IVGCVSW-1978: Support NHWC for ResizeBilinear CpuRef
* Adds implementation to plumb DataLayout parameter
for ResizeBilinear on CpuRef.
* Adds unit tests to execute ResizeBilinear on CpuRef
using the NHWC data layout.
* Adds DataLayoutIndexed API, allowing easy access to
the Channels, Height and Width of a tensor based on
its data layout. This reduces code duplication.
* Refactors original ResizeBilinear implementation and
tests to use the DataLayoutIndexed API when required.
Change-Id: Ic2b8916cdd2e370d070175547079d774daf6d7bf
diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp
index c2efa21..c051005 100644
--- a/include/armnn/Descriptors.hpp
+++ b/include/armnn/Descriptors.hpp
@@ -313,9 +313,9 @@
, m_DataLayout(DataLayout::NCHW)
{}
- uint32_t m_TargetWidth;
- uint32_t m_TargetHeight;
- DataLayout m_DataLayout;
+ uint32_t m_TargetWidth;
+ uint32_t m_TargetHeight;
+ DataLayoutIndexed m_DataLayout;
};
struct ReshapeDescriptor
diff --git a/include/armnn/Types.hpp b/include/armnn/Types.hpp
index 4afc50b..bb0b1e6 100644
--- a/include/armnn/Types.hpp
+++ b/include/armnn/Types.hpp
@@ -31,12 +31,56 @@
Signed32 = 3
};
+// Begin: DataLayout
+
enum class DataLayout
{
NCHW = 1,
NHWC = 2
};
+// Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout
+class DataLayoutIndexed
+{
+public:
+ DataLayoutIndexed(DataLayout dataLayout) : m_DataLayout(dataLayout)
+ {
+ switch (dataLayout)
+ {
+ case DataLayout::NHWC:
+ m_ChannelsIndex = 3;
+ m_HeightIndex = 1;
+ m_WidthIndex = 2;
+ break;
+ case DataLayout::NCHW:
+ m_ChannelsIndex = 1;
+ m_HeightIndex = 2;
+ m_WidthIndex = 3;
+ break;
+ default:
+ throw InvalidArgumentException("Unknown DataLayout value: " +
+ std::to_string(static_cast<int>(dataLayout)));
+ }
+ }
+
+ DataLayout GetDataLayout() const { return m_DataLayout; }
+ unsigned int GetChannelsIndex() const { return m_ChannelsIndex; }
+ unsigned int GetHeightIndex() const { return m_HeightIndex; }
+ unsigned int GetWidthIndex() const { return m_WidthIndex; }
+
+private:
+ DataLayout m_DataLayout;
+ unsigned int m_ChannelsIndex;
+ unsigned int m_HeightIndex;
+ unsigned int m_WidthIndex;
+};
+
+// Conversion methods - implementations in src/armnn/InternalTypes.cpp
+bool operator==(const DataLayout& dataLayout, const DataLayoutIndexed& indexed);
+bool operator==(const DataLayoutIndexed& indexed, const DataLayout& dataLayout);
+
+// End: DataLayout
+
enum class ActivationFunction
{
Sigmoid = 0,
diff --git a/src/armnn/InternalTypes.cpp b/src/armnn/InternalTypes.cpp
index fce1e95..67c596e 100644
--- a/src/armnn/InternalTypes.cpp
+++ b/src/armnn/InternalTypes.cpp
@@ -48,4 +48,16 @@
}
}
+// Definition in include/armnn/Types.hpp
+bool operator==(const DataLayout& dataLayout, const DataLayoutIndexed& indexed)
+{
+ return dataLayout == indexed.GetDataLayout();
+}
+
+// Definition in include/armnn/Types.hpp
+bool operator==(const DataLayoutIndexed& indexed, const DataLayout& dataLayout)
+{
+ return indexed.GetDataLayout() == dataLayout;
+}
+
}
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index b9735f4..a33189e 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -820,31 +820,26 @@
template <typename ResizeBilinearWorkload, armnn::DataType DataType>
std::unique_ptr<ResizeBilinearWorkload> CreateResizeBilinearWorkloadTest(armnn::IWorkloadFactory& factory,
armnn::Graph& graph,
- DataLayout dataLayout = DataLayout::NCHW)
+ DataLayoutIndexed dataLayout =
+ DataLayout::NCHW)
{
TensorShape inputShape;
TensorShape outputShape;
- unsigned int heightIndex;
- unsigned int widthIndex;
- switch (dataLayout) {
+ switch (dataLayout.GetDataLayout()) {
case DataLayout::NHWC:
- inputShape = { 2, 4, 4, 3 };
+ inputShape = { 2, 4, 4, 3 };
outputShape = { 2, 2, 2, 3 };
- heightIndex = 1;
- widthIndex = 2;
break;
default: // NCHW
- inputShape = { 2, 3, 4, 4 };
+ inputShape = { 2, 3, 4, 4 };
outputShape = { 2, 3, 2, 2 };
- heightIndex = 2;
- widthIndex = 3;
}
// Creates the layer we're testing.
