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tf_graph_simplifier.cpp
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tf_graph_simplifier.cpp
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// 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.
#include "../precomp.hpp"
#ifdef HAVE_PROTOBUF
#include "../graph_simplifier.hpp"
#include "tf_graph_simplifier.hpp"
#include <queue>
namespace cv { namespace dnn {
CV__DNN_INLINE_NS_BEGIN
using ::google::protobuf::RepeatedField;
using ::google::protobuf::MapPair;
static Mat getTensorContentRef_(const tensorflow::TensorProto& tensor);
static inline
bool isAlignedMat(const Mat& m)
{
int depth = m.depth();
int alignment = CV_ELEM_SIZE1(depth);
return (((size_t)m.data) & (alignment - 1)) == 0;
}
class TFNodeWrapper : public ImportNodeWrapper
{
public:
TFNodeWrapper(tensorflow::NodeDef* _node) : node(_node) {}
virtual int getNumInputs() const CV_OVERRIDE
{
return node->input_size();
}
virtual std::string getInputName(int idx) const CV_OVERRIDE
{
// If operation produces several tensors, they are specified by index
// after ':' character. In example, "input:0".
std::string name = node->input(idx);
return name.substr(0, name.rfind(':'));
}
virtual std::string getType() const CV_OVERRIDE
{
return node->op();
}
virtual void setType(const std::string& type) CV_OVERRIDE
{
node->set_op(type);
}
virtual void setInputNames(const std::vector<std::string>& inputs) CV_OVERRIDE
{
node->clear_input();
for (int i = 0; i < inputs.size(); ++i)
node->add_input(inputs[i]);
}
tensorflow::NodeDef* node;
};
class TFGraphWrapper : public ImportGraphWrapper
{
public:
TFGraphWrapper(tensorflow::GraphDef& _net) : net(_net) {}
virtual Ptr<ImportNodeWrapper> getNode(int idx) const CV_OVERRIDE
{
return makePtr<TFNodeWrapper>(net.mutable_node(idx));
}
virtual int getNumNodes() const CV_OVERRIDE
{
return net.node_size();
}
virtual int getNumOutputs(int nodeId) const CV_OVERRIDE
{
return 1;
}
virtual std::string getOutputName(int nodeId, int outId) const CV_OVERRIDE
{
CV_Assert(outId == 0);
return net.node(nodeId).name();
}
virtual void removeNode(int idx) CV_OVERRIDE
{
net.mutable_node()->DeleteSubrange(idx, 1);
}
virtual inline bool isCommutativeOp(const std::string& type) const CV_OVERRIDE
{
return type == "Add" || type == "Sum" ||
type == "Mul" || type == "Prod" ||
type == "Max" || type == "Maximum" || type == "Minimum" ||
type == "Mean" || type == "SquaredDifference";
}
tensorflow::GraphDef& net;
};
class TFSubgraph : public Subgraph
{
virtual void finalize(const Ptr<ImportGraphWrapper>& netWrapper,
const Ptr<ImportNodeWrapper>& fusedNodeWrapper,
std::vector<Ptr<ImportNodeWrapper> >& inputs) CV_OVERRIDE
{
std::vector<tensorflow::NodeDef*> inputNodes(inputs.size());
for (int i = 0; i < inputs.size(); ++i)
inputNodes[i] = inputs[i].dynamicCast<TFNodeWrapper>()->node;
finalize(netWrapper.dynamicCast<TFGraphWrapper>()->net,
fusedNodeWrapper.dynamicCast<TFNodeWrapper>()->node, inputNodes);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) {}
};
class BatchNormSubgraph : public TFSubgraph
{
public:
BatchNormSubgraph()
{
int input = addNodeToMatch("");
int epsilon = addNodeToMatch("Const");
int moving_variance = addNodeToMatch("Const");
int moving_mean = addNodeToMatch("Const");
int beta = addNodeToMatch("Const");
int gamma = addNodeToMatch("Const");
int add = addNodeToMatch("Add", moving_variance, epsilon);
int rsqrt = addNodeToMatch("Rsqrt", add);
int mul = addNodeToMatch("Mul", rsqrt, gamma);
int mul_1 = addNodeToMatch("Mul", input, mul);
int mul_2 = addNodeToMatch("Mul", moving_mean, mul);
int sub = addNodeToMatch("Sub", beta, mul_2);
addNodeToMatch("Add", mul_1, sub);
setFusedNode("FusedBatchNorm", input, gamma, beta, moving_mean, moving_variance, epsilon);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
fusedNode->mutable_input()->RemoveLast();
fusedNode->clear_attr();
tensorflow::AttrValue epsilon;
epsilon.set_f(epsMat.at<float>(0));
fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon));
}
};
class BatchNormNoGammaSubgraph : public TFSubgraph
{
public:
BatchNormNoGammaSubgraph()
{
int input = addNodeToMatch("");
int epsilon = addNodeToMatch("Const");
int moving_variance = addNodeToMatch("Const");
int moving_mean = addNodeToMatch("Const");
int beta = addNodeToMatch("Const");
int add = addNodeToMatch("Add", moving_variance, epsilon);
int rsqrt = addNodeToMatch("Rsqrt", add);
int mul = addNodeToMatch("Mul", input, rsqrt);
int mul_1 = addNodeToMatch("Mul", moving_mean, rsqrt);
int sub = addNodeToMatch("Sub", beta, mul_1);
addNodeToMatch("Add", mul, sub);
// There is a fake reference to beta that will be replaced to a new gamma tensor.
setFusedNode("FusedBatchNorm", input, beta, beta, moving_mean, moving_variance, epsilon);
}
virtual void finalize(tensorflow::GraphDef& net, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
fusedNode->mutable_input()->RemoveLast();
fusedNode->clear_attr();
tensorflow::AttrValue epsilon;
epsilon.set_f(epsMat.at<float>(0));
fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon));
tensorflow::NodeDef* gamma = net.add_node();
gamma->set_op("Const");
gamma->set_name(fusedNode->name() + "/gamma");
// Just put a single value to recognize this node as Const.
gamma->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("value", epsilon));
fusedNode->set_input(1, gamma->name());
}
};
// tf.contrib.layers.flatten
class FlattenSubgraph : public Subgraph
{
public:
FlattenSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Const");
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int shape_pack = addNodeToMatch("Const");
int pack = addNodeToMatch("Pack", strided_slice, shape_pack);
addNodeToMatch("Reshape", input, pack);
setFusedNode("Flatten", input);
}
};
// tf.contrib.layers.flatten in case of unknown batch size
class FlattenShapeSubgraph : public Subgraph
{
public:
FlattenShapeSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int shape_pack = addNodeToMatch("Const");
int pack = addNodeToMatch("Pack", strided_slice, shape_pack);
addNodeToMatch("Reshape", input, pack);
setFusedNode("Flatten", input);
}
};
class FlattenProdSubgraph : public Subgraph
{
public:
FlattenProdSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int prod = addNodeToMatch("Prod", strided_slice, addNodeToMatch("Const"));
int shape_pack = addNodeToMatch("Const");
int pack = addNodeToMatch("Pack", shape_pack, prod);
addNodeToMatch("Reshape", input, pack);
setFusedNode("Flatten", input);
}
};
// K.layers.Softmax
class SoftMaxKerasSubgraph : public Subgraph
{
public:
SoftMaxKerasSubgraph()
{
int input = addNodeToMatch("");
int maxReductionIndices = addNodeToMatch("Const");
int smMax = addNodeToMatch("Max", input, maxReductionIndices);
int smSub = addNodeToMatch("Sub", input, smMax);
int smExp = addNodeToMatch("Exp", smSub);
int sumReductionIndices = addNodeToMatch("Const");
int smSum = addNodeToMatch("Sum", smExp, sumReductionIndices);
addNodeToMatch("RealDiv", smExp, smSum);
setFusedNode("Softmax", input);
}
};
class ReLU6KerasSubgraph : public Subgraph
{
public:
ReLU6KerasSubgraph()
{
int input = addNodeToMatch("");
int relu = addNodeToMatch("Relu", input);
maxValueId = addNodeToMatch("Const");
int clipValue = addNodeToMatch("Const");
int minimum = addNodeToMatch("Minimum", relu, maxValueId);
addNodeToMatch("Maximum", minimum, clipValue);
setFusedNode("Relu6", input);
}
virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
std::vector<int>& matchedNodesIds) CV_OVERRIDE
{
if (!Subgraph::match(net, nodeId, matchedNodesIds))
return false;
tensorflow::NodeDef* node = net->getNode(matchedNodesIds[maxValueId]).dynamicCast<TFNodeWrapper>()->node;
Mat maxValue = getTensorContent(node->attr().at("value").tensor());
return maxValue.type() == CV_32FC1 && maxValue.total() == 1 && maxValue.at<float>(0) == 6;
}
private:
int maxValueId;
};
// Keras' reshape stores output shape in separate Const nodes by one value.
