IVGCVSW-1656 Add Mean support to Tf Parser
Change-Id: I3d31d6b72be1984acdb51fd9e7b5488a7aa5d832
diff --git a/src/armnnTfParser/TfParser.cpp b/src/armnnTfParser/TfParser.cpp
index 90bd992..0087ef8 100755
--- a/src/armnnTfParser/TfParser.cpp
+++ b/src/armnnTfParser/TfParser.cpp
@@ -2,40 +2,27 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
+
#include "TfParser.hpp"
-#include <armnn/INetwork.hpp>
-#include <armnn/Utils.hpp>
#include <armnn/TypesUtils.hpp>
-#include <armnn/Exceptions.hpp>
#include <armnn/Descriptors.hpp>
#include <GraphTopologicalSort.hpp>
#include <ParserHelper.hpp>
#include <Permute.hpp>
-#include <VerificationHelpers.hpp>
#include <DataLayoutIndexed.hpp>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>
#include "tensorflow/core/framework/graph.pb.h"
-#include "tensorflow/core/framework/node_def.pb.h"
-#include "tensorflow/core/framework/types.pb.h"
-#include "tensorflow/core/framework/tensor.pb.h"
-#include "tensorflow/core/framework/tensor_shape.pb.h"
-#include <boost/assert.hpp>
#include <boost/format.hpp>
#include <boost/core/ignore_unused.hpp>
-#include <boost/log/trivial.hpp>
-#include <boost/numeric/conversion/cast.hpp>
#include <boost/polymorphic_cast.hpp>
-#include <memory>
-#include <sstream>
#include <numeric>
-#include <functional>
using namespace armnnUtils;
using namespace armnn;
@@ -141,6 +128,17 @@
return attribValue;
}
+bool ReadMandatoryNodeBoolAttribute(const tensorflow::NodeDef& nodeDef, const std::string& name)
+{
+ bool attribValue = false;
+ ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kB,
+ [&attribValue](const tensorflow::AttrValue& attrValue)
+ {
+ attribValue = static_cast<bool>(attrValue.b());
+ });
+ return attribValue;
+}
+
uint32_t ReadMandatoryNodeUint32Attribute(const tensorflow::NodeDef& nodeDef, const std::string& name)
{
uint32_t attribValue = 0u;
@@ -338,6 +336,7 @@
{ "ConcatV2", &TfParser::ParseConcat },
{ "LRN", &TfParser::ParseLrn },
{ "MatMul", &TfParser::ParseMatMul },
+ { "Mean", &TfParser::ParseMean },
{ "Mul", &TfParser::ParseMul },
{ "Placeholder", &TfParser::ParsePlaceholder },
{ "RealDiv", &TfParser::ParseRealDiv },
@@ -2349,6 +2348,60 @@
return std::make_unique<ParsedMatMulTfOperation>(this, nodeDef);
}
+ParsedTfOperationPtr TfParser::ParseMean(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef)
+{
+ std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);
+ IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);
+ TensorInfo inputTensorInfo = inputSlot.GetTensorInfo();
+
+ if (inputs.size() != 2)
+ {
+ throw ParseException(
+ boost::str(boost::format("Mean expects two inputs!. Got %1% for Node %2% %3%")
+ % inputs.size()
+ % nodeDef.name()
+ % CHECK_LOCATION().AsString()));
+ }
+
+ bool keepDims = ReadMandatoryNodeBoolAttribute(nodeDef, "keep_dims");
+
+ ParsedConstTfOperation<int32_t>* axisNode =
+ boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue);
+
+ const TensorInfo& axisTensorInfo = axisNode->GetTensorInfo();
+
+ ConstTensor axisTensor(axisTensorInfo, axisNode->GetStorage());
+ const int* axisData = static_cast<const int*>(axisTensor.GetMemoryArea());
+
+ TensorInfo outputTensorInfo;
+ MeanDescriptor meanDescriptor;
+ meanDescriptor.m_KeepDims = keepDims;
+
+ // Negative axis values are supported so that the process requires
+ // to convert them into the corresponding positive ones.
+ // Duplicate values are also removed.
+ std::vector<int> rawAxisVector(axisData, axisData + axisTensorInfo.GetNumElements());
+ std::set<unsigned int> positiveAxisSet;
+ int rank = static_cast<int>(inputTensorInfo.GetNumDimensions());
+
+ std::transform(rawAxisVector.begin(), rawAxisVector.end(),
+ std::inserter(positiveAxisSet, positiveAxisSet.begin()),
+ [rank](int i) -> unsigned int { return static_cast<unsigned int>((i + rank) % rank); });
+
+ CalculateReducedOutputTensoInfo(inputTensorInfo, axisTensorInfo, positiveAxisSet, keepDims, outputTensorInfo);
+
+ if (inputTensorInfo.GetNumDimensions() > positiveAxisSet.size())
+ {
+ meanDescriptor.m_Axis.assign(positiveAxisSet.begin(), positiveAxisSet.end());
+ }
+
+ IConnectableLayer* layer = m_Network->AddMeanLayer(meanDescriptor, nodeDef.name().c_str());
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ inputSlot.Connect(layer->GetInputSlot(0));
+
+ return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer);
+}
+
/// An ParsedTfOperation for a Mul node.
/// Creation of the armnn Mul layer is deferred until it is actually needed, because Mul nodes
/// are also used for the first part of a leaky relu activation function (Mul followed by Maximum)
diff --git a/src/armnnTfParser/TfParser.hpp b/src/armnnTfParser/TfParser.hpp
index 4421768..f1b7205 100644
--- a/src/armnnTfParser/TfParser.hpp
+++ b/src/armnnTfParser/TfParser.hpp
@@ -140,6 +140,7 @@
ParsedTfOperationPtr ParseIdentity(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
ParsedTfOperationPtr ParseLrn(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
ParsedTfOperationPtr ParseMatMul(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
+ ParsedTfOperationPtr ParseMean(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
ParsedTfOperationPtr ParseMul(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
ParsedTfOperationPtr ParsePlaceholder(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
ParsedTfOperationPtr ParseRealDiv(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef);
@@ -260,4 +261,5 @@
/// Maps output layer names to their corresponding ids and tensor info.
std::unordered_map<std::string, BindingPointInfo> m_NetworkOutputsBindingInfo;
};
+
}
diff --git a/src/armnnTfParser/test/Mean.cpp b/src/armnnTfParser/test/Mean.cpp
new file mode 100644
index 0000000..1304162
--- /dev/null
+++ b/src/armnnTfParser/test/Mean.cpp
@@ -0,0 +1,175 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "armnnTfParser/ITfParser.hpp"
+#include "ParserPrototxtFixture.hpp"
+
+BOOST_AUTO_TEST_SUITE(TensorflowParser)
+
+struct MeanFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+ explicit MeanFixture(const armnn::TensorShape& inputShape, const armnn::TensorShape& outputShape,
+ const std::vector<unsigned int>& axis, bool keepDims)
+ {
+ std::string protobufAxisString;
+ std::vector<unsigned int> protobufAxis(axis);
+
+ // If no axis range is specified, the reduction is applied to
+ // all dimensions of the input tensor
+ if (protobufAxis.size() == 0)
+ {
+ for (unsigned int i = 0; i < inputShape.GetNumDimensions(); ++i)
+ {
