Merge pull request #10940 from dkurt:dnn_tf_graph_optim
Alexander Alekhin
7 years ago
commit
d68466bb6a
5 changed files with 696 additions and 134 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2018, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#ifdef HAVE_PROTOBUF |
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#include "tf_graph_simplifier.hpp" |
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namespace cv { namespace dnn { |
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CV__DNN_EXPERIMENTAL_NS_BEGIN |
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using ::google::protobuf::RepeatedField; |
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using ::google::protobuf::MapPair; |
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class Subgraph // Interface to match and replace TensorFlow subgraphs.
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{ |
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public: |
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// Add a node to be matched in the origin graph. Specify ids of nodes that
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// are expected to be inputs. Returns id of a newly added node.
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// TODO: Replace inputs to std::vector<int> in C++11
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int addNodeToMatch(const std::string& op, int input_0 = -1, int input_1 = -1, |
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int input_2 = -1, int input_3 = -1) |
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{ |
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int nodeInputs[] = {input_0, input_1, input_2, input_3}; |
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int numInputs = 0; |
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for (int i = 0; i < 4; ++i) |
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{ |
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numInputs += (int)(nodeInputs[i] != -1); |
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} |
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return addNodeToMatch(op, std::vector<int>(&nodeInputs[0], &nodeInputs[0] + numInputs)); |
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} |
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int addNodeToMatch(const std::string& op, const std::vector<int>& inputs_) |
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{ |
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for (int i = 0; i < inputs_.size(); ++i) |
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{ |
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CV_Assert(inputs_[i] < (int)nodes.size()); |
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} |
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nodes.push_back(op); |
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inputs.push_back(inputs_); |
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return nodes.size() - 1; |
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} |
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// Specify resulting node. All the matched nodes in subgraph excluding
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// input nodes will be fused into this single node.
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// TODO: Replace inputs to std::vector<int> in C++11
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void setFusedNode(const std::string& op, int input_0 = -1, int input_1 = -1, |
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int input_2 = -1, int input_3 = -1, int input_4 = -1, |
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int input_5 = -1) |
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{ |
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int nodeInputs[] = {input_0, input_1, input_2, input_3, input_4, input_5}; |
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int numInputs = 0; |
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for (int i = 0; i < 6; ++i) |
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{ |
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CV_Assert(nodeInputs[i] < (int)nodes.size()); |
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numInputs += (int)(nodeInputs[i] != -1); |
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} |
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setFusedNode(op, std::vector<int>(&nodeInputs[0], &nodeInputs[0] + numInputs)); |
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} |
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void setFusedNode(const std::string& op, const std::vector<int>& inputs_) |
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{ |
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fusedNodeInputs = inputs_; |
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fusedNodeOp = op; |
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nodesToFuse.clear(); |
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for (int i = 0; i < nodes.size(); ++i) |
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{ |
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if (std::find(fusedNodeInputs.begin(), fusedNodeInputs.end(), i) == fusedNodeInputs.end() && |
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nodes[i] != "Const") |
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nodesToFuse.push_back(i); |
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} |
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} |
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static const tensorflow::NodeDef& getInputNode(const tensorflow::GraphDef& net, |
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const tensorflow::NodeDef& node, |
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int inpId) |
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{ |
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CV_Assert(inpId < node.input_size()); |
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std::string name = node.input(inpId); |
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const int numNodes = net.node_size(); |
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for (int i = 0; i < numNodes; ++i) |
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{ |
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if (net.node(i).name() == name) |
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return net.node(i); |
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} |
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CV_Error(Error::StsParseError, "Input node with name " + name + " not found"); |
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return net.node(0); // just return something
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} |
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// Match TensorFlow subgraph starting from <nodeId> with a set of nodes to be fused.
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// Const nodes are skipped during matching. Returns true if nodes are matched and can be fused.
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virtual bool match(const tensorflow::GraphDef& net, int nodeId, std::vector<int>& matchedNodesIds) |
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{ |
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matchedNodesIds.clear(); |
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matchedNodesIds.reserve(nodesToFuse.size()); |
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int numNodes = net.node_size(); |
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for (int i = 0; i < nodesToFuse.size(); ++i) |
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{ |
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while (nodeId < numNodes && net.node(nodeId).op() == "Const") |
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{ |
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nodeId += 1; |
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} |
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if (nodeId > numNodes - 1) |
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return false; |
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const tensorflow::NodeDef& node = net.node(nodeId); |
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if (node.op() != nodes[nodesToFuse[i]]) |
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return false; |
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std::vector<int>& inputNodes = inputs[nodesToFuse[i]]; |
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if (inputNodes.size() != node.input_size()) |
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return false; |
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for (int j = 0; j < inputNodes.size(); ++j) |
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{ |
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if (nodes[inputNodes[j]].empty()) // Unknown input node type.
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continue; |
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const tensorflow::NodeDef& inpNode = getInputNode(net, node, j); |
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if (inpNode.op() != nodes[inputNodes[j]]) |
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return false; |
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} |
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matchedNodesIds.push_back(nodeId); |
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nodeId += 1; |
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} |
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return true; |
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} |
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// Fuse matched subgraph.
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void replace(tensorflow::GraphDef& net, const std::vector<int>& matchedNodesIds) |
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{ |
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// Extract names of input nodes.
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std::vector<std::string> inputsNames(fusedNodeInputs.size()); |
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for (int i = 0; i < fusedNodeInputs.size(); ++i) |
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{ |
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std::string inpName; |
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// Find input node name looking at inputs of fused nodes.
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for (int j = 0; j < matchedNodesIds.size() && inpName.empty(); ++j) |
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{ |
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const tensorflow::NodeDef &node = net.node(matchedNodesIds[j]); |
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std::vector<int>& inpIndices = inputs[nodesToFuse[j]]; |
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CV_Assert(node.input_size() == inpIndices.size()); |
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for (int k = 0; k < inpIndices.size(); ++k) |
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{ |
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if (inpIndices[k] == fusedNodeInputs[i]) |
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{ |
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inpName = node.input(k); |
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break; |
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} |
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} |
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} |
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CV_Assert(!inpName.empty()); |
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inputsNames[i] = inpName; |
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} |
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// Remove matched nodes except the last one. Indices in ascending order are expected.
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tensorflow::NodeDef* node = net.mutable_node(matchedNodesIds.back()); |
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for (int i = matchedNodesIds.size() - 2; i >= 0; --i) |
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net.mutable_node()->DeleteSubrange(matchedNodesIds[i], 1); |
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// Modify the last node to be a fused one.
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node->set_op(fusedNodeOp); |
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node->clear_input(); |
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for (int i = 0; i < inputsNames.size(); ++i) |
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{ |
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node->add_input(inputsNames[i]); |
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} |
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std::vector<tensorflow::NodeDef*> inputNodes(inputsNames.size()); |
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for (int i = 0; i < inputsNames.size(); ++i) |
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{ |
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inputNodes[i] = (tensorflow::NodeDef*)&getInputNode(net, *node, i); |
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} |
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finalize(net, node, inputNodes); |
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} |
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virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef*, |
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std::vector<tensorflow::NodeDef*>&) {} |
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private: |
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std::vector<std::string> nodes; // Nodes to be matched in the origin graph.
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std::vector<std::vector<int> > inputs; // Connections of an every node to it's inputs.
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std::string fusedNodeOp; // Operation name of resulting fused node.
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std::vector<int> nodesToFuse; // Set of nodes to be fused.
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std::vector<int> fusedNodeInputs; // Inputs of fused node.