// Need to merge them into a single Const node.
class ReshapeKerasSubgraph : public TFSubgraph
{
public:
ReshapeKerasSubgraph(int _numOutDims) : numOutDims(_numOutDims)
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
std::vector<int> ids(1 + numOutDims);
ids[0] = strided_slice;
for (int i = 0; i < numOutDims; ++i)
ids[1 + i] = addNodeToMatch("Const");
int pack = addNodeToMatch("Pack", ids);
addNodeToMatch("Reshape", input, pack);
ids[0] = input;
setFusedNode("Reshape", ids);
}
virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
std::vector<int>& matchedNodesIds) CV_OVERRIDE
{
Ptr<ImportNodeWrapper> node = net->getNode(nodeId);
if (node->getNumInputs() == 0)
return false;
inpName = node->getInputName(0);
return Subgraph::match(net, nodeId, matchedNodesIds);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
std::vector<int> shape(numOutDims + 1); // batch size in Keras is implicit.
shape[0] = -1;
for (int i = 0; i < numOutDims; ++i)
{
shape[1 + i] = inputNodes[1 + i]->attr().at("value").tensor().int_val(0);
}
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)
{
shapeTensor->add_int_val(shape[i]);
}
}
private:
int numOutDims;
std::string inpName;
};
class L2NormalizeSubgraph : public Subgraph
{
public:
L2NormalizeSubgraph()
{
int input = addNodeToMatch("");
int square = addNodeToMatch("Square", input);
int reductionIndices = addNodeToMatch("Const");
int sum = addNodeToMatch("Sum", square, reductionIndices);
int y = addNodeToMatch("Const");
int maximum = addNodeToMatch("Maximum", sum, y);
int rsqrt = addNodeToMatch("Rsqrt", maximum);
addNodeToMatch("Mul", input, rsqrt);
setFusedNode("L2Normalize", input, reductionIndices);
}
};
class DeconvolutionValidKerasSubgraph : public TFSubgraph
{
public:
DeconvolutionValidKerasSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int kernel = addNodeToMatch("Const");
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
int strided_slice_1 = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
int strided_slice_2 = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int mul = addNodeToMatch("Mul", strided_slice_1, addNodeToMatch("Const"));
int add = addNodeToMatch("Add", mul, addNodeToMatch("Const"));
int mul_1 = addNodeToMatch("Mul", strided_slice_2, addNodeToMatch("Const"));
int add_1 = addNodeToMatch("Add", mul_1, addNodeToMatch("Const"));
int pack = addNodeToMatch("Pack", strided_slice, add, add_1, addNodeToMatch("Const"));
addNodeToMatch("Conv2DBackpropInput", pack, kernel, input);
// Put any unused Const op to the first input.
setFusedNode("Conv2DBackpropInput", stack, kernel, input);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
// Disable adjusted paddings (see Conv2DBackpropInput layer at tf_importer.cpp)
// adj_w = (outW - (pad == "SAME") ? 1 : kernelW) % strideX;
// adj_h = (outH - (pad == "SAME") ? 1 : kernelH) % strideY;
// Where outH and outW are 1st and 2nd dimensions (NHWC) or 2nd and third (NCHW).
std::string padMode = fusedNode->attr().at("padding").s();
CV_Assert(padMode == "VALID");
const tensorflow::TensorShapeProto& kernelShape =
inputNodes[1]->mutable_attr()->at("value").tensor().tensor_shape();
CV_Assert(kernelShape.dim_size() == 4);
const int kernelHeight = kernelShape.dim(0).size();
const int kernelWidth = kernelShape.dim(1).size();
tensorflow::TensorProto* outShape = inputNodes[0]->mutable_attr()->at("value").mutable_tensor();
outShape->clear_int_val();
outShape->add_int_val(-1);
outShape->add_int_val(kernelHeight);
outShape->add_int_val(kernelWidth);
outShape->add_int_val(-1);
}
};
class DeconvolutionSameKerasSubgraph : public TFSubgraph
{
public:
DeconvolutionSameKerasSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int kernel = addNodeToMatch("Const");
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
int strided_slice_1 = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
int strided_slice_2 = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int mul = addNodeToMatch("Mul", strided_slice_1, addNodeToMatch("Const"));
int mul_1 = addNodeToMatch("Mul", strided_slice_2, addNodeToMatch("Const"));
int pack = addNodeToMatch("Pack", strided_slice, mul, mul_1, addNodeToMatch("Const"));
addNodeToMatch("Conv2DBackpropInput", pack, kernel, input);
// Put any unused Const op to the first input.
setFusedNode("Conv2DBackpropInput", stack, kernel, input);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
// Disable adjusted paddings (see Conv2DBackpropInput layer at tf_importer.cpp)
// adj_w = (outW - (pad == "SAME") ? 1 : kernelW) % strideX;
// adj_h = (outH - (pad == "SAME") ? 1 : kernelH) % strideY;
// Where outH and outW are 1st and 2nd dimensions (NHWC) or 2nd and third (NCHW).
std::string padMode = fusedNode->attr().at("padding").s();
CV_Assert(padMode == "SAME");
const tensorflow::AttrValue_ListValue& strides = fusedNode->attr().at("strides").list();
CV_Assert(strides.i_size() == 4);
const int strideY = strides.i(1);
const int strideX = strides.i(2);
tensorflow::TensorProto* outShape = inputNodes[0]->mutable_attr()->at("value").mutable_tensor();
outShape->clear_int_val();
outShape->add_int_val(-1);
outShape->add_int_val(strideY);
outShape->add_int_val(strideX);
outShape->add_int_val(-1);
}
};
// In case of resizing by factor.
class ResizeBilinearSubgraph : public Subgraph
{
public:
ResizeBilinearSubgraph()
{
int input = addNodeToMatch("");
int shapeSource = addNodeToMatch("");
int shape = addNodeToMatch("Shape", shapeSource);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int factorY = addNodeToMatch("Const");
int mul = addNodeToMatch("Mul", strided_slice, factorY);
shape = addNodeToMatch("Shape", shapeSource);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int factorX = addNodeToMatch("Const");
int mul_1 = addNodeToMatch("Mul", strided_slice, factorX);
int pack = addNodeToMatch("Pack", mul, mul_1);
addNodeToMatch("ResizeBilinear", input, pack);
setFusedNode("ResizeBilinear", input, factorY, factorX);
}
};
// In case of resizing by factor.
class ResizeBilinearSubgraphDown : public TFSubgraph
{
public:
ResizeBilinearSubgraphDown()
{
int input = addNodeToMatch("");
int shapeSource = addNodeToMatch("");
int shape = addNodeToMatch("Shape", shapeSource);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int factorY = addNodeToMatch("Const");
int div = addNodeToMatch("RealDiv", addNodeToMatch("Cast", strided_slice), factorY);
int cast = addNodeToMatch("Cast", div);
shape = addNodeToMatch("Shape", shapeSource);
stack = addNodeToMatch("Const");
stack_1 = addNodeToMatch("Const");
stack_2 = addNodeToMatch("Const");
strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int factorX = addNodeToMatch("Const");
int div_1 = addNodeToMatch("RealDiv", addNodeToMatch("Cast", strided_slice), factorX);
int cast_1 = addNodeToMatch("Cast", div_1);
int pack = addNodeToMatch("Pack", cast, cast_1);
addNodeToMatch("ResizeBilinear", input, pack);
setFusedNode("ResizeBilinear", input, factorY, factorX);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
for (int i = 1; i < 3; ++i)
{
tensorflow::TensorProto* factor = inputNodes[i]->mutable_attr()->at("value").mutable_tensor();
factor->set_double_val(0, 1.0 / factor->double_val(0));
}
}
};
// In case of resizing by factor.
