Merge pull request #13746 from allnes:fix_step_dnn

modules/dnn/src/layers/prior_box_layer.cpp

@@ -487,9 +487,7 @@ public:
         if (_explicitSizes)
         {
             InferenceEngine::Builder::PriorBoxClusteredLayer ieLayer(name);
-
+            ieLayer.setSteps({_stepY, _stepX});
-            CV_Assert(_stepX == _stepY);
-            ieLayer.setStep(_stepX);
 
             CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
             ieLayer.setOffset(_offsetsX[0]);
@@ -516,9 +514,6 @@ public:
             if (_maxSize > 0)
                 ieLayer.setMaxSize(_maxSize);
 
-            CV_Assert(_stepX == _stepY);
-            ieLayer.setStep(_stepX);
-
             CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
             ieLayer.setOffset(_offsetsX[0]);
 
@@ -526,6 +521,18 @@ public:
             ieLayer.setFlip(false);  // We already flipped aspect ratios.
 
             InferenceEngine::Builder::Layer l = ieLayer;
+            if (_stepX == _stepY)
+            {
+                l.getParameters()["step"] = _stepX;
+                l.getParameters()["step_h"] = 0.0;
+                l.getParameters()["step_w"] = 0.0;
+            }
+            else
+            {
+                l.getParameters()["step"] = 0.0;
+                l.getParameters()["step_h"] = _stepY;
+                l.getParameters()["step_w"] = _stepX;
+            }
             if (!_aspectRatios.empty())
             {
                 l.getParameters()["aspect_ratio"] = _aspectRatios;

modules/dnn/test/test_backends.cpp

@@ -162,6 +162,18 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
                inp, "detection_out", "", diffScores);
 }
 
+TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
+{
+    if (backend == DNN_BACKEND_HALIDE)
+        throw SkipTestException("");
+    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
+    float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
+    float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09  : 0.0;
+    processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
+               inp, "detection_out", "", diffScores, diffSquares);
+}
+
 TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
 {
     if (backend == DNN_BACKEND_HALIDE)
@@ -174,6 +186,18 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
                inp, "detection_out", "", l1, lInf);
 }
 
+TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
+{
+    if (backend == DNN_BACKEND_HALIDE)
+        throw SkipTestException("");
+    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
+    float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
+    float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
+    processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
+               inp, "detection_out", "", l1, lInf);
+}
+
 TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
 {
     if (backend == DNN_BACKEND_HALIDE)