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Merge pull request #14454 from dkurt:dnn_tf_subgraph_fusion

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pull/14462/head^2
Alexander Alekhin 6 years ago
parent ddfaa41f07 9408c3e640
commit 1c092a181d
3 changed files with 77 additions and 42 deletions
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  1. 69
      modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
  2. 43
      modules/dnn/src/tensorflow/tf_importer.cpp
  3. 7
      modules/dnn/test/test_tf_importer.cpp

69
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
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@ -79,9 +79,9 @@ public:
} }
} }
static const tensorflow::NodeDef& getInputNode(const tensorflow::GraphDef& net, static int getInputNodeId(const tensorflow::GraphDef& net,
const tensorflow::NodeDef& node, const tensorflow::NodeDef& node,
int inpId) int inpId)
{ {
CV_Assert(inpId < node.input_size()); CV_Assert(inpId < node.input_size());
std::string name = node.input(inpId); std::string name = node.input(inpId);
@ -92,7 +92,7 @@ public:
for (int i = 0; i < numNodes; ++i) for (int i = 0; i < numNodes; ++i)
{ {
if (net.node(i).name() == name) if (net.node(i).name() == name)
return net.node(i); return i;
} }
CV_Error(Error::StsParseError, "Input node with name " + name + " not found"); CV_Error(Error::StsParseError, "Input node with name " + name + " not found");
} }
@ -104,36 +104,46 @@ public:
matchedNodesIds.clear(); matchedNodesIds.clear();
matchedNodesIds.reserve(nodesToFuse.size()); matchedNodesIds.reserve(nodesToFuse.size());
int numNodes = net.node_size(); std::queue<int> nodesToMatch;
for (int i = 0; i < nodesToFuse.size(); ++i) std::queue<int> targetNodes;
nodesToMatch.push(nodeId);
targetNodes.push(nodesToFuse.back());
while (!nodesToMatch.empty())
{ {
while (nodeId < numNodes && net.node(nodeId).op() == "Const") int nodeToMatch = nodesToMatch.front();
{ int targetNodeId = targetNodes.front();
nodeId += 1; nodesToMatch.pop();
} targetNodes.pop();
if (nodeId > numNodes - 1)
return false;
const tensorflow::NodeDef& node = net.node(nodeId); if (std::find(matchedNodesIds.begin(), matchedNodesIds.end(), nodeToMatch) !=
matchedNodesIds.end())
continue;
if (node.op() != nodes[nodesToFuse[i]]) const tensorflow::NodeDef& node = net.node(nodeToMatch);
if (node.op() != nodes[targetNodeId])
return false; return false;
std::vector<int>& inputNodes = inputs[nodesToFuse[i]]; std::vector<int>& inputNodes = inputs[targetNodeId];
if (inputNodes.size() != node.input_size()) if (inputNodes.size() != node.input_size())
return false; return false;
for (int j = 0; j < inputNodes.size(); ++j) for (int j = 0; j < inputNodes.size(); ++j)
{ {
if (nodes[inputNodes[j]].empty()) // Unknown input node type. if (nodes[inputNodes[j]].empty()) // Unknown input node type.
continue; continue;
const tensorflow::NodeDef& inpNode = getInputNode(net, node, j); nodeId = getInputNodeId(net, node, j);
if (inpNode.op() != nodes[inputNodes[j]]) const tensorflow::NodeDef& inpNode = net.node(nodeId);
if (inpNode.op() != "Const")
{
nodesToMatch.push(nodeId);
targetNodes.push(inputNodes[j]);
}
else if (nodes[inputNodes[j]] != "Const")
return false; return false;
} }
matchedNodesIds.push_back(nodeToMatch);
matchedNodesIds.push_back(nodeId);
nodeId += 1;
} }
std::sort(matchedNodesIds.begin(), matchedNodesIds.end());
return true; return true;
} }
@ -181,7 +191,7 @@ public:
std::vector<tensorflow::NodeDef*> inputNodes(inputsNames.size()); std::vector<tensorflow::NodeDef*> inputNodes(inputsNames.size());
for (int i = 0; i < inputsNames.size(); ++i) for (int i = 0; i < inputsNames.size(); ++i)
{ {
inputNodes[i] = (tensorflow::NodeDef*)&getInputNode(net, *node, i); inputNodes[i] = net.mutable_node(getInputNodeId(net, *node, i));
} }
finalize(net, node, inputNodes); finalize(net, node, inputNodes);
} }
@ -354,7 +364,7 @@ public:
{ {
if (!Subgraph::match(net, nodeId, matchedNodesIds)) if (!Subgraph::match(net, nodeId, matchedNodesIds))
return false; return false;
Mat maxValue = getTensorContent(net.node(nodeId + 1).attr().at("value").tensor()); Mat maxValue = getTensorContent(net.node(matchedNodesIds.front() + 1).attr().at("value").tensor());
return maxValue.type() == CV_32FC1 && maxValue.total() == 1 && maxValue.at<float>(0) == 6; return maxValue.type() == CV_32FC1 && maxValue.total() == 1 && maxValue.at<float>(0) == 6;
} }
}; };
@ -384,6 +394,17 @@ public:
setFusedNode("Reshape", ids); setFusedNode("Reshape", ids);
} }
virtual bool match(const tensorflow::GraphDef& net, int nodeId, std::vector<int>& matchedNodesIds) CV_OVERRIDE
{
const tensorflow::NodeDef& node = net.node(nodeId);
if (node.input_size() == 0)
return false;
inpName = node.input(0);
return Subgraph::match(net, nodeId, matchedNodesIds);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode, virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{ {
@ -395,6 +416,7 @@ public:
} }
tensorflow::TensorProto* shapeTensor = inputNodes[1]->mutable_attr()->at("value").mutable_tensor(); tensorflow::TensorProto* shapeTensor = inputNodes[1]->mutable_attr()->at("value").mutable_tensor();
fusedNode->mutable_input()->DeleteSubrange(2, numOutDims - 1); fusedNode->mutable_input()->DeleteSubrange(2, numOutDims - 1);
fusedNode->set_input(0, inpName);
shapeTensor->clear_int_val(); shapeTensor->clear_int_val();
for (int i = 0; i < shape.size(); ++i) for (int i = 0; i < shape.size(); ++i)
@ -405,6 +427,7 @@ public:
private: private:
int numOutDims; int numOutDims;
std::string inpName;
}; };
class L2NormalizeSubgraph : public Subgraph class L2NormalizeSubgraph : public Subgraph
@ -685,9 +708,9 @@ void simplifySubgraphs(tensorflow::GraphDef& net)
subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionSameKerasSubgraph())); subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionSameKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraph())); subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new UpsamplingKerasSubgraph())); subgraphs.push_back(Ptr<Subgraph>(new UpsamplingKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ReshapeAsShapeSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimSubgraph())); subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimV2Subgraph())); subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimV2Subgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ReshapeAsShapeSubgraph()));
int numNodes = net.node_size(); int numNodes = net.node_size();
std::vector<int> matchedNodesIds; std::vector<int> matchedNodesIds;