ResizeBilinearDescriptor resizeDesc;
- resizeDesc.m_TargetWidth = outputShape[widthIndex];
- resizeDesc.m_TargetHeight = outputShape[heightIndex];
+ resizeDesc.m_TargetWidth = outputShape[dataLayout.GetWidthIndex()];
+ resizeDesc.m_TargetHeight = outputShape[dataLayout.GetHeightIndex()];
resizeDesc.m_DataLayout = dataLayout;
Layer* const layer = graph.AddLayer<ResizeBilinearLayer>(resizeDesc, "layer");
@@ -865,7 +860,7 @@
ResizeBilinearQueueDescriptor queueDescriptor = workload->GetData();
BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);
BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);
- BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
+ BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout.GetDataLayout() == dataLayout));
// Returns so we can do extra, backend-specific tests.
return workload;
diff --git a/src/backends/WorkloadData.cpp b/src/backends/WorkloadData.cpp
index a6a87f3..d562b73 100644
--- a/src/backends/WorkloadData.cpp
+++ b/src/backends/WorkloadData.cpp
@@ -663,11 +663,10 @@
}
{
- // DataLayout is NCHW by default (channelsIndex = 1)
- const unsigned int channelsIndex = this->m_Parameters.m_DataLayout == armnn::DataLayout::NHWC ? 3 : 1;
-
- const unsigned int inputChannelCount = workloadInfo.m_InputTensorInfos[0].GetShape()[channelsIndex];
- const unsigned int outputChannelCount = workloadInfo.m_OutputTensorInfos[0].GetShape()[channelsIndex];
+ const unsigned int inputChannelCount =
+ workloadInfo.m_InputTensorInfos[0].GetShape()[this->m_Parameters.m_DataLayout.GetChannelsIndex()];
+ const unsigned int outputChannelCount =
+ workloadInfo.m_OutputTensorInfos[0].GetShape()[this->m_Parameters.m_DataLayout.GetChannelsIndex()];
if (inputChannelCount != outputChannelCount)
{
throw InvalidArgumentException(
diff --git a/src/backends/cl/workloads/ClResizeBilinearFloatWorkload.cpp b/src/backends/cl/workloads/ClResizeBilinearFloatWorkload.cpp
index ced3a06..4ee6d5e 100644
--- a/src/backends/cl/workloads/ClResizeBilinearFloatWorkload.cpp
+++ b/src/backends/cl/workloads/ClResizeBilinearFloatWorkload.cpp
@@ -26,7 +26,7 @@
arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
- arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
+ arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout.GetDataLayout());
input.info()->set_data_layout(aclDataLayout);
output.info()->set_data_layout(aclDataLayout);
diff --git a/src/backends/reference/test/RefCreateWorkloadTests.cpp b/src/backends/reference/test/RefCreateWorkloadTests.cpp
index e88fbed..e8d536f 100644
--- a/src/backends/reference/test/RefCreateWorkloadTests.cpp
+++ b/src/backends/reference/test/RefCreateWorkloadTests.cpp
@@ -420,27 +420,46 @@
}
template <typename ResizeBilinearWorkloadType, armnn::DataType DataType>
-static void RefCreateResizeBilinearTest()
+static void RefCreateResizeBilinearTest(DataLayout dataLayout)
{
Graph graph;
RefWorkloadFactory factory;
- auto workload = CreateResizeBilinearWorkloadTest<ResizeBilinearWorkloadType, DataType>(factory, graph);
+ auto workload = CreateResizeBilinearWorkloadTest<ResizeBilinearWorkloadType, DataType>(factory, graph, dataLayout);
+
+ TensorShape inputShape;
+ TensorShape outputShape;
+
+ switch (dataLayout)
+ {
+ case DataLayout::NHWC:
+ inputShape = { 2, 4, 4, 3 };
+ outputShape = { 2, 2, 2, 3 };
+ break;
+ default: // NCHW
+ inputShape = { 2, 3, 4, 4 };
+ outputShape = { 2, 3, 2, 2 };
+ }
// Checks that outputs and inputs are as we expect them (see definition of CreateResizeBilinearWorkloadTest).
CheckInputOutput(
- std::move(workload),
- TensorInfo({ 2, 3, 4, 4 }, DataType),
- TensorInfo({ 2, 3, 2, 2 }, DataType));
+ std::move(workload),
+ TensorInfo(inputShape, DataType),
+ TensorInfo(outputShape, DataType));
}
BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32)
{
- RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>();
+ RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>(DataLayout::NCHW);
}
BOOST_AUTO_TEST_CASE(CreateResizeBilinearUint8)
{
- RefCreateResizeBilinearTest<RefResizeBilinearUint8Workload, armnn::DataType::QuantisedAsymm8>();
+ RefCreateResizeBilinearTest<RefResizeBilinearUint8Workload, armnn::DataType::QuantisedAsymm8>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat32Nhwc)
+{
+ RefCreateResizeBilinearTest<RefResizeBilinearFloat32Workload, armnn::DataType::Float32>(DataLayout::NHWC);
}
BOOST_AUTO_TEST_CASE(CreateL2NormalizationFloat32)
diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp
index de2c2fe..48bffa9 100644
--- a/src/backends/reference/test/RefLayerTests.cpp
+++ b/src/backends/reference/test/RefLayerTests.cpp
@@ -178,7 +178,7 @@
ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest)
ARMNN_AUTO_TEST_CASE(BatchNormUint8, BatchNormUint8Test)
-// Resize Bilinear
+// Resize Bilinear - NCHW
ARMNN_AUTO_TEST_CASE(SimpleResizeBilinear, SimpleResizeBilinearTest)
ARMNN_AUTO_TEST_CASE(SimpleResizeBilinearUint8, SimpleResizeBilinearUint8Test)
ARMNN_AUTO_TEST_CASE(ResizeBilinearNop, ResizeBilinearNopTest)
@@ -190,6 +190,13 @@
ARMNN_AUTO_TEST_CASE(ResizeBilinearMag, ResizeBilinearMagTest)
ARMNN_AUTO_TEST_CASE(ResizeBilinearMagUint8, ResizeBilinearMagUint8Test)
+// Resize Bilinear - NHWC
+ARMNN_AUTO_TEST_CASE(ResizeBilinearNopNhwc, ResizeBilinearNopNhwcTest)
+ARMNN_AUTO_TEST_CASE(SimpleResizeBilinearNhwc, SimpleResizeBilinearNhwcTest)
+ARMNN_AUTO_TEST_CASE(ResizeBilinearSqMinNhwc, ResizeBilinearSqMinNhwcTest)
+ARMNN_AUTO_TEST_CASE(ResizeBilinearMinNhwc, ResizeBilinearMinNhwcTest)
+ARMNN_AUTO_TEST_CASE(ResizeBilinearMagNhwc, ResizeBilinearMagNhwcTest)
+
// Fake Quantization
ARMNN_AUTO_TEST_CASE(FakeQuantization, FakeQuantizationTest)
diff --git a/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp b/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
index 50ee7a2..8d86bdc 100644
--- a/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
+++ b/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
@@ -23,7 +23,8 @@
ResizeBilinear(GetInputTensorDataFloat(0, m_Data),
inputInfo,
GetOutputTensorDataFloat(0, m_Data),
- outputInfo);
+ outputInfo,
+ m_Data.m_Parameters.m_DataLayout);
}
} //namespace armnn
diff --git a/src/backends/reference/workloads/ResizeBilinear.cpp b/src/backends/reference/workloads/ResizeBilinear.cpp
index 0bce3c7..e098c6c 100644
--- a/src/backends/reference/workloads/ResizeBilinear.cpp
+++ b/src/backends/reference/workloads/ResizeBilinear.cpp
@@ -25,27 +25,31 @@
}
-void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo)
+void ResizeBilinear(const float* in,
+ const TensorInfo& inputInfo,
+ float* out,
+ const TensorInfo& outputInfo,
+ DataLayoutIndexed dataLayout)
{
// We follow the definition of TensorFlow and AndroidNN: the top-left corner of a texel in the output
// image is projected into the input image to figure out the interpolants and weights. Note that this
// will yield different results than if projecting the centre of output texels.
const unsigned int batchSize = inputInfo.GetShape()[0];
- const unsigned int channelCount = inputInfo.GetShape()[1];
+ const unsigned int channelCount = inputInfo.GetShape()[dataLayout.GetChannelsIndex()];
- const unsigned int inputHeight = inputInfo.GetShape()[2];
- const unsigned int inputWidth = inputInfo.GetShape()[3];
- const unsigned int outputHeight = outputInfo.GetShape()[2];
- const unsigned int outputWidth = outputInfo.GetShape()[3];
+ const unsigned int inputHeight = inputInfo.GetShape()[dataLayout.GetHeightIndex()];
+ const unsigned int inputWidth = inputInfo.GetShape()[dataLayout.GetWidthIndex()];
+ const unsigned int outputHeight = outputInfo.GetShape()[dataLayout.GetHeightIndex()];
+ const unsigned int outputWidth = outputInfo.GetShape()[dataLayout.GetWidthIndex()];
// How much to scale pixel coordinates in the output image, to get the corresponding pixel coordinates
// in the input image.
const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight);
const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth);
- TensorBufferArrayView<const float> input(inputInfo.GetShape(), in);
- TensorBufferArrayView<float> output(outputInfo.GetShape(), out);
+ TensorBufferArrayView<const float> input(inputInfo.GetShape(), in, dataLayout);
+ TensorBufferArrayView<float> output(outputInfo.GetShape(), out, dataLayout);
for (unsigned int n = 0; n < batchSize; ++n)
{
diff --git a/src/backends/reference/workloads/ResizeBilinear.hpp b/src/backends/reference/workloads/ResizeBilinear.hpp
index 847b8e8..92b229d 100644
--- a/src/backends/reference/workloads/ResizeBilinear.hpp
+++ b/src/backends/reference/workloads/ResizeBilinear.hpp
@@ -10,6 +10,10 @@
namespace armnn
{
-void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo);
+void ResizeBilinear(const float* in,
+ const TensorInfo& inputInfo,
+ float* out,
+ const TensorInfo& outputInfo,
+ DataLayoutIndexed dataLayout = DataLayout::NCHW);
} //namespace armnn
diff --git a/src/backends/reference/workloads/TensorBufferArrayView.hpp b/src/backends/reference/workloads/TensorBufferArrayView.hpp
index e19810c..aba44e4 100644
--- a/src/backends/reference/workloads/TensorBufferArrayView.hpp
+++ b/src/backends/reference/workloads/TensorBufferArrayView.hpp
@@ -15,28 +15,33 @@
class TensorBufferArrayView
{
public:
- TensorBufferArrayView(const TensorShape& shape, DataType* data)
+ TensorBufferArrayView(const TensorShape& shape, DataType* data, DataLayoutIndexed dataLayout = DataLayout::NCHW)
: m_Shape(shape)
, m_Data(data)
+ , m_DataLayout(dataLayout)
{
}
DataType& Get(unsigned int b, unsigned int c, unsigned int h, unsigned int w) const
{
- BOOST_ASSERT( b < m_Shape[0] || (m_Shape[0] == 0 && b == 0) );
- BOOST_ASSERT( c < m_Shape[1] || (m_Shape[1] == 0 && c == 0) );
- BOOST_ASSERT( h < m_Shape[2] || (m_Shape[2] == 0 && h == 0) );
- BOOST_ASSERT( w < m_Shape[3] || (m_Shape[3] == 0 && w == 0) );
+ BOOST_ASSERT( b < m_Shape[0] || ( m_Shape[0] == 0 && b == 0 ) );
+ BOOST_ASSERT( c < m_Shape[m_DataLayout.GetChannelsIndex()] ||
+ ( m_Shape[m_DataLayout.GetChannelsIndex()] == 0 && c == 0) );
+ BOOST_ASSERT( h < m_Shape[m_DataLayout.GetHeightIndex()] ||
+ ( m_Shape[m_DataLayout.GetHeightIndex()] == 0 && h == 0) );
+ BOOST_ASSERT( w < m_Shape[m_DataLayout.GetWidthIndex()] ||
+ ( m_Shape[m_DataLayout.GetWidthIndex()] == 0 && w == 0) );
return m_Data[b * m_Shape[1] * m_Shape[2] * m_Shape[3]
- + c * m_Shape[2] * m_Shape[3]
- + h * m_Shape[3]
+ + c * m_Shape[m_DataLayout.GetHeightIndex()] * m_Shape[m_DataLayout.GetWidthIndex()]
+ + h * m_Shape[m_DataLayout.GetWidthIndex()]
+ w];
}
private:
const TensorShape m_Shape;
- DataType* m_Data;
+ DataType* m_Data;
+ DataLayoutIndexed m_DataLayout;
};
} //namespace armnn