dnn(test): add YOLOv4 tests

modules/dnn/perf/perf_net.cpp

@@ -197,9 +197,23 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
     Mat sample = imread(findDataFile("dnn/dog416.png"));
+    cvtColor(sample, sample, COLOR_BGR2RGB);
     Mat inp;
-    sample.convertTo(inp, CV_32FC3);
-    processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp / 255);
+    sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
+    processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp);
+}
+
+PERF_TEST_P_(DNNTestNetwork, YOLOv4)
+{
+    if (backend == DNN_BACKEND_HALIDE)
+        throw SkipTestException("");
+    if (target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+    Mat sample = imread(findDataFile("dnn/dog416.png"));
+    cvtColor(sample, sample, COLOR_BGR2RGB);
+    Mat inp;
+    sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
+    processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp);
 }
 
 PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)

modules/dnn/test/test_darknet_importer.cpp

@@ -245,6 +245,13 @@ public:
                 nms_boxes.push_back(box);
                 nms_confidences.push_back(conf);
                 nms_classIds.push_back(class_id);
+#if 0  // use to update test reference data
+                std::cout << b << ", " << class_id << ", " << conf << "f, "
+                          << box.x << "f, " << box.y << "f, "
+                          << box.x + box.width << "f, " << box.y + box.height << "f,"
+                          << std::endl;
+#endif
+
             }
 
             normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds,
@@ -395,6 +402,9 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
 
     std::string prefix = get<0>(GetParam());
 
+    if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4")  // NC_OUT_OF_MEMORY
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
@@ -439,7 +449,7 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
 }
 
 INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine(
-    Values("yolo-voc", "tiny-yolo-voc", "yolov3"),
+    Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4"),
     dnnBackendsAndTargets()
 ));
 
@@ -453,15 +463,21 @@ TEST_P(Test_Darknet_nets, YOLOv3)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 
     // batchId, classId, confidence, left, top, right, bottom
-    Mat ref = (Mat_<float>(9, 7) << 0, 7,  0.952983f, 0.614622f, 0.150257f, 0.901369f, 0.289251f,  // a truck
-                                    0, 1,  0.987908f, 0.150913f, 0.221933f, 0.742255f, 0.74626f,   // a bicycle
-                                    0, 16, 0.998836f, 0.160024f, 0.389964f, 0.417885f, 0.943716f,  // a dog (COCO)
-                                    1, 9,  0.384801f, 0.659824f, 0.372389f, 0.673926f, 0.429412f,  // a traffic light
-                                    1, 9,  0.733283f, 0.376029f, 0.315694f, 0.401776f, 0.395165f,  // a traffic light
-                                    1, 9,  0.785352f, 0.665503f, 0.373543f, 0.688893f, 0.439245f,  // a traffic light
-                                    1, 0,  0.980052f, 0.195856f, 0.378454f, 0.258626f, 0.629258f,  // a person
-                                    1, 2,  0.989633f, 0.450719f, 0.463353f, 0.496305f, 0.522258f,  // a car
-                                    1, 2,  0.997412f, 0.647584f, 0.459939f, 0.821038f, 0.663947f); // a car
+    const int N0 = 3;
+    const int N1 = 6;
+    static const float ref_[/* (N0 + N1) * 7 */] = {
+0, 16, 0.998836f, 0.160024f, 0.389964f, 0.417885f, 0.943716f,
+0, 1, 0.987908f, 0.150913f, 0.221933f, 0.742255f, 0.746261f,
+0, 7, 0.952983f, 0.614621f, 0.150257f, 0.901368f, 0.289251f,
+
+1, 2, 0.997412f, 0.647584f, 0.459939f, 0.821037f, 0.663947f,
+1, 2, 0.989633f, 0.450719f, 0.463353f, 0.496306f, 0.522258f,
+1, 0, 0.980053f, 0.195856f, 0.378454f, 0.258626f, 0.629257f,
+1, 9, 0.785341f, 0.665503f, 0.373543f, 0.688893f, 0.439244f,
+1, 9, 0.733275f, 0.376029f, 0.315694f, 0.401776f, 0.395165f,
+1, 9, 0.384815f, 0.659824f, 0.372389f, 0.673927f, 0.429412f,
+    };
+    Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
 
     double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
     double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.042 : 3e-4;
@@ -480,8 +496,8 @@ TEST_P(Test_Darknet_nets, YOLOv3)
 #endif
 
     {
-    SCOPED_TRACE("batch size 1");
-    testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff);
+        SCOPED_TRACE("batch size 1");
+        testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
     }
 
 #if defined(INF_ENGINE_RELEASE)
@@ -503,6 +519,75 @@ TEST_P(Test_Darknet_nets, YOLOv3)
     }
 }
 
+TEST_P(Test_Darknet_nets, YOLOv4)
+{
+    applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
+
+#if defined(INF_ENGINE_RELEASE)
+    if (target == DNN_TARGET_MYRIAD)  // NC_OUT_OF_MEMORY
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+
+    // batchId, classId, confidence, left, top, right, bottom
+    const int N0 = 3;
+    const int N1 = 7;
+    static const float ref_[/* (N0 + N1) * 7 */] = {
+0, 16, 0.992194f, 0.172375f, 0.402458f, 0.403918f, 0.932801f,
+0, 1, 0.988326f, 0.166708f, 0.228236f, 0.737208f, 0.735803f,
+0, 7, 0.94639f, 0.602523f, 0.130399f, 0.901623f, 0.298452f,
+
+1, 2, 0.99761f, 0.646556f, 0.45985f, 0.816041f, 0.659067f,
+1, 0, 0.988913f, 0.201726f, 0.360282f, 0.266181f, 0.631728f,
+1, 2, 0.98233f, 0.452007f, 0.462217f, 0.495612f, 0.521687f,
+1, 9, 0.919195f, 0.374642f, 0.316524f, 0.398126f, 0.393714f,
+1, 9, 0.856303f, 0.666842f, 0.372215f, 0.685539f, 0.44141f,
+1, 9, 0.313516f, 0.656791f, 0.374734f, 0.671959f, 0.438371f,
+1, 9, 0.256625f, 0.940232f, 0.326931f, 0.967586f, 0.374002f,
+    };
+    Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
+
+    double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.006 : 8e-5;
+    double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.042 : 3e-4;
+
+    std::string config_file = "yolov4.cfg";
+    std::string weights_file = "yolov4.weights";
+
+#if defined(INF_ENGINE_RELEASE)
+    if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
+         backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD &&
+        getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
+    {
+        scoreDiff = 0.04;
+        iouDiff = 0.2;
+    }
+#endif
+
+    {
+        SCOPED_TRACE("batch size 1");
+        testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
+    }
+
+    {
+        SCOPED_TRACE("batch size 2");
+
+#if defined(INF_ENGINE_RELEASE)
+        if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        {
+            if (target == DNN_TARGET_OPENCL)
+                applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+            else if (target == DNN_TARGET_OPENCL_FP16 && INF_ENGINE_VER_MAJOR_LE(202010000))
+                applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+            else if (target == DNN_TARGET_MYRIAD &&
+                     getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
+                applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
+        }
+#endif
+
+        testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
+    }
+}
+
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
 
 TEST_P(Test_Darknet_layers, shortcut)