From 6a669d757aec8790d4548486cdbf073d8f81b646 Mon Sep 17 00:00:00 2001 From: Ferran Balaguer Date: Tue, 11 Dec 2018 10:29:05 +0000 Subject: IVGCVSW-2266 Remove the input swizzling from ParseDepthwiseConv2D Change-Id: I72d94fff4cdad2c62dff98c8fd52eba78a1908f0 --- src/armnnTfParser/TfParser.cpp | 141 ++++++++++------------ src/armnnTfParser/test/DepthwiseConvolution2d.cpp | 82 ++++++++----- 2 files changed, 115 insertions(+), 108 deletions(-) diff --git a/src/armnnTfParser/TfParser.cpp b/src/armnnTfParser/TfParser.cpp index 210b825e43..3c7d16e7b4 100644 --- a/src/armnnTfParser/TfParser.cpp +++ b/src/armnnTfParser/TfParser.cpp @@ -48,37 +48,6 @@ namespace const PermutationVector NHWCToArmNN = { 0, 2, 3, 1 }; const PermutationVector ArmNNToNHWC = { 0, 3, 1, 2 }; -IConnectableLayer* AddSwizzleLayer(INetwork& network, IOutputSlot& input, const PermutationVector& mapping, - const std::string& name) -{ - // Adds swizzle layer. - IConnectableLayer* const layer = network.AddPermuteLayer(mapping, name.c_str()); - - // Connects intput to swizzle layer. - input.Connect(layer->GetInputSlot(0)); - - // Sets up swizzled output. - const TensorInfo outInfo = armnnUtils::Permuted(input.GetTensorInfo(), mapping); - layer->GetOutputSlot(0).SetTensorInfo(outInfo); - - return layer; -} - -IConnectableLayer* SwizzleInDeswizzleOut(INetwork& network, IOutputSlot& input, IConnectableLayer& layer, - const std::string& name) -{ - // Adds swizzle layer. - IConnectableLayer* const swizzleLayer = AddSwizzleLayer(network, input, NHWCToArmNN, "swizzle_for-" + name); - - // Connects swizzledInput to layer. - swizzleLayer->GetOutputSlot(0).Connect(layer.GetInputSlot(0)); - - // Adds deswizzle layer. - IConnectableLayer* const deswizzleLayer = AddSwizzleLayer(network, layer.GetOutputSlot(0), ArmNNToNHWC, - "deswizzle_for-" + name); - - return deswizzleLayer; -} template void ReadMandatoryNodeAttributeImpl(const tensorflow::NodeDef& nodeDef, @@ -1181,10 +1150,10 @@ ParsedTfOperationPtr TfParser::ParseDepthwiseConv2D(const tensorflow::NodeDef& n % nodeDef.name() % CHECK_LOCATION().AsString())); } + ParsedConstTfOperation* weightNode = boost::polymorphic_downcast *>(inputs[1].m_IndexedValue); - std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, "padding"); std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); std::vector strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, "strides"); @@ -1194,59 +1163,85 @@ ParsedTfOperationPtr TfParser::ParseDepthwiseConv2D(const tensorflow::NodeDef& n CHECK_DATA_FORMAT(nodeDef, dataFormat, "DepthwiseConv2dNative"); - if (dataFormat == "NHWC") - { - desc.m_StrideX = strides[2]; - desc.m_StrideY = strides[1]; - // Swizzles input to supported memory layout. - inputTensorInfo = armnnUtils::Permuted(inputSlot.GetTensorInfo(), NHWCToArmNN); - } - else if (dataFormat == "NCHW") - { - desc.m_StrideX = strides[3]; - desc.m_StrideY = strides[2]; - } + DataLayout dataLayout = dataFormat == "NHWC" ? DataLayout::NHWC : DataLayout::NCHW; - uint32_t inputHeight = inputTensorInfo.GetShape()[2]; - uint32_t inputWidth = inputTensorInfo.GetShape()[3]; + desc.m_DataLayout = dataLayout; - std::vector outputTensorData; + DataLayoutIndexed dataLayoutIndexed(dataLayout); + + desc.m_StrideX = strides[dataLayoutIndexed.GetWidthIndex()]; + desc.m_StrideY = strides[dataLayoutIndexed.GetHeightIndex()]; + + uint32_t inputHeight = inputTensorInfo.GetShape()[dataLayoutIndexed.GetHeightIndex()]; + uint32_t inputWidth = inputTensorInfo.GetShape()[dataLayoutIndexed.GetWidthIndex()]; + + // Mappings from TensorFlow filter tensors to the ArmNN filter tensors. + // Tensorflow weights are [H, W, In, Out]. + // ArmNN weights have to be [Out, H, W, In] when the data layout is NHWC, + // and [Out, In, H, W] when the data layout is NCHW. + PermutationVector permutationVector = + dataLayout == DataLayout::NHWC ? + std::initializer_list{ 1, 2, 3, 0 } : // NHWC: [H, W, In, Out] -> [Out, H, W, In] + std::initializer_list{ 2, 3, 1, 0 }; // NCHW: [H, W, In, Out] -> [Out, In, H, W] + + // Swizzle