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add fused batchNorm Upsample

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ashishiva3@gmail.com 2020-03-19 22:22:36 +05:30 committed by AshihsKrShrivastava
parent 775d031366
commit e0ac0cfbe2
3 changed files with 69 additions and 0 deletions
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31
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
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@ -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);
}

19
modules/dnn/src/onnx/onnx_importer.cpp
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@ -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);

19
modules/dnn/test/test_onnx_importer.cpp
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@ -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);