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Fuse deconvolution layer subgraphs from Keras

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This commit is contained in:
Dmitry Kurtaev 2018-04-20 16:44:05 +03:00
parent 7ea5029ae5
commit d959d7b9f0
3 changed files with 125 additions and 2 deletions
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121
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
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@ -419,6 +419,125 @@ public:
}
};
class DeconvolutionValidKerasSubgraph : public Subgraph
{
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 Subgraph
{
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);
}
};
void simplifySubgraphs(tensorflow::GraphDef& net)
{
std::vector<Ptr<Subgraph> > subgraphs;
@ -430,6 +549,8 @@ void simplifySubgraphs(tensorflow::GraphDef& net)
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()));
int numNodes = net.node_size();
std::vector<int> matchedNodesIds;

4
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1303,8 +1303,8 @@ void TFImporter::populateNet(Net dstNet)
const int strideY = layerParams.get<int>("stride_h");
const int strideX = layerParams.get<int>("stride_w");
Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0));
const int outH = outShape.at<int>(2);
const int outW = outShape.at<int>(1);
const int outH = outShape.at<int>(1);
const int outW = outShape.at<int>(2);
if (layerParams.get<String>("pad_mode") == "SAME")
{
layerParams.set("adj_w", (outW - 1) % strideX);

2
modules/dnn/test/test_tf_importer.cpp
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@ -173,6 +173,8 @@ TEST_P(Test_TensorFlow_layers, deconvolution)
runTensorFlowNet("deconvolution_stride_2_same", targetId);
runTensorFlowNet("deconvolution_adj_pad_valid", targetId);
runTensorFlowNet("deconvolution_adj_pad_same", targetId);
runTensorFlowNet("keras_deconv_valid", targetId);
runTensorFlowNet("keras_deconv_same", targetId);
}
TEST_P(Test_TensorFlow_layers, matmul)