Add missing parameter to DetectionOutput layer from Intel's Inference Engine

modules/dnn/src/layers/detection_output_layer.cpp

@@ -919,6 +919,7 @@ public:
         ieLayer->params["nms_threshold"] = format("%f", _nmsThreshold);
         ieLayer->params["top_k"] = format("%d", _topK);
         ieLayer->params["keep_top_k"] = format("%d", _keepTopK);
+        ieLayer->params["eta"] = "1.0";
         ieLayer->params["confidence_threshold"] = format("%f", _confidenceThreshold);
         ieLayer->params["variance_encoded_in_target"] = _varianceEncodedInTarget ? "1" : "0";
         ieLayer->params["code_type"] = "caffe.PriorBoxParameter." + _codeType;

modules/dnn/test/test_backends.cpp

@@ -175,7 +175,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
     Mat sample = imread(findDataFile("dnn/street.png", false));
     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
-    float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
+    float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
     processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
                inp, "detection_out", "", l1, lInf, 0.25);
 }
@@ -233,11 +233,8 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Size inpSize;
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        inpSize = Size(300, 300);
     Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
-    Mat inp = blobFromImage(img, 1.0, inpSize, Scalar(104.0, 177.0, 123.0), false, false);
+    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
                inp, "detection_out");
 }
@@ -249,7 +246,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
     Mat sample = imread(findDataFile("dnn/street.png", false));
     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : 0.0;
-    float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.07 : 0.0;
+    float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
     processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
                inp, "detection_out", "", l1, lInf);
 }