class UpsamplingKerasSubgraph : public TFSubgraph
{
public:
UpsamplingKerasSubgraph(const std::string& type)
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int stack = addNodeToMatch("Const");
int stack_1 = addNodeToMatch("Const");
int stack_2 = addNodeToMatch("Const");
int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
int factors = addNodeToMatch("Const");
int mul = addNodeToMatch("Mul", strided_slice, factors);
addNodeToMatch(type, input, mul);
setFusedNode(type, input, factors);
}
virtual void finalize(tensorflow::GraphDef& net, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat factorsMat = getTensorContent(inputNodes[1]->attr().at("value").tensor());
CV_CheckEQ(factorsMat.total(), (size_t)2, ""); CV_CheckTypeEQ(factorsMat.type(), CV_32SC1, "");
// Height scale factor
tensorflow::TensorProto* factorY = inputNodes[1]->mutable_attr()->at("value").mutable_tensor();
factorY->clear_int_val();
factorY->clear_tensor_content();
factorY->add_int_val(factorsMat.at<int>(0, 0));
// Width scale factor.
tensorflow::NodeDef* factorXNode = net.add_node();
factorXNode->set_op("Const");
factorXNode->set_name(fusedNode->name() + "/factor_y");
tensorflow::AttrValue factorX;
factorX.mutable_tensor()->set_dtype(tensorflow::DT_INT32);
factorX.mutable_tensor()->add_int_val(factorsMat.at<int>(0, 1));
factorXNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("value", factorX));
fusedNode->add_input(factorXNode->name());
}
};
class ReshapeAsShapeSubgraph : public Subgraph
{
public:
ReshapeAsShapeSubgraph()
{
int input = addNodeToMatch("");
int shapeSrc = addNodeToMatch("");
int shape = addNodeToMatch("Shape", shapeSrc);
addNodeToMatch("Reshape", input, shape);
setFusedNode("Reshape", input, shapeSrc);
}
};
class SoftMaxSlimSubgraph : public Subgraph
{
public:
SoftMaxSlimSubgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Const");
int shapeOp = addNodeToMatch("Shape", input);
int reshape = addNodeToMatch("Reshape", input, shape);
int softmax = addNodeToMatch("Softmax", reshape);
addNodeToMatch("Reshape", softmax, shapeOp);
setFusedNode("Softmax", input);
}
};
class SoftMaxSlimV2Subgraph : public Subgraph
{
public:
SoftMaxSlimV2Subgraph()
{
int input = addNodeToMatch("");
int shape = addNodeToMatch("Shape", input);
int shape_2 = addNodeToMatch("Shape", input);
int rank = addNodeToMatch("Const");
int y = addNodeToMatch("Const");
int sub = addNodeToMatch("Sub", rank, y);
int begin = addNodeToMatch("Pack", sub);
int size = addNodeToMatch("Const");
int slice = addNodeToMatch("Slice", shape, begin, size);
int values = addNodeToMatch("Const");
int axis = addNodeToMatch("Const");
int concat = addNodeToMatch("ConcatV2", values, slice, axis);
int reshape = addNodeToMatch("Reshape", input, concat);
int softmax = addNodeToMatch("Softmax", reshape);
addNodeToMatch("Reshape", softmax, shape_2);
setFusedNode("Softmax", input);
}
};
class KerasMVNSubgraph : public TFSubgraph
{
public:
KerasMVNSubgraph()
{
int input = addNodeToMatch("");
int mean = addNodeToMatch("Mean", input, addNodeToMatch("Const"));
int grad = addNodeToMatch("StopGradient", mean);
int diff = addNodeToMatch("SquaredDifference", input, grad);
int var = addNodeToMatch("Mean", diff, addNodeToMatch("Const"));
int sub = addNodeToMatch("Sub", input, mean);
int add_y = addNodeToMatch("Const");
int add = addNodeToMatch("Add", var, add_y);
int pow_y = addNodeToMatch("Const");
int powNode = addNodeToMatch("Pow", add, pow_y);