the tensor using the given permutation vector. + const TensorInfo& weightTensorInfo = weightNode->GetTensorInfo(); + const TensorInfo weightTensorSwizzledInfo = armnnUtils::Permuted(weightTensorInfo, permutationVector); + + // Swizzles the content of the tensor's permanent storage into a local storage. + std::vector weightTensorSwizzledData(weightTensorInfo.GetNumElements()); + armnnUtils::Permute(weightTensorSwizzledInfo.GetShape(), permutationVector, + weightNode->GetStorage(), weightTensorSwizzledData.data()); - ConstTensor weightTensor = weightNode->GetConstTensor(true, outputTensorData); + // Create a weight tensor with the newly swizzled data. + ConstTensor weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData); - uint32_t weightHeight = weightTensor.GetShape()[2]; - uint32_t weightWidth = weightTensor.GetShape()[3]; + uint32_t weightHeight = weightTensor.GetShape()[dataLayoutIndexed.GetHeightIndex()]; + uint32_t weightWidth = weightTensor.GetShape()[dataLayoutIndexed.GetWidthIndex()]; bool padding = false; TensorInfo outputInfo; + unsigned int outputHeight = 0; + unsigned int outputWidth = 0; CHECK_PADDING_TYPE(nodeDef, paddingString); if (paddingString == "SAME") { padding = true; - outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], - weightTensor.GetShape()[0] * weightTensor.GetShape()[1], - static_cast(ceil( - static_cast(inputHeight) / - static_cast(desc.m_StrideY))), - static_cast(ceil( - static_cast(inputWidth) / - static_cast(desc.m_StrideX))) - }, DataType::Float32); + + outputHeight = static_cast(ceil(static_cast(inputHeight) / + static_cast(desc.m_StrideY))); + outputWidth = static_cast(ceil(static_cast(inputWidth) / + static_cast(desc.m_StrideX))); } else if (paddingString == "VALID") { padding = false; - outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], - weightTensor.GetShape()[0] * weightTensor.GetShape()[1], - static_cast(ceil( - static_cast(inputHeight - weightHeight + 1) / - static_cast(desc.m_StrideY))), - static_cast(ceil( - static_cast(inputWidth - weightWidth + 1) / - static_cast(desc.m_StrideX))) - }, DataType::Float32); + + outputHeight = static_cast(ceil(static_cast(inputHeight - weightHeight + 1) / + static_cast(desc.m_StrideY))); + outputWidth = static_cast(ceil(static_cast(inputWidth - weightWidth + 1) / + static_cast(desc.m_StrideX))); + } + + switch (dataLayout) + { + case DataLayout::NHWC: + outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], + outputHeight, + outputWidth, + weightTensor.GetShape()[0] * weightTensor.GetShape()[3]}, + DataType::Float32); + break; + case DataLayout::NCHW: + default: + outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], + weightTensor.GetShape()[0] * weightTensor.GetShape()[1], + outputHeight, + outputWidth }, + DataType::Float32); + break; } CalcPadding(inputHeight, weightHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, padding); @@ -1254,15 +1249,7 @@ ParsedTfOperationPtr TfParser::ParseDepthwiseConv2D(const tensorflow::NodeDef& n IConnectableLayer* layer = m_Network->AddDepthwiseConvolution2dLayer(desc, weightTensor, nodeDef.name().c_str()); layer->GetOutputSlot(0).SetTensorInfo(outputInfo); - - if (dataFormat == "NHWC") - { - layer = SwizzleInDeswizzleOut(*m_Network, inputSlot, *layer, nodeDef.name()); - } - else - { - inputSlot.Connect(layer->GetInputSlot(0)); - } + inputSlot.Connect(layer->GetInputSlot(0)); return std::make_unique(this, nodeDef, layer); } diff --git a/src/armnnTfParser/test/DepthwiseConvolution2d.cpp b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp index 5a8105cfa5..464e62fc23 100644 --- a/src/armnnTfParser/test/DepthwiseConvolution2d.cpp +++ b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp @@ -9,11 +9,15 @@ #include #include +#include +using namespace armnnUtils; +using namespace armnn; + BOOST_AUTO_TEST_SUITE(TensorflowParser) struct DepthwiseConvolution2dFixture : public armnnUtils::ParserPrototxtFixture { - explicit DepthwiseConvolution2dFixture(const char* paddingType) + explicit DepthwiseConvolution2dFixture(const std::string& dataLayout, const char* paddingType) { m_Prototext = "node { \n" " name: \"graphInput\" \n" @@ -25,26 +29,9 @@ struct DepthwiseConvolution2dFixture : public armnnUtils::ParserPrototxtFixture< " } \n" " } \n" " attr { \n" - " key: \"value\" \n" + " key: \"shape\" \n" " value { \n" - " tensor { \n" - " dtype: DT_FLOAT \n" - " tensor_shape { \n" - " dim { \n" - " size: 1 \n" - " } \n" - " dim { \n" - " size: 1 \n" - " } \n" - " dim { \n" - " size: 3 \n" - " } \n" - " dim { \n" - " size: 3 \n" - " } \n" - " } \n" - " tensor_content: \"\\000\\000\\200?\\000\\000\\000@\\000\\000@@\\000\\000\\200@" - "\\000\\000\\240@\\000\\000\\300@\\000\\000\\340@\\000\\000\\000A\\000\\000\\020A\" \n" + " shape { \n" " } \n" " } \n" " } \n" @@ -104,13 +91,15 @@ struct DepthwiseConvolution2dFixture : public armnnUtils::ParserPrototxtFixture< " attr { \n" " key: \"data_format\" \n" " value { \n" - " s: \"NHWC\" \n" + " s: \""; + m_Prototext.append(dataLayout); + m_Prototext.append("\"\n" " } \n" " } \n" " attr { \n" " key: \"padding\" \n" " value { \n" - " s: \""; + " s: \""); m_Prototext.append(paddingType); m_Prototext.append("\"\n" " } \n" @@ -134,32 +123,63 @@ struct DepthwiseConvolution2dFixture : public armnnUtils::ParserPrototxtFixture< " } \n" "} \n"); - SetupSingleInputSingleOutput({ 1, 1, 3, 3 }, "graphInput", "potato"); + if(dataLayout == "NHWC") + { + SetupSingleInputSingleOutput({ 1u, 1u, 3u, 3u }, "graphInput", "potato"); + } + else + { + SetupSingleInputSingleOutput({ 1u, 3u, 1u, 3u }, "graphInput", "potato"); + } } }; -struct DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture +struct DepthwiseConvolution2dNhwcSameFixture : DepthwiseConvolution2dFixture { - DepthwiseConvolution2dSameFixture() : DepthwiseConvolution2dFixture("SAME") { } + DepthwiseConvolution2dNhwcSameFixture() : DepthwiseConvolution2dFixture("NHWC", "SAME") { } }; -BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSame, DepthwiseConvolution2dSameFixture) +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNhwcSame, DepthwiseConvolution2dNhwcSameFixture) { RunTest<4>({ 1, 2, 3, 4, 5, 6, 7, 8, 9 }, { 2.5f, 5.f, 2.5f, 3.5f, 7.f, 3.5f, 4.5f, 9.f, 4.5f, 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f, - 5.5f, 11.f, 5.5f, 6.5f, 13.f, 6.5f, 7.5f, 15.f, 7.5f}); + 5.5f, 11.f, 5.5f, 6.5f, 13.f, 6.5f, 7.5f, 15.f, 7.5f }); } -struct DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture +struct DepthwiseConvolution2dNchwSameFixture : DepthwiseConvolution2dFixture { - DepthwiseConvolution2dValidFixture() : DepthwiseConvolution2dFixture("VALID") { } + DepthwiseConvolution2dNchwSameFixture() : DepthwiseConvolution2dFixture("NCHW", "SAME") { } }; -BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DValid, DepthwiseConvolution2dValidFixture) +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNchwSame, DepthwiseConvolution2dNchwSameFixture) +{ + RunTest<4>({ 1, 4, 7, 2, 5, 8, 3, 6, 9 }, + { 2.5f, 6.f, 5.5f, 5.f, 12.f, 11.f, 2.5f, 6.f, 5.5f, + 3.5f, 7.5f, 6.5f, 7.f, 15.f, 13.f, 3.5f, 7.5f, 6.5f, + 4.5f, 9.f, 7.5f, 9.f, 18.f, 15.f, 4.5f, 9.f, 7.5f }); +} + +struct DepthwiseConvolution2dNhwcValidFixture : DepthwiseConvolution2dFixture +{ + DepthwiseConvolution2dNhwcValidFixture() : DepthwiseConvolution2dFixture("NHWC", "VALID") { } +}; + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNhwcValid, DepthwiseConvolution2dNhwcValidFixture) { RunTest<4>({ 1, 2, 3, 4, 5, 6, 7, 8, 9 }, // input data - { 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f }); // output expected data + { 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f }); // output expected data +} + +struct DepthwiseConvolution2dNchwValidFixture : DepthwiseConvolution2dFixture +{ + DepthwiseConvolution2dNchwValidFixture() : DepthwiseConvolution2dFixture("NCHW", "VALID") { } +}; + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNchwValid, DepthwiseConvolution2dNchwValidFixture) +{ + RunTest<4>({ 1, 4, 7, 2, 5, 8, 3, 6, 9 }, + { 6.f, 12.f, 6.f, 7.5f, 15.f, 7.5f, 9.f, 18.f, 9.f }); } -- cgit v1.2.1