+ protobufAxis.push_back(i);
+ }
+ }
+
+ for (unsigned int i = 0; i < protobufAxis.size(); ++i)
+ {
+ protobufAxisString.append(ConvertInt32ToOctalString(static_cast<int>(protobufAxis[i])));
+ }
+
+ m_Prototext = R"(node {
+ name: "input"
+ op: "Placeholder"
+ attr {
+ key: "dtype"
+ value {
+ type: DT_FLOAT
+ }
+ }
+ attr {
+ key: "shape"
+ value {
+ shape {
+ }
+ }
+ }
+ }
+ node {
+ name: "Const"
+ op: "Const"
+ attr {
+ key: "dtype"
+ value {
+ type: DT_INT32
+ }
+ }
+ attr {
+ key: "value"
+ value { )";
+
+ if (axis.size() == 1)
+ {
+ m_Prototext.append(R"( tensor {
+ dtype: DT_INT32
+ tensor_shape {
+ }
+ int_val: )").append(std::to_string(protobufAxis[0])).append(R"(
+ } )");
+ }
+ else
+ {
+ m_Prototext.append(R"( tensor {
+ dtype: DT_INT32
+ tensor_shape {
+ dim {
+ size: 2
+ }
+ }
+ tensor_content: ")").append(protobufAxisString).append(R"("
+ } )");
+ }
+
+ m_Prototext.append(R"( }
+ }
+ }
+ node {
+ name: "output"
+ op: "Mean"
+ input: "input"
+ input: "Const"
+ attr {
+ key: "T"
+ value {
+ type: DT_FLOAT
+ }
+ }
+ attr {
+ key: "Tidx"
+ value {
+ type: DT_INT32
+ }
+ }
+ attr {
+ key: "keep_dims"
+ value {
+ b: )").append(keepDims ? "true" : "false").append(R"(
+ }
+ }
+ })");
+
+ SetupSingleInputSingleOutput(inputShape, outputShape, "input", "output");
+ }
+};
+
+struct MeanNoAxisNoKeepDimsFixture: MeanFixture
+{
+ MeanNoAxisNoKeepDimsFixture() : MeanFixture({ 2, 3 }, { 1 }, {}, false) {}
+};
+
+struct MeanWithAxis0NoKeepDimsFixture: MeanFixture
+{
+ MeanWithAxis0NoKeepDimsFixture() : MeanFixture({ 2, 3 }, { 3 }, { 0 }, false) {}
+};
+
+struct MeanWithAxis1NoKeepDimsFixture: MeanFixture
+{
+ MeanWithAxis1NoKeepDimsFixture() : MeanFixture({ 2, 3 }, { 2 }, { 1 }, false) {}
+};
+
+struct MeanWithAxis0KeepDimsFixture: MeanFixture
+{
+ MeanWithAxis0KeepDimsFixture() : MeanFixture({ 2, 3 }, { 1, 3 }, { 0 }, true) {}
+};
+
+struct MeanWithAxis1KeepDimsFixture: MeanFixture
+{
+ MeanWithAxis1KeepDimsFixture() : MeanFixture({ 2, 3 }, { 2, 1 }, { 1 }, true) {}
+};
+
+
+BOOST_FIXTURE_TEST_CASE(MeanNoAxisNoKeepDims, MeanNoAxisNoKeepDimsFixture)
+{
+ RunTest<1>({ { "input", { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f } } },
+ { { "output", { 1.5f } } });
+}
+
+BOOST_FIXTURE_TEST_CASE(MeanWithAxis0NoKeepDims, MeanWithAxis0NoKeepDimsFixture)
+{
+ RunTest<1>({ { "input", { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f } } },
+ { { "output", { 1.5f, 1.5f, 1.5f } } });
+}
+
+BOOST_FIXTURE_TEST_CASE(MeanWithAxis1NoKeepDims, MeanWithAxis1NoKeepDimsFixture)
+{
+ RunTest<1>({ { "input", { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f } } },
+ { { "output", { 1.f, 2.f } } });
+}
+
+BOOST_FIXTURE_TEST_CASE(MeanWithAxis0KeepDims, MeanWithAxis0KeepDimsFixture)
+{
+ RunTest<2>({ { "input", { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f } } },
+ { { "output", { 1.5f, 1.5f, 1.5f } } });
+}
+
+BOOST_FIXTURE_TEST_CASE(MeanWithAxis1KeepDims, MeanWithAxis1KeepDimsFixture)
+{
+ RunTest<2>({ { "input", { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f } } },
+ { { "output", { 1.f, 2.f } } });
+}
+
+BOOST_AUTO_TEST_SUITE_END()