addNodeToMatch("RealDiv", sub, powNode);
setFusedNode("MVN", input, add_y);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
tensorflow::AttrValue eps;
Mat epsMat = getTensorContent(inputNodes[1]->attr().at("value").tensor());
CV_CheckEQ(epsMat.total(), (size_t)1, "");
CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
eps.set_f(epsMat.at<float>(0));
fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("eps", eps));
fusedNode->mutable_input()->RemoveLast();
}
};
class PReLUSubgraph : public TFSubgraph
{
public:
PReLUSubgraph(bool negativeScales_) : negativeScales(negativeScales_)
{
int input = addNodeToMatch("");
int scales = addNodeToMatch("Const");
int neg = addNodeToMatch("Neg", input);
int relu_neg = addNodeToMatch("Relu", neg);
int finalScales = negativeScales ? addNodeToMatch("Neg", scales) : scales;
int mul = addNodeToMatch("Mul", finalScales, relu_neg);
int relu_pos = addNodeToMatch("Relu", input);
addNodeToMatch("Add", relu_pos, mul);
setFusedNode("PReLU", input, scales);
}
virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
if (!negativeScales)
{
Mat scalesRef = getTensorContentRef_(inputNodes[1]->attr().at("value").tensor());
// FIXME: This breaks the const guarantees of tensor() by writing to scalesRef
if (isAlignedMat(scalesRef))
{
scalesRef *= -1;
}
else
{
Mat scales = scalesRef.clone() * -1;
CV_Assert(scalesRef.isContinuous());
CV_Assert(scales.isContinuous());
memcpy(scalesRef.data, scales.data, scales.total() * scales.elemSize());
}
}
}
private:
bool negativeScales;
};
class ClipByValueSubgraph : public TFSubgraph
{
public:
ClipByValueSubgraph()
{
int input = addNodeToMatch("");
int maxValue = addNodeToMatch("Const");
int minimum = addNodeToMatch("Minimum", input, maxValue);
int minValue = addNodeToMatch("Const");
addNodeToMatch("Maximum", minimum, minValue);
setFusedNode("ClipByValue", input, minValue, maxValue);
}
};
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)));
subgraphs.push_back(Ptr<Subgraph>(new L2NormalizeSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionValidKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionSameKerasSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new UpsamplingKerasSubgraph("ResizeNearestNeighbor")));
subgraphs.push_back(Ptr<Subgraph>(new UpsamplingKerasSubgraph("ResizeBilinear")));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new SoftMaxSlimV2Subgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ReshapeAsShapeSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new KerasMVNSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new PReLUSubgraph(true)));
subgraphs.push_back(Ptr<Subgraph>(new PReLUSubgraph(false)));
subgraphs.push_back(Ptr<Subgraph>(new FlattenProdSubgraph()));
subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraphDown()));
subgraphs.push_back(Ptr<Subgraph>(new ClipByValueSubgraph()));
for (int i = 0; i < net.node_size(); ++i)
{
tensorflow::NodeDef* layer = net.mutable_node(i);
if (layer->op() == "AddV2")
layer->set_op("Add");
}
simplifySubgraphs(Ptr<ImportGraphWrapper>(new TFGraphWrapper(net)), subgraphs);
}
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" || type == "PlaceholderWithDefault") {
identity_ops_idx.push_back(li);
CV_Assert(layer.input_size() != 0);
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);
input_op_name = input_op_name.substr(input_op_name.find('^') + 1,
input_op_name.rfind(':'));
