IVGCVSW-2266 Remove the input swizzling from ParseDepthwiseConv2D
Change-Id: I72d94fff4cdad2c62dff98c8fd52eba78a1908f0
diff --git a/src/armnnTfParser/TfParser.cpp b/src/armnnTfParser/TfParser.cpp
index 210b825..3c7d16e 100644
--- a/src/armnnTfParser/TfParser.cpp
+++ b/src/armnnTfParser/TfParser.cpp
@@ -48,37 +48,6 @@
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 <typename Callable>
void ReadMandatoryNodeAttributeImpl(const tensorflow::NodeDef& nodeDef,
@@ -1181,10 +1150,10 @@
% nodeDef.name()
% CHECK_LOCATION().AsString()));
}
+
ParsedConstTfOperation<float>* weightNode =
boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue);
-
std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, "padding");
std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format");
std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, "strides");
@@ -1194,59 +1163,85 @@
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<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] * weightTensor.GetShape()[1],
- 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;
- outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0],
- weightTensor.GetShape()[0] * weightTensor.GetShape()[1],
- 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 = 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] * 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 @@
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<SingleLayerParsedTfOperation>(this, nodeDef, layer);
}
diff --git a/src/armnnTfParser/test/DepthwiseConvolution2d.cpp b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp
index 5a8105c..464e62f 100644
--- a/src/armnnTfParser/test/DepthwiseConvolution2d.cpp
+++ b/src/armnnTfParser/test/DepthwiseConvolution2d.cpp
@@ -9,11 +9,15 @@
#include <string>
#include <iostream>
+#include <Permute.hpp>
+using namespace armnnUtils;
+using namespace armnn;
+
BOOST_AUTO_TEST_SUITE(TensorflowParser)
struct DepthwiseConvolution2dFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser>
{
- 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 @@
" } \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 @@
" 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 @@
" } \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 });
}