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-rwxr-xr-xsrc/backends/backendsCommon/test/LayerTests.cpp6125
1 files changed, 6125 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp
new file mode 100755
index 0000000000..12a7063e22
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+++ b/src/backends/backendsCommon/test/LayerTests.cpp
@@ -0,0 +1,6125 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "LayerTests.hpp"
+
+#include "test/TensorHelpers.hpp"
+#include "TensorCopyUtils.hpp"
+#include "Permute.hpp"
+
+#include <boost/test/unit_test.hpp>
+#include <boost/assert.hpp>
+
+#include <armnn/LayerSupport.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <algorithm>
+#include <boost/cast.hpp>
+
+#include "WorkloadTestUtils.hpp"
+#include "Conv2dTestImpl.hpp"
+#include "BatchNormTestImpl.hpp"
+#include "ActivationTestImpl.hpp"
+#include "Pooling2dTestImpl.hpp"
+#include "ReshapeTestImpl.hpp"
+#include "FullyConnectedTestImpl.hpp"
+#include "SplitterTestImpl.hpp"
+#include "SoftmaxTestImpl.hpp"
+#include "NormTestImpl.hpp"
+#include "PermuteTestImpl.hpp"
+#include "LstmTestImpl.hpp"
+#include "ConvertFp16ToFp32TestImpl.hpp"
+#include "ConvertFp32ToFp16TestImpl.hpp"
+
+// 3-channel 16x8 image used as common input data for a number of Conv2d tests.
+static std::vector<float> ConvInput3x8x16({
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
+});
+
+// 2-channel bias used by a number of Conv2d tests.
+static std::vector<float> Bias2({0, 2});
+
+// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled.
+template<typename T>
+boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset)
+{
+ if(biasEnabled)
+ {
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, armnn::GetDataType<T>());
+ boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2));
+ return bias;
+ }
+ else
+ {
+ return boost::multi_array<T, 1>();
+ }
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ // Use common single-batch 3-channel 16x8 image.
+ armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
+
+ // Use a 2-element batch with 3-channel 3x5 kernels.
+ armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ 1, 1, 1,
+ 1, -1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0
+ })));
+
+ // Expected output is 2 batch elements of a 1-channel 14x4 image.
+ armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,
+ -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,
+ -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
+ -23.5f, -23.5f, -23.5f,
+ -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
+ -23.5f, -23.5f, -23.5f,
+
+ 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+ })));
+
+ return SimpleConvolution2dTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ layout);
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path.
+
+ // Use common single-batch 3-channel 16x8 image.
+ armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
+
+ // Use a 2-element batch of 3-channel 3x3 kernels.
+ armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ 1, 1, 1,
+ 1, -1, 1,
+ 1, 1, 1,
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0
+ })));
+
+ // Expected output is 1 batch of a 2-channel 14x6 image.
+ armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,
+ -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,
+ -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
+ -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
+ -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
+ -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
+
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+ })));
+
+ return SimpleConvolution2dTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ layout);
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset,
+ bool biasEnabled,
+ armnn::DataLayout dataLayout)
+{
+ // Use common single-batch 5x5 image.
+
+ armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,
+ {
+ 1, 5, 2, 3,
+ 8, 7, 3, 6,
+ 3, 3, 9, 1
+ });
+
+
+ // Use a 2-element batch of 3-channel 3x3 kernels.
+ armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, {
+ 4, 5, 6,
+ 0, 0, 0,
+ 3, 2, 1
+ });
+
+ // Expected output is 1 batch of a 5x5 image.
+ armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType<T>());
+
+ const std::vector<float> outputData =
+ {
+ 23, 41, 33, 21,
+ 44, 65, 76, 52,
+ 82, 85, 79, 42
+ };
+
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);
+
+ return SimpleConvolution2dNhwcTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ boost::multi_array<T, 1>(),
+ expectedOutput,
+ dataLayout,
+ qScale,
+ qOffset);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled)
+{
+ return SimpleConvolution2d3x3NhwcTestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
+}
+
+template<typename T>
+LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& layout,
+ float qScale,
+ int32_t qOffset)
+{
+ // Use a single-batch 1-channel 3x3 image as input.
+ armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ 11,21,31,
+ 12,22,32,
+ 13,23,33
+ })));
+
+ // Use 1 batch of a 1-channel 2x2 kernel.
+ armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ -11,-21,
+ -12,-22,
+ })));
+
+// Expected output is 1 batch of a 1-channel 6x8 image.
+// Manually calculated like this:
+//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
+//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..]
+//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]
+//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]
+//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..]
+//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
+//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..]
+ armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ 0, 0, 0, 0, 0, 0,
+ -242, -594, -934, -372, 0, 0,
+ -495, -1190, -1850, -725, 0, 0,
+ -538, -1256, -1916, -748, 0, 0,
+ -273, -626, -946, -363, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0
+ })));
+
+ return SimpleConvolution2dTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ layout,
+ 1, // Padding left.
+ 2, // Padding top.
+ 3, // Padding right.
+ 4); // Padding bottom.
+}
+
+template<typename T>
+LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& layout,
+ float qScale,
+ int32_t qOffset)
+{
+ // Use a single-batch 1-channel 5x5 image as input.
+ armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ 11,21,31,41,51,
+ 12,22,32,42,52,
+ 13,23,33,43,53,
+ 14,24,34,44,54,
+ 15,25,35,45,55,
+ })));
+
+ // Use 1 batch of a 1-channel 4x4 kernel.
+ armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ -11,-21,-31,-41,
+ -12,-22,-32,-42,
+ -13,-23,-33,-43,
+ -14,-24,-34,-44,
+ })));
+
+ // Expected output is 1 batch of a 1-channel 5x5 image.
+ armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
+ std::vector<T> myVec(outputDesc.GetNumElements(), 0);
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ -7140, -10580, -13940, -9300, -5230,
+ -9590, -14120, -18520, -12290, -6860,
+ -9980, -14560, -18960, -12560, -7000,
+ -7518, -10904, -14144, -9318, -5152,
+ -5032, -7256, -9376, -6142, -3368,
+ })));
+
+ return SimpleConvolution2dTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ layout,
+ 1, // Padding left.
+ 1, // Padding top.
+ 2, // Padding right.
+ 2); // Padding bottom.
+}
+
+template<typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ // Use a single-batch 2-channel 5x5 image as input.
+ armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
+ auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
+ 0, 1, 2, 3, 4,
+ 5, 6, 7, 8, 9,
+ 10, 11, 12, 13, 14,
+ 15, 16, 17, 18, 19,
+ 20, 21, 22, 23, 24,
+
+ 25, 26, 27, 28, 29,
+ 30, 31, 32, 33, 34,
+ 35, 36, 37, 38, 39,
+ 40, 41, 42, 43, 44,
+ 45, 46, 47, 48, 49
+ })));
+
+ // Use a depth multiplier of 1 on a 2-channel 4x4 kernel.
+ armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>());
+ auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
+ QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), {
+ 32, 31, 30, 29,
+ 28, 27, 26, 25,
+ 24, 23, 22, 21,
+ 20, 19, 18, 17,
+
+ 16, 15, 14, 13,
+ 12, 11, 10, 9,
+ 8, 7, 6, 5,
+ 4, 3, 2, 1
+ })));
+
+ // Expected output is 1 batch of a 2-channel 5x5 image.
+ // Calculated using the python tensorflow library with strideX=1, strideY=1.
+ armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
+ 1062, 1580, 1850, 1530, 1117,
+ 2140, 3108, 3500, 2842, 2042,
+ 3580, 5068, 5460, 4342, 3062,
+ 3618, 5072, 5390, 4248, 2971,
+ 3074, 4282, 4510, 3533, 2457,
+ 1550, 2284, 2362, 1955, 1428,
+ 2910, 4206, 4342, 3528, 2536,
+ 3390, 4886, 5022, 4068, 2916,
+ 3566, 5056, 5182, 4133, 2922,
+ 3100, 4352, 4452, 3517, 2465
+ })));
+
+ return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ layout,
+ 1, // Padding left.
+ 1, // Padding top.
+ 2, // Padding right.
+ 2, // Padding bottom.
+ 1, // strideX
+ 1); // strideY
+}
+
+template<typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset,
+ bool biasEnabled)
+{
+ armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>());
+ auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
+ 0, 25,
+ 1, 26,
+ 2, 27,
+ 3, 28,
+ 4, 29,
+
+ 5, 30,
+ 6, 31,
+ 7, 32,
+ 8, 33,
+ 9, 34,
+
+ 10, 35,
+ 11, 36,
+ 12, 37,
+ 13, 38,
+ 14, 39,
+
+ 15, 40,
+ 16, 41,
+ 17, 42,
+ 18, 43,
+ 19, 44,
+
+ 20, 45,
+ 21, 46,
+ 22, 47,
+ 23, 48,
+ 24, 49
+ })));
+
+ armnn::TensorInfo kernelTensorInfo({ 1, 4, 4, 2}, armnn::GetDataType<T>());
+ auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
+ QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), {
+ 32, 16,
+ 31, 15,
+ 30, 14,
+ 29, 13,
+
+ 28, 12,
+ 27, 11,
+ 26, 10,
+ 25, 9,
+
+ 24, 8,
+ 23, 7,
+ 22, 6,
+ 21, 5,
+
+ 20, 4,
+ 19, 3,
+ 18, 2,
+ 17, 1
+ })));
+
+ armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>());
+ boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
+ 1062, 1550,
+ 1580, 2284,
+ 1850, 2362,
+ 1530, 1955,
+ 1117, 1428,
+
+ 2140, 2910,
+ 3108, 4206,
+ 3500, 4342,
+ 2842, 3528,
+ 2042, 2536,
+
+ 3580, 3390,
+ 5068, 4886,
+ 5460, 5022,
+ 4342, 4068,
+ 3062, 2916,
+
+ 3618, 3566,
+ 5072, 5056,
+ 5390, 5182,
+ 4248, 4133,
+ 2971, 2922,
+
+ 3074, 3100,
+ 4282, 4352,
+ 4510, 4452,
+ 3533, 3517,
+ 2457, 2465
+ })));
+
+ return DepthwiseConvolution2dNhwcTestImpl<T>(workloadFactory,
+ input,
+ kernel,
+ GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
+ expectedOutput,
+ qScale,
+ qOffset,
+ 1, // Padding left.
+ 1, // Padding top.
+ 2, // Padding right.
+ 2, // Padding bottom.
+ 1, // strideX
+ 1); // strideY
+}
+
+LayerTestResult<float, 4>
+Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>(workloadFactory, layout, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return SimpleConvolution2dAsymmetricPaddingTestCommon<float>(workloadFactory, layout, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return DepthwiseConvolution2dTestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled)
+{
+ return DepthwiseConvolution2dNhwcTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return DepthwiseConvolution2dDepthMul1TestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return DepthwiseConvolution2dAsymmetricTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
+{
+ return Convolution1dTestImpl<float>(workloadFactory, 0.0f, 0, biasEnabled);
+}
+
+LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
+{
+ return Convolution1dTestImpl<uint8_t>(workloadFactory, 0.1f, 128, biasEnabled);
+}
+
+LayerTestResult<float,4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory)
+{
+ return CompareConvolution2dTestImpl<float>(workloadFactory, refWorkloadFactory);
+}
+
+template<typename T>
+LayerTestResult<T,4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ const armnn::DataLayoutIndexed& layout)
+{
+ return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, refWorkloadFactory, layout);
+}
+
+template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>(
+ armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&);
+template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>(
+ armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&);
+
+LayerTestResult<float,4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
+ auto normChannel = armnn::NormalizationAlgorithmChannel::Across;
+ return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
+}
+
+LayerTestResult<float,4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
+ auto normChannel = armnn::NormalizationAlgorithmChannel::Within;
+ return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
+}
+
+LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
+ auto normChannel = armnn::NormalizationAlgorithmChannel::Across;
+ return SimpleNormalizationNhwcTestImpl(workloadFactory, normChannel, normMethod);
+}
+
+LayerTestResult<float,2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta)
+{
+ return SimpleSoftmaxTestImpl<float>(workloadFactory, beta);
+}
+
+LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta)
+{
+ return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, beta);
+}
+
+LayerTestResult<float,4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::NormalizationAlgorithmChannel normChannel,
+ armnn::NormalizationAlgorithmMethod normMethod)
+{
+ return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod);
+}
+
+LayerTestResult<float,2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ float beta)
+{
+ return CompareSoftmaxTestImpl<float>(workloadFactory, refWorkloadFactory, beta);
+}
+
+LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ float beta)
+{
+ return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, beta);
+}
+
+std::vector<LayerTestResult<float,3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SplitterTestCommon<float>(workloadFactory);
+}
+
+std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SplitterTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return CopyViaSplitterTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory)
+{
+ armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ { 2., 3., 3., 4. }));
+
+ armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
+ {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
+ -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}));
+ return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
+}
+
+LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(
+ armnn::IWorkloadFactory& workloadFactory)
+{
+ armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
+ 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}));
+
+ armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
+ {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f,
+ -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f,
+ -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f,
+ 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f,
+ -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f,
+ 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f,
+ 0.02168f}));
+ return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput);
+}
+
+LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
+ {2., 3., 3., 4.}));
+
+
+ armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>());
+ boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
+ {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
+ -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}));
+
+ return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
+}
+
+LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int outputWidth = 3;
+ unsigned int outputHeight = 6;
+ unsigned int outputChannels = 3;
+
+ unsigned int inputWidth1 = 3;
+ unsigned int inputHeight1 = 6;
+ unsigned int inputChannels1 = 2;
+
+ unsigned int inputWidth2 = 3;
+ unsigned int inputHeight2 = 6;
+ unsigned int inputChannels2 = 1;
+
+ // Define the tensor descriptors.
+ armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32);
+ armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32);
+ armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32);
+
+ LayerTestResult<float,3> ret(outputTensorInfo);
+
+ ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>(
+ {
+ 1.0f, 2.0f, 3.0f,
+ 4.0f, 5.0f, 6.0f,
+ 7.0f, 8.0f, 9.0f,
+ 10.0f, 11.0f, 12.0f,
+ 13.0f, 14.0f, 15.0f,
+ 16.0f, 17.0f, 18.0f,
+
+ 19.0f, 20.0f, 21.0f,
+ 22.0f, 23.0f, 24.0f,
+ 25.0f, 26.0f, 27.0f,
+ 28.0f, 29.0f, 30.0f,
+ 31.0f, 32.0f, 33.0f,
+ 34.0f, 35.0f, 36.0f,
+
+ 37.0f, 38.0f, 39.0f,
+ 40.0f, 41.0f, 42.0f,
+ 43.0f, 44.0f, 45.0f,
+ 46.0f, 47.0f, 48.0f,
+ 49.0f, 50.0f, 51.0f,
+ 52.0f, 53.0f, 54.0f,
+ })
+ );
+
+ auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>(
+ {
+ 1.0f, 2.0f, 3.0f,
+ 4.0f, 5.0f, 6.0f,
+ 7.0f, 8.0f, 9.0f,
+ 10.0f, 11.0f, 12.0f,
+ 13.0f, 14.0f, 15.0f,
+ 16.0f, 17.0f, 18.0f,
+
+ 19.0f, 20.0f, 21.0f,
+ 22.0f, 23.0f, 24.0f,
+ 25.0f, 26.0f, 27.0f,
+ 28.0f, 29.0f, 30.0f,
+ 31.0f, 32.0f, 33.0f,
+ 34.0f, 35.0f, 36.0f,
+ })
+ );
+
+ auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>(
+ {
+ 37.0f, 38.0f, 39.0f,
+ 40.0f, 41.0f, 42.0f,
+ 43.0f, 44.0f, 45.0f,
+ 46.0f, 47.0f, 48.0f,
+ 49.0f, 50.0f, 51.0f,
+ 52.0f, 53.0f, 54.0f,
+ })
+ );
+
+ std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0].
+ armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
+
+ std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1].
+ armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ bool subTensorsSupported = workloadFactory.SupportsSubTensors();
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo1);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo2);
+
+ armnn::MergerQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ data.m_ViewOrigins.push_back(window1);
+ data.m_ViewOrigins.push_back(window2);
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+LayerTestResult<float,4> AdditionTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int batchSize = 2;
+ unsigned int channels = 2;
+ unsigned int height = 2;
+ unsigned int width = 3;
+
+ armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
+ armnn::TensorInfo outputTensorInfo;
+
+ unsigned int shape[] = {batchSize, channels, height, width};
+
+ inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+
+
+ auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>(
+ {
+ 0.0f, 2.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 1.0f, 2.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 0.0f, 2.0f, 1.0f,
+ 4.2f, 1.0f, 2.0f,
+
+ 0.0f, 0.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+ }));
+
+ auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>(
+ {
+ 1.0f, 2.0f, 1.0f,
+ 0.0f, 1.0f, 2.0f,
+
+ 1.0f, 2.0f, -2.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 0.0f, 2.0f, 1.0f,
+ 4.2f, 0.0f, -3.0f,
+
+ 0.0f, 0.0f, 1.0f,
+ 0.7f, 1.0f, 5.0f,
+ }));
+
+ LayerTestResult<float,4> ret(outputTensorInfo);
+ ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>(
+ {
+ 1.0f, 4.0f, 2.0f,
+ 0.2f, 2.0f, 4.0f,
+
+ 2.0f, 4.0f, -1.0f,
+ 0.4f, 2.0f, 4.0f,
+
+ 0.0f, 4.0f, 2.0f,
+ 8.4f, 1.0f, -1.0f,
+
+ 0.0f, 0.0f, 2.0f,
+ 0.9f, 2.0f, 7.0f,
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+template <typename T>
+LayerTestResult<T, 4> AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
+
+ if (armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo1.SetQuantizationScale(qScale);
+ inputTensorInfo1.SetQuantizationOffset(qOffset);
+ inputTensorInfo2.SetQuantizationScale(qScale);
+ inputTensorInfo2.SetQuantizationOffset(qOffset);
+ outputTensorInfo.SetQuantizationScale(qScale);
+ outputTensorInfo.SetQuantizationOffset(qOffset);
+ }
+
+ auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.0f,
+ 1.0f,
+
+ 2.0f,
+ 3.0f,
+
+ 4.0f,
+ 5.0f,
+ }));
+
+ auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.5f, 1.5f, 2.5f,
+ 3.5f, 4.5f, 5.5f,
+ }));
+
+ LayerTestResult<T,4> ret(outputTensorInfo);
+ ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.5f, 1.5f, 2.5f,
+ 4.5f, 5.5f, 6.5f,
+
+ 2.5f, 3.5f, 4.5f,
+ 6.5f, 7.5f, 8.5f,
+
+ 4.5f, 5.5f, 6.5f,
+ 8.5f, 9.5f, 10.5f,
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+template <typename T>
+LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
+
+ if (armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo1.SetQuantizationScale(qScale);
+ inputTensorInfo1.SetQuantizationOffset(qOffset);
+ inputTensorInfo2.SetQuantizationScale(qScale);
+ inputTensorInfo2.SetQuantizationOffset(qOffset);
+ outputTensorInfo.SetQuantizationScale(qScale);
+ outputTensorInfo.SetQuantizationOffset(qOffset);
+ }
+
+ auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.0f, 1.0f, 2.0f,
+ 3.0f, 4.0f, 5.0f,
+ 6.0f, 7.0f, 8.0f,
+ 9.0f, 10.0f, 11.0f,
+ 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f,
+ }));
+
+ auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.5f,
+ }));
+
+ LayerTestResult<T,4> ret(outputTensorInfo);
+ ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
+ {
+ 0.5f, 1.5f, 2.5f,
+ 3.5f, 4.5f, 5.5f,
+ 6.5f, 7.5f, 8.5f,
+ 9.5f, 10.5f, 11.5f,
+ 12.5f, 13.5f, 14.5f,
+ 15.5f, 16.5f, 17.5f,
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AdditionBroadcastTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, 2.f, 0);
+}
+
+LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, 0.1333333f, 128);
+}
+
+LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory)
+{
+ unsigned int batchSize = 4;
+ unsigned int channels = 1;
+ unsigned int height = 2;
+ unsigned int width = 3;
+
+ armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
+ armnn::TensorInfo outputTensorInfo;
+
+ unsigned int shape[] = {batchSize, channels, height, width};
+
+ inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+
+ auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232);
+ auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456);
+
+ LayerTestResult<float,4> ret(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ armnn::AdditionQueueDescriptor refData = data;
+ armnn::WorkloadInfo refInfo = info;
+ SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get());
+ SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get());
+ SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
+ std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+ inputHandle1Ref->Allocate();
+ inputHandle2Ref->Allocate();
+ outputHandleRef->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+ refWorkloadFactory.Finalize();
+ workloadRef->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+ CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+ return ret;
+}
+
+namespace {
+template <typename T>
+LayerTestResult<T, 4> DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int shape0[4],
+ const std::vector<T>& values0,
+ float scale0,
+ int32_t offset0,
+ const unsigned int shape1[4],
+ const std::vector<T> & values1,
+ float scale1,
+ int32_t offset1,
+ const unsigned int outShape[4],
+ const std::vector<T> & outValues,
+ float outScale,
+ int32_t outOffset)
+{
+ auto dataType = (std::is_same<T, uint8_t>::value ?
+ armnn::DataType::QuantisedAsymm8 :
+ armnn::DataType::Float32);
+
+ armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
+ armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
+ armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
+
+ inputTensorInfo0.SetQuantizationScale(scale0);
+ inputTensorInfo0.SetQuantizationOffset(offset0);
+
+ inputTensorInfo1.SetQuantizationScale(scale1);
+ inputTensorInfo1.SetQuantizationOffset(offset1);
+
+ outputTensorInfo.SetQuantizationScale(outScale);
+ outputTensorInfo.SetQuantizationOffset(outOffset);
+
+ auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
+ auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
+
+ LayerTestResult<T, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::DivisionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info);
+
+ inputHandle0->Allocate();
+ inputHandle1->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+} // anonymous namespace
+
+LayerTestResult<float,4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 2;
+ const unsigned int channelCount = 2;
+ const unsigned int batchSize = 2;
+
+ unsigned int shape[] = { batchSize, channelCount, height, width };
+
+ std::vector<float> input0({
+ 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f,
+ -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f });
+
+ std::vector<float> input1({
+ 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f,
+ 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f });
+
+ std::vector<float> output({
+ INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN,
+ -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 });
+
+ return DivisionTestHelper<float>(workloadFactory,
+ shape, input0, 1.0f, 0,
+ shape, input1, 1.0f, 0,
+ shape, output, 1.0f, 0);
+}
+
+LayerTestResult<float,4> DivisionTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 2;
+ const unsigned int channelCount = 2;
+ const unsigned int batchSize = 2;
+
+ unsigned int shape[] = { batchSize, channelCount, height, width };
+
+ std::vector<float> input0({
+ 2, 2, 2, 2, 3, 3, 3, 3,
+ 4, 4, 4, 4, 5, 5, 5, 5 });
+
+ std::vector<float> input1({
+ 1, 1, 1, 1, 2, 2, 2, 2,
+ 4, 4, 4, 4, 4, 4, 4, 4 });
+
+ std::vector<float> output({
+ 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5,
+ 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 });
+
+
+ return DivisionTestHelper<float>(workloadFactory,
+ shape, input0, 1.0f, 0,
+ shape, input1, 1.0f, 0,
+ shape, output, 1.0f, 0);
+}
+
+LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 2, 2, 2 };
+ std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
+
+ unsigned int shape1[] = { 1, 1, 1, 1 };
+ std::vector<float> input1({ 2 });
+
+ std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8});
+
+
+ return DivisionTestHelper<float>(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 3, 3, 2 };
+ std::vector<float> input0({
+ 1, 4, 3, 8, 5, 12,
+ 7, 16, 9, 20, 11, 24,
+ 13, 28, 15, 32, 17, 36});
+
+ unsigned int shape1[] = { 1, 1, 1, 2 };
+ std::vector<float> input1({ 1, 2 });
+
+ std::vector<float> output({
+ 1, 2, 3, 4, 5, 6,
+ 7, 8, 9, 10, 11, 12,
+ 13, 14, 15, 16, 17, 18});
+
+ return DivisionTestHelper<float>(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+
+LayerTestResult<uint8_t,4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 2;
+ const unsigned int channelCount = 2;
+ const unsigned int batchSize = 2;
+
+ unsigned int shape[] = { batchSize, channelCount, height, width };
+
+ std::vector<uint8_t> input0({2, 2, 2, 2, 3, 3, 3, 3,
+ 4, 4, 4, 4, 5, 5, 5, 5 });
+
+ std::vector<uint8_t> input1({1, 1, 1, 1, 2, 2, 2, 2,
+ 4, 4, 4, 4, 4, 4, 4, 4 });
+
+ std::vector<uint8_t> output({8, 8, 8, 8, 6, 6, 6, 6,
+ 4, 4, 4, 4, 5, 5, 5, 5});
+
+
+ return DivisionTestHelper<uint8_t>(workloadFactory,
+ shape, input0, 1.0f, 0,
+ shape, input1, 1.0f, 0,
+ shape, output, 0.25f, 0);
+}
+
+LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 2, 2, 2 };
+ std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
+
+ unsigned int shape1[] = { 1, 1, 1, 1 };
+ std::vector<uint8_t> input1({ 2 });
+
+ std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8});
+
+ return DivisionTestHelper<uint8_t>(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 3, 3, 2 };
+ std::vector<uint8_t> input0({1, 4, 3, 8, 5, 12,
+ 7, 16, 9, 20, 11, 24,
+ 13, 28, 15, 32, 17, 36});
+
+ unsigned int shape1[] = { 1, 1, 1, 2 };
+ std::vector<uint8_t> input1({ 1, 2 });
+
+ std::vector<uint8_t> output({1, 2, 3, 4, 5, 6,
+ 7, 8, 9, 10, 11, 12,
+ 13, 14, 15, 16, 17, 18});
+
+ return DivisionTestHelper<uint8_t>(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+namespace {
+LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int shape0[4],
+ const std::vector<float> & values0,
+ const unsigned int shape1[4],
+ const std::vector<float> & values1,
+ const unsigned int outShape[4],
+ const std::vector<float> & outValues)
+{
+ const size_t dimensionCount = 4;
+ armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32};
+ armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32};
+ armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32};
+
+ auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0);
+ auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1);
+
+ LayerTestResult<float,4> ret(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::MultiplicationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
+
+ inputHandle0->Allocate();
+ inputHandle1->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues);
+ return ret;
+}
+} // anonymous namespace
+
+
+LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 2;
+ const unsigned int channelCount = 2;
+ const unsigned int batchSize = 2;
+
+ unsigned int shape[] = { batchSize, channelCount, height, width };
+
+ std::vector<float> input0({
+ 1, 1, 1, 1, 2, 2, 2, 2,
+ 3, 3, 3, 3, 4, 4, 4, 4 });
+
+ std::vector<float> input1({
+ 2, 2, 2, 2, 3, 3, 3, 3,
+ 4, 4, 4, 4, 5, 5, 5, 5 });
+
+ std::vector<float> output({
+ 2, 2, 2, 2, 6, 6, 6, 6,
+ 12, 12, 12, 12, 20, 20, 20, 20 });
+
+ return MultiplicationTestHelper(workloadFactory,
+ shape,
+ input0,
+ shape,
+ input1,
+ shape,
+ output);
+}
+
+LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 2, 2, 2 };
+ std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8});
+
+ unsigned int shape1[] = { 1, 1, 1, 1 };
+ std::vector<float> input1({ 2 });
+
+ std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16});
+
+ return MultiplicationTestHelper(workloadFactory,
+ shape0,
+ input0,
+ shape1,
+ input1,
+ shape0,
+ output);
+}
+
+LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int shape0[] = { 1, 3, 3, 2 };
+ std::vector<float> input0({
+ 1, 2, 3, 4, 5, 6,
+ 7, 8, 9, 10, 11, 12,
+ 13, 14, 15, 16, 17, 18});
+
+ unsigned int shape1[] = { 1, 1, 1, 2 };
+ std::vector<float> input1({ 1, 2 });
+
+ std::vector<float> output({
+ 1, 4, 3, 8, 5, 12,
+ 7, 16, 9, 20, 11, 24,
+ 13, 28, 15, 32, 17, 36});
+
+ return MultiplicationTestHelper(workloadFactory,
+ shape0,
+ input0,
+ shape1,
+ input1,
+ shape0,
+ output);
+}
+
+LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory)
+{
+ const unsigned int width = 16;
+ const unsigned int height = 32;
+ const unsigned int channelCount = 2;
+ const unsigned int batchSize = 5;
+
+ armnn::TensorInfo inputTensorInfo0;
+ armnn::TensorInfo inputTensorInfo1;
+ armnn::TensorInfo outputTensorInfo;
+
+ constexpr unsigned int shape[] = { batchSize, channelCount, height, width };
+
+ inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+
+ LayerTestResult<float,4> comparisonResult(outputTensorInfo);
+
+ auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992);
+ auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::MultiplicationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ armnn::MultiplicationQueueDescriptor refData = data;
+ armnn::WorkloadInfo refInfo = info;
+ SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get());
+ SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get());
+ SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
+ std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo);
+
+ inputHandle0->Allocate();
+ inputHandle1->Allocate();
+ outputHandle->Allocate();
+ inputHandle0Ref->Allocate();
+ inputHandle1Ref->Allocate();
+ outputHandleRef->Allocate();
+
+ CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+ refWorkloadFactory.Finalize();
+ workloadRef->Execute();
+
+ CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get());
+ CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get());
+
+ return comparisonResult;
+}
+
+LayerTestResult<float,4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory)
+{
+ const unsigned int width = 2;
+ const unsigned int height = 3;
+ const unsigned int channels = 5;
+ const unsigned int batchSize = 3;
+
+ armnn::TensorInfo inputTensorInfo;
+ armnn::TensorInfo outputTensorInfo;
+ armnn::TensorInfo tensorInfo;
+
+ constexpr unsigned int shape[] = {batchSize, channels, height, width};
+ constexpr unsigned int tensorShape[] = {channels};
+
+ inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
+ tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32);
+
+ auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312);
+
+ auto mean = MakeRandomTensor<float, 1>(tensorInfo, 123);
+ auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f);
+ auto beta = MakeRandomTensor<float, 1>(tensorInfo, 123);
+ auto gamma = MakeRandomTensor<float, 1>(tensorInfo, 345);
+
+ LayerTestResult<float,4> ret(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::BatchNormalizationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
+ armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
+
+ AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
+ AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
+ AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
+ AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
+
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+ data.m_Mean = &meanTensor;
+ data.m_Variance = &varianceTensor;
+ data.m_Beta = &betaTensor;
+ data.m_Gamma = &gammaTensor;
+ data.m_Parameters.m_Eps = 0.01f;
+
+ armnn::BatchNormalizationQueueDescriptor refData = data;
+ armnn::WorkloadInfo refInfo = info;
+ SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
+ SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
+ std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ inputHandleRef->Allocate();
+ outputHandleRef->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+ refWorkloadFactory.Finalize();
+ workloadRef->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+ CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+ return ret;
+}
+
+template<typename T>
+void PermuteTensorData(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::PermutationVector& mappings,
+ armnn::TensorInfo & inputTensorInfo,
+ const T * inputData,
+ std::vector<T>& outputData)
+{
+ BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null");
+ if (inputData == nullptr)
+ {
+ // Nullptr is an error in the test. By returning without doing the concatenation
+ // I expect the caller to fail the test. It still makes sense to report this as
+ // an assert for Debug builds.
+ return;
+ }
+
+ armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::PermuteQueueDescriptor queueDescriptor;
+ queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings};
+ armnn::WorkloadInfo workloadInfo;
+ AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), inputData);
+
+ workload->Execute();
+
+ outputData.resize(outputTensorInfo.GetNumElements());
+ CopyDataFromITensorHandle(&outputData[0], outputHandle.get());
+ inputTensorInfo = outputTensorInfo;
+}
+
+armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation(
+ const std::vector<armnn::TensorInfo> & inputTensorInfos,
+ unsigned int concatDim)
+{
+ std::vector<armnn::TensorShape> shapes;
+ shapes.reserve(inputTensorInfos.size());
+ for (const armnn::TensorInfo& it: inputTensorInfos)
+ {
+ shapes.push_back(it.GetShape());
+ }
+
+ return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(),
+ shapes.end(),
+ concatDim);
+}
+
+//
+// Concatenation is only supported for N and C dimensions for NCHW. In case of
+// <4 dimensions we need to make sure that the concat dimensions are at least
+// the 3rd slowest iterating one.
+//
+
+bool NeedPermuteForConcat(
+ const std::vector<armnn::TensorInfo> & inputTensorInfos,
+ unsigned int concatDim)
+{
+ // See note above. Additionally we expect the input shapes to have the
+ // same number of dimensions.
+ unsigned int nDimensions = 0;
+
+ // Determine the number of dimensions as well as sanity check them
+ // agains test implementation issues.
+ for (auto && tensorInfo : inputTensorInfos)
+ {
+ if (!nDimensions)
+ {
+ nDimensions = tensorInfo.GetShape().GetNumDimensions();
+ }
+ else
+ {
+ BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(),
+ "Input shapes must have the same number of dimensions");
+ }
+ }
+
+ return (nDimensions-concatDim) < 3;
+}
+
+armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape)
+{
+ unsigned int numDims = inputShape.GetNumDimensions();
+ if (numDims >= 3)
+ {
+ // Nothing to do if the inputShape has at least 3 dimensions.
+ return inputShape;
+ }
+
+ std::vector<unsigned int> newDims(size_t(3), 1u);
+ unsigned int expandedBy = 3 - numDims;
+ for (unsigned int i=0; i<numDims; ++i)
+ {
+ newDims[expandedBy+i] = inputShape[i];
+ }
+ return armnn::TensorShape(3u, &newDims[0]);
+}
+
+void Generate3dPermuteVectorForConcat(
+ unsigned int numDimensions,
+ unsigned int & concatDim,
+ std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations)
+{
+ BOOST_ASSERT_MSG(numDimensions <= 3,
+ "Only dimensions 1,2 and 3 are supported by this helper");
+
+ unsigned int expandedBy = 3 - numDimensions;
+ unsigned int expandedConcatAxis = concatDim + expandedBy;
+
+ if (expandedConcatAxis == 2)
+ {
+ concatDim = 0;
+ armnn::PermutationVector forwardPermutation({1, 2, 0});
+ armnn::PermutationVector reversePermutation({2, 0, 1});
+ permutations = std::make_pair(forwardPermutation, reversePermutation);
+ }
+ else if (expandedConcatAxis == 1)
+ {
+ concatDim = 0;
+ armnn::PermutationVector forwardPermutation({2, 0, 1});
+ armnn::PermutationVector reversePermutation({1, 2, 0});
+ permutations = std::make_pair(forwardPermutation, reversePermutation);
+ }
+ else
+ {
+ BOOST_ASSERT(expandedConcatAxis == 0);
+ concatDim = 0;
+ }
+}
+
+//
+// Permute the input tensors so we can do a supported concatenation.
+// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions
+// at the front. Finally this function tells what the output shape
+// of the permuted concatenated tensor is going to be.
+//
+template <typename T>
+void PermuteInputsForConcat(
+ armnn::IWorkloadFactory& workloadFactory,
+ std::vector<armnn::TensorInfo> & inputTensorInfos,
+ std::vector<T *> & inputData,
+ std::vector<std::vector<T>> & inputDataStorage,
+ armnn::PermutationVector & permuteVector,
+ unsigned int & concatDim,
+ armnn::TensorInfo & outputTensorInfo)
+{
+ BOOST_ASSERT_MSG(inputTensorInfos.size() > 1,
+ "Expecting more than one tensor to be concatenated here");
+
+ unsigned int numDims = 0;
+ unsigned int nthInput = 0;
+ const armnn::PermutationVector identity({0, 1, 2});
+
+ std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations =
+ std::make_pair(identity, identity);
+
+ inputDataStorage.resize(inputData.size());
+
+ for (auto && tensorInfo : inputTensorInfos)
+ {
+ if (numDims == 0)
+ {
+ numDims = tensorInfo.GetShape().GetNumDimensions();
+ Generate3dPermuteVectorForConcat(numDims, concatDim, permutations);
+ // Store the reverese permutation.
+ permuteVector = permutations.second;
+ BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity),
+ "Test logic error, we don't need permutation, so we shouldn't arrive here");
+ }
+ else
+ {
+ BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(),
+ "All inputs must have the same number of dimensions");
+ }
+
+ armnn::TensorInfo newTensorInfo = tensorInfo;
+ newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape()));
+
+ PermuteTensorData<T>(workloadFactory,
+ permutations.first,
+ newTensorInfo,
+ inputData[nthInput],
+ inputDataStorage[nthInput]);
+
+ inputData[nthInput] = inputDataStorage[nthInput].data();
+ inputTensorInfos[nthInput] = newTensorInfo;
+
+ ++nthInput;
+ }
+
+ outputTensorInfo.SetShape(
+ armnnUtils::Permuted(
+ ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()),
+ permutations.first));
+}
+
+
+//
+// This is the pair of PermuteInputsForConcat(...) which permutes back
+// the output of the concatenation so we can check it against an expected
+// output.
+//
+template <typename T>
+void PermuteOutputForConcat(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorInfo & tensorInfo,
+ const armnn::PermutationVector & permuteVector,
+ std::unique_ptr<armnn::ITensorHandle> && inputDataHandle,
+ T * data)
+{
+ BOOST_ASSERT_MSG(data != nullptr, "data must not be null");
+ if (data == nullptr)
+ {
+ // Nullptr is an error in the test. By returning without doing the permutation
+ // I expect the caller to fail the test. It still makes sense to report this as
+ // an assert for Debug builds.
+ return;
+ }
+
+ armnn::TensorInfo resultTensorInfo = tensorInfo;
+ std::vector<T> inputData(tensorInfo.GetNumElements());
+ std::vector<T> outputData;
+
+ CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get());
+
+ PermuteTensorData<T>(workloadFactory,
+ permuteVector,
+ resultTensorInfo,
+ &inputData[0],
+ outputData);
+
+ ::memcpy(data, &outputData[0], sizeof(T)*outputData.size());
+}
+
+template <typename T>
+void Concatenate(armnn::IWorkloadFactory& workloadFactory,
+ std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig,
+ std::initializer_list<T *> inputsOrig,
+ const armnn::TensorInfo& outputTensorInfoOrig,
+ T * output,
+ unsigned int concatDim)
+{
+ BOOST_ASSERT_MSG(output != nullptr, "output must not be null");
+ if (output == nullptr)
+ {
+ // Nullptr is an error in the test. By returning without doing the permutation
+ // I expect the caller to fail the test. It still makes sense to report this as
+ // an assert for Debug builds.
+ return;
+ }
+
+ armnn::MergerQueueDescriptor queueDescriptor;
+
+ // Saves a copy of the parameters which we might need to change.
+ std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end());
+ std::vector<T *> inputs = inputsOrig;
+ armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig;
+
+ armnn::PermutationVector permuteVector{0, 1, 2};
+
+ // Holds and automatically releases memory for the reshaped input data.
+ std::vector<std::vector<T>> tmpInputDataStorage;
+
+ const size_t inputCount = inputTensorInfos.size();
+
+ bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim);
+
+ if (needPermuteForConcat)
+ {
+ //
+ // We need to permute the inputs, because concatenation along
+ // the requested axis is not supported.
+ //
+ PermuteInputsForConcat<T>(workloadFactory,
+ inputTensorInfos,
+ inputs,
+ tmpInputDataStorage,
+ permuteVector,
+ concatDim,
+ outputTensorInfo);
+ }
+
+ armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim);
+
+ queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews());
+ for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i)
+ {
+ queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i),
+ viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions()));
+ }
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles;
+ inputHandles.reserve(inputCount);
+
+ const bool subTensorsSupported = workloadFactory.SupportsSubTensors();
+ for (unsigned int i = 0; i < inputCount; ++i)
+ {
+ const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i];
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(),
+ queueDescriptor.m_ViewOrigins[i].m_Origin.data())
+ : workloadFactory.CreateTensorHandle(inputTensorInfo);
+
+ inputHandles.emplace_back(std::move(inputHandle));
+ }
+
+ armnn::WorkloadInfo workloadInfo;
+
+ for (unsigned int i = 0; i < inputCount; ++i)
+ {
+ AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get());
+ }
+
+ AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo);
+
+ for (auto& inputHandle : inputHandles)
+ {
+ inputHandle->Allocate();
+ }
+
+ outputHandle->Allocate();
+
+ unsigned int nextInputId = 0;
+ for (auto& inputHandle : inputHandles)
+ {
+ CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]);
+ ++nextInputId;
+ }
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ if (needPermuteForConcat)
+ {
+ PermuteOutputForConcat<T>(workloadFactory,
+ outputTensorInfo,
+ permuteVector,
+ std::move(outputHandle),
+ output);
+ }
+ else
+ {
+ CopyDataFromITensorHandle(output, outputHandle.get());
+ }
+}
+
+template <typename T>
+LayerTestResult<T, 1> Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+ armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>());
+
+ auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f }));
+ auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f }));
+ auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f }));
+
+ armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 1> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { inputTensorInfo, inputTensorInfo, inputTensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 0);
+
+ result.output = MakeTensor<T, 1>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation1dTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 2> Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorInfo& outputTensorInfo,
+ unsigned int dimension,
+ const float qScale,
+ const int32_t qOffset)
+{
+ armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>());
+
+ auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f,
+ }));
+
+ auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 4.0f, 5.0f, 6.0f,
+
+ // Batch 1
+ 13.0f, 14.0f, 15.0f,
+ }));
+
+ auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 7.0f, 8.0f, 9.0f,
+
+ // Batch 1
+ 16.0f, 17.0f, 18.0f,
+ }));
+
+ LayerTestResult<T, 2> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { inputTensorInfo, inputTensorInfo, inputTensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ dimension);
+
+ result.output = MakeTensor<T, 2>(outputTensorInfo, output);
+ return result;
+}
+
+template <typename T>
+LayerTestResult<T, 2> Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale, int32_t qOffset)
+{
+ armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 0, qScale, qOffset);
+ result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f,
+
+ // Batch 2
+ 4.0f, 5.0f, 6.0f,
+
+ // Batch 3
+ 13.0f, 14.0f, 15.0f,
+
+ // Batch 4
+ 7.0f, 8.0f, 9.0f,
+
+ // Batch 5
+ 16.0f, 17.0f, 18.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 2> Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale, int32_t qOffset)
+{
+ armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
+ result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
+ auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f,
+ }));
+
+ armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>());
+ auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 4.0f, 5.0f, 6.0f,
+
+ // Batch 1
+ 13.0f, 14.0f, 15.0f,
+
+ // Batch 0
+ 7.0f, 8.0f, 9.0f,
+ }));
+
+ armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>());
+ auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 1
+ 16.0f, 17.0f, 18.0f,
+ }));
+
+ armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
+ LayerTestResult<T, 2> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { input0TensorInfo, input1TensorInfo, input2TensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 0);
+
+ result.output = MakeTensor<T, 2>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f,
+
+ // Batch 2
+ 4.0f, 5.0f, 6.0f,
+
+ // Batch 3
+ 13.0f, 14.0f, 15.0f,
+
+ // Batch 4
+ 7.0f, 8.0f, 9.0f,
+
+ // Batch 5
+ 16.0f, 17.0f, 18.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
+ auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f,
+ }));
+
+ armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>());
+ auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
+
+ // Batch 1
+ 13.0f, 14.0f, 15.0f, 16.0f, 17.0f,
+ }));
+
+ armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>());
+ auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 9.0f,
+
+ // Batch 1
+ 18.0f
+ }));
+
+ armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
+ LayerTestResult<T, 2> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { input0TensorInfo, input1TensorInfo, input2TensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 1);
+
+ result.output = MakeTensor<T, 2>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0
+ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
+
+ // Batch 1
+ 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorInfo& outputTensorInfo,
+ unsigned int dimension,
+ float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
+
+ auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f
+ }));
+
+ auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 7.0f, 8.0f,
+
+ // Batch 0, Channel 1
+ 9.0f, 10.0f,
+
+ // Batch 0, Channel 2
+ 11.0f, 12.0f,
+
+ // Batch 1, Channel 0
+ 25.0f, 26.0f,
+
+ // Batch 1, Channel 1
+ 27.0f, 28.0f,
+
+ // Batch 1, Channel 2
+ 29.0f, 30.0f
+ }));
+
+ auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 13.0f, 14.0f,
+
+ // Batch 0, Channel 1
+ 15.0f, 16.0f,
+
+ // Batch 0, Channel 2
+ 17.0f, 18.0f,
+
+ // Batch 1, Channel 0
+ 31.0f, 32.0f,
+
+ // Batch 1, Channel 1
+ 33.0f, 34.0f,
+
+ // Batch 1, Channel 2
+ 35.0f, 36.0f
+ }));
+
+ LayerTestResult<T, 3> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { inputTensorInfo, inputTensorInfo, inputTensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ dimension);
+
+ result.output = MakeTensor<T, 3>(outputTensorInfo, output);
+ return result;
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 0,
+ qScale, qOffset);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f,
+
+ // Batch 2, Channel 0
+ 7.0f, 8.0f,
+
+ // Batch 2, Channel 1
+ 9.0f, 10.0f,
+
+ // Batch 2, Channel 2
+ 11.0f, 12.0f,
+
+ // Batch 3, Channel 0
+ 25.0f, 26.0f,
+
+ // Batch 3, Channel 1
+ 27.0f, 28.0f,
+
+ // Batch 3, Channel 2
+ 29.0f, 30.0f,
+
+ // Batch 4, Channel 0
+ 13.0f, 14.0f,
+
+ // Batch 4, Channel 1
+ 15.0f, 16.0f,
+
+ // Batch 4, Channel 2
+ 17.0f, 18.0f,
+
+ // Batch 5, Channel 0
+ 31.0f, 32.0f,
+
+ // Batch 5, Channel 1
+ 33.0f, 34.0f,
+
+ // Batch 5, Channel 2
+ 35.0f, 36.0f
+ }));
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale, int32_t qOffset)
+{
+ armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 0, Channel 3
+ 7.0f, 8.0f,
+
+ // Batch 0, Channel 4
+ 9.0f, 10.0f,
+
+ // Batch 0, Channel 5
+ 11.0f, 12.0f,
+
+ // Batch 0, Channel 6
+ 13.0f, 14.0f,
+
+ // Batch 0, Channel 7
+ 15.0f, 16.0f,
+
+ // Batch 0, Channel 8
+ 17.0f, 18.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f,
+
+ // Batch 1, Channel 3
+ 25.0f, 26.0f,
+
+ // Batch 1, Channel 4
+ 27.0f, 28.0f,
+
+ // Batch 1, Channel 5
+ 29.0f, 30.0f,
+
+ // Batch 1, Channel 6
+ 31.0f, 32.0f,
+
+ // Batch 1, Channel 7
+ 33.0f, 34.0f,
+
+ // Batch 1, Channel 8
+ 35.0f, 36.0f
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale, int32_t qOffset)
+{
+ armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
+
+ LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 2, qScale, qOffset);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim2TestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
+ auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f
+ }));
+
+ armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>());
+ auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 7.0f, 8.0f,
+
+ // Batch 0, Channel 1
+ 9.0f, 10.0f,
+
+ // Batch 0, Channel 2
+ 11.0f, 12.0f,
+ }));
+
+ armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>());
+ auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 25.0f, 26.0f,
+
+ // Batch 0, Channel 1
+ 27.0f, 28.0f,
+
+ // Batch 0, Channel 2
+ 29.0f, 30.0f,
+
+ // Batch 1, Channel 0
+ 13.0f, 14.0f,
+
+ // Batch 1, Channel 1
+ 15.0f, 16.0f,
+
+ // Batch 1, Channel 2
+ 17.0f, 18.0f,
+
+ // Batch 2, Channel 0
+ 31.0f, 32.0f,
+
+ // Batch 2, Channel 1
+ 33.0f, 34.0f,
+
+ // Batch 2, Channel 2
+ 35.0f, 36.0f
+ }));
+
+ armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
+ LayerTestResult<T, 3> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { input0TensorInfo, input1TensorInfo, input2TensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 0);
+
+ result.output = MakeTensor<T, 3>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f,
+
+ // Batch 2, Channel 0
+ 7.0f, 8.0f,
+
+ // Batch 2, Channel 1
+ 9.0f, 10.0f,
+
+ // Batch 2, Channel 2
+ 11.0f, 12.0f,
+
+ // Batch 3, Channel 0
+ 25.0f, 26.0f,
+
+ // Batch 3, Channel 1
+ 27.0f, 28.0f,
+
+ // Batch 3, Channel 2
+ 29.0f, 30.0f,
+
+ // Batch 4, Channel 0
+ 13.0f, 14.0f,
+
+ // Batch 4, Channel 1
+ 15.0f, 16.0f,
+
+ // Batch 4, Channel 2
+ 17.0f, 18.0f,
+
+ // Batch 5, Channel 0
+ 31.0f, 32.0f,
+
+ // Batch 5, Channel 1
+ 33.0f, 34.0f,
+
+ // Batch 5, Channel 2
+ 35.0f, 36.0f
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
+ auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f
+ }));
+
+ armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>());
+ auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 7.0f, 8.0f,
+
+ // Batch 0, Channel 1
+ 9.0f, 10.0f,
+
+ // Batch 0, Channel 2
+ 11.0f, 12.0f,
+
+ // Batch 0, Channel 3
+ 25.0f, 26.0f,
+
+ // Batch 1, Channel 0
+ 27.0f, 28.0f,
+
+ // Batch 1, Channel 1
+ 29.0f, 30.0f,
+
+ // Batch 1, Channel 2
+ 13.0f, 14.0f,
+
+ // Batch 1, Channel 3
+ 15.0f, 16.0f,
+ }));
+
+ armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>());
+ auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 17.0f, 18.0f,
+
+ // Batch 1, Channel 0
+ 31.0f, 32.0f,
+ }));
+
+ armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>());
+ LayerTestResult<T, 3> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { input0TensorInfo, input1TensorInfo, input2TensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 1);
+
+ result.output = MakeTensor<T, 3>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 0, Channel 3
+ 7.0f, 8.0f,
+
+ // Batch 0, Channel 4
+ 9.0f, 10.0f,
+
+ // Batch 0, Channel 5
+ 11.0f, 12.0f,
+
+ // Batch 0, Channel 6
+ 25.0f, 26.0f,
+
+ // Batch 0, Channel 7
+ 17.0f, 18.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f,
+
+ // Batch 1, Channel 3
+ 27.0f, 28.0f,
+
+ // Batch 1, Channel 4
+ 29.0f, 30.0f,
+
+ // Batch 1, Channel 5
+ 13.0f, 14.0f,
+
+ // Batch 1, Channel 6
+ 15.0f, 16.0f,
+
+ // Batch 1, Channel 7
+ 31.0f, 32.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
+ int32_t qOffset)
+{
+ armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
+ auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f
+ }));
+
+ armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>());
+ auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 7.0f,
+
+ // Batch 0, Channel 1
+ 9.0f,
+
+ // Batch 0, Channel 2
+ 11.0f,
+
+ // Batch 1, Channel 0
+ 25.0f,
+
+ // Batch 1, Channel 1
+ 27.0f,
+
+ // Batch 1, Channel 2
+ 29.0f
+ }));
+
+ armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>());
+ auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 13.0f, 14.0f, 50.0f,
+
+ // Batch 0, Channel 1
+ 15.0f, 16.0f, 51.0f,
+
+ // Batch 0, Channel 2
+ 17.0f, 18.0f, 52.0f,
+
+ // Batch 1, Channel 0
+ 31.0f, 32.0f, 53.0f,
+
+ // Batch 1, Channel 1
+ 33.0f, 34.0f, 54.0f,
+
+ // Batch 1, Channel 2
+ 35.0f, 36.0f, 55.0f,
+ }));
+
+ armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
+ LayerTestResult<T, 3> result(outputTensorInfo);
+
+ std::vector<T> output;
+ output.resize(outputTensorInfo.GetNumElements());
+ Concatenate<T>(workloadFactory,
+ { input0TensorInfo, input1TensorInfo, input2TensorInfo },
+ { input0.data(), input1.data(), input2.data() },
+ outputTensorInfo,
+ output.data(),
+ 2);
+
+ result.output = MakeTensor<T, 3>(outputTensorInfo, output);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f,
+
+ // Batch 0, Channel 1
+ 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f,
+
+ // Batch 0, Channel 2
+ 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f,
+
+ // Batch 1, Channel 0
+ 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f,
+
+ // Batch 1, Channel 1
+ 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f,
+
+ // Batch 1, Channel 2
+ 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f,
+ }));
+
+ return result;
+}
+
+LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
+ const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
+
+ std::vector<float> inputData({
+ 1.0f, 2.0f, 3.0f, 4.0f,
+ 2.0f, 3.0f, 4.0f, 5.0f,
+ 3.0f, 4.0f, 5.0f, 6.0f,
+ 4.0f, 5.0f, 6.0f, 7.0f,
+
+ 1.0f, 2.0f, 3.0f, 4.0f,
+ 2.0f, 3.0f, 4.0f, 5.0f,
+ 3.0f, 4.0f, 5.0f, 6.0f,
+ 4.0f, 5.0f, 6.0f, 7.0f
+ });
+
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<float> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+ }
+
+ auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = input;
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 2, 2, dataLayout);
+ const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 1, 1, dataLayout);
+
+ std::vector<float> inputData({
+ 1.0f, 255.0f,
+ 200.0f, 250.0f,
+
+ 250.0f, 200.0f,
+ 250.0f, 1.0f
+ });
+
+ // The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
+ // then figures out the interpolants and weights. Note this is different to projecting the centre of the
+ // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as
+ // its single element, the value that was at position (0,0) of the input matrix (rather than an average,
+ // which we would expect if projecting the centre).
+
+ std::vector<float> outputData({
+ 1.0f,
+
+ 250.0f
+ });
+
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<float> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+
+ std::vector<float> tmp1(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
+ outputData = tmp1;
+ }
+
+ auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
+ const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 2, 2, dataLayout);
+
+ std::vector<float> inputData({
+ 1.0f, 2.0f, 3.0f, 4.0f,
+ 2.0f, 3.0f, 4.0f, 5.0f,
+ 3.0f, 4.0f, 5.0f, 6.0f,
+ 4.0f, 5.0f, 6.0f, 7.0f,
+
+ 7.0f, 6.0f, 5.0f, 4.0f,
+ 6.0f, 5.0f, 4.0f, 3.0f,
+ 5.0f, 4.0f, 3.0f, 2.0f,
+ 4.0f, 3.0f, 2.0f, 1.0f
+ });
+
+ std::vector<float> outputData({
+ 1.0f, 3.0f,
+ 3.0f, 5.0f,
+
+ 7.0f, 5.0f,
+ 5.0f, 3.0f
+ });
+
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<float> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+
+ std::vector<float> tmp1(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
+ outputData = tmp1;
+ }
+
+ auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 3, 5, dataLayout);
+ const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 2, 3, dataLayout);
+
+ std::vector<float> inputData({
+ 1.0f, 2.0f, 3.0f, 5.0f, 8.0f,
+ 13.0f, 21.0f, 34.0f, 55.0f, 89.0f,
+ 144.0f, 233.0f, 377.0f, 610.0f, 987.0f,
+
+ 987.0f, 610.0f, 377.0f, 233.0f, 144.0f,
+ 89.0f, 55.0f, 34.0f, 21.0f, 13.0f,
+ 8.0f, 5.0f, 3.0f, 2.0f, 1.0f
+ });
+
+ std::vector<float> outputData({
+ 1.0f, 2.6666f, 6.00f,
+ 78.5f, 179.3333f, 401.00f,
+
+ 987.0f, 454.6670f, 203.33f,
+ 48.5f, 22.3333f, 10.00f
+ });
+
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<float> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+
+ std::vector<float> tmp1(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
+ outputData = tmp1;
+ }
+
+ auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 3, 2, dataLayout);
+ const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 3, 5, dataLayout);
+
+ std::vector<float> inputData({
+ 1.0f, 2.0f,
+ 13.0f, 21.0f,
+ 144.0f, 233.0f,
+
+ 233.0f, 144.0f,
+ 21.0f, 13.0f,
+ 2.0f, 1.0f
+ });
+
+ std::vector<float> outputData({
+ 1.0f, 1.4f, 1.8f, 2.0f, 2.0f,
+ 13.0f, 16.2f, 19.4f, 21.0f, 21.0f,
+ 144.0f, 179.6f, 215.2f, 233.0f, 233.0f,
+
+ 233.0f, 197.4f, 161.8f, 144.0f, 144.0f,
+ 21.0f, 17.8f, 14.6f, 13.0f, 13.0f,
+ 2.0f, 1.6f, 1.2f, 1.0f, 1.0f
+ });
+
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ std::vector<float> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
+ inputData = tmp;
+
+ std::vector<float> tmp1(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
+ outputData = tmp1;
+ }
+
+ auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int width = 2;
+ constexpr unsigned int height = 3;
+
+ const armnn::TensorInfo tensorInfo({height, width },
+ armnn::DataType::Float32);
+ auto input = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
+ -10.0f, -5.0f,
+ 0.0f, 5.0f,
+ 10.0f, 10.0f
+ }));
+
+ LayerTestResult<float, 2> ret(tensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(tensorInfo);
+
+ armnn::FakeQuantizationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+
+ AddInputToWorkload(data, info, tensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, tensorInfo, outputHandle.get());
+ float min = -10.f;
+ float max = 10.f;
+
+ data.m_Parameters.m_Min = min;
+ data.m_Parameters.m_Max = max;
+
+ armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]);
+ armnn::FakeQuantizationQueueDescriptor refData = data;
+ armnn::WorkloadInfo refInfo = info;
+ SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle);
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFakeQuantization(data, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+
+ ret.outputExpected = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
+ 0.0f, 63.0f,
+ 128.0f, 191.0f,
+ 255.0f, 255.0f
+ }));
+ return ret;
+}
+
+namespace
+{
+
+LayerTestResult<float, 4> L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorShape& inputOutputTensorShape,
+ const std::vector<float>& inputValues,
+ const std::vector<float>& expectedOutputValues,
+ armnn::DataLayout dataLayout)
+{
+ const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
+ const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
+
+ auto inputTensor = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(inputValues));
+
+ LayerTestResult<float, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(expectedOutputValues));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::L2NormalizationQueueDescriptor descriptor;
+ descriptor.m_Parameters.m_DataLayout = dataLayout;
+ armnn::WorkloadInfo info;
+
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+
+float CalcInvL2Norm(std::initializer_list<float> elements)
+{
+ const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f,
+ [](float acc, float element) { return acc + element * element; });
+ return 1.0f / sqrtf(reduction);
+}
+
+} // anonymous namespace
+
+template<typename T>
+LayerTestResult<T, 2> Pad2dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+ const armnn::TensorShape inputShape{ 3, 3 };
+ const armnn::TensorShape outputShape{ 7, 7 };
+
+ const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
+ const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
+
+ std::vector<T> inputValues(
+ QuantizedVector<T>(qScale, qOffset,
+ {
+ // Height (3) x Width (3)
+ 4, 8, 6,
+ 7, 4, 4,
+ 3, 2, 4
+ }));
+
+ std::vector<T> expectedOutputValues(
+ QuantizedVector<T>(qScale, qOffset,
+ {
+ 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 4, 8, 6, 0, 0,
+ 0, 0, 7, 4, 4, 0, 0,
+ 0, 0, 3, 2, 4, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0
+ }));
+
+ auto inputTensor = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(inputValues));
+
+ LayerTestResult<T, 2> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>(expectedOutputValues));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::PadQueueDescriptor descriptor;
+
+ std::vector<std::pair<unsigned int, unsigned int>> PadList;
+ PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
+
+ descriptor.m_Parameters.m_PadList = PadList;
+ armnn::WorkloadInfo info;
+
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get());
+
+ return result;
+}
+
+template <typename T>
+LayerTestResult<T, 3> Pad3dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+ const armnn::TensorShape inputShape{ 2, 2, 2 };
+ const armnn::TensorShape outputShape{ 3, 5, 6 };
+
+ const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
+ const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
+
+ std::vector<T> inputValues(
+ QuantizedVector<T>(qScale,qOffset,
+ {
+ // Channel 0, Height (2) x Width (2)
+ 0, 4,
+ 2, 5,
+
+ // Channel 1, Height (2) x Width (2)
+ 6, 1,
+ 5, 2
+ }));
+
+ std::vector<T> expectedOutputValues(
+ QuantizedVector<T>(qScale,qOffset,
+ {
+
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 4, 0, 0,
+ 0, 0, 2, 5, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 6, 1, 0, 0,
+ 0, 0, 5, 2, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0
+
+ }));
+
+ auto inputTensor = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(inputValues));
+
+ LayerTestResult<T, 3> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>(expectedOutputValues));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::PadQueueDescriptor descriptor;
+
+ std::vector<std::pair<unsigned int, unsigned int>> PadList;
+ PadList.push_back(std::pair<unsigned int, unsigned int>(0,1));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(2,1));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
+
+ descriptor.m_Parameters.m_PadList = PadList;
+ armnn::WorkloadInfo info;
+
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0], outputHandle.get());
+
+ return result;
+}
+
+template <typename T>
+LayerTestResult<T, 4> Pad4dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
+{
+ const armnn::TensorShape inputShape{ 2, 2, 3, 2 };
+ const armnn::TensorShape outputShape{ 4, 5, 7, 4 };
+
+ const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
+ const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
+
+ std::vector<T> inputValues(
+ QuantizedVector<T>(qScale,qOffset,
+ {
+ // Batch 0, Channel 0, Height (3) x Width (2)
+ 0, 1,
+ 2, 3,
+ 4, 5,
+
+ // Batch 0, Channel 1, Height (3) x Width (2)
+ 6, 7,
+ 8, 9,
+ 10, 11,
+
+ // Batch 1, Channel 0, Height (3) x Width (2)
+ 12, 13,
+ 14, 15,
+ 16, 17,
+
+ // Batch 1, Channel 1, Height (3) x Width (2)
+ 18, 19,
+ 20, 21,
+ 22, 23
+ }));
+
+ std::vector<T> expectedOutputValues(
+ QuantizedVector<T>(qScale,qOffset,
+ {
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 1, 0,
+ 0, 2, 3, 0,
+ 0, 4, 5, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 6, 7, 0,
+ 0, 8, 9, 0,
+ 0, 10, 11, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 12, 13, 0,
+ 0, 14, 15, 0,
+ 0, 16, 17, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 18, 19, 0,
+ 0, 20, 21, 0,
+ 0, 22, 23, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0,
+ 0, 0, 0, 0
+ }));
+
+ auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(inputValues));
+
+ LayerTestResult<T, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(expectedOutputValues));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::PadQueueDescriptor descriptor;
+
+ std::vector<std::pair<unsigned int, unsigned int>> PadList;
+ PadList.push_back(std::pair<unsigned int, unsigned int>(1,1));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(2,1));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(3,1));
+ PadList.push_back(std::pair<unsigned int, unsigned int>(1,1));
+
+ descriptor.m_Parameters.m_PadList = PadList;
+ armnn::WorkloadInfo info;
+
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
+
+ workloadFactory.Finalize();
+
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+
+LayerTestResult<uint8_t, 2> PadUint82dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad2dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 3> PadUint83dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad3dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> PadUint84dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad4dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<float, 2> PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad2dTestCommon<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<float, 3> PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad3dTestCommon<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Pad4dTestCommon<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 1
+ // Height: 1
+ // Channels: 10
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (1)
+ 1.0f,
+
+ // Batch 0, Channel 1, Height (1) x Width (1)
+ 2.0f,
+
+ // Batch 0, Channel 2, Height (1) x Width (1)
+ 3.0f,
+
+ // Batch 0, Channel 3, Height (1) x Width (1)
+ 4.0f,
+
+ // Batch 0, Channel 4, Height (1) x Width (1)
+ 5.0f,
+
+ // Batch 0, Channel 5, Height (1) x Width (1)
+ 6.0f,
+
+ // Batch 0, Channel 6, Height (1) x Width (1)
+ 7.0f,
+
+ // Batch 0, Channel 7, Height (1) x Width (1)
+ 8.0f,
+
+ // Batch 0, Channel 8, Height (1) x Width (1)
+ 9.0f,
+
+ // Batch 0, Channel 9, Height (1) x Width (1)
+ 10.0f
+ };
+ const float approxInvL2Norm = 0.050964719f;
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (1)
+ 1.0f * approxInvL2Norm,
+ 2.0f * approxInvL2Norm,
+ 3.0f * approxInvL2Norm,
+ 4.0f * approxInvL2Norm,
+ 5.0f * approxInvL2Norm,
+ 6.0f * approxInvL2Norm,
+ 7.0f * approxInvL2Norm,
+ 8.0f * approxInvL2Norm,
+ 9.0f * approxInvL2Norm,
+ 10.0f * approxInvL2Norm
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 1
+ // Height: 1
+ // Channels: 10
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (1) x Channel (10)
+ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f
+ };
+ const float approxInvL2Norm = 0.050964719f;
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (1) x Channel (10)
+ 1.0f * approxInvL2Norm,
+ 2.0f * approxInvL2Norm,
+ 3.0f * approxInvL2Norm,
+ 4.0f * approxInvL2Norm,
+ 5.0f * approxInvL2Norm,
+ 6.0f * approxInvL2Norm,
+ 7.0f * approxInvL2Norm,
+ 8.0f * approxInvL2Norm,
+ 9.0f * approxInvL2Norm,
+ 10.0f * approxInvL2Norm
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 5
+ // Height: 1
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (5)
+ 1.0f, 3.0f, 5.0f, 7.0f, 9.0f,
+
+ // Batch 0, Channel 1, Height (1) x Width (5)
+ 2.0f, 4.0f, 6.0f, 8.0f, 10.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (1) x Width (5)
+ 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
+
+ // Batch 0, Channel 1, Height (1) x Width (5)
+ 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 5
+ // Height: 1
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (5) x Channel (2)
+ 1.0f, 2.0f,
+ 3.0f, 4.0f,
+ 5.0f, 6.0f,
+ 7.0f, 8.0f,
+ 9.0f, 10.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (5) x Channel (2)
+ 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
+ 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
+ 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
+ 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
+ 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
+ 10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
+ 119.0f, 21.0f, 150.0f,
+ 149.0f, 32.0f, 179.0f,
+ 15.0f, 227.0f, 141.0f,
+ 147.0f, 199.0f, 220.0f,
+
+ // Batch 0, Channel 1, Height (4) x Width (3)
+ 110.0f, 140.0f, 73.0f,
+ 211.0f, 212.0f, 89.0f,
+ 24.0f, 138.0f, 188.0f,
+ 162.0f, 12.0f, 161.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
+ 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
+ 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
+ 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
+ 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
+
+ // Batch 0, Channel 1, Height (4) x Width (3)
+ 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
+ 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
+ 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
+ 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 2
+ // BatchSize: 1
+
+ const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (2)
+ 119.0f, 110.0f,
+ 21.0f, 140.0f,
+ 150.0f, 73.0f,
+
+ // Batch 0, Height 1, Width (3) x Channel (2)
+ 149.0f, 211.0f,
+ 32.0f, 212.0f,
+ 179.0f, 89.0f,
+
+ // Batch 0, Height 2, Width (3) x Channel (2)
+ 15.0f, 24.0f,
+ 227.0f, 138.0f,
+ 141.0f, 188.0f,
+
+ // Batch 0, Height 3, Width (3) x Channel (2)
+ 147.0f, 162.0f,
+ 199.0f, 12.0f,
+ 220.0f, 161.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (2)
+ 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
+ 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
+ 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
+ 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
+
+ // Batch 0, Height 1, Width (3) x Channel (2)
+ 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
+ 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
+ 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
+ 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
+
+ // Batch 0, Height 2, Width (3) x Channel (2)
+ 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
+ 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
+ 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
+ 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
+
+ // Batch 0, Height 3, Width (3) x Channel (2)
+ 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
+ 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
+ 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
+ 161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 3
+ // BatchSize: 2
+
+ const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
+ 235.0f, 46.0f, 178.0f,
+ 100.0f, 123.0f, 19.0f,
+ 172.0f, 74.0f, 250.0f,
+ 6.0f, 195.0f, 80.0f,
+
+ // Batch 0, Channel 1, Height (4) x Width (3)
+ 113.0f, 95.0f, 202.0f,
+ 77.0f, 114.0f, 71.0f,
+ 122.0f, 246.0f, 166.0f,
+ 82.0f, 28.0f, 37.0f,
+
+ // Batch 0, Channel 2, Height (4) x Width (3)
+ 56.0f, 170.0f, 162.0f,
+ 194.0f, 89.0f, 254.0f,
+ 12.0f, 209.0f, 200.0f,
+ 1.0f, 64.0f, 54.0f,
+
+ // Batch 1, Channel 0, Height (4) x Width (3)
+ 67.0f, 90.0f, 49.0f,
+ 7.0f, 163.0f, 18.0f,
+ 25.0f, 117.0f, 103.0f,
+ 247.0f, 59.0f, 189.0f,
+
+ // Batch 1, Channel 1, Height (4) x Width (3)
+ 239.0f, 104.0f, 199.0f,
+ 17.0f, 124.0f, 153.0f,
+ 222.0f, 217.0f, 75.0f,
+ 32.0f, 126.0f, 21.0f,
+
+ // Batch 1, Channel 2, Height (4) x Width (3)
+ 97.0f, 145.0f, 215.0f,
+ 115.0f, 116.0f, 238.0f,
+ 226.0f, 16.0f, 132.0f,
+ 92.0f, 125.0f, 88.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (4) x Width (3)
+ 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+
+ // Batch 0, Channel 1, Height (4) x Width (3)
+ 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+
+ // Batch 0, Channel 2, Height (4) x Width (3)
+ 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+
+ // Batch 1, Channel 0, Height (4) x Width (3)
+ 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+
+ // Batch 1, Channel 1, Height (4) x Width (3)
+ 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+
+ // Batch 1, Channel 2, Height (4) x Width (3)
+ 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Width: 3
+ // Height: 4
+ // Channels: 3
+ // BatchSize: 2
+
+ const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (3)
+ 235.0f, 113.0f, 56.0f,
+ 46.0f, 95.0f, 170.0f,
+ 178.0f, 202.0f, 162.0f,
+
+ // Batch 0, Height 1, Width (3) x Channel (3)
+ 100.0f, 77.0f, 194.0f,
+ 123.0f, 114.0f, 89.0f,
+ 19.0f, 71.0f, 254.0f,
+
+ // Batch 0, Height 2, Width (3) x Channel (3)
+ 172.0f, 122.0f, 12.0f,
+ 74.0f, 246.0f, 209.0f,
+ 250.0f, 166.0f, 200.0f,
+
+ // Batch 0, Height 3, Width (3) x Channel (3)
+ 6.0f, 82.0f, 1.0f,
+ 195.0f, 28.0f, 64.0f,
+ 80.0f, 37.0f, 54.0f,
+
+ // Batch 1, Height 0, Width (3) x Channel (3)
+ 67.0f, 239.0f, 97.0f,
+ 90.0f, 104.0f, 145.0f,
+ 49.0f, 199.0f, 215.0f,
+
+ // Batch 1, Height 1, Width (3) x Channel (3)
+ 7.0f, 17.0f, 115.0f,
+ 163.0f, 124.0f, 116.0f,
+ 18.0f, 153.0f, 238.0f,
+
+ // Batch 1, Height 2, Width (3) x Channel (3)
+ 25.0f, 222.0f, 226.0f,
+ 117.0f, 217.0f, 16.0f,
+ 103.0f, 75.0f, 132.0f,
+
+ // Batch 1, Height 3, Width (3) x Channel (3)
+ 247.0f, 32.0f, 92.0f,
+ 59.0f, 126.0f, 125.0f,
+ 189.0f, 21.0f, 88.0f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (3) x Channel (3)
+ 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
+ 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
+ 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+ 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
+
+ // Batch 0, Height 1, Width (3) x Channel (3)
+ 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
+ 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
+ 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+ 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
+
+ // Batch 0, Height 2, Width (3) x Channel (3)
+ 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
+ 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
+ 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+ 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
+
+ // Batch 0, Height 3, Width (3) x Channel (3)
+ 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
+ 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
+ 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+ 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+ 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
+
+ // Batch 1, Height 0, Width (3) x Channel (3)
+ 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
+ 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
+ 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+ 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
+
+ // Batch 1, Height 1, Width (3) x Channel (3)
+ 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
+ 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
+ 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+ 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
+
+ // Batch 1, Height 2, Width (3) x Channel (3)
+ 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
+ 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
+ 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+ 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
+
+ // Batch 1, Height 3, Width (3) x Channel (3)
+ 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
+ 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
+ 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+ 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
+ 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f })
+ };
+
+ return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
+ inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
+}
+
+template <typename T>
+LayerTestResult<T, 4> ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ float qScale,
+ int32_t qOffset)
+{
+ constexpr unsigned int inputWidth = 3;
+ constexpr unsigned int inputHeight = 4;
+ constexpr unsigned int inputChannels = 3;
+ constexpr unsigned int inputBatchSize = 2;
+
+ constexpr unsigned int outputWidth = inputWidth;
+ constexpr unsigned int outputHeight = inputHeight;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::GetDataType<T>());
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::GetDataType<T>());
+
+ // Set quantization parameters if the requested type is a quantized type.
+ if(armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo.SetQuantizationScale(qScale);
+ inputTensorInfo.SetQuantizationOffset(qOffset);
+ outputTensorInfo.SetQuantizationScale(qScale);
+ outputTensorInfo.SetQuantizationOffset(qOffset);
+ }
+
+ auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
+ // Batch 0, Channel 0
+ 235.0f, 46.0f, 178.0f,
+ 100.0f, 123.0f, 19.0f,
+ 172.0f, 74.0f, 250.0f,
+ 6.0f, 195.0f, 80.0f,
+
+ // Batch 0, Channel 1
+ 113.0f, 95.0f, 202.0f,
+ 77.0f, 114.0f, 71.0f,
+ 122.0f, 246.0f, 166.0f,
+ 82.0f, 28.0f, 37.0f,
+
+ // Batch 0, Channel 2
+ 56.0f, 170.0f, 162.0f,
+ 194.0f, 89.0f, 254.0f,
+ 12.0f, 209.0f, 200.0f,
+ 1.0f, 64.0f, 54.0f,
+
+ // Batch 1, Channel 0
+ 67.0f, 90.0f, 49.0f,
+ 7.0f, 163.0f, 18.0f,
+ 25.0f, 117.0f, 103.0f,
+ 247.0f, 59.0f, 189.0f,
+
+ // Batch 1, Channel 1
+ 239.0f, 104.0f, 199.0f,
+ 17.0f, 124.0f, 153.0f,
+ 222.0f, 217.0f, 75.0f,
+ 32.0f, 126.0f, 21.0f,
+
+ // Batch 1, Channel 2
+ 97.0f, 145.0f, 215.0f,
+ 115.0f, 116.0f, 238.0f,
+ 226.0f, 16.0f, 132.0f,
+ 92.0f, 125.0f, 88.0f,
+ })));
+
+ LayerTestResult<T, 4> result(outputTensorInfo);
+ result.outputExpected = input;
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo);
+ AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]);
+
+ armnn::ConstantQueueDescriptor descriptor;
+ descriptor.m_LayerOutput = &constantTensor;
+
+ armnn::WorkloadInfo info;
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConstant(descriptor, info);
+
+ outputHandle->Allocate();
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return ConstantTestImpl<float>(workloadFactory, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory)
+{
+ return ConstantTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int outputWidth = 3;
+ unsigned int outputHeight = 6;
+ unsigned int outputChannels = 3;
+
+ unsigned int inputWidth1 = 3;
+ unsigned int inputHeight1 = 6;
+ unsigned int inputChannels1 = 2;
+
+ unsigned int inputWidth2 = 3;
+ unsigned int inputHeight2 = 6;
+ unsigned int inputChannels2 = 1;
+
+ // Defines the tensor descriptors.
+ armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8);
+
+ // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them.
+ const float scale = 0.13497836f;
+ const int32_t offset = -7;
+
+ outputTensorInfo.SetQuantizationScale(scale);
+ outputTensorInfo.SetQuantizationOffset(offset);
+ inputTensorInfo1.SetQuantizationScale(scale);
+ inputTensorInfo1.SetQuantizationOffset(offset);
+ inputTensorInfo2.SetQuantizationScale(scale);
+ inputTensorInfo2.SetQuantizationOffset(offset);
+
+ LayerTestResult<uint8_t, 3> ret(outputTensorInfo);
+
+ ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>(
+ {
+ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9,
+ 10, 11, 12,
+ 13, 14, 15,
+ 16, 17, 18,
+
+ 19, 20, 21,
+ 22, 23, 24,
+ 25, 26, 27,
+ 28, 29, 30,
+ 31, 32, 33,
+ 34, 35, 36,
+
+ 37, 38, 39,
+ 40, 41, 42,
+ 43, 44, 45,
+ 46, 47, 48,
+ 49, 50, 51,
+ 52, 53, 54,
+ })
+ );
+
+ auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>(
+ {
+ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9,
+ 10, 11, 12,
+ 13, 14, 15,
+ 16, 17, 18,
+
+ 19, 20, 21,
+ 22, 23, 24,
+ 25, 26, 27,
+ 28, 29, 30,
+ 31, 32, 33,
+ 34, 35, 36,
+ })
+ );
+
+ auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>(
+ {
+ 37, 38, 39,
+ 40, 41, 42,
+ 43, 44, 45,
+ 46, 47, 48,
+ 49, 50, 51,
+ 52, 53, 54,
+ })
+ );
+
+ std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0].
+ armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
+
+ std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1].
+ armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
+
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ bool subTensorsSupported = workloadFactory.SupportsSubTensors();
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo1);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo2);
+
+
+ armnn::MergerQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ data.m_ViewOrigins.push_back(window1);
+ data.m_ViewOrigins.push_back(window2);
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int batchSize = 1;
+ unsigned int channels = 2;
+ unsigned int height = 2;
+ unsigned int width = 3;
+
+ const float scale = 7.0f;
+ const int32_t offset = 3;
+
+ armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
+ armnn::TensorInfo outputTensorInfo;
+
+ const unsigned int shape[] = { batchSize, channels, height, width };
+ inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo1.SetQuantizationScale(scale);
+ inputTensorInfo1.SetQuantizationOffset(offset);
+
+ inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo2.SetQuantizationScale(scale);
+ inputTensorInfo2.SetQuantizationOffset(offset);
+
+ outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(scale);
+ outputTensorInfo.SetQuantizationOffset(offset);
+
+ // See dequantized values to the right.
+ auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
+ {
+ 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763
+ 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616
+ }));
+
+ // See dequantized values to the right.
+ auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
+ {
+ 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449
+ 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861
+ }));
+
+ // See dequantized values to the right.
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>(
+ {
+ 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped)
+ 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+
+namespace
+{
+LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int shape0[4],
+ const std::vector<uint8_t> & values0,
+ float scale0,
+ int32_t offset0,
+ const unsigned int shape1[4],
+ const std::vector<uint8_t> & values1,
+ float scale1,
+ int32_t offset1,
+ const unsigned int outShape[4],
+ const std::vector<uint8_t> & outValues,
+ float outScale,
+ int32_t outOffset)
+{
+ armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8);
+ armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8);
+
+ inputTensorInfo0.SetQuantizationScale(scale0);
+ inputTensorInfo0.SetQuantizationOffset(offset0);
+
+ inputTensorInfo1.SetQuantizationScale(scale1);
+ inputTensorInfo1.SetQuantizationOffset(offset1);
+
+ outputTensorInfo.SetQuantizationScale(outScale);
+ outputTensorInfo.SetQuantizationOffset(outOffset);
+
+ auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0);
+ auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1);
+
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::MultiplicationQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
+
+ inputHandle0->Allocate();
+ inputHandle1->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+} // anonymous namespace
+
+LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ unsigned int batchSize = 1;
+ unsigned int channels = 2;
+ unsigned int height = 2;
+ unsigned int width = 3;
+ const unsigned int shape[] = { batchSize, channels, height, width };
+
+ // See dequantized values to the right.
+ std::vector<uint8_t> input0({
+ 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440,
+ 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120
+ });
+
+ // See dequantized values to the right.
+ std::vector<uint8_t> input1({
+ 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747,
+ 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297
+ });
+
+ // See dequantized values to the right.
+ std::vector<uint8_t> output(
+ {
+ 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680,
+ 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640
+ });
+
+ return MultiplicationUint8TestHelper(workloadFactory,
+ shape,
+ input0,
+ 4.0f,
+ 1,
+ shape,
+ input1,
+ 3.0f,
+ -2,
+ shape,
+ output,
+ 1366.255f, // Scale/offset chosen to have output values out of range.
+ -5);
+}
+
+LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 2, 2, 3 };
+ const unsigned int shape1[] = { 1, 1, 1, 1 };
+
+ std::vector<uint8_t> input0({
+ 1, 2, 3, 4, 5, 6,
+ 7, 8, 9, 10, 11, 12
+ });
+
+ std::vector<uint8_t> input1({2});
+
+ std::vector<uint8_t> output({
+ 2, 4, 6, 8, 10, 12,
+ 14, 16, 18, 20, 22, 24
+ });
+
+ return MultiplicationUint8TestHelper(workloadFactory,
+ shape0,
+ input0,
+ 1.0f,
+ 0,
+ shape1,
+ input1,
+ 1.0f,
+ 0,
+ shape0,
+ output,
+ 1.0f,
+ 0);
+}
+
+LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 2, 2, 3 };
+ const unsigned int shape1[] = { 1, 1, 1, 3 };
+
+ std::vector<uint8_t> input0({
+ 1, 2, 3, 4, 5, 6,
+ 7, 8, 9, 10, 11, 12
+ });
+
+ std::vector<uint8_t> input1({1, 2, 3});
+
+ std::vector<uint8_t> output({
+ 1, 4, 9, 4, 10, 18,
+ 7, 16, 27, 10, 22, 36
+ });
+
+ return MultiplicationUint8TestHelper(workloadFactory,
+ shape0,
+ input0,
+ 1.0f,
+ 0,
+ shape1,
+ input1,
+ 1.0f,
+ 0,
+ shape0,
+ output,
+ 1.0f,
+ 0);
+}
+
+namespace
+{
+template <typename T>
+LayerTestResult<T, 4> SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int shape0[4],
+ const std::vector<T>& values0,
+ float scale0,
+ int32_t offset0,
+ const unsigned int shape1[4],
+ const std::vector<T> & values1,
+ float scale1,
+ int32_t offset1,
+ const unsigned int outShape[4],
+ const std::vector<T> & outValues,
+ float outScale,
+ int32_t outOffset)
+{
+ auto dataType = (std::is_same<T, uint8_t>::value ?
+ armnn::DataType::QuantisedAsymm8 :
+ armnn::DataType::Float32);
+
+ armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
+ armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
+ armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
+
+ inputTensorInfo0.SetQuantizationScale(scale0);
+ inputTensorInfo0.SetQuantizationOffset(offset0);
+
+ inputTensorInfo1.SetQuantizationScale(scale1);
+ inputTensorInfo1.SetQuantizationOffset(offset1);
+
+ outputTensorInfo.SetQuantizationScale(outScale);
+ outputTensorInfo.SetQuantizationOffset(outOffset);
+
+ auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
+ auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
+
+ LayerTestResult<T, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::SubtractionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info);
+
+ inputHandle0->Allocate();
+ inputHandle1->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+
+ return result;
+}
+} // anonymous namespace
+
+LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 2, 2 };
+
+ std::vector<uint8_t> input0({ 10, 12, 14, 16 });
+ std::vector<uint8_t> input1({ 1, 2, 1, 2 });
+ std::vector<uint8_t> output({ 3, 3, 5, 5 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 0.5f, 2,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 1, 1 };
+
+ std::vector<uint8_t> input0({ 10, 12, 14, 16 });
+ std::vector<uint8_t> input1({ 2 });
+ std::vector<uint8_t> output({ 5, 6, 7, 8 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 0.5f, 2,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 3);
+}
+
+LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 2, 1 };
+
+ std::vector<uint8_t> input0({ 10, 12, 14, 16 });
+ std::vector<uint8_t> input1({ 2, 1 });
+ std::vector<uint8_t> output({ 8, 11, 12, 15 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 2, 2 };
+
+ std::vector<float> input0({ 1, 2, 3, 4 });
+ std::vector<float> input1({ 1, -1, 0, 2 });
+ std::vector<float> output({ 0, 3, 3, 2 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 1, 1 };
+
+ std::vector<float> input0({ 1, 2, 3, 4 });
+ std::vector<float> input1({ 10 });
+ std::vector<float> output({ -9, -8, -7, -6 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int shape0[] = { 1, 1, 2, 2 };
+ const unsigned int shape1[] = { 1, 1, 1, 2 };
+
+ std::vector<float> input0({ 1, 2, 3, 4 });
+ std::vector<float> input1({ 10, -5 });
+ std::vector<float> output({ -9, 7, -7, 9 });
+
+ return SubtractionTestHelper(workloadFactory,
+ shape0, input0, 1.0f, 0,
+ shape1, input1, 1.0f, 0,
+ shape0, output, 1.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int inputWidth = 4;
+ constexpr unsigned int inputHeight = 4;
+ constexpr unsigned int inputChannels = 1;
+ constexpr unsigned int inputBatchSize = 1;
+
+ constexpr unsigned int outputWidth = inputWidth;
+ constexpr unsigned int outputHeight = inputHeight;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationScale(1.5f);
+ inputTensorInfo.SetQuantizationOffset(-3);
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(1.5f);
+ outputTensorInfo.SetQuantizationOffset(-3);
+
+ auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
+ 1, 2, 3, 4,
+ 2, 3, 4, 5,
+ 3, 4, 5, 6,
+ 4, 5, 6, 7
+ }));
+
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = input;
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int inputWidth = 2;
+ constexpr unsigned int inputHeight = 2;
+ constexpr unsigned int inputChannels = 1;
+ constexpr unsigned int inputBatchSize = 1;
+
+ constexpr unsigned int outputWidth = inputWidth / 2;
+ constexpr unsigned int outputHeight = inputHeight / 2;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationScale(0.1567f);
+ inputTensorInfo.SetQuantizationOffset(1);
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(0.1567f);
+ outputTensorInfo.SetQuantizationOffset(1);
+
+ auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
+ 1, 255,
+ 200, 250
+ }));
+
+ // The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
+ // then figures out the interpolants and weights. Note this is different to projecting the centre of the
+ // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value
+ // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting
+ // the centre).
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
+ 1
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int inputWidth = 4;
+ constexpr unsigned int inputHeight = 4;
+ constexpr unsigned int inputChannels = 1;
+ constexpr unsigned int inputBatchSize = 1;
+
+ constexpr unsigned int outputWidth = inputWidth / 2;
+ constexpr unsigned int outputHeight = inputHeight / 2;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationScale(3.141592f);
+ inputTensorInfo.SetQuantizationOffset(3);
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(3.141592f);
+ outputTensorInfo.SetQuantizationOffset(3);
+
+ auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
+ 1, 2, 3, 4,
+ 2, 3, 4, 5,
+ 3, 4, 5, 6,
+ 4, 5, 6, 7
+ }));
+
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
+ 1, 3,
+ 3, 5
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int inputWidth = 3;
+ constexpr unsigned int inputHeight = 2;
+ constexpr unsigned int inputChannels = 1;
+ constexpr unsigned int inputBatchSize = 1;
+
+ constexpr unsigned int outputWidth = 2;
+ constexpr unsigned int outputHeight = 1;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationScale(1.5f);
+ inputTensorInfo.SetQuantizationOffset(-1);
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(1.5f);
+ outputTensorInfo.SetQuantizationOffset(-1);
+
+ auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
+ 1, 2, 3, // 3.0, 4.5, 6.0
+ 5, 8, 13 // 9.0, 13.5, 21.0
+ }));
+
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
+ 1, 3 // 3.0, 5.25
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ constexpr unsigned int inputWidth = 2;
+ constexpr unsigned int inputHeight = 3;
+ constexpr unsigned int inputChannels = 1;
+ constexpr unsigned int inputBatchSize = 1;
+
+ constexpr unsigned int outputWidth = 5;
+ constexpr unsigned int outputHeight = 3;
+ constexpr unsigned int outputChannels = inputChannels;
+ constexpr unsigned int outputBatchSize = inputBatchSize;
+
+ armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationScale(0.010765f);
+ inputTensorInfo.SetQuantizationOffset(7);
+
+ armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
+ armnn::DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationScale(0.010132f);
+ outputTensorInfo.SetQuantizationOffset(-18);
+
+ auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
+ 24, 228, // 0.183005, 2.379065,
+ 105, 128, // 1.05497, 1.302565
+ 230, 71 // 2.400595, 0.68896
+ }));
+
+ LayerTestResult<uint8_t, 4> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
+ 0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504
+ 86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498
+ 219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ResizeBilinearQueueDescriptor descriptor;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+ CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
+ return result;
+}
+
+LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // BatchSize: 1
+ // Channels: 2
+ // Height: 3
+ // Width: 2
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (3) x Width (2)
+ 1.f, 4.f,
+ 4.f, 2.f,
+ 1.f, 6.f,
+
+ // Batch 0, Channel 1, Height (3) x Width (2)
+ 1.f, 1.f,
+ 4.f, 1.f,
+ -2.f, 4.f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (3) x Width (2)
+ 1.f, 4.f,
+ 4.f, 2.f,
+ 1.f, 6.f,
+
+ // Batch 0, Channel 1, Height (3) x Width (2)
+ 3.f, 3.f,
+ 4.f, 3.f,
+ 2.f, 4.f
+ };
+
+ return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+ 0.f, 0, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // BatchSize: 1
+ // Height: 3
+ // Width: 2
+ // Channels: 2
+
+ const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (2) x Channel (2)
+ 1.f, 1.f,
+ 4.f, 1.f,
+
+ // Batch 0, Height 1, Width (2) x Channel (2)
+ 4.f, 4.f,
+ 2.f, 1.f,
+
+ // Batch 0, Height 2, Width (2) x Channel (2)
+ 1.f, -2.f,
+ 6.f, 4.f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (2) x Channel (2)
+ 1.f, 3.f,
+ 4.f, 3.f,
+
+ // Batch 0, Height 1, Width (2) x Channel (2)
+ 4.f, 4.f,
+ 2.f, 3.f,
+
+ // Batch 0, Height 2, Width (2) x Channel (2)
+ 1.f, 2.f,
+ 6.f, 4.f
+ };
+
+ return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+ 0.f, 0, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ // BatchSize: 1
+ // Channels: 2
+ // Height: 3
+ // Width: 2
+
+ const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Channel 0, Height (3) x Width (2)
+ 1.f, 4.f,
+ 4.f, 2.f,
+ 1.f, 6.f,
+
+ // Batch 0, Channel 1, Height (3) x Width (2)
+ 1.f, 1.f,
+ 4.f, 1.f,
+ -2.f, 4.f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Channel 0, Height (3) x Width (2)
+ 1.f, 4.f,
+ 4.f, 2.f,
+ 1.f, 6.f,
+
+ // Batch 0, Channel 1, Height (3) x Width (2)
+ 3.f, 3.f,
+ 4.f, 3.f,
+ 2.f, 4.f
+ };
+
+ return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+ 1.f/20.f, 50, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // BatchSize: 1
+ // Height: 3
+ // Width: 2
+ // Channels: 2
+
+ const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+ std::vector<float> inputValues
+ {
+ // Batch 0, Height 0, Width (2) x Channel (2)
+ 1.f, 1.f,
+ 4.f, 1.f,
+
+ // Batch 0, Height 1, Width (2) x Channel (2)
+ 4.f, 4.f,
+ 2.f, 1.f,
+
+ // Batch 0, Height 2, Width (2) x Channel (2)
+ 1.f, -2.f,
+ 6.f, 4.f
+ };
+ std::vector<float> expectedOutputValues
+ {
+ // Batch 0, Height 0, Width (2) x Channel (2)
+ 1.f, 3.f,
+ 4.f, 3.f,
+
+ // Batch 0, Height 1, Width (2) x Channel (2)
+ 4.f, 4.f,
+ 2.f, 3.f,
+
+ // Batch 0, Height 2, Width (2) x Channel (2)
+ 1.f, 2.f,
+ 6.f, 4.f
+ };
+
+ return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+ 1.f/20.f, 50, armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return ConstantTestImpl<uint8_t>(workloadFactory, 2e-6f, 1);
+}
+
+LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation1dTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
+}
+
+LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
+ bool forceNoPadding)
+{
+ return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
+}
+
+LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool forceNoPadding)
+{
+ return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>(workloadFactory, forceNoPadding, 3.0f, -5);
+}
+
+LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory,
+ bool forceNoPadding)
+{
+ return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, forceNoPadding);
+}
+
+LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory,
+ bool forceNoPadding)
+{
+ return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>(workloadFactory, forceNoPadding, 0.1f, 128);
+}
+
+LayerTestResult<float, 4> SimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleMaxPooling2dTestCommon<float>(workloadFactory, dataLayout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, dataLayout);
+}
+
+LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleAveragePooling2dTestCommon<float>(workloadFactory, dataLayout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, dataLayout, 0.5, -1);
+}
+
+LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
+ bool forceNoPadding)
+{
+ return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
+}
+
+LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1);
+}
+
+LayerTestResult<float, 4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleL2Pooling2dTestCommon<float>(workloadFactory, dataLayout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::DataLayoutIndexed& dataLayout)
+{
+ return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory, dataLayout);
+}
+
+LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize7TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize9TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::PoolingAlgorithm poolingType)
+{
+ return ComparePooling2dTestCommon<float>(workloadFactory, refWorkloadFactory, poolingType);
+}
+
+LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ armnn::PoolingAlgorithm poolingType)
+{
+ return ComparePooling2dTestCommon<uint8_t>(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128);
+}
+
+LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory,
+ bool transposeWeights)
+{
+ return FullyConnectedLargeTestCommon<float>(workloadFactory, transposeWeights);
+}
+
+LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, 1.0f, -5);
+}
+
+LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, 1.0f, -5);
+}
+
+LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test(
+ armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory);
+}
+
+LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory);
+}
+
+LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SimplePermuteFloat32TestCommon(workloadFactory);
+};
+
+LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return SimplePermuteUint8TestCommon(workloadFactory);
+};
+
+LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return PermuteFloat32ValueSet1TestCommon(workloadFactory);
+};
+
+LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return PermuteFloat32ValueSet2TestCommon(workloadFactory);
+};
+
+LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ return PermuteFloat32ValueSet3TestCommon(workloadFactory);
+};
+
+namespace
+{
+
+template <typename T, std::size_t InputDim, std::size_t OutputDim>
+LayerTestResult<T, OutputDim> MeanTestHelper(armnn::IWorkloadFactory& workloadFactory,
+ const unsigned int* inputShape,
+ const std::vector<T>& inputData,
+ const std::vector<unsigned int>& axis,
+ bool keepDims,
+ const unsigned int* outputShape,
+ const std::vector<T>& outputData,
+ float scale = 1.0f,
+ int32_t offset = 0)
+{
+ auto dataType = (std::is_same<T, uint8_t>::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32);
+
+ armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType);
+ armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType);
+
+ inputTensorInfo.SetQuantizationScale(scale);
+ inputTensorInfo.SetQuantizationOffset(offset);
+
+ outputTensorInfo.SetQuantizationScale(scale);
+ outputTensorInfo.SetQuantizationOffset(offset);
+
+ auto input = MakeTensor<T, InputDim>(inputTensorInfo, inputData);
+
+ LayerTestResult<T, OutputDim> result(outputTensorInfo);
+ result.outputExpected = MakeTensor<T, OutputDim>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::MeanQueueDescriptor data;
+ data.m_Parameters.m_Axis = axis;
+ data.m_Parameters.m_KeepDims = keepDims;
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMean(data, info);
+
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), input.origin());
+
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(result.output.origin(), outputHandle.get());
+
+ return result;
+}
+
+} // anonymous namespace
+
+LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 3, 2 };
+ const unsigned int outputShape[] = { 1 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2 });
+
+ return MeanTestHelper<uint8_t, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 2 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2, 2 });
+
+ return MeanTestHelper<uint8_t, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 1, 2 };
+
+ std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
+ std::vector<uint8_t> output({ 2, 2 });
+
+ return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 1, 3, 1, 1 };
+
+ std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 });
+ std::vector<uint8_t> output({ 1, 3, 5 });
+
+ return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output);
+}
+
+LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 4, 3, 2 };
+ const unsigned int outputShape[] = { 2 };
+
+ std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
+ 24 });
+ std::vector<uint8_t> output({ 12, 13 });
+
+ return MeanTestHelper<uint8_t, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape,
+ output, 0.8f, 5);
+}
+
+LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 3, 2 };
+ const unsigned int outputShape[] = { 1 };
+
+ std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f });
+ std::vector<float> output({ 2.0f });
+
+ return MeanTestHelper<float, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
+}
+
+LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 3, 1, 2 };
+
+ std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
+ std::vector<float> output({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
+
+ return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 0 }, false, outputShape, output);
+}
+
+LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 1, 3, 2 };
+ const unsigned int outputShape[] = { 1, 1, 1, 2 };
+
+ std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f });
+ std::vector<float> output({ 2.0f, 2.0f });
+
+ return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output);
+}
+
+LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 2, 3, 1, 2 };
+ const unsigned int outputShape[] = { 1, 3, 1, 1 };
+
+ std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
+ std::vector<float> output({ 1.5f, 3.5f, 5.5f });
+
+ return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output);
+}
+
+LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 4, 3, 2 };
+ const unsigned int outputShape[] = { 2 };
+
+ std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f });
+ std::vector<float> output({ 12.0f, 13.0f });
+
+ return MeanTestHelper<float, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, output);
+}
+
+LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 4, 3, 2 };
+ const unsigned int outputShape[] = { 1, 3, 1 };
+
+ std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f });
+ std::vector<float> output({ 10.5f, 12.5f, 14.5f });
+
+ return MeanTestHelper<float, 3, 3>(workloadFactory, inputShape, input, { 0, 2 }, true, outputShape, output);
+}
+
+LayerTestResult<float, 3> MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory)
+{
+ const unsigned int inputShape[] = { 1, 2, 2, 1 };
+ const unsigned int outputShape[] = { 1, 2, 1 };
+
+ std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f });
+ std::vector<float> output({ 1.5f, 3.5f });
+
+ return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output);
+}
+
+LayerTestResult<float, 4> AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory)
+{
+ // Create Initial Tensor
+ // 1, 2, 3
+ // 4, 5, 6
+ // 7, 8, 9
+
+ armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType<float>());
+ armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType<float>());
+
+ boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo,
+ {1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9
+ });
+
+ std::unique_ptr<armnn::ITensorHandle> poolingInputHandle =
+ workloadFactory.CreateTensorHandle(poolingInputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> poolingOutputHandle =
+ workloadFactory.CreateTensorHandle(poolingOutputTensorInfo);
+
+ // Apply MaxPool poolSize = 1x1, stride=2x2
+ // Result =
+ // 1, 3
+ // 7, 9
+ armnn::Pooling2dDescriptor descriptor;
+ descriptor.m_PoolHeight = 1;
+ descriptor.m_PoolWidth = 1;
+ descriptor.m_StrideX = 2;
+ descriptor.m_StrideY = 2;
+ descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
+
+ armnn::Pooling2dQueueDescriptor queueDescriptor;
+ queueDescriptor.m_Parameters = descriptor;
+ armnn::WorkloadInfo workloadInfo;
+ AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get());
+ AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get());
+
+ // Create the MaxPool
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo);
+
+ //LayerTestResult<float, 4> result(poolingOutputTensorInfo);
+ auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo));
+ boost::multi_array<float, 4> resultMaxPool;
+ resultMaxPool.resize(shape);
+
+
+ // Create addition with another tensor the same size
+ // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1
+ // with the initial tensor.
+ // 12, 16
+ // 24, 28
+
+ armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>());
+ armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>());
+
+ boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo,
+ {12, 16,
+ 24, 28,
+ });
+
+ // Expected output tensor after MaxPool and Addition.
+ LayerTestResult<float,4> addRet(addOutputTensorInfo);
+ addRet.outputExpected = MakeTensor<float, 4>(addOutputTensorInfo, std::vector<float>(
+ {
+ 13, 19,
+ 31, 37
+ }));
+
+ std::unique_ptr<armnn::ITensorHandle> addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo);
+
+ armnn::AdditionQueueDescriptor data;
+ armnn::WorkloadInfo info;
+
+ // Add the output of the MaxPool and the new tensor
+ AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get());
+ AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get());
+ AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> addWorkload = workloadFactory.CreateAddition(data, info);
+
+ poolingInputHandle->Allocate();
+ poolingOutputHandle->Allocate();
+ addInputHandle->Allocate();
+ addOutputHandle->Allocate();
+
+ CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]);
+ CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get());
+
+ CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]);
+ CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]);
+
+ workload->Execute();
+ addWorkload->Execute();
+
+ CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get());
+
+ workloadFactory.Finalize();
+
+ return addRet;
+}
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-16 18:22:27 +0000