text: // This file is part of OpenCV project.
  // It is subject to the license terms in the LICENSE file found in the top-level directory
  // of this distribution and at http://opencv.org/license.html.
  //
  // Copyright (C) 2017, Intel Corporation, all rights reserved.
  // Third party copyrights are property of their respective owners.
  
  #include "perf_precomp.hpp"
  #include "opencv2/core/ocl.hpp"
  
  #include "opencv2/dnn/shape_utils.hpp"
  
  #include "../test/test_common.hpp"
  
  namespace opencv_test {
  
  class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<Backend, Target> >
  {
  public:
      dnn::Backend backend;
      dnn::Target target;
  
      dnn::Net net;
  
      DNNTestNetwork()
      {
          backend = (dnn::Backend)(int)get<0>(GetParam());
          target = (dnn::Target)(int)get<1>(GetParam());
      }
  
      void processNet(std::string weights, std::string proto, std::string halide_scheduler,
                      const Mat& input, const std::string& outputLayer = "")
      {
          randu(input, 0.0f, 1.0f);
  
          weights = findDataFile(weights, false);
          if (!proto.empty())
              proto = findDataFile(proto);
          if (backend == DNN_BACKEND_HALIDE)
          {
              if (halide_scheduler == "disabled")
                  throw cvtest::SkipTestException("Halide test is disabled");
              if (!halide_scheduler.empty())
                  halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
          }
          net = readNet(proto, weights);
          net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
          net.setPreferableBackend(backend);
          net.setPreferableTarget(target);
          if (backend == DNN_BACKEND_HALIDE)
          {
              net.setHalideScheduler(halide_scheduler);
          }
  
          MatShape netInputShape = shape(1, 3, input.rows, input.cols);
          size_t weightsMemory = 0, blobsMemory = 0;
          net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
          int64 flops = net.getFLOPS(netInputShape);
          CV_Assert(flops > 0);
  
          net.forward(outputLayer); // warmup
  
          std::cout << "Memory consumption:" << std::endl;
          std::cout << "    Weights(parameters): " << divUp(weightsMemory, 1u<<20) << " Mb" << std::endl;
          std::cout << "    Blobs: " << divUp(blobsMemory, 1u<<20) << " Mb" << std::endl;
          std::cout << "Calculation complexity: " << flops * 1e-9 << " GFlops" << std::endl;
  
          PERF_SAMPLE_BEGIN()
              net.forward();
          PERF_SAMPLE_END()
  
          SANITY_CHECK_NOTHING();
      }
  };
  
  
  PERF_TEST_P_(DNNTestNetwork, AlexNet)
  {
      processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
              "alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
  {
      processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
              "", Mat(cv::Size(224, 224), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, ResNet_50)
  {
      processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
              "resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
  {
      processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
              "squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, Inception_5h)
  {
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) throw SkipTestException("");
      processNet("dnn/tensorflow_inception_graph.pb", "",
              "inception_5h.yml",
              Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
  }
  
  PERF_TEST_P_(DNNTestNetwork, ENet)
  {
      if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
          (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
          throw SkipTestException("");
      processNet("dnn/Enet-model-best.net", "", "enet.yml",
              Mat(cv::Size(512, 256), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, SSD)
  {
      processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
              Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, OpenFace)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
          throw SkipTestException("");
  #endif
      processNet("dnn/openface_nn4.small2.v1.t7", "", "",
              Mat(cv::Size(96, 96), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
              Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", "",
              Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", "",
              Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
                 Mat(cv::Size(224, 224), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
  {
      if (backend == DNN_BACKEND_HALIDE ||
          (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD))
          throw SkipTestException("");
      // The same .caffemodel but modified .prototxt
      // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
      processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", "",
                 Mat(cv::Size(368, 368), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, opencv_face_detector)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "",
                 Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "",
              Mat(cv::Size(300, 300), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, YOLOv3)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      Mat sample = imread(findDataFile("dnn/dog416.png"));
      Mat inp;
      sample.convertTo(inp, CV_32FC3);
      processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp / 255);
  }
  
  PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
  {
      if (backend == DNN_BACKEND_HALIDE)
          throw SkipTestException("");
      processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
  }
  
  PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
  {
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019010000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
          throw SkipTestException("Test is disabled in OpenVINO 2019R1");
  #endif
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
          throw SkipTestException("Test is disabled in OpenVINO 2019R2");
  #endif
      if (backend == DNN_BACKEND_HALIDE ||
          (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
          (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
          throw SkipTestException("");
      processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb",
                 "dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", "",
                 Mat(cv::Size(800, 600), CV_32FC3));
  }
  
  INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets());
  
  } // namespace
  