}
}
- static const tensorflow::NodeDef& getInputNode(const tensorflow::GraphDef& net,
- const tensorflow::NodeDef& node,
- int inpId)
+ static int getInputNodeId(const tensorflow::GraphDef& net,
+ const tensorflow::NodeDef& node,
+ int inpId)
{
CV_Assert(inpId < node.input_size());
std::string name = node.input(inpId);
for (int i = 0; i < numNodes; ++i)
{
if (net.node(i).name() == name)
- return net.node(i);
+ return i;
}
CV_Error(Error::StsParseError, "Input node with name " + name + " not found");
}
matchedNodesIds.clear();
matchedNodesIds.reserve(nodesToFuse.size());
- int numNodes = net.node_size();
- for (int i = 0; i < nodesToFuse.size(); ++i)
+ std::queue<int> nodesToMatch;
+ std::queue<int> targetNodes;
+ nodesToMatch.push(nodeId);
+ targetNodes.push(nodesToFuse.back());
+ while (!nodesToMatch.empty())
{
- while (nodeId < numNodes && net.node(nodeId).op() == "Const")
- {
- nodeId += 1;
- }
- if (nodeId > numNodes - 1)
- return false;
+ int nodeToMatch = nodesToMatch.front();
+ int targetNodeId = targetNodes.front();
+ nodesToMatch.pop();
+ targetNodes.pop();
- 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;
- std::vector<int>& inputNodes = inputs[nodesToFuse[i]];
+ std::vector<int>& inputNodes = inputs[targetNodeId];
if (inputNodes.size() != node.input_size())
return false;
+
for (int j = 0; j < inputNodes.size(); ++j)
{
if (nodes[inputNodes[j]].empty()) // Unknown input node type.
continue;
- const tensorflow::NodeDef& inpNode = getInputNode(net, node, j);
- if (inpNode.op() != nodes[inputNodes[j]])
+ nodeId = getInputNodeId(net, node, 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;
}
-
- matchedNodesIds.push_back(nodeId);
- nodeId += 1;
+ matchedNodesIds.push_back(nodeToMatch);
}
+ std::sort(matchedNodesIds.begin(), matchedNodesIds.end());
return true;
}
std::vector<tensorflow::NodeDef*> inputNodes(inputsNames.size());
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);
}
{
if (!Subgraph::match(net, nodeId, matchedNodesIds))
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;
}
};
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,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
}
tensorflow::TensorProto* shapeTensor = inputNodes[1]->mutable_attr()->at("value").mutable_tensor();
fusedNode->mutable_input()->DeleteSubrange(2, numOutDims - 1);
+ fusedNode->set_input(0, inpName);
shapeTensor->clear_int_val();
for (int i = 0; i < shape.size(); ++i)
private:
int numOutDims;
+ std::string inpName;
};
class L2NormalizeSubgraph : public Subgraph
subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionSameKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraph()));
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 SoftMaxSlimV2Subgraph()));
+ subgraphs.push_back(Ptr<Subgraph>(new ReshapeAsShapeSubgraph()));
int numNodes = net.node_size();
std::vector<int> matchedNodesIds;
{
Mat newShape = getTensorContent(getConstBlob(layer, value_id, 1));
- if (newShape.total() != 4 && 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)
+ if (inpLayout == DATA_LAYOUT_NHWC)
{
- // 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)
+ {
+ // 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()));
// num_split
// 1st blob is dims tensor
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);
layer_id[name] = id;