Merge pull request #11182 from dkurt:fix_11102_part_2

modules/dnn/src/layers/prior_box_layer.cpp

@@ -419,8 +419,6 @@ public:
           stepY = _stepY;
         }
 
-        int _outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
-
         float* outputPtr = outputs[0].ptr<float>();
         float _boxWidth, _boxHeight;
         for (size_t h = 0; h < _layerHeight; ++h)
@@ -444,6 +442,8 @@ public:
         // clip the prior's coordidate such that it is within [0, 1]
         if (_clip)
         {
+            int _outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
+            outputPtr = outputs[0].ptr<float>();
             for (size_t d = 0; d < _outChannelSize; ++d)
             {
                 outputPtr[d] = std::min<float>(std::max<float>(outputPtr[d], 0.), 1.);

modules/dnn/src/tensorflow/tf_graph_simplifier.cpp

@@ -219,7 +219,7 @@ public:
         Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
         CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
 
-        fusedNode->mutable_input()->ReleaseLast();
+        fusedNode->mutable_input()->RemoveLast();
         fusedNode->clear_attr();
         tensorflow::AttrValue epsilon;
         epsilon.set_f(epsMat.at<float>(0));
@@ -254,7 +254,7 @@ public:
         Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
         CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
 
-        fusedNode->mutable_input()->ReleaseLast();
+        fusedNode->mutable_input()->RemoveLast();
         fusedNode->clear_attr();
         tensorflow::AttrValue epsilon;
         epsilon.set_f(epsMat.at<float>(0));

modules/dnn/test/test_layers.cpp

@@ -842,7 +842,7 @@ TEST(Layer_PriorBox, squares)
     LayerParams lp;
     lp.name = "testPriorBox";
     lp.type = "PriorBox";
-    lp.set("min_size", 32);
+    lp.set("min_size", 2);
     lp.set("flip", true);
     lp.set("clip", true);
     float variance[] = {0.1f, 0.1f, 0.2f, 0.2f};
@@ -858,8 +858,8 @@ TEST(Layer_PriorBox, squares)
     net.setInput(blobFromImage(inp));
     Mat out = net.forward();
 
-    Mat target = (Mat_<float>(4, 4) << -7.75f, -15.5f, 8.25f, 16.5f,
-                                       -7.25f, -15.5f, 8.75f, 16.5f,
+    Mat target = (Mat_<float>(4, 4) << 0.0, 0.0, 0.75, 1.0,
+                                       0.25, 0.0, 1.0, 1.0,
                                        0.1f, 0.1f, 0.2f, 0.2f,
                                        0.1f, 0.1f, 0.2f, 0.2f);
     normAssert(out.reshape(1, 4), target);