IVGCVSW-2074: SimpleConvolution2dTestImpl updated for NHWC
* Added a defaulted DataLayoutIndexed argument to the SimpleConvolution2dTestImpl
* Permute test data tensors just before use if necessary
* Add DataLayout to descriptor
* Added a DataLayoutIndexed argument to SimpleConvolution2d3x5TestCommon
* Added NHWC versions of the SimpleConvolution2d3x5Test for Neon and CL
Change-Id: I10e3ece42a50108baeabe4d8b0f0ac3e6d532261
diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp
index 937c58c..4946515 100755
--- a/src/backends/cl/test/ClLayerTests.cpp
+++ b/src/backends/cl/test/ClLayerTests.cpp
@@ -54,10 +54,12 @@
// Convolution
ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true)
-ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true)
-ARMNN_AUTO_TEST_CASE(SimpleConvolution2dSquare, SimpleConvolution2d3x3Test, true)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2dNhwc, SimpleConvolution2d3x5Test, true, armnn::DataLayout::NHWC)
ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x3Uint8, SimpleConvolution2d3x3Uint8Test, true)
-ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dNhwc, SimpleConvolution2d3x5Test, false, armnn::DataLayout::NHWC)
+
ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false)
ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest)
diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp
index 568a236..ffd0d29 100644
--- a/src/backends/neon/test/NeonLayerTests.cpp
+++ b/src/backends/neon/test/NeonLayerTests.cpp
@@ -26,9 +26,12 @@
// Convolution
ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true)
-ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2d, SimpleConvolution2d3x5Test, true, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2dNhwc, SimpleConvolution2d3x5Test, true, armnn::DataLayout::NHWC)
ARMNN_AUTO_TEST_CASE(SimpleConvolution2dSquare, SimpleConvolution2d3x3Test, true)
-ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dNhwc, SimpleConvolution2d3x5Test, false, armnn::DataLayout::NHWC)
+
ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2dSquare, SimpleConvolution2d3x3Test, false)
ARMNN_AUTO_TEST_CASE(SimpleConvolution2dAsymmetricPadding, Convolution2dAsymmetricPaddingTest)
diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp
index 38ce94d..45239be 100644
--- a/src/backends/reference/test/RefLayerTests.cpp
+++ b/src/backends/reference/test/RefLayerTests.cpp
@@ -18,11 +18,11 @@
// UNIT tests
// Convolution
-ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5, SimpleConvolution2d3x5Test, true)
-ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5Uint8, SimpleConvolution2d3x5Uint8Test, true)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5, SimpleConvolution2d3x5Test, true, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(SimpleConvolution2d3x5Uint8, SimpleConvolution2d3x5Uint8Test, true, armnn::DataLayout::NCHW)
-ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false)
-ARMNN_AUTO_TEST_CASE(UnbiasedConvolutionUint8, SimpleConvolution2d3x5Uint8Test, false)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolution2d, SimpleConvolution2d3x5Test, false, armnn::DataLayout::NCHW)
+ARMNN_AUTO_TEST_CASE(UnbiasedConvolutionUint8, SimpleConvolution2d3x5Uint8Test, false, armnn::DataLayout::NCHW)
ARMNN_AUTO_TEST_CASE(SimpleConvolution1d, Convolution1dTest, true)
ARMNN_AUTO_TEST_CASE(SimpleConvolution1dUint8, Convolution1dUint8Test, true)
diff --git a/src/backends/test/Conv2dTestImpl.hpp b/src/backends/test/Conv2dTestImpl.hpp
index d8c1040..41a0d1b 100755
--- a/src/backends/test/Conv2dTestImpl.hpp
+++ b/src/backends/test/Conv2dTestImpl.hpp
@@ -4,6 +4,7 @@
//
#pragma once
+#include <string>
#include <armnn/ArmNN.hpp>
#include <armnn/Tensor.hpp>
#include <armnn/TypesUtils.hpp>
@@ -13,6 +14,8 @@
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
+#include "Permute.hpp"
+#include <boost/numeric/conversion/cast.hpp>
// Mapping from input type to bias type for fully connected layers.