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}; |
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class BatchNormSubgraph : public Subgraph |
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{ |
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public: |
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BatchNormSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int epsilon = addNodeToMatch("Const"); |
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int moving_variance = addNodeToMatch("Const"); |
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int moving_mean = addNodeToMatch("Const"); |
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int beta = addNodeToMatch("Const"); |
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int gamma = addNodeToMatch("Const"); |
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int add = addNodeToMatch("Add", moving_variance, epsilon); |
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int rsqrt = addNodeToMatch("Rsqrt", add); |
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int mul = addNodeToMatch("Mul", rsqrt, gamma); |
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int mul_1 = addNodeToMatch("Mul", input, mul); |
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int mul_2 = addNodeToMatch("Mul", moving_mean, mul); |
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int sub = addNodeToMatch("Sub", beta, mul_2); |
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addNodeToMatch("Add", mul_1, sub); |
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setFusedNode("FusedBatchNorm", input, gamma, beta, moving_mean, moving_variance, epsilon); |
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} |
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virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode, |
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std::vector<tensorflow::NodeDef*>& inputNodes) |
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{ |
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Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor()); |
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CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1); |
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fusedNode->mutable_input()->ReleaseLast(); |
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fusedNode->clear_attr(); |
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tensorflow::AttrValue epsilon; |
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epsilon.set_f(epsMat.at<float>(0)); |
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fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon)); |
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} |
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}; |
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class BatchNormNoGammaSubgraph : public Subgraph |
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{ |
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public: |
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BatchNormNoGammaSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int epsilon = addNodeToMatch("Const"); |
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int moving_variance = addNodeToMatch("Const"); |
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int moving_mean = addNodeToMatch("Const"); |
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int beta = addNodeToMatch("Const"); |
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int add = addNodeToMatch("Add", moving_variance, epsilon); |
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int rsqrt = addNodeToMatch("Rsqrt", add); |
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int mul = addNodeToMatch("Mul", input, rsqrt); |
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int mul_1 = addNodeToMatch("Mul", moving_mean, rsqrt); |
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int sub = addNodeToMatch("Sub", beta, mul_1); |
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addNodeToMatch("Add", mul, sub); |
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// There is a fake reference to beta that will be replaced to a new gamma tensor.
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setFusedNode("FusedBatchNorm", input, beta, beta, moving_mean, moving_variance, epsilon); |
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} |
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virtual void finalize(tensorflow::GraphDef& net, tensorflow::NodeDef* fusedNode, |
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std::vector<tensorflow::NodeDef*>& inputNodes) |
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{ |
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Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor()); |
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CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1); |
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fusedNode->mutable_input()->ReleaseLast(); |
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fusedNode->clear_attr(); |
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tensorflow::AttrValue epsilon; |
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epsilon.set_f(epsMat.at<float>(0)); |
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fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon)); |
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tensorflow::NodeDef* gamma = net.add_node(); |
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gamma->set_op("Const"); |
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gamma->set_name(fusedNode->name() + "/gamma"); |
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// Just put a single value to recognize this node as Const.
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gamma->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("value", epsilon)); |
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fusedNode->set_input(1, gamma->name()); |
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} |
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}; |
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// tf.contrib.layers.flatten
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class FlattenSubgraph : public Subgraph |
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{ |
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public: |
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FlattenSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int shape = addNodeToMatch("Const"); |
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int stack = addNodeToMatch("Const"); |
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int stack_1 = addNodeToMatch("Const"); |
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int stack_2 = addNodeToMatch("Const"); |
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int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2); |
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int shape_pack = addNodeToMatch("Const"); |
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int pack = addNodeToMatch("Pack", strided_slice, shape_pack); |
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addNodeToMatch("Reshape", input, pack); |
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setFusedNode("Flatten", input); |
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} |
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}; |
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// tf.contrib.layers.flatten in case of unknown batch size
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class FlattenShapeSubgraph : public Subgraph |
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{ |
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public: |
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FlattenShapeSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int shape = addNodeToMatch("Shape", input); |
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int stack = addNodeToMatch("Const"); |
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int stack_1 = addNodeToMatch("Const"); |
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int stack_2 = addNodeToMatch("Const"); |
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int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2); |
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int shape_pack = addNodeToMatch("Const"); |
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int pack = addNodeToMatch("Pack", strided_slice, shape_pack); |
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addNodeToMatch("Reshape", input, pack); |
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setFusedNode("Flatten", input); |
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} |
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}; |
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// K.layers.Softmax
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class SoftMaxKerasSubgraph : public Subgraph |
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{ |
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public: |
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SoftMaxKerasSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int maxReductionIndices = addNodeToMatch("Const"); |
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int smMax = addNodeToMatch("Max", input, maxReductionIndices); |
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int smSub = addNodeToMatch("Sub", input, smMax); |
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int smExp = addNodeToMatch("Exp", smSub); |
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int sumReductionIndices = addNodeToMatch("Const"); |
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int smSum = addNodeToMatch("Sum", smExp, sumReductionIndices); |
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addNodeToMatch("RealDiv", smExp, smSum); |
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setFusedNode("Softmax", input); |
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} |
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}; |
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class ReLU6KerasSubgraph : public Subgraph |
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{ |
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public: |
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ReLU6KerasSubgraph() |
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{ |
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int input = addNodeToMatch(""); |
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int relu = addNodeToMatch("Relu", input); |
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int maxValue = addNodeToMatch("Const"); |
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int clipValue = addNodeToMatch("Const"); |
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int minimum = addNodeToMatch("Minimum", relu, maxValue); |
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addNodeToMatch("Maximum", minimum, clipValue); |
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setFusedNode("Relu6", input); |
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} |
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virtual bool match(const tensorflow::GraphDef& net, int nodeId, std::vector<int>& matchedNodesIds) |
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{ |
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if (!Subgraph::match(net, nodeId, matchedNodesIds)) |
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return false; |
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Mat maxValue = getTensorContent(net.node(nodeId + 1).attr().at("value").tensor()); |
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return maxValue.type() == CV_32FC1 && maxValue.total() == 1 && maxValue.at<float>(0) == 6; |
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} |
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}; |
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// Keras' reshape stores output shape in separate Const nodes by one value.
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// Need to merge them into a single Const node.
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class ReshapeKerasSubgraph : public Subgraph |
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{ |
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public: |
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ReshapeKerasSubgraph(int _numOutDims) : numOutDims(_numOutDims) |
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{ |
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int input = addNodeToMatch(""); |
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int shape = addNodeToMatch("Shape", input); |
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int stack = addNodeToMatch("Const"); |
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int stack_1 = addNodeToMatch("Const"); |
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int stack_2 = addNodeToMatch("Const"); |
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int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2); |
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std::vector<int> ids(1 + numOutDims); |
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ids[0] = strided_slice; |
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for (int i = 0; i < numOutDims; ++i) |
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ids[1 + i] = addNodeToMatch("Const"); |
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int pack = addNodeToMatch("Pack", ids); |
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addNodeToMatch("Reshape", input, pack); |
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ids[0] = input; |
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setFusedNode("Reshape", ids); |
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} |
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virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode, |
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std::vector<tensorflow::NodeDef*>& inputNodes) |
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{ |
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std::vector<int> shape(numOutDims + 1); // batch size in Keras is implicit.
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shape[0] = -1; |
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for (int i = 0; i < numOutDims; ++i) |
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{ |
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shape[1 + i] = inputNodes[1 + i]->attr().at("value").tensor().int_val(0); |
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} |
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tensorflow::TensorProto* shapeTensor = inputNodes[1]->mutable_attr()->at("value").mutable_tensor(); |
||||||
|
fusedNode->mutable_input()->DeleteSubrange(2, numOutDims - 1); |
||||||
|
|
||||||
|
shapeTensor->clear_int_val(); |
||||||
|
for (int i = 0; i < shape.size(); ++i) |
||||||
|
{ |
||||||
|
shapeTensor->add_int_val(shape[i]); |
||||||
|
} |
||||||
|
} |
||||||
|
|
||||||
|
private: |
||||||
|
int numOutDims; |
||||||
|
}; |
||||||
|
|
||||||
|
void simplifySubgraphs(tensorflow::GraphDef& net) |
||||||
|
{ |
||||||
|
std::vector<Ptr<Subgraph> > subgraphs; |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new BatchNormSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new BatchNormNoGammaSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new FlattenSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new FlattenShapeSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxKerasSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new ReLU6KerasSubgraph())); |
||||||
|
subgraphs.push_back(Ptr<Subgraph>(new ReshapeKerasSubgraph(3))); |
||||||
|
|
||||||
|
int numNodes = net.node_size(); |
||||||
|
std::vector<int> matchedNodesIds; |
||||||
|
for (int i = 0; i < numNodes; ++i) |
||||||
|
{ |
||||||
|
for (int j = 0; j < subgraphs.size(); ++j) |
||||||
|
{ |
||||||
|
if (subgraphs[j]->match(net, i, matchedNodesIds)) |
||||||
|
{ |
||||||
|
subgraphs[j]->replace(net, matchedNodesIds); |
||||||
|
numNodes -= matchedNodesIds.size() - 1; // #matchedNodes removed and one added.
|
||||||
|
break; |
||||||
|
} |
||||||
|
} |
||||||
|
} |
||||||
|
} |
||||||
|
|
||||||
|
void RemoveIdentityOps(tensorflow::GraphDef& net) |
||||||
|
{ |
||||||
|
typedef std::map<String, String> IdentityOpsMap; |
||||||
|
IdentityOpsMap identity_ops; |
||||||
|
|
||||||
|
std::vector<int> identity_ops_idx; |
||||||
|
|
||||||
|
int layersCount = net.node_size(); |
||||||
|
for (int li = 0; li < layersCount; li++) |
||||||
|
{ |
||||||
|
const tensorflow::NodeDef &layer = net.node(li); |
||||||
|
String type = layer.op(); |
||||||
|
|
||||||
|
if (type == "Identity" || type == "Dropout") { |
||||||
|
identity_ops_idx.push_back(li); |
||||||
|
identity_ops[layer.name()] = layer.input(0); |
||||||
|
} |
||||||
|
} |
||||||
|
|
||||||
|
for (int li = 0; li < layersCount; li++) |
||||||
|
{ |
||||||
|
tensorflow::NodeDef* layer = net.mutable_node(li); |
||||||
|
for (int input_id = 0; input_id < layer->input_size(); input_id++) { |
||||||
|
String input_op_name = layer->input(input_id); |
||||||
|
IdentityOpsMap::iterator it = identity_ops.find(input_op_name); |
||||||
|
|
||||||
|
if (it != identity_ops.end()) { |
||||||
|
layer->set_input(input_id, it->second); |
||||||
|
} |
||||||
|
} |
||||||
|
} |
||||||
|
|
||||||
|
std::sort(identity_ops_idx.begin(), identity_ops_idx.end()); |
||||||
|
|
||||||
|
int removed_nodes = 0; |
||||||
|
for(size_t i = 0; i < identity_ops_idx.size(); i++) { |
||||||
|
int start_id = identity_ops_idx[i] - removed_nodes; |
||||||
|
net.mutable_node()->DeleteSubrange(start_id, 1); |
||||||
|
removed_nodes++; |
||||||
|
} |
||||||
|
} |
||||||
|
|
||||||
|
Mat getTensorContent(const tensorflow::TensorProto &tensor) |
||||||
|
{ |
||||||
|
std::string content = tensor.tensor_content(); |
||||||
|
switch (tensor.dtype()) |
||||||
|
{ |
||||||
|
case tensorflow::DT_FLOAT: |
||||||
|
{ |
||||||
|
if (!content.empty()) |
||||||
|
return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone(); |
||||||
|
else |
||||||
|
{ |
||||||
|
const RepeatedField<float>& field = tensor.float_val(); |
||||||
|
CV_Assert(!field.empty()); |
||||||
|
return Mat(1, field.size(), CV_32FC1, (void*)field.data()).clone(); |
||||||
|
} |
||||||
|
} |
||||||
|
case tensorflow::DT_DOUBLE: |
||||||
|
{ |
||||||
|
if (!content.empty()) |
||||||
|
return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone(); |
||||||
|
else |
||||||
|
{ |
||||||
|
const RepeatedField<double>& field = tensor.double_val(); |
||||||
|
CV_Assert(!field.empty()); |
||||||
|
return Mat(1, field.size(), CV_64FC1, (void*)field.data()).clone(); |
||||||
|
} |
||||||
|
} |
||||||
|
case tensorflow::DT_INT32: |
||||||
|
{ |
||||||
|
if (!content.empty()) |
||||||
|
return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone(); |