IdentityOpsMap::iterator it = identity_ops.find(input_op_name);
if (it != identity_ops.end()) {
std::set<String> loopCheckSet;
// In case of Identity after Identity
while (true)
{
IdentityOpsMap::iterator nextIt = identity_ops.find(it->second);
if (nextIt != identity_ops.end())
{
// Loop check
if (loopCheckSet.find(it->second) != loopCheckSet.end())
CV_Error(Error::StsError, "Found a loop in your input Tensorflow model, which is illegal!");
loopCheckSet.insert(it->second);
it = nextIt;
}
else
break;
}
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++;
}
}
// NB: returned Mat::data pointer may be unaligned
Mat getTensorContentRef_(const tensorflow::TensorProto& tensor)
{
const std::string& content = tensor.tensor_content();
Mat m;
switch (tensor.dtype())
{
case tensorflow::DT_FLOAT:
{
if (!content.empty())
m = Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str());
else
{
const RepeatedField<float>& field = tensor.float_val();
CV_Assert(!field.empty());
m = Mat(1, field.size(), CV_32FC1, (void*)field.data());
}
break;
}
case tensorflow::DT_DOUBLE:
{
if (!content.empty())
m = Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str());
else
{
const RepeatedField<double>& field = tensor.double_val();
CV_Assert(!field.empty());
m = Mat(1, field.size(), CV_64FC1, (void*)field.data());
}
break;
}
case tensorflow::DT_INT32:
{
if (!content.empty())
m = Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str());
else
{
const RepeatedField<int32_t>& field = tensor.int_val();
CV_Assert(!field.empty());
m = Mat(1, field.size(), CV_32SC1, (void*)field.data());
}
break;
}
case tensorflow::DT_HALF:
{
if (!content.empty())
{
static const int kHalfSize = 2;
Mat halfs(1, content.size() / kHalfSize, CV_16FC1, (void*)content.c_str());
halfs.convertTo(m, CV_32F);
}
else
{
const RepeatedField<int32_t>& field = tensor.half_val();
CV_Assert(!field.empty());
Mat ints(1, field.size(), CV_32SC1, (void*)field.data());
Mat halfs;
ints.convertTo(halfs, CV_16UC1);
Mat halfsSigned(halfs.size(), CV_16FC1, halfs.data);
halfsSigned.convertTo(m, CV_32F);
}
break;
}
case tensorflow::DT_QUINT8:
{
CV_Assert(!content.empty());
m = Mat(1, content.size(), CV_8UC1, (void*)content.c_str());
break;
}
default:
CV_Error(Error::StsError, "Tensor's data type is not supported");
break;
}
return m;
}
Mat getTensorContent(const tensorflow::TensorProto& tensor, bool forceCopy)
{
// If necessary clone m to have aligned data pointer
Mat m = getTensorContentRef_(tensor);
if (forceCopy || !isAlignedMat(m))
return m.clone();
else
return m;
}
void releaseTensor(tensorflow::TensorProto* tensor)
{
if (!tensor->mutable_tensor_content()->empty())
{
delete tensor->release_tensor_content();
}
}
static void permute(google::protobuf::RepeatedPtrField<tensorflow::NodeDef>* data,
const std::vector<int>& indices)
{
const int num = data->size();
CV_Assert(num == indices.size());
std::vector<int> elemIdToPos(num);
std::vector<int> posToElemId(num);
for (int i = 0; i < num; ++i)
{
elemIdToPos[i] = i;
posToElemId[i] = i;
}
for (int i = 0; i < num; ++i)
{
int elemId = indices[i];
int pos = elemIdToPos[elemId];
if (pos != i)
{
data->SwapElements(i, pos);
const int swappedElemId = posToElemId[i];
elemIdToPos[elemId] = i;
elemIdToPos[swappedElemId] = pos;
posToElemId[i] = elemId;
posToElemId[pos] = swappedElemId;
}
}
}
// Is based on tensorflow::graph_transforms::SortByExecutionOrder
void sortByExecutionOrder(tensorflow::GraphDef& net)
{