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Merge pull request #17233 from l-bat:onnx_bn

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* Added ONNX BatchNorm subgraph

* Move removing constant inputs to addConstantNodesForInitializers

* Added initializers to ONNXGraphWrapper
pull/17270/head
Liubov Batanina 5 years ago committed by GitHub
parent 1bf353b876
commit 79f8b7fd73
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3 changed files with 66 additions and 44 deletions
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  1. 84
      modules/dnn/src/onnx/onnx_graph_simplifier.cpp
  2. 19
      modules/dnn/src/onnx/onnx_importer.cpp
  3. 7
      modules/dnn/test/test_onnx_importer.cpp

84
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
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@ -61,27 +61,28 @@ public:
ONNXGraphWrapper(opencv_onnx::GraphProto& _net) : net(_net)
{
numInputs = net.input_size();
numInitializers = net.initializer_size();
}
virtual Ptr<ImportNodeWrapper> getNode(int idx) const CV_OVERRIDE
{
opencv_onnx::NodeProto* node = 0;
if (idx >= numInputs)
node = net.mutable_node(idx - numInputs);
if (idx >= numInputs + numInitializers)
node = net.mutable_node(idx - numInputs - numInitializers);
return makePtr<ONNXNodeWrapper>(node);
}
virtual int getNumNodes() const CV_OVERRIDE
{
return numInputs + net.node_size();
return numInputs + numInitializers + net.node_size();
}
virtual int getNumOutputs(int nodeId) const CV_OVERRIDE
{
if (nodeId < numInputs)
if (nodeId < numInputs + numInitializers)
return 1;
else
return net.node(nodeId - numInputs).output_size();
return net.node(nodeId - numInputs - numInitializers).output_size();
}
virtual std::string getOutputName(int nodeId, int outId) const CV_OVERRIDE
@ -89,18 +90,20 @@ public:
CV_Assert(outId < getNumOutputs(nodeId));
if (nodeId < numInputs)
return net.input(nodeId).name();
else if (nodeId < numInputs + numInitializers)
return net.initializer(nodeId - numInputs).name();
else
return net.node(nodeId - numInputs).output(outId);
return net.node(nodeId - numInputs - numInitializers).output(outId);
}
virtual void removeNode(int idx) CV_OVERRIDE
{
CV_Assert(idx >= numInputs);
net.mutable_node()->DeleteSubrange(idx - numInputs, 1);
CV_Assert(idx >= numInputs + numInitializers);
net.mutable_node()->DeleteSubrange(idx - numInputs - numInitializers, 1);
}
private:
int numInputs;
int numInputs, numInitializers;
opencv_onnx::GraphProto& net;
};
@ -382,33 +385,63 @@ public:
}
};
class BatchNormalizationSubgraph : public Subgraph
class BatchNormalizationSubgraphBase : public Subgraph
{
public:
BatchNormalizationSubgraph()
BatchNormalizationSubgraphBase()
{
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);
input = addNodeToMatch("");
var = addNodeToMatch("");
mean = addNodeToMatch("");
weight = addNodeToMatch("");
bias = addNodeToMatch("");
A = addNodeToMatch("");
shape1 = addNodeToMatch("");
shape2 = addNodeToMatch("");
}
protected:
int input, var, mean, weight, bias, A, shape1, shape2;
};
class BatchNormalizationSubgraph1 : public BatchNormalizationSubgraphBase
{
public:
BatchNormalizationSubgraph1()
{
int reshape1 = addNodeToMatch("Reshape", weight, shape1);
int reshape2 = addNodeToMatch("Reshape", bias, shape2);
int shape3 = addNodeToMatch("Constant");
int reshape3 = addNodeToMatch("Reshape", data3, shape3);
int reshape3 = addNodeToMatch("Reshape", var, shape3);
int shape4 = addNodeToMatch("Constant");
int reshape4 = addNodeToMatch("Reshape", data4, shape4);
int reshape4 = addNodeToMatch("Reshape", mean, 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);
setFusedNode("BatchNormalization", input, weight, bias, mean, var);
}
};
class BatchNormalizationSubgraph2 : public BatchNormalizationSubgraphBase
{
public:
BatchNormalizationSubgraph2()
{
int sqrtNode = addNodeToMatch("Sqrt", var);
int divNode = addNodeToMatch("Div", A, sqrtNode);
int mul1 = addNodeToMatch("Mul", weight, divNode);
int reshape2 = addNodeToMatch("Reshape", mul1, shape2);
int mulMean = addNodeToMatch("Mul", mean, mul1);
int sub = addNodeToMatch("Sub", bias, mulMean);
int reshape1 = addNodeToMatch("Reshape", sub, shape1);
int mulInput = addNodeToMatch("Mul", input, reshape2);
addNodeToMatch("Add", mulInput, reshape1);
setFusedNode("BatchNormalization", input, weight, bias, mean, var);
}
};
@ -424,7 +457,8 @@ 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>());
subgraphs.push_back(makePtr<BatchNormalizationSubgraph1>());
subgraphs.push_back(makePtr<BatchNormalizationSubgraph2>());
simplifySubgraphs(Ptr<ImportGraphWrapper>(new ONNXGraphWrapper(net)), subgraphs);
}

19
modules/dnn/src/onnx/onnx_importer.cpp
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@ -309,30 +309,11 @@ 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);

7
modules/dnn/test/test_onnx_importer.cpp
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@ -306,6 +306,13 @@ TEST_P(Test_ONNX_layers, BatchNormalizationUnfused)
testONNXModels("frozenBatchNorm2d");
}
TEST_P(Test_ONNX_layers, BatchNormalizationSubgraph)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("batch_norm_subgraph");
}
TEST_P(Test_ONNX_layers, Transpose)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)

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