43
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1126,25 +1126,28 @@ void TFImporter::populateNet(Net dstNet)
{ {
Mat newShape = getTensorContent(getConstBlob(layer, value_id, 1)); Mat newShape = getTensorContent(getConstBlob(layer, value_id, 1));
if (newShape.total() != 4 && inpLayout == DATA_LAYOUT_NHWC) if (inpLayout == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
inpLayout = DATA_LAYOUT_NCHW;
}
else if (newShape.total() == 4 && inpLayout == DATA_LAYOUT_NHWC)
{ {
// NHWC->NCHW if (newShape.total() == 4)
std::swap(*newShape.ptr<int32_t>(0, 2), *newShape.ptr<int32_t>(0, 3)); {
std::swap(*newShape.ptr<int32_t>(0, 1), *newShape.ptr<int32_t>(0, 2)); // NHWC->NCHW
std::swap(*newShape.ptr<int32_t>(0, 2), *newShape.ptr<int32_t>(0, 3));
std::swap(*newShape.ptr<int32_t>(0, 1), *newShape.ptr<int32_t>(0, 2));
}
if (newShape.total() != 4 || newShape.at<int>(1) == 1)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
inpLayout = DATA_LAYOUT_NCHW;
}
} }
layerParams.set("dim", DictValue::arrayInt<int*>(newShape.ptr<int>(), newShape.total())); layerParams.set("dim", DictValue::arrayInt<int*>(newShape.ptr<int>(), newShape.total()));
@ -1381,7 +1384,9 @@ void TFImporter::populateNet(Net dstNet)
// num_split // num_split
// 1st blob is dims tensor // 1st blob is dims tensor
int axis = getConstBlob(layer, value_id, 0).int_val().Get(0); int axis = getConstBlob(layer, value_id, 0).int_val().Get(0);
layerParams.set("axis", toNCHW(axis)); if (getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
axis = toNCHW(axis);
layerParams.set("axis", axis);
int id = dstNet.addLayer(name, "Slice", layerParams); int id = dstNet.addLayer(name, "Slice", layerParams);
layer_id[name] = id; layer_id[name] = id;

7
modules/dnn/test/test_tf_importer.cpp
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@ -675,6 +675,13 @@ TEST_P(Test_TensorFlow_layers, relu6)
runTensorFlowNet("keras_relu6", /*hasText*/ true); runTensorFlowNet("keras_relu6", /*hasText*/ true);
} }
TEST_P(Test_TensorFlow_layers, subpixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("subpixel");
}
TEST_P(Test_TensorFlow_layers, keras_mobilenet_head) TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
{ {
runTensorFlowNet("keras_mobilenet_head"); runTensorFlowNet("keras_mobilenet_head");

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