IVGCVSW-2264 Remove input swizzling from ParseConv2D in the TF parser
* Removed the input swizzling when the data layout is NHWC
* Permuting weights depending on the data layout used
* Added getter methods to ParsedConstTfOperation to get the tensor
info and the storage memory area, needed for swizzling the weights
* Added unit tests for both NHWC and NCHW data layouts
Change-Id: I6543900c594417df630b2663d8551158b93b7836
diff --git a/src/armnnTfParser/TfParser.cpp b/src/armnnTfParser/TfParser.cpp
index 53cdfa3..b40b054 100644
--- a/src/armnnTfParser/TfParser.cpp
+++ b/src/armnnTfParser/TfParser.cpp
@@ -14,6 +14,7 @@
#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>
@@ -36,6 +37,7 @@
#include <numeric>
#include <functional>
+using namespace armnnUtils;
using namespace armnn;
namespace armnnTfParser
@@ -752,6 +754,16 @@
return constTensor;
}
+ const T* GetStorage() const
+ {
+ return m_Storage.data();
+ }
+
+ const TensorInfo& GetTensorInfo() const
+ {
+ return m_TensorInfo;
+ }
+
private:
///< Manages the lifetime of the tensor data.
std::vector<T> m_Storage;
@@ -1059,59 +1071,85 @@
CHECK_DATA_FORMAT(nodeDef, dataFormat, "Conv2D");
- 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<float> outputTensorData;
+ DataLayoutIndexed dataLayoutIndexed(dataLayout);
- ConstTensor weightTensor = weightNode->GetConstTensor(true, outputTensorData);
+ desc.m_StrideX = strides[dataLayoutIndexed.GetWidthIndex()];
+ desc.m_StrideY = strides[dataLayoutIndexed.GetHeightIndex()];
- uint32_t weightHeight = weightTensor.GetShape()[2];
- uint32_t weightWidth = weightTensor.GetShape()[3];
+ 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<unsigned int>{ 1, 2, 3, 0 } : // NHWC: [H, W, In, Out] -> [Out, H, W, In]
+ std::initializer_list<unsigned int>{ 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<float> weightTensorSwizzledData(weightTensorInfo.GetNumElements());
+ armnnUtils::Permute(weightTensorSwizzledInfo.GetShape(), permutationVector,
+ weightNode->GetStorage(), weightTensorSwizzledData.data());
+
+ // Create a weight tensor with the newly swizzled data.
+ ConstTensor weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData);
+
+ 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],
- static_cast<uint32_t>(ceil(
- static_cast<float>(inputHeight) /
- static_cast<float>(desc.m_StrideY))),
- static_cast<uint32_t>(ceil(
- static_cast<float>(inputWidth) /
- static_cast<float>(desc.m_StrideX)))
- }, DataType::Float32);
+
+ outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight) /
+ static_cast<float>(desc.m_StrideY)));
+ outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth) /
+ static_cast<float>(desc.m_StrideX)));
}
else if (paddingString == "VALID")
{
padding = false;
+
+ outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight - weightHeight + 1) /
+ static_cast<float>(desc.m_StrideY)));
+ outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth - weightWidth + 1) /
+ static_cast<float>(desc.m_StrideX)));
+ }
+
+ switch (dataLayout)
+ {
+ case DataLayout::NHWC:
+ outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0],
+ outputHeight,
+ outputWidth,
+ weightTensor.GetShape()[0] },
+ DataType::Float32);
+ break;
+ case DataLayout::NCHW:
+ default:
outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0],
weightTensor.GetShape()[0],
- static_cast<uint32_t>(ceil(
- static_cast<float>(inputHeight - weightHeight + 1) /
- static_cast<float>(desc.m_StrideY))),
- static_cast<uint32_t>(ceil(
- static_cast<float>(inputWidth - weightWidth + 1) /
- static_cast<float>(desc.m_StrideX)))
- }, DataType::Float32);
+ outputHeight,
+ outputWidth },
+ DataType::Float32);
+ break;
}
CalcPadding(inputHeight, weightHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, padding);
@@ -1119,15 +1157,7 @@
IConnectableLayer* layer = m_Network->AddConvolution2dLayer(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<SingleLayerParsedTfOperation>(this, nodeDef, layer);
}
diff --git a/src/armnnTfParser/test/Convolution2d.cpp b/src/armnnTfParser/test/Convolution2d.cpp
index cc534df..aead1fe 100644
--- a/src/armnnTfParser/test/Convolution2d.cpp
+++ b/src/armnnTfParser/test/Convolution2d.cpp
@@ -6,6 +6,8 @@
#include <boost/test/unit_test.hpp>
#include "armnnTfParser/ITfParser.hpp"
#include "ParserPrototxtFixture.hpp"
+
+#include <array>
#include <string>
#include <iostream>
@@ -13,15 +15,28 @@
struct Convolution2dFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
{
- explicit Convolution2dFixture(const char* paddingType)
- : Convolution2dFixture(paddingType, 1)
+ explicit Convolution2dFixture(const std::string& dataLayout, const std::string& paddingType)
+ : Convolution2dFixture(dataLayout, paddingType, 1)
{}
// Dilation: 0 - dilations attribute is not included;
// Dilation: >0 - dilations attribute set to [1,v,v,1], where v is the value of the dilation arg
- explicit Convolution2dFixture(const char* paddingType, int stride, int dilation = 0)
+ explicit Convolution2dFixture(const std::string& dataLayout, const std::string& paddingType,
+ int stride, int dilation = 0)
{
- std::string strideString = std::to_string(stride);
+ std::string strideString (" i: 1 \n"
+ " i: 1 \n");
+ if (dataLayout == "NHWC")
+ {
+ strideString.append(" i: " + std::to_string(stride) + " \n"
+ " i: 1 \n");
+ }
+ else // dataLayout == "NCHW"
+ {
+ strideString.append(" i: 1 \n"
+ " i: " + std::to_string(stride) + " \n");
+ }
+
std::string dilationString = std::to_string(dilation);
m_Prototext = "node { \n"
" name: \"graphInput\" \n"
@@ -87,13 +102,15 @@
" attr { \n"
" key: \"data_format\" \n"
" value { \n"
- " s: \"NHWC\" \n"
- " } \n"
- " } \n"
- " attr { \n"
- " key: \"padding\" \n"
- " value { \n"
" s: \"";
+ m_Prototext.append(dataLayout);
+ m_Prototext.append("\"\n"
+ " } \n"
+ " } \n"
+ " attr { \n"
+ " key: \"padding\" \n"
+ " value { \n"
+ " s: \"");
m_Prototext.append(paddingType);
m_Prototext.append("\"\n"
" } \n"
@@ -101,14 +118,10 @@
" attr { \n"
" key: \"strides\" \n"
" value { \n"
- " list { \n"
- " i: 1 \n"
- " i: 1 \n"
- " i: ");
+ " list { \n");
m_Prototext.append(strideString);
- m_Prototext.append(" \n"
- " i: 1 \n"
- " } \n"
+
+ m_Prototext.append(" } \n"
" } \n"
" } \n");
@@ -139,67 +152,118 @@
"} \n");
// Manual height computation based on stride parameter.
- BOOST_ASSERT_MSG(stride == 1 || stride==2, "Add support for strides other than 1 or 2.");
- unsigned int dims[] = {1,2,3,1};
- if (stride == 2)
+ BOOST_ASSERT_MSG(stride == 1 || stride == 2, "Add support for strides other than 1 or 2.");
+ std::array<unsigned int, 4> dims;
+ if (dataLayout == "NHWC")
{
- dims[1]=3;
+ dims = { 1u, (stride == 2 ? 3u : 2u), 3u, 1u };
+ }
+ else // dataLayout == "NCHW"
+ {
+ dims = { 1u, 1u, (stride == 2 ? 3u : 2u), 3u };
}
- SetupSingleInputSingleOutput(armnn::TensorShape(4, dims), "graphInput", "potato");
+ SetupSingleInputSingleOutput(armnn::TensorShape(4, dims.data()), "graphInput", "potato");
}
};
-struct Convolution2dSameFixture : Convolution2dFixture
+struct Convolution2dNhwcSameFixture : Convolution2dFixture
{
- Convolution2dSameFixture() : Convolution2dFixture("SAME", 1){}
+ Convolution2dNhwcSameFixture() : Convolution2dFixture("NHWC", "SAME", 1){}
};
-BOOST_FIXTURE_TEST_CASE(ParseConv2DSame, Convolution2dSameFixture)
+BOOST_FIXTURE_TEST_CASE(ParseConv2dNhwcSame, Convolution2dNhwcSameFixture)
{
RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
}
-struct Convolution2dValidFixture : Convolution2dFixture
+struct Convolution2dNchwSameFixture : Convolution2dFixture
{
- Convolution2dValidFixture() : Convolution2dFixture("VALID", 1){}
+ Convolution2dNchwSameFixture() : Convolution2dFixture("NCHW", "SAME", 1){}
};
-BOOST_FIXTURE_TEST_CASE(ParseConv2DValid, Convolution2dValidFixture)
+BOOST_FIXTURE_TEST_CASE(ParseConv2dNchwSame, Convolution2dNchwSameFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
+}
+
+
+struct Convolution2dNhwcValidFixture : Convolution2dFixture
+{
+ Convolution2dNhwcValidFixture() : Convolution2dFixture("NHWC", "VALID", 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2dNhwcValid, Convolution2dNhwcValidFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {4, 10});
+}
+
+struct Convolution2dNchwValidFixture : Convolution2dFixture
+{
+ Convolution2dNchwValidFixture() : Convolution2dFixture("NCHW", "VALID", 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2dNchwValid, Convolution2dNchwValidFixture)
{
RunTest<4>({1, 2, 3, 4, 5, 6}, {4, 10});
}
-struct Convolution2dStride2SameFixture : Convolution2dFixture
+struct Convolution2dStride2NhwcSameFixture : Convolution2dFixture
{
- Convolution2dStride2SameFixture() : Convolution2dFixture("SAME", 2){}
+ Convolution2dStride2NhwcSameFixture() : Convolution2dFixture("NHWC", "SAME", 2){}
};
-BOOST_FIXTURE_TEST_CASE(ParseConv2DStride2Same, Convolution2dStride2SameFixture)
+BOOST_FIXTURE_TEST_CASE(ParseConv2dStride2NhwcSame, Convolution2dStride2NhwcSameFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 4, 6.5, 8.5, 11, 13});
+}
+
+struct Convolution2dStride2NchwSameFixture : Convolution2dFixture
+{
+ Convolution2dStride2NchwSameFixture() : Convolution2dFixture("NCHW", "SAME", 2){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2dStride2NchwSame, Convolution2dStride2NchwSameFixture)
{
RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 4, 6.5, 8.5, 11, 13});
}
-struct Convolution2dStride2ValidFixture : Convolution2dFixture
+struct Convolution2dStride2NhwcValidFixture : Convolution2dFixture
{
- Convolution2dStride2ValidFixture() : Convolution2dFixture("VALID", 2){}
+ Convolution2dStride2NhwcValidFixture() : Convolution2dFixture("NHWC", "VALID", 2){}
};
-BOOST_FIXTURE_TEST_CASE(ParseConv2DStride2Valid, Convolution2dStride2ValidFixture)
+BOOST_FIXTURE_TEST_CASE(ParseConv2dStride2NhwcValid, Convolution2dStride2NhwcValidFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {4, 10, 16});
+}
+
+struct Convolution2dStride2NchwValidFixture : Convolution2dFixture
+{
+ Convolution2dStride2NchwValidFixture() : Convolution2dFixture("NCHW", "VALID", 2){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2dStride2NchwValid, Convolution2dStride2NchwValidFixture)
{
RunTest<4>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {4, 10, 16});
}
-struct Convolution2dDilation1Fixture : Convolution2dFixture
+struct Convolution2dDilation1NhwcFixture : Convolution2dFixture
{
- Convolution2dDilation1Fixture() : Convolution2dFixture("SAME", 1, 1){}
+ Convolution2dDilation1NhwcFixture() : Convolution2dFixture("NHWC", "SAME", 1, 1){}
};
-BOOST_FIXTURE_TEST_CASE(ParseConv2DDilation1, Convolution2dDilation1Fixture)
+BOOST_FIXTURE_TEST_CASE(ParseConv2dDilation1Nhwc, Convolution2dDilation1NhwcFixture)
{
RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
}
-BOOST_AUTO_TEST_CASE(ParseConv2DDilation2)
+struct Convolution2dDilation1NchwFixture : Convolution2dFixture
+{
+ Convolution2dDilation1NchwFixture() : Convolution2dFixture("NCHW", "SAME", 1, 1){}
+};
+BOOST_FIXTURE_TEST_CASE(ParseConv2dDilation1Nchw, Convolution2dDilation1NchwFixture)
+{
+ RunTest<4>({1, 2, 3, 4, 5, 6}, {2, 4, 4, 6.5f, 10 , 8.5f});
+}
+
+
+BOOST_AUTO_TEST_CASE(ParseConv2dDilation2)
{
const char* prototext = ""
"node {\n"
@@ -309,8 +373,7 @@
armnn::TensorShape tensorShape = { 1, 3, 3, 1 };
inputShapes["graphInput"] = tensorShape;
armnnTfParser::ITfParserPtr parser = armnnTfParser::ITfParser::Create();
- BOOST_CHECK_THROW(parser->CreateNetworkFromString(prototext, inputShapes, { "potato" }),
- armnn::ParseException);
+ BOOST_CHECK_THROW(parser->CreateNetworkFromString(prototext, inputShapes, { "potato" }), armnn::ParseException);
}
diff --git a/src/backends/reference/workloads/ConvImpl.hpp b/src/backends/reference/workloads/ConvImpl.hpp
index b8e2dea..704bc36 100644
--- a/src/backends/reference/workloads/ConvImpl.hpp
+++ b/src/backends/reference/workloads/ConvImpl.hpp
@@ -15,6 +15,8 @@
#include <boost/assert.hpp>
#include <boost/numeric/conversion/cast.hpp>
+#include <DataLayoutIndexed.hpp>
+
#include <cmath>
#include <limits>
@@ -74,6 +76,7 @@
data.m_Parameters.m_DataLayout);
const armnnUtils::DataLayoutIndexed dataLayoutIndexed(data.m_Parameters.m_DataLayout);
+
const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
@@ -91,10 +94,10 @@
unsigned int heightFilter = filterInfo.GetShape()[heightIndex];
unsigned int widthFilter = filterInfo.GetShape()[widthIndex];
- unsigned int paddingTop = data.m_Parameters.m_PadTop;
+ unsigned int paddingTop = data.m_Parameters.m_PadTop;
unsigned int paddingLeft = data.m_Parameters.m_PadLeft;
- unsigned int hStride = data.m_Parameters.m_StrideY;
- unsigned int xStride = data.m_Parameters.m_StrideX;
+ unsigned int xStride = data.m_Parameters.m_StrideX;
+ unsigned int yStride = data.m_Parameters.m_StrideY;
// The world's least efficient convolution.
for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
@@ -168,7 +171,7 @@
AccumulatorType filterValue = filterData[filterIndex] -
boost::numeric_cast<AccumulatorType>(filterOffset);
- unsigned int yInput = yOutput * hStride + yFilter;
+ unsigned int yInput = yOutput * yStride + yFilter;
unsigned int xInput = xOutput * xStride + xFilter;
AccumulatorType inputValue;