enable_mkldnn, char_list_file,
- FLAGS_use_tensorrt, FLAGS_precision);
+ FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
 
  for (int i = 0; i < cv_all_img_names.size(); ++i) {
  LOG(INFO) << "The predict img: " << cv_all_img_names[i];
@@ -228,19 +228,22 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  time_info_det[1] += det_times[1];
  time_info_det[2] += det_times[2];
 
- cv::Mat crop_img;
+ std::vector<cv::Mat> img_list;
  for (int j = 0; j < boxes.size(); j++) {
- crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
-
- if (cls != nullptr) {
- crop_img = cls->Run(crop_img);
- }
- rec.Run(crop_img, &rec_times);
- time_info_rec[0] += rec_times[0];
- time_info_rec[1] += rec_times[1];
- time_info_rec[2] += rec_times[2];
+ cv::Mat crop_img;
+ crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
+ if (cls != nullptr) {
+ crop_img = cls->Run(crop_img);
+ }
+ img_list.push_back(crop_img);
  }
+
+ rec.Run(img_list, &rec_times);
+ time_info_rec[0] += rec_times[0];
+ time_info_rec[1] += rec_times[1];
+ time_info_rec[2] += rec_times[2];
  }
+
  if (FLAGS_benchmark) {
  AutoLogger autolog_det("ocr_det", 
  FLAGS_use_gpu,
@@ -257,7 +260,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
  FLAGS_use_tensorrt,
  FLAGS_enable_mkldnn,
  FLAGS_cpu_threads,
- 1, 
+ FLAGS_rec_batch_num, 
  "dynamic", 
  FLAGS_precision, 
  time_info_rec, 

deploy/cpp_infer/src/ocr_rec.cpp

@@ -15,83 +15,108 @@
 #include <include/ocr_rec.h>
 
 namespace PaddleOCR {
-
-void CRNNRecognizer::Run(cv::Mat &img, std::vector<double> *times) {
- cv::Mat srcimg;
- img.copyTo(srcimg);
- cv::Mat resize_img;
-
- float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
- auto preprocess_start = std::chrono::steady_clock::now();
- this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);
-
- this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
- this->is_scale_);
-
- std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
-
- this->permute_op_.Run(&resize_img, input.data());
- auto preprocess_end = std::chrono::steady_clock::now();
-
- // Inference.
- auto input_names = this->predictor_->GetInputNames();
- auto input_t = this->predictor_->GetInputHandle(input_names[0]);
- input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
- 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,
+
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, 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_);
+ float max_wh_ratio = 0;
+ 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);
+ }
+ 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->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
+ norm_img_batch.push_back(resize_img);
+ }
+
+ int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
+ std::vector<float> input(this->rec_batch_num_ * 3 * 32 * 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({this->rec_batch_num_, 3, 32, 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);
-
- output_t->CopyToCpu(predict_batch.data());
- auto inference_end = std::chrono::steady_clock::now();
-
- // ctc decode
- auto postprocess_start = std::chrono::steady_clock::now();
- std::vector<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++) {
- argmax_idx =
- int(Utility::argmax(&predict_batch[n * predict_shape[2]],
- &predict_batch[(n + 1) * predict_shape[2]]));
- max_value =
- float(*std::max_element(&predict_batch[n * predict_shape[2]],
- &predict_batch[(n + 1) * predict_shape[2]]));
-
- if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
- score += max_value;
- count += 1;
- str_res.push_back(label_list_[argmax_idx]);
+ predict_batch.resize(out_num);
+
+ 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::vector<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++) {
+ 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]]));
+ 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.push_back(label_list_[argmax_idx]);
+ }
+ last_index = argmax_idx;
+ }
+ score /= count;
+ if (isnan(score))
+ continue;
+ for (int i = 0; i < str_res.size(); i++) {
+ std::cout << str_res[i];
+ }
+ std::cout << "\tscore: " << score << std::endl;
+ }
+ auto postprocess_end = std::chrono::steady_clock::now();
+ postprocess_diff += postprocess_end - postprocess_start;
  }
- last_index = argmax_idx;
- }
- auto postprocess_end = std::chrono::steady_clock::now();
- score /= count;
- for (int i = 0; i < str_res.size(); i++) {
- std::cout << str_res[i];
- }
- std::cout << "\tscore: " << score << std::endl;
-
- std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
- times->push_back(double(preprocess_diff.count() * 1000));
- std::chrono::duration<float> inference_diff = inference_end - inference_start;
- times->push_back(double(inference_diff.count() * 1000));
- std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
- times->push_back(double(postprocess_diff.count() * 1000));
+ 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;

deploy/cpp_infer/src/preprocess_op.cpp

@@ -40,6 +40,17 @@ void Permute::Run(const cv::Mat *im, float *data) {
  }
 }
 
+void PermuteBatch::Run(const std::vector<cv::Mat> imgs, float *data) {
+ for (int j = 0; j < imgs.size(); j ++){
+ int rh = imgs[j].rows;
+ int rw = imgs[j].cols;
+ int rc = imgs[j].channels();
+ for (int i = 0; i < rc; ++i) {
+ cv::extractChannel(imgs[j], cv::Mat(rh, rw, CV_32FC1, data + (j * rc + i) * rh * rw), i);
+ }
+ }
+}
+
 void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
  const std::vector<float> &scale, const bool is_scale) {
  double e = 1.0;
@@ -90,16 +101,17 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
  imgC = rec_image_shape[0];
  imgH = rec_image_shape[1];
  imgW = rec_image_shape[2];
-
+ 
  imgW = int(32 * wh_ratio);
 
  float ratio = float(img.cols) / float(img.rows);
  int resize_w, resize_h;
+
  if (ceilf(imgH * ratio) > imgW)
  resize_w = imgW;
  else
  resize_w = int(ceilf(imgH * ratio));
-
+ 
  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
  cv::INTER_LINEAR);
  cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,