FastDeploy/fastdeploy/vision/ocr/ppocr/classifier.cc

// Copyright (c) 2022 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 "fastdeploy/vision/ocr/ppocr/classifier.h"

#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"

namespace fastdeploy {
namespace vision {
namespace ocr {

Classifier::Classifier() {}
Classifier::Classifier(const std::string& model_file,
                       const std::string& params_file,
                       const RuntimeOption& custom_option,
                       const ModelFormat& model_format) {
  if (model_format == ModelFormat::ONNX) {
    valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};
    valid_gpu_backends = {Backend::ORT, Backend::TRT};
  } else {
    valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO,
                          Backend::LITE};
    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
    valid_kunlunxin_backends = {Backend::LITE};
    valid_ascend_backends = {Backend::LITE};
    valid_sophgonpu_backends = {Backend::SOPHGOTPU};
    valid_rknpu_backends = {Backend::RKNPU2};
  }
  runtime_option = custom_option;
  runtime_option.model_format = model_format;
  runtime_option.model_file = model_file;
  runtime_option.params_file = params_file;

  initialized = Initialize();
}

bool Classifier::Initialize() {
  if (!InitRuntime()) {
    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
    return false;
  }

  return true;
}

std::unique_ptr<Classifier> Classifier::Clone() const {
  std::unique_ptr<Classifier> clone_model =
      utils::make_unique<Classifier>(Classifier(*this));
  clone_model->SetRuntime(clone_model->CloneRuntime());
  return clone_model;
}

bool Classifier::Predict(const cv::Mat& img, int32_t* cls_label,
                         float* cls_score) {
  std::vector<int32_t> cls_labels(1);
  std::vector<float> cls_scores(1);
  bool success = BatchPredict({img}, &cls_labels, &cls_scores);
  if (!success) {
    return success;
  }
  *cls_label = cls_labels[0];
  *cls_score = cls_scores[0];
  return true;
}

bool Classifier::Predict(const cv::Mat& img, vision::OCRResult* ocr_result) {
  ocr_result->cls_labels.resize(1);
  ocr_result->cls_scores.resize(1);
  if (!Predict(img, &(ocr_result->cls_labels[0]),
               &(ocr_result->cls_scores[0]))) {
    return false;
  }
  return true;
}

bool Classifier::BatchPredict(const std::vector<cv::Mat>& images,
                              vision::OCRResult* ocr_result) {
  return BatchPredict(images, &(ocr_result->cls_labels),
                      &(ocr_result->cls_scores));
}

bool Classifier::BatchPredict(const std::vector<cv::Mat>& images,
                              std::vector<int32_t>* cls_labels,
                              std::vector<float>* cls_scores) {
  return BatchPredict(images, cls_labels, cls_scores, 0, images.size());
}

bool Classifier::BatchPredict(const std::vector<cv::Mat>& images,
                              std::vector<int32_t>* cls_labels,
                              std::vector<float>* cls_scores,
                              size_t start_index, size_t end_index) {
  size_t total_size = images.size();
  std::vector<FDMat> fd_images = WrapMat(images);
  if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, start_index,
                         end_index)) {
    FDERROR << "Failed to preprocess the input image." << std::endl;
    return false;
  }
  reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
  if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
    FDERROR << "Failed to inference by runtime." << std::endl;
    return false;
  }

  if (!postprocessor_.Run(reused_output_tensors_, cls_labels, cls_scores,
                          start_index, total_size)) {
    FDERROR << "Failed to postprocess the inference cls_results by runtime."
            << std::endl;
    return false;
  }
  return true;
}

}  // namespace ocr
}  // namespace vision
}  // namespace fastdeploy