modules/dnn/test/test_common.hpp

   1 // This file is part of OpenCV project.
   2 // It is subject to the license terms in the LICENSE file found in the top-level directory
   3 // of this distribution and at http://opencv.org/license.html.
   4
   5 #ifndef __OPENCV_TEST_COMMON_HPP__
   6 #define __OPENCV_TEST_COMMON_HPP__
   7
   8 #include "opencv2/dnn/utils/inference_engine.hpp"
   9
  10 #ifdef HAVE_OPENCL
  11 #include "opencv2/core/ocl.hpp"
  12 #endif
  13
  14 #define CV_TEST_TAG_DNN_SKIP_HALIDE              "dnn_skip_halide"
  15 #define CV_TEST_TAG_DNN_SKIP_OPENCL              "dnn_skip_ocl"
  16 #define CV_TEST_TAG_DNN_SKIP_OPENCL_FP16         "dnn_skip_ocl_fp16"
  17 #define CV_TEST_TAG_DNN_SKIP_IE                  "dnn_skip_ie"
  18 #define CV_TEST_TAG_DNN_SKIP_IE_2018R5           "dnn_skip_ie_2018r5"
  19 #define CV_TEST_TAG_DNN_SKIP_IE_2019R1           "dnn_skip_ie_2019r1"
  20 #define CV_TEST_TAG_DNN_SKIP_IE_2019R1_1         "dnn_skip_ie_2019r1_1"
  21 #define CV_TEST_TAG_DNN_SKIP_IE_2019R2           "dnn_skip_ie_2019r2"
  22 #define CV_TEST_TAG_DNN_SKIP_IE_2019R3           "dnn_skip_ie_2019r3"
  23 #define CV_TEST_TAG_DNN_SKIP_IE_OPENCL           "dnn_skip_ie_ocl"
  24 #define CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16      "dnn_skip_ie_ocl_fp16"
  25 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2         "dnn_skip_ie_myriad2"
  26 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X         "dnn_skip_ie_myriadx"
  27 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD           CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
  28
  29 #define CV_TEST_TAG_DNN_SKIP_VULKAN              "dnn_skip_vulkan"
  30
  31 #define CV_TEST_TAG_DNN_SKIP_CUDA                "dnn_skip_cuda"
  32 #define CV_TEST_TAG_DNN_SKIP_CUDA_FP16           "dnn_skip_cuda_fp16"
  33 #define CV_TEST_TAG_DNN_SKIP_CUDA_FP32           "dnn_skip_cuda_fp32"
  34
  35 namespace cv { namespace dnn {
  36 CV__DNN_INLINE_NS_BEGIN
  37
  38 void PrintTo(const cv::dnn::Backend& v, std::ostream* os);
  39 void PrintTo(const cv::dnn::Target& v, std::ostream* os);
  40 using opencv_test::tuple;
  41 using opencv_test::get;
  42 void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os);
  43
  44 CV__DNN_INLINE_NS_END
  45 }} // namespace cv::dnn
  46
  47
  48
  49 namespace opencv_test {
  50
  51 void initDNNTests();
  52
  53 using namespace cv::dnn;
  54
  55 static inline const std::string &getOpenCVExtraDir()
  56 {
  57     return cvtest::TS::ptr()->get_data_path();
  58 }
  59
  60 void normAssert(
  61         cv::InputArray ref, cv::InputArray test, const char *comment = "",
  62         double l1 = 0.00001, double lInf = 0.0001);
  63
  64 std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m);
  65
  66 void normAssertDetections(
  67         const std::vector<int>& refClassIds,
  68         const std::vector<float>& refScores,
  69         const std::vector<cv::Rect2d>& refBoxes,
  70         const std::vector<int>& testClassIds,
  71         const std::vector<float>& testScores,
  72         const std::vector<cv::Rect2d>& testBoxes,
  73         const char *comment = "", double confThreshold = 0.0,
  74         double scores_diff = 1e-5, double boxes_iou_diff = 1e-4);
  75
  76 // For SSD-based object detection networks which produce output of shape 1x1xNx7
  77 // where N is a number of detections and an every detection is represented by
  78 // a vector [batchId, classId, confidence, left, top, right, bottom].
  79 void normAssertDetections(
  80         cv::Mat ref, cv::Mat out, const char *comment = "",
  81         double confThreshold = 0.0, double scores_diff = 1e-5,
  82         double boxes_iou_diff = 1e-4);
  83
  84 void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content);
  85
  86 #ifdef HAVE_INF_ENGINE
  87 bool validateVPUType();
  88 #endif
  89
  90 testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
  91         bool withInferenceEngine = true,
  92         bool withHalide = false,
  93         bool withCpuOCV = true,
  94         bool withVkCom = true,
  95         bool withCUDA = true
  96 );
  97
  98
  99 class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
 100 {
 101 public:
 102     dnn::Backend backend;
 103     dnn::Target target;
 104     double default_l1, default_lInf;
 105
 106     DNNTestLayer()
 107     {
 108         backend = (dnn::Backend)(int)get<0>(GetParam());
 109         target = (dnn::Target)(int)get<1>(GetParam());
 110         getDefaultThresholds(backend, target, &default_l1, &default_lInf);
 111     }
 112
 113     static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
 114     {
 115         if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 116         {
 117             *l1 = 4e-3;
 118             *lInf = 2e-2;
 119         }
 120         else
 121         {
 122             *l1 = 1e-5;
 123             *lInf = 1e-4;
 124         }
 125     }
 126
 127     static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
 128     {
 129         if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 130         {
 131             if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
 132                 inp->size[0] != 1 && inp->size[0] != ref->size[0])
 133             {
 134                 applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 135                 throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
 136             }
 137         }
 138     }
 139
 140     void expectNoFallbacks(Net& net)
 141     {
 142         // Check if all the layers are supported with current backend and target.
 143         // Some layers might be fused so their timings equal to zero.
 144         std::vector<double> timings;
 145         net.getPerfProfile(timings);
 146         std::vector<String> names = net.getLayerNames();
 147         CV_Assert(names.size() == timings.size());
 148
 149         for (int i = 0; i < names.size(); ++i)
 150         {
 151             Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
 152             bool fused = !timings[i];
 153             if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
 154                 CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
 155                          l->type + "] is expected to has backend implementation");
 156         }
 157     }
 158
 159     void expectNoFallbacksFromIE(Net& net)
 160     {
 161         if (backend == DNN_BACKEND_INFERENCE_ENGINE)
 162             expectNoFallbacks(net);
 163     }
 164
 165     void expectNoFallbacksFromCUDA(Net& net)
 166     {
 167         if (backend == DNN_BACKEND_CUDA)
 168             expectNoFallbacks(net);
 169     }
 170
 171 protected:
 172     void checkBackend(Mat* inp = 0, Mat* ref = 0)
 173     {
 174         checkBackend(backend, target, inp, ref);
 175     }
 176 };
 177
 178 } // namespace
 179
 180
 181 // src/op_inf_engine.hpp
 182 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
 183 #define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
 184 #define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000))
 185 #define INF_ENGINE_VER_MAJOR_LE(ver) (((INF_ENGINE_RELEASE) / 10000) <= ((ver) / 10000))
 186 #define INF_ENGINE_VER_MAJOR_EQ(ver) (((INF_ENGINE_RELEASE) / 10000) == ((ver) / 10000))
 187
 188 #endif