Merge pull request #16887 from ashishkrshrivastava:fasterrcnn

modules/dnn/src/onnx/onnx_graph_simplifier.cpp

@@ -382,6 +382,36 @@ public:
     }
 };
 
+class BatchNormalizationSubgraph : public Subgraph
+{
+public:
+    BatchNormalizationSubgraph()
+    {
+        int input = addNodeToMatch("");
+        int data1 = addNodeToMatch("Constant");
+        int data2 = addNodeToMatch("Constant");
+        int data3 = addNodeToMatch("Constant");
+        int data4 = addNodeToMatch("Constant");
+        int shape1 = addNodeToMatch("Constant");
+        int reshape1 = addNodeToMatch("Reshape", data1, shape1);
+        int shape2 = addNodeToMatch("Constant");
+        int reshape2 = addNodeToMatch("Reshape", data2, shape2);
+        int shape3 = addNodeToMatch("Constant");
+        int reshape3 = addNodeToMatch("Reshape", data3, shape3);
+        int shape4 = addNodeToMatch("Constant");
+        int reshape4 = addNodeToMatch("Reshape", data4, shape4);
+        int sqrtNode = addNodeToMatch("Sqrt", reshape3);
+        int A = addNodeToMatch("Constant");
+        int divNode = addNodeToMatch("Div", A, sqrtNode);
+        int mul1 = addNodeToMatch("Mul", reshape1, divNode);
+        int mul2 = addNodeToMatch("Mul", reshape4, mul1);
+        int sub = addNodeToMatch("Sub", reshape2, mul2);
+        int mul3 = addNodeToMatch("Mul", input, mul1);
+        addNodeToMatch("Add", mul3, sub);
+        setFusedNode("BatchNormalization", input, data1, data2, data4 ,data3);
+    }
+};
+
 void simplifySubgraphs(opencv_onnx::GraphProto& net)
 {
     std::vector<Ptr<Subgraph> > subgraphs;
@@ -394,6 +424,7 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
     subgraphs.push_back(makePtr<NormalizeSubgraph1>());
     subgraphs.push_back(makePtr<NormalizeSubgraph2>());
     subgraphs.push_back(makePtr<NormalizeSubgraph3>());
+    subgraphs.push_back(makePtr<BatchNormalizationSubgraph>());
 
     simplifySubgraphs(Ptr<ImportGraphWrapper>(new ONNXGraphWrapper(net)), subgraphs);
 }

modules/dnn/src/onnx/onnx_importer.cpp

@@ -309,11 +309,30 @@ static void addConstant(const std::string& name,
     outShapes.insert(std::make_pair(name, shape(blob)));
 }
 
+void addConstantNodesForInitializers(opencv_onnx::GraphProto& graph_proto)
+{
+    int num_initializers = graph_proto.initializer_size();
+    for (int id = 0; id < num_initializers; id++)
+    {
+        opencv_onnx::TensorProto initializer = graph_proto.initializer(id);
+        opencv_onnx::NodeProto* constant_node = graph_proto.add_node();
+        constant_node->set_op_type("Constant");
+        constant_node->set_name(initializer.name());
+        constant_node->add_output(initializer.name());
+        opencv_onnx::AttributeProto* value = constant_node->add_attribute();
+        opencv_onnx::TensorProto* tensor = initializer.New();
+        tensor->CopyFrom(initializer);
+        releaseONNXTensor(initializer);
+        value->set_allocated_t(tensor);
+    }
+}
+
 void ONNXImporter::populateNet(Net dstNet)
 {
     CV_Assert(model_proto.has_graph());
     opencv_onnx::GraphProto graph_proto = model_proto.graph();
 
+    addConstantNodesForInitializers(graph_proto);
     simplifySubgraphs(graph_proto);
 
     std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);

modules/dnn/test/test_onnx_importer.cpp

@@ -290,6 +290,15 @@ TEST_P(Test_ONNX_layers, BatchNormalization3D)
     testONNXModels("batch_norm_3d");
 }
 
+TEST_P(Test_ONNX_layers, BatchNormalizationUnfused)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+    testONNXModels("frozenBatchNorm2d");
+}
+
 TEST_P(Test_ONNX_layers, Transpose)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -372,6 +381,16 @@ TEST_P(Test_ONNX_layers, ResizeUnfused)
     testONNXModels("resize_bilinear_unfused_opset11_torch1.4");
 }
 
+TEST_P(Test_ONNX_layers, ResizeUnfusedTwoInputs)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+    testONNXModels("upsample_unfused_two_inputs_opset9_torch1.4", npy, 0, 0, false, true, 2);
+    testONNXModels("upsample_unfused_two_inputs_opset11_torch1.4", npy, 0, 0, false, true, 2);
+}
+
 TEST_P(Test_ONNX_layers, MultyInputs)
 {
     testONNXModels("multy_inputs", npy, 0, 0, false, true, 2);