From d3f9ad11459bfc63f04246771dfc42f6d275700a Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev+github@gmail.com>
Date: Sat, 28 Mar 2020 18:53:57 +0300
Subject: [PATCH] Enable ONNX SSD from https://github.com/amdegroot/ssd.pytorch

---
 .../dnn/src/onnx/onnx_graph_simplifier.cpp    | 54 ++++++++++++++-----
 modules/dnn/src/onnx/onnx_importer.cpp        | 19 +++++++
 modules/dnn/test/test_onnx_importer.cpp       |  1 +
 3 files changed, 60 insertions(+), 14 deletions(-)

diff --git a/modules/dnn/src/onnx/onnx_graph_simplifier.cpp b/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
index ff474224cc..bf992feb2c 100644
--- a/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
+++ b/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
@@ -154,16 +154,10 @@ private:
     int axis;
 };
 
-class NormalizeSubgraph1 : public Subgraph
+class NormalizeSubgraphBase : public Subgraph
 {
 public:
-    NormalizeSubgraph1() : axis(1)
-    {
-        input = addNodeToMatch("");
-        norm = addNodeToMatch("ReduceL2", input);
-        addNodeToMatch("Div", input, norm);
-        setFusedNode("Normalize", input);
-    }
+    NormalizeSubgraphBase(int _normNodeOrder = 0) : axis(1), normNodeOrder(_normNodeOrder) {}
 
     virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
                        std::vector<int>& matchedNodesIds,
@@ -171,7 +165,7 @@ public:
     {
         if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds))
         {
-            Ptr<ImportNodeWrapper> norm = net->getNode(matchedNodesIds[0]);
+            Ptr<ImportNodeWrapper> norm = net->getNode(matchedNodesIds[normNodeOrder]);
             opencv_onnx::NodeProto* node = norm.dynamicCast<ONNXNodeWrapper>()->node;
 
             for (int i = 0; i < node->attribute_size(); i++)
@@ -204,20 +198,51 @@ public:
     }
 
 protected:
-    int input, norm;
-    int axis;
+    int axis, normNodeOrder;
 };
 
-
-class NormalizeSubgraph2 : public NormalizeSubgraph1
+class NormalizeSubgraph1 : public NormalizeSubgraphBase
 {
 public:
-    NormalizeSubgraph2() : NormalizeSubgraph1()
+    NormalizeSubgraph1()
     {
+        int input = addNodeToMatch("");
+        int norm = addNodeToMatch("ReduceL2", input);
+        addNodeToMatch("Div", input, norm);
+        setFusedNode("Normalize", input);
+    }
+};
+
+class NormalizeSubgraph2 : public NormalizeSubgraphBase
+{
+public:
+    NormalizeSubgraph2()
+    {
+        int input = addNodeToMatch("");
+        int norm = addNodeToMatch("ReduceL2", input);
         int clip = addNodeToMatch("Clip", norm);
         int shape = addNodeToMatch("Shape", input);
         int expand = addNodeToMatch("Expand", clip, shape);
         addNodeToMatch("Div", input, expand);
+        setFusedNode("Normalize", input);
+    }
+};
+
+class NormalizeSubgraph3 : public NormalizeSubgraphBase
+{
+public:
+    NormalizeSubgraph3() : NormalizeSubgraphBase(1)
+    {
+        int input = addNodeToMatch("");
+        int power = addNodeToMatch("Constant");
+        int squared = addNodeToMatch("Pow", input, power);
+        int sum = addNodeToMatch("ReduceSum", squared);
+        int sqrtNode = addNodeToMatch("Sqrt", sum);
+        int eps = addNodeToMatch("Constant");
+        int add = addNodeToMatch("Add", sqrtNode, eps);
+
+        addNodeToMatch("Div", input, add);
+        setFusedNode("Normalize", input);
     }
 };
 
@@ -368,6 +393,7 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
     subgraphs.push_back(makePtr<SoftMaxSubgraph>());
     subgraphs.push_back(makePtr<NormalizeSubgraph1>());
     subgraphs.push_back(makePtr<NormalizeSubgraph2>());
+    subgraphs.push_back(makePtr<NormalizeSubgraph3>());
 
     simplifySubgraphs(Ptr<ImportGraphWrapper>(new ONNXGraphWrapper(net)), subgraphs);
 }
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index 9c60dcaae9..716c9ad39c 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -1457,6 +1457,25 @@ void ONNXImporter::populateNet(Net dstNet)
             layerParams.type = "Softmax";
             layerParams.set("log_softmax", layer_type == "LogSoftmax");
         }
+        else if (layer_type == "DetectionOutput")
+        {
+            CV_CheckEQ(node_proto.input_size(), 3, "");
+            if (constBlobs.find(node_proto.input(2)) != constBlobs.end())
+            {
+                Mat priors = getBlob(node_proto, constBlobs, 2);
+
+                LayerParams constParams;
+                constParams.name = layerParams.name + "/priors";
+                constParams.type = "Const";
+                constParams.blobs.push_back(priors);
+
+                opencv_onnx::NodeProto priorsProto;
+                priorsProto.add_output(constParams.name);
+                addLayer(dstNet, constParams, priorsProto, layer_id, outShapes);
+
+                node_proto.set_input(2, constParams.name);
+            }
+        }
         else
         {
             for (int j = 0; j < node_proto.input_size(); j++) {
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 6f36f8d3d1..6e47a86631 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -440,6 +440,7 @@ TEST_P(Test_ONNX_layers, ReduceL2)
 {
     testONNXModels("reduceL2");
     testONNXModels("reduceL2_subgraph");
+    testONNXModels("reduceL2_subgraph_2");
 }
 
 TEST_P(Test_ONNX_layers, Split)