||||||
|
else |
||||||
|
{ |
||||||
|
const RepeatedField<int32_t>& field = tensor.int_val(); |
||||||
|
CV_Assert(!field.empty()); |
||||||
|
return Mat(1, field.size(), CV_32SC1, (void*)field.data()).clone(); |
||||||
|
} |
||||||
|
} |
||||||
|
case tensorflow::DT_HALF: |
||||||
|
{ |
||||||
|
Mat halfs; |
||||||
|
if (!content.empty()) |
||||||
|
{ |
||||||
|
static const int kHalfSize = 2; |
||||||
|
halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str()); |
||||||
|
} |
||||||
|
else |
||||||
|
{ |
||||||
|
const RepeatedField<int32_t>& field = tensor.half_val(); |
||||||
|
CV_Assert(!field.empty()); |
||||||
|
Mat ints(1, field.size(), CV_32SC1, (void*)field.data()); |
||||||
|
ints.convertTo(halfs, CV_16UC1); |
||||||
|
} |
||||||
|
// Reinterpret as a signed shorts just for a convertFp16 call.
|
||||||
|
Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data); |
||||||
|
Mat floats(halfs.size(), CV_32FC1); |
||||||
|
convertFp16(halfsSigned, floats); |
||||||
|
return floats; |
||||||
|
} |
||||||
|
case tensorflow::DT_QUINT8: |
||||||
|
{ |
||||||
|
CV_Assert(!content.empty()); |
||||||
|
return Mat(1, content.size(), CV_8UC1, (void*)content.c_str()).clone(); |
||||||
|
} |
||||||
|
default: |
||||||
|
CV_Error(Error::StsError, "Tensor's data type is not supported"); |
||||||
|
break; |
||||||
|
} |
||||||
|
return Mat(); |
||||||
|
} |
||||||
|
|
||||||
|
CV__DNN_EXPERIMENTAL_NS_END |
||||||
|
}} // namespace dnn, namespace cv
|
||||||
|
|
||||||
|
#endif // HAVE_PROTOBUF
|
||||||
@ -0,0 +1,30 @@ |
|||||||
|
// This file is part of OpenCV project.
|
||||||
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||||
|
// of this distribution and at http://opencv.org/license.html.
|
||||||
|
|
||||||
|
// Copyright (C) 2018, Intel Corporation, all rights reserved.
|
||||||
|
// Third party copyrights are property of their respective owners.
|
||||||
|
|
||||||
|
#ifndef __OPENCV_DNN_TF_SIMPLIFIER_HPP__ |
||||||
|
#define __OPENCV_DNN_TF_SIMPLIFIER_HPP__ |
||||||
|
|
||||||
|
#include "../precomp.hpp" |
||||||
|
|
||||||
|
#ifdef HAVE_PROTOBUF |
||||||
|
|
||||||
|
#include "tf_io.hpp" |
||||||
|
|
||||||
|
namespace cv { namespace dnn { |
||||||
|
CV__DNN_EXPERIMENTAL_NS_BEGIN |
||||||
|
|
||||||
|
void RemoveIdentityOps(tensorflow::GraphDef& net); |
||||||
|
|
||||||
|
void simplifySubgraphs(tensorflow::GraphDef& net); |
||||||
|
|
||||||
|
Mat getTensorContent(const tensorflow::TensorProto &tensor); |
||||||
|
|
||||||
|
CV__DNN_EXPERIMENTAL_NS_END |
||||||
|
}} // namespace dnn, namespace cv
|
||||||
|
|
||||||
|
#endif // HAVE_PROTOBUF
|
||||||
|
#endif // __OPENCV_DNN_TF_SIMPLIFIER_HPP__
|
||||||
Loading…
Reference in new issue