Support for several min and max sizes in PriorBox layer (Merge pull request #15076)

* Support for several min and max sizes in PriorBox layer

* Fix minSize

* Check size

* Modify initInfEngine

* Fix tests

* Fix IE support

* Add priorbox test

* Remove inputs

modules/dnn/src/caffe/opencv-caffe.proto

@@ -116,9 +116,9 @@ message PriorBoxParameter {
     CENTER_SIZE = 2;
   }
   // Minimum box size (in pixels). Required!
-  optional float min_size = 1;
+  repeated float min_size = 1;
   // Maximum box size (in pixels). Required!
-  optional float max_size = 2;
+  repeated float max_size = 2;
   // Various of aspect ratios. Duplicate ratios will be ignored.
   // If none is provided, we use default ratio 1.
   repeated float aspect_ratio = 3;

modules/dnn/src/layers/prior_box_layer.cpp

@@ -180,21 +180,20 @@ public:
     PriorBoxLayerImpl(const LayerParams &params)
     {
         setParamsFrom(params);
-        _minSize = getParameter<float>(params, "min_size", 0, false, 0);
         _flip = getParameter<bool>(params, "flip", 0, false, true);
         _clip = getParameter<bool>(params, "clip", 0, false, true);
         _bboxesNormalized = getParameter<bool>(params, "normalized_bbox", 0, false, true);
 
-        _aspectRatios.clear();
-
+        getParams("min_size", params, &_minSize);
         getAspectRatios(params);
         getVariance(params);
 
-        _maxSize = -1;
         if (params.has("max_size"))
         {
-            _maxSize = params.get("max_size").get<float>(0);
-            CV_Assert(_maxSize > _minSize);
+            getParams("max_size", params, &_maxSize);
+            CV_Assert(_minSize.size() == _maxSize.size());
+            for (int i = 0; i < _maxSize.size(); i++)
+                CV_Assert(_minSize[i] < _maxSize[i]);
         }
 
         std::vector<float> widths, heights;
@@ -213,25 +212,28 @@ public:
         }
         else
         {
-            CV_Assert(_minSize > 0);
-            _boxWidths.resize(1 + (_maxSize > 0 ? 1 : 0) + _aspectRatios.size());
-            _boxHeights.resize(_boxWidths.size());
-            _boxWidths[0] = _boxHeights[0] = _minSize;
-
-            int i = 1;
-            if (_maxSize > 0)
+            CV_Assert(!_minSize.empty());
+            for (int i = 0; i < _minSize.size(); ++i)
             {
-                // second prior: aspect_ratio = 1, size = sqrt(min_size * max_size)
-                _boxWidths[i] = _boxHeights[i] = sqrt(_minSize * _maxSize);
-                i += 1;
-            }
+                float minSize = _minSize[i];
+                CV_Assert(minSize > 0);
+                _boxWidths.push_back(minSize);
+                _boxHeights.push_back(minSize);
 
-            // rest of priors
-            for (size_t r = 0; r < _aspectRatios.size(); ++r)
-            {
-                float arSqrt = sqrt(_aspectRatios[r]);
-                _boxWidths[i + r] = _minSize * arSqrt;
-                _boxHeights[i + r] = _minSize / arSqrt;
+                if (_maxSize.size() > 0)
+                {
+                    float size = sqrt(minSize * _maxSize[i]);
+                    _boxWidths.push_back(size);
+                    _boxHeights.push_back(size);
+                }
+
+                // rest of priors
+                for (size_t r = 0; r < _aspectRatios.size(); ++r)
+                {
+                    float arSqrt = sqrt(_aspectRatios[r]);
+                    _boxWidths.push_back(minSize * arSqrt);
+                    _boxHeights.push_back(minSize / arSqrt);
+                }
             }
         }
         CV_Assert(_boxWidths.size() == _boxHeights.size());
@@ -271,7 +273,8 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
+               ( _explicitSizes || (_minSize.size() == 1 && _maxSize.size() <= 1)));
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -508,10 +511,9 @@ public:
             InferenceEngine::Builder::PriorBoxLayer ieLayer(name);
 
             CV_Assert(!_explicitSizes);
-
-            ieLayer.setMinSize(_minSize);
-            if (_maxSize > 0)
-                ieLayer.setMaxSize(_maxSize);
+            ieLayer.setMinSize(_minSize[0]);
+            if (!_maxSize.empty())
+                ieLayer.setMaxSize(_maxSize[0]);
 
             CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
             ieLayer.setOffset(_offsetsX[0]);
@@ -558,8 +560,8 @@ public:
     }
 
 private:
-    float _minSize;
-    float _maxSize;
+    std::vector<float> _minSize;
+    std::vector<float> _maxSize;
 
     float _stepX, _stepY;
 

modules/dnn/test/test_layers.cpp

@@ -742,6 +742,22 @@ TEST_P(Test_Caffe_layers, Average_pooling_kernel_area)
     normAssert(out, blobFromImage(ref));
 }
 
+TEST_P(Test_Caffe_layers, PriorBox_repeated)
+{
+    Net net = readNet(_tf("prior_box.prototxt"));
+    int inp_size[] = {1, 3, 10, 10};
+    int shape_size[] = {1, 2, 3, 4};
+    Mat inp(4, inp_size, CV_32F);
+    randu(inp, -1.0f, 1.0f);
+    Mat shape(4, shape_size, CV_32F);
+    randu(shape, -1.0f, 1.0f);
+    net.setInput(inp, "data");
+    net.setInput(shape, "shape");
+    Mat out = net.forward();
+    Mat ref = blobFromNPY(_tf("priorbox_output.npy"));
+    normAssert(out, ref, "");
+}
+
 // Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
 TEST_P(Test_Caffe_layers, PriorBox_squares)
 {