// float => float, uint8_t => int32_t
@@ -59,33 +62,55 @@
}
}
+template<typename T>
+armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches,
+ unsigned int numberOfChannels,
+ unsigned int height,
+ unsigned int width,
+ const armnn::DataLayoutIndexed& layout)
+{
+ switch (layout.GetDataLayout())
+ {
+ case armnn::DataLayout::NCHW:
+ return armnn::TensorInfo({numberOfBatches, numberOfChannels, height, width}, armnn::GetDataType<T>());
+ case armnn::DataLayout ::NHWC:
+ return armnn::TensorInfo({numberOfBatches, height, width, numberOfChannels}, armnn::GetDataType<T>());
+ default:
+ throw armnn::InvalidArgumentException("unknown data layout ["
+ + std::to_string(static_cast<int>(layout.GetDataLayout())) + "]");
+ }
+}
+
+
+
template<typename T, typename B>
LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
- const boost::multi_array<T, 4>& input,
- const boost::multi_array<T, 4>& kernel,
+ const boost::multi_array<T, 4>& originalInput,
+ const boost::multi_array<T, 4>& originalKernel,
const boost::multi_array<B, 1>& bias,
- const boost::multi_array<T, 4>& outputExpected,
+ const boost::multi_array<T, 4>& originalOutputExpected,
float qScale,
int32_t qOffset,
+ const armnn::DataLayoutIndexed& layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
uint32_t padBottom = 0)
{
- unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]);
- unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]);
- unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]);
- unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]);
+ unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]);
+ unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]);
+ unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]);
+ unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]);
- unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
- unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
- unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
- unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
+ unsigned int outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]);
+ unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]);
+ unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]);
+ unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]);
- unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
- unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
- unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
- unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
+ unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
+ unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
+ unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
+ unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
bool biasEnabled = bias.size() > 0;
@@ -98,10 +123,11 @@
// Note these tensors will use two (identical) batches.
- armnn::TensorInfo inputTensorInfo({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth},
- armnn::GetDataType<T>());
- armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
+ // NOTE: if layout is unknown we will get an exception at this point.
+ armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(2*inputNum, inputChannels, inputHeight, inputWidth, layout);
+ armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(
+ 2*outputNum, outputChannels, outputHeight, outputWidth, layout);
+ armnn::TensorInfo kernelDesc = GetTensorInfo<T>(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -121,14 +147,25 @@
// Construct input data - two batches of the same input image.
std::vector<T> inputImage;
- inputImage.assign(input.data(), input.data() + 1*inputChannels*inputHeight*inputWidth);
+ inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
std::vector<T> inputData;
inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
+
+ // at this point if we require it permute the input data
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<T> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+ }
+
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<T> outputImage;
- outputImage.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
+ outputImage.assign(originalOutputExpected.data(),
+ originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
// Apply bias to output image if it is enabled.
if(biasEnabled)
@@ -145,6 +182,13 @@
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
+ // at this point if we require it permute the expected output
+ if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<T> tmp(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data());
+ outputData = tmp;
+ }
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
// Todo: nontrivial padding and strides.
@@ -158,7 +202,12 @@
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
+ // Permute the kernel if necessary
+ boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
+ if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
+ }
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
if(biasEnabled)
@@ -178,6 +227,7 @@
data.m_Parameters.m_PadTop = padTop;
data.m_Parameters.m_PadBottom = padBottom;
data.m_Parameters.m_BiasEnabled = biasEnabled;
+ data.m_Parameters.m_DataLayout = layout.GetDataLayout();
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
inputHandle->Allocate();
diff --git a/src/backends/test/LayerTests.cpp b/src/backends/test/LayerTests.cpp
index e5a4258..e536cc9 100755
--- a/src/backends/test/LayerTests.cpp
+++ b/src/backends/test/LayerTests.cpp
@@ -85,7 +85,8 @@
LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset,
- bool biasEnabled)
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
{
// Use common single-batch 3-channel 16x8 image.
armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
@@ -156,7 +157,8 @@
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
expectedOutput,
qScale,
- qOffset);
+ qOffset,
+ layout);
}
template<typename T>
@@ -278,15 +280,17 @@
}
LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
- bool biasEnabled)
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
{
- return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled);
+ return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout);
}
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
- bool biasEnabled)
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
{
- return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled);
+ return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
@@ -359,6 +363,7 @@
expectedOutput,
qScale,
qOffset,
+ armnn::DataLayout::NCHW,
1, // Padding left.
2, // Padding top.
3, // Padding right.
@@ -410,6 +415,7 @@
expectedOutput,
qScale,
qOffset,
+ armnn::DataLayout::NCHW,
1, // Padding left.
1, // Padding top.
2, // Padding right.
diff --git a/src/backends/test/LayerTests.hpp b/src/backends/test/LayerTests.hpp
index ebd3841..6c3b9e1 100644
--- a/src/backends/test/LayerTests.hpp
+++ b/src/backends/test/LayerTests.hpp
@@ -50,7 +50,8 @@
};
LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
- bool biasEnabled);
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout);
LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled);
@@ -312,7 +313,8 @@
LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory);
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
- bool biasEnabled);
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout);
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled);