diff options
| author | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-10-22 10:00:28 +0100 |
|---|---|---|
| committer | Matteo Martincigh <matteo.martincigh@arm.com> | 2019-10-25 15:01:24 +0000 |
| commit | 48623a0f6f4681ce0d9525b1587b7f96bfd58519 (patch) | |
| tree | f5dbf25937e7b6641274e0953d09ca84acb51772 | |
| parent | c82c8732fb514b412012002bd951a84039eca696 (diff) | |
| download | armnn-48623a0f6f4681ce0d9525b1587b7f96bfd58519.tar.gz | |
IVGCVSW-4018 Move QuantizeHelper.hpp to armnnUtils
* Moved QuntizeHelper.hpp to armnnUtils
* Reordered parameters for QuantizedVector and added default
values for qScale and qOffset to make life easier when
using the function for non-quantized types such as Float16
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: I28c263dfa425f1316feccb4116839a84f5d568e5
44 files changed, 2255 insertions, 1988 deletions
diff --git a/CMakeLists.txt b/CMakeLists.txt index 6edc57f196..38605cac18 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -63,6 +63,7 @@ list(APPEND armnnUtils_sources src/armnnUtils/ParserPrototxtFixture.hpp src/armnnUtils/PrototxtConversions.hpp src/armnnUtils/PrototxtConversions.cpp + src/armnnUtils/QuantizeHelper.hpp src/armnnUtils/TensorIOUtils.hpp src/armnnUtils/TensorUtils.hpp src/armnnUtils/TensorUtils.cpp @@ -612,6 +613,7 @@ if(BUILD_UNIT_TESTS) src/armnn/test/UnitTests.cpp src/armnn/test/UnitTests.hpp src/armnn/test/UtilsTests.cpp + src/armnnUtils/test/QuantizeHelperTest.cpp src/armnnUtils/test/PrototxtConversionsTest.cpp src/armnnUtils/test/ParserHelperTest.cpp src/armnnUtils/test/TensorUtilsTest.cpp diff --git a/src/armnn/test/EndToEndTest.cpp b/src/armnn/test/EndToEndTest.cpp index d25e197f63..df84be4277 100644 --- a/src/armnn/test/EndToEndTest.cpp +++ b/src/armnn/test/EndToEndTest.cpp @@ -7,8 +7,6 @@ #include <armnn/IRuntime.hpp> #include <armnn/INetwork.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> - #include <boost/core/ignore_unused.hpp> #include <boost/test/unit_test.hpp> @@ -16,37 +14,6 @@ BOOST_AUTO_TEST_SUITE(EndToEnd) -namespace -{ - -template<typename T> -bool IsFloatIterFunc(T iter) -{ - boost::ignore_unused(iter); - return IsFloatingPointIterator<T>::value; -} - -} //namespace - -BOOST_AUTO_TEST_CASE(QuantizedHelper) -{ - std::vector<float> fArray; - BOOST_TEST(IsFloatIterFunc(fArray.begin()) == true); - BOOST_TEST(IsFloatIterFunc(fArray.cbegin()) == true); - - std::vector<double> dArray; - BOOST_TEST(IsFloatIterFunc(dArray.begin()) == true); - - std::vector<int> iArray; - BOOST_TEST(IsFloatIterFunc(iArray.begin()) == false); - - float floats[5]; - BOOST_TEST(IsFloatIterFunc(&floats[0]) == true); - - int ints[5]; - BOOST_TEST(IsFloatIterFunc(&ints[0]) == false); -} - BOOST_AUTO_TEST_CASE(ErrorOnLoadNetwork) { using namespace armnn; diff --git a/src/armnn/test/QuantizerTest.cpp b/src/armnn/test/QuantizerTest.cpp index 90fd5e90dd..3f57ce83b2 100644 --- a/src/armnn/test/QuantizerTest.cpp +++ b/src/armnn/test/QuantizerTest.cpp @@ -4,17 +4,19 @@ // #include <armnn/INetwork.hpp> +#include <armnn/LayerVisitorBase.hpp> #include <armnn/Tensor.hpp> -#include <armnnQuantizer/INetworkQuantizer.hpp> #include <armnn/Types.hpp> -#include "armnn/LayerVisitorBase.hpp" +#include <armnnQuantizer/INetworkQuantizer.hpp> + +#include <QuantizeHelper.hpp> + #include "../Graph.hpp" #include "../Network.hpp" #include "../NetworkQuantizerUtils.hpp" #include "../OverrideInputRangeVisitor.hpp" #include "../RangeTracker.hpp" -#include "../backends/backendsCommon/test/QuantizeHelper.hpp" #include "../../armnnQuantizer/CommandLineProcessor.hpp" #include <boost/test/unit_test.hpp> @@ -2294,9 +2296,9 @@ std::vector<uint8_t> SetupQuantize(float value) std::vector<float> input({ value, 0.0f, 0.0f, 1.0f }); const std::vector<float> &inputRef = input; - auto output = QuantizedVector<uint8_t>(inputInfo.GetQuantizationScale(), - inputInfo.GetQuantizationOffset(), - inputRef); + auto output = armnnUtils::QuantizedVector<uint8_t>(inputRef, + inputInfo.GetQuantizationScale(), + inputInfo.GetQuantizationOffset()); return output; } diff --git a/src/armnn/test/TensorHelpers.hpp b/src/armnn/test/TensorHelpers.hpp index 35e471e0f2..3f8589353c 100644 --- a/src/armnn/test/TensorHelpers.hpp +++ b/src/armnn/test/TensorHelpers.hpp @@ -4,23 +4,21 @@ // #pragma once -#include <armnn/TensorFwd.hpp> -#include <boost/test/unit_test.hpp> -#include <boost/multi_array.hpp> -#include <vector> -#include <array> +#include <armnn/Tensor.hpp> + +#include <QuantizeHelper.hpp> #include <boost/assert.hpp> -#include <boost/test/tools/floating_point_comparison.hpp> +#include <boost/multi_array.hpp> +#include <boost/numeric/conversion/cast.hpp> #include <boost/random/uniform_real_distribution.hpp> #include <boost/random/mersenne_twister.hpp> -#include <boost/numeric/conversion/cast.hpp> - -#include <armnn/Tensor.hpp> - -#include <backendsCommon/test/QuantizeHelper.hpp> +#include <boost/test/tools/floating_point_comparison.hpp> +#include <boost/test/unit_test.hpp> +#include <array> #include <cmath> +#include <vector> constexpr float g_FloatCloseToZeroTolerance = 1.0e-6f; @@ -235,7 +233,9 @@ boost::multi_array<T, n> MakeRandomTensor(const armnn::TensorInfo& tensorInfo, { init[i] = dist(gen); } - float qScale = tensorInfo.GetQuantizationScale(); - int32_t qOffset = tensorInfo.GetQuantizationOffset(); - return MakeTensor<T, n>(tensorInfo, QuantizedVector<T>(qScale, qOffset, init)); + + const float qScale = tensorInfo.GetQuantizationScale(); + const int32_t qOffset = tensorInfo.GetQuantizationOffset(); + + return MakeTensor<T, n>(tensorInfo, armnnUtils::QuantizedVector<T>(init, qScale, qOffset)); } diff --git a/src/armnnDeserializer/test/DeserializeComparison.cpp b/src/armnnDeserializer/test/DeserializeComparison.cpp index 9a2fabf5bd..6616398b73 100644 --- a/src/armnnDeserializer/test/DeserializeComparison.cpp +++ b/src/armnnDeserializer/test/DeserializeComparison.cpp @@ -6,10 +6,9 @@ #include "ParserFlatbuffersSerializeFixture.hpp" #include "../Deserializer.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> - #include <boost/test/unit_test.hpp> #include <string> @@ -32,8 +31,8 @@ BOOST_FIXTURE_TEST_CASE(operation##dataType, Simple##operation##dataType##Fixtur constexpr int32_t qOffset = 0; \ RunTest<4, armnn::DataType::dataType, armnn::DataType::Boolean>( \ 0, \ - {{ "InputLayer0", QuantizedVector<T>(qScale, qOffset, s_TestData.m_InputData0) }, \ - { "InputLayer1", QuantizedVector<T>(qScale, qOffset, s_TestData.m_InputData1) }}, \ + {{ "InputLayer0", armnnUtils::QuantizedVector<T>(s_TestData.m_InputData0, qScale, qOffset) }, \ + { "InputLayer1", armnnUtils::QuantizedVector<T>(s_TestData.m_InputData1, qScale, qOffset) }}, \ {{ "OutputLayer", s_TestData.m_Output##operation }}); \ } diff --git a/src/armnnTfLiteParser/test/DetectionPostProcess.cpp b/src/armnnTfLiteParser/test/DetectionPostProcess.cpp index 638238db57..1ec87f97d5 100644 --- a/src/armnnTfLiteParser/test/DetectionPostProcess.cpp +++ b/src/armnnTfLiteParser/test/DetectionPostProcess.cpp @@ -12,6 +12,8 @@ #include "ParserPrototxtFixture.hpp" #include "ParserHelper.hpp" +#include <QuantizeHelper.hpp> + BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) struct DetectionPostProcessFixture : ParserFlatbuffersFixture @@ -200,8 +202,9 @@ BOOST_FIXTURE_TEST_CASE( ParseDetectionPostProcess, ParseDetectionPostProcessCus // Quantize inputs and outputs using QuantizedContainer = std::vector<uint8_t>; - QuantizedContainer quantBoxEncodings = QuantizedVector<uint8_t>(1.0f, 1, boxEncodings); - QuantizedContainer quantScores = QuantizedVector<uint8_t>(0.01f, 0, scores); + + QuantizedContainer quantBoxEncodings = armnnUtils::QuantizedVector<uint8_t>(boxEncodings, 1.00f, 1); + QuantizedContainer quantScores = armnnUtils::QuantizedVector<uint8_t>(scores, 0.01f, 0); std::map<std::string, QuantizedContainer> input = { diff --git a/src/backends/backendsCommon/test/QuantizeHelper.hpp b/src/armnnUtils/QuantizeHelper.hpp index b7ca3b34c0..a7f68c5ef0 100644 --- a/src/backends/backendsCommon/test/QuantizeHelper.hpp +++ b/src/armnnUtils/QuantizeHelper.hpp @@ -17,6 +17,9 @@ #include <boost/core/ignore_unused.hpp> #include <boost/numeric/conversion/cast.hpp> +namespace armnnUtils +{ + template<typename T, bool DoQuantize=true> struct SelectiveQuantizer { @@ -84,7 +87,7 @@ struct IsFloatingPointIterator template <typename T, typename FloatIt, typename std::enable_if<IsFloatingPointIterator<FloatIt>::value, int>::type=0 // Makes sure fp iterator is valid. > -std::vector<T> QuantizedVector(float qScale, int32_t qOffset, FloatIt first, FloatIt last) +std::vector<T> QuantizedVector(FloatIt first, FloatIt last, float qScale, int32_t qOffset) { std::vector<T> quantized; quantized.reserve(boost::numeric_cast<size_t>(std::distance(first, last))); @@ -92,7 +95,7 @@ std::vector<T> QuantizedVector(float qScale, int32_t qOffset, FloatIt first, Flo for (auto it = first; it != last; ++it) { auto f = *it; - T q =SelectiveQuantize<T>(f, qScale, qOffset); + T q = SelectiveQuantize<T>(f, qScale, qOffset); quantized.push_back(q); } @@ -100,13 +103,15 @@ std::vector<T> QuantizedVector(float qScale, int32_t qOffset, FloatIt first, Flo } template<typename T> -std::vector<T> QuantizedVector(float qScale, int32_t qOffset, const std::vector<float>& array) +std::vector<T> QuantizedVector(const std::vector<float>& array, float qScale = 1.f, int32_t qOffset = 0) { - return QuantizedVector<T>(qScale, qOffset, array.begin(), array.end()); + return QuantizedVector<T>(array.begin(), array.end(), qScale, qOffset); } template<typename T> -std::vector<T> QuantizedVector(float qScale, int32_t qOffset, std::initializer_list<float> array) +std::vector<T> QuantizedVector(std::initializer_list<float> array, float qScale = 1.f, int32_t qOffset = 0) { - return QuantizedVector<T>(qScale, qOffset, array.begin(), array.end()); + return QuantizedVector<T>(array.begin(), array.end(), qScale, qOffset); } + +} // namespace armnnUtils diff --git a/src/armnnUtils/test/QuantizeHelperTest.cpp b/src/armnnUtils/test/QuantizeHelperTest.cpp new file mode 100644 index 0000000000..7e781d0b5d --- /dev/null +++ b/src/armnnUtils/test/QuantizeHelperTest.cpp @@ -0,0 +1,46 @@ +// +// Copyright © 2019 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <QuantizeHelper.hpp> + +#include <boost/core/ignore_unused.hpp> +#include <boost/test/unit_test.hpp> + +#include <vector> + +BOOST_AUTO_TEST_SUITE(QuantizeHelper) + +namespace +{ + +template<typename T> +bool IsFloatIterFunc(T iter) +{ + boost::ignore_unused(iter); + return armnnUtils::IsFloatingPointIterator<T>::value; +} + +} // anonymous namespace + +BOOST_AUTO_TEST_CASE(IsFloatIterFuncTest) +{ + std::vector<float> fArray; + BOOST_TEST(IsFloatIterFunc(fArray.begin()) == true); + BOOST_TEST(IsFloatIterFunc(fArray.cbegin()) == true); + + std::vector<double> dArray; + BOOST_TEST(IsFloatIterFunc(dArray.begin()) == true); + + std::vector<int> iArray; + BOOST_TEST(IsFloatIterFunc(iArray.begin()) == false); + + float floats[5]; + BOOST_TEST(IsFloatIterFunc(&floats[0]) == true); + + int ints[5]; + BOOST_TEST(IsFloatIterFunc(&ints[0]) == false); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/backendsCommon/test/AbsEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/AbsEndToEndTestImpl.hpp index c46376b80e..dd851e3960 100644 --- a/src/backends/backendsCommon/test/AbsEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/AbsEndToEndTestImpl.hpp @@ -7,6 +7,7 @@ #include "CommonTestUtils.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -53,8 +54,8 @@ void AbsEndToEnd(const std::vector<armnn::BackendId>& backends) }; // quantize data - std::vector<T> qInputData = QuantizedVector<T>(qScale, qOffset, inputData); - std::vector<T> qExpectedOutputData = QuantizedVector<T>(qScale, qOffset, expectedOutputData); + std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); + std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); INetworkPtr network = CreateAbsNetwork(tensorInfo); diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt index 8a96318d8f..f310ef7564 100644 --- a/src/backends/backendsCommon/test/CMakeLists.txt +++ b/src/backends/backendsCommon/test/CMakeLists.txt @@ -36,7 +36,6 @@ list(APPEND armnnBackendsCommonUnitTests_sources OptimizeSubgraphViewTests.cpp OptimizationViewsTests.cpp PreluEndToEndTestImpl.hpp - QuantizeHelper.hpp QuantizedLstmEndToEndTestImpl.cpp QuantizedLstmEndToEndTestImpl.hpp ResizeEndToEndTestImpl.hpp diff --git a/src/backends/backendsCommon/test/DepthToSpaceEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/DepthToSpaceEndToEndTestImpl.hpp index cf4db1d2d4..fd0b12ffd3 100644 --- a/src/backends/backendsCommon/test/DepthToSpaceEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/DepthToSpaceEndToEndTestImpl.hpp @@ -9,8 +9,9 @@ #include <armnn/ArmNN.hpp> +#include <QuantizeHelper.hpp> + #include <backendsCommon/test/DataLayoutUtils.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> namespace { @@ -58,8 +59,8 @@ void DepthToSpaceEndToEndImpl(const std::vector<armnn::BackendId>& backends, outputInfo.SetQuantizationOffset(qOffset); } - std::vector<T> inputData = QuantizedVector<T>(qScale, qOffset, floatInputData); - std::vector<T> expectedOutputData = QuantizedVector<T>(qScale, qOffset, floatExpectedOutputData); + std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData, qScale, qOffset); + std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData, qScale, qOffset); // Permute tensors from NHWC to NCHW (if needed) if (descriptor.m_DataLayout == DataLayout::NCHW) diff --git a/src/backends/backendsCommon/test/EndToEndTestImpl.hpp b/src/backends/backendsCommon/test/EndToEndTestImpl.hpp index ee9d2bc026..d6f589fa00 100644 --- a/src/backends/backendsCommon/test/EndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/EndToEndTestImpl.hpp @@ -4,13 +4,12 @@ // #pragma once -#include <ResolveType.hpp> - #include <armnn/ArmNN.hpp> #include <armnn/INetwork.hpp> -#include <Profiling.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> +#include <Profiling.hpp> +#include <QuantizeHelper.hpp> +#include <ResolveType.hpp> #include <boost/test/unit_test.hpp> @@ -99,9 +98,9 @@ inline bool ConstantUsageUint8Test(const std::vector<BackendId>& backends) return ConstantUsageTest(backends, commonTensorInfo, - QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input. - QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input. - QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output. + armnnUtils::QuantizedVector<uint8_t>({ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, scale, offset), // Input. + armnnUtils::QuantizedVector<uint8_t>({ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, scale, offset), // Const input. + armnnUtils::QuantizedVector<uint8_t>({ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }, scale, offset) // Expected output. ); } diff --git a/src/backends/backendsCommon/test/ResizeEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ResizeEndToEndTestImpl.hpp index 4bf9d510a6..1eeb944805 100644 --- a/src/backends/backendsCommon/test/ResizeEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/ResizeEndToEndTestImpl.hpp @@ -9,6 +9,7 @@ #include <armnn/ArmNN.hpp> #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <backendsCommon/test/CommonTestUtils.hpp> @@ -119,8 +120,8 @@ void ResizeEndToEnd(const std::vector<armnn::BackendId>& backends, } // quantize data - std::vector<T> qInputData = QuantizedVector<T>(qScale, qOffset, inputData); - std::vector<T> qExpectedOutputData = QuantizedVector<T>(qScale, qOffset, expectedOutputData); + std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); + std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); INetworkPtr network = CreateResizeNetwork(descriptor, inputInfo, outputInfo); @@ -144,4 +145,4 @@ void ResizeNearestNeighborEndToEnd(const std::vector<armnn::BackendId>& backends armnn::DataLayout dataLayout) { ResizeEndToEnd<ArmnnType>(backends, dataLayout, armnn::ResizeMethod::NearestNeighbor); -}
\ No newline at end of file +} diff --git a/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp index 9d6312ea53..4935a18b51 100644 --- a/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp @@ -9,6 +9,7 @@ #include <armnn/ArmNN.hpp> #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <backendsCommon/test/CommonTestUtils.hpp> @@ -129,12 +130,12 @@ void TransposeConvolution2dEndToEnd(const std::vector<armnn::BackendId>& backend } // quantize data - std::vector<T> qInputData = QuantizedVector<T>(qScale, qOffset, inputData); - std::vector<T> qWeightsData = QuantizedVector<T>(qScale, qOffset, weightsData); - std::vector<T> qExpectedOutputData = QuantizedVector<T>(qScale, qOffset, expectedOutputData); + std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); + std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset); + std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); using BT = ResolveType<ArmnnBType>; - std::vector<BT> qBiasesData = QuantizedVector<BT>(qScale * qScale, 0, biasesData); + std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); ConstTensor weights(weightsInfo, qWeightsData); ConstTensor biases(biasesInfo, qBiasesData); @@ -150,4 +151,4 @@ void TransposeConvolution2dEndToEnd(const std::vector<armnn::BackendId>& backend { { 0, qInputData } }, { { 0, qExpectedOutputData } }, backends); -}
\ No newline at end of file +} diff --git a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp index 075c29de67..a45c6d5500 100644 --- a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp @@ -5,12 +5,12 @@ #include "ActivationTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> #include <backendsCommon/test/ActivationFixture.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -424,7 +424,7 @@ LayerTestResult<T, 4> SimpleActivationTest( LayerTestResult<T, 4> result(inputTensorInfo); - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(scale, offset, inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, scale, offset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -451,8 +451,8 @@ LayerTestResult<T, 4> SimpleActivationTest( CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); // Calculated manually. - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(outScale, outOffset, - outputExpectedData)); + result.outputExpected = + MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>(outputExpectedData, outScale, outOffset)); return result; } @@ -812,7 +812,7 @@ LayerTestResult<float, 5> SqrtNNTest( LayerTestResult<float, 5> result(inputTensorInfo); - auto input = MakeTensor<float, 5>(inputTensorInfo, QuantizedVector<float>(0.f, 0.f, inputData)); + auto input = MakeTensor<float, 5>(inputTensorInfo, inputData); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -836,8 +836,7 @@ LayerTestResult<float, 5> SqrtNNTest( CopyDataFromITensorHandle(&result.output[0][0][0][0][0], outputHandle.get()); // Calculated manually. - result.outputExpected = MakeTensor<float, 5>(outputTensorInfo, QuantizedVector<float>(0.f, 0.f, - outputExpectedData)); + result.outputExpected = MakeTensor<float, 5>(outputTensorInfo, outputExpectedData); return result; }; diff --git a/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp index c6d3982f92..247821b293 100644 --- a/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp @@ -7,6 +7,8 @@ #include "ElementwiseTestImpl.hpp" +#include <QuantizeHelper.hpp> + template<> std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::AdditionQueueDescriptor>( const armnn::IWorkloadFactory& workloadFactory, @@ -177,7 +179,7 @@ LayerTestResult<T, 4> AdditionBroadcastTestImpl( outputTensorInfo.SetQuantizationOffset(qOffset); } - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, armnnUtils::QuantizedVector<T>( { 0.0f, 1.0f, @@ -187,16 +189,18 @@ LayerTestResult<T, 4> AdditionBroadcastTestImpl( 4.0f, 5.0f, - })); + }, + qScale, qOffset)); - auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, + auto input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>( { 0.5f, 1.5f, 2.5f, 3.5f, 4.5f, 5.5f, - })); + }, + qScale, qOffset)); LayerTestResult<T,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>( { 0.5f, 1.5f, 2.5f, 4.5f, 5.5f, 6.5f, @@ -206,7 +210,8 @@ LayerTestResult<T, 4> AdditionBroadcastTestImpl( 4.5f, 5.5f, 6.5f, 8.5f, 9.5f, 10.5f, - })); + }, + qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); @@ -256,31 +261,34 @@ LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl( outputTensorInfo.SetQuantizationOffset(qOffset); } - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, armnnUtils::QuantizedVector<T>( { - 0.0f, 1.0f, 2.0f, - 3.0f, 4.0f, 5.0f, - 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, + 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, - })); + }, + qScale, qOffset)); - auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, + auto input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>( { 0.5f, - })); + }, + qScale, qOffset)); LayerTestResult<T,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>( { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - 6.5f, 7.5f, 8.5f, - 9.5f, 10.5f, 11.5f, + 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, - })); + }, + qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); diff --git a/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp index ef430883d4..68cda7ccf3 100644 --- a/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp @@ -6,6 +6,7 @@ #include "BatchNormalizationTestImpl.hpp" #include <DataLayoutIndexed.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -14,7 +15,6 @@ #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/WorkloadFactory.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -23,6 +23,8 @@ namespace { +using namespace armnnUtils; + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> BatchNormTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -53,19 +55,18 @@ LayerTestResult<T, 4> BatchNormTestImpl( tensorInfo.SetQuantizationOffset(qOffset); } - auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, inputValues)); + auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputValues, qScale, qOffset)); // These values are per-channel of the input. - auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2})); - auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9})); - auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2})); - auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1})); + auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, -2 }, qScale, qOffset)); + auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 4, 9 }, qScale, qOffset)); + auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, 2 }, qScale, qOffset)); + auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 2, 1 }, qScale, qOffset)); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, expectedOutputValues)); + QuantizedVector<T>(expectedOutputValues, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -134,17 +135,18 @@ LayerTestResult<T,4> BatchNormTestNhwcImpl( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, + QuantizedVector<T>( { 1.f, 1.f, 4.f, 1.f, 4.f, 4.f, 2.f, 1.f, 1.f, -2.f, 6.f, 4.f - })); + }, + qScale, qOffset)); // These values are per-channel of the input. - auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2})); - auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9})); - auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2})); - auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1})); + auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, -2 }, qScale, qOffset)); + auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 4, 9 }, qScale, qOffset)); + auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, 2 }, qScale, qOffset)); + auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 2, 1 }, qScale, qOffset)); LayerTestResult<T,4> ret(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); @@ -175,12 +177,13 @@ LayerTestResult<T,4> BatchNormTestNhwcImpl( // substract mean, divide by standard deviation (with an epsilon to avoid div by 0), // multiply by gamma and add beta ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, + QuantizedVector<T>( { 1.f, 3.f, 4.f, 3.f, 4.f, 4.f, 2.f, 3.f, 1.f, 2.f, 6.f, 4.f - })); + }, + qScale, qOffset)); std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info); diff --git a/src/backends/backendsCommon/test/layerTests/ComparisonTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ComparisonTestImpl.cpp index 9da1d425d9..1c54b85d06 100644 --- a/src/backends/backendsCommon/test/layerTests/ComparisonTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ComparisonTestImpl.cpp @@ -8,12 +8,12 @@ #include <armnn/ArmNN.hpp> #include <Half.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <backendsCommon/Workload.hpp> #include <backendsCommon/WorkloadData.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -155,8 +155,8 @@ LayerTestResult<uint8_t, 4> ComparisonTestImpl(armnn::IWorkloadFactory& workload { using T = armnn::ResolveType<ArmnnInType>; - std::vector<T> inputData0 = QuantizedVector<T>(quantScale, quantOffset, testData.m_InputData0); - std::vector<T> inputData1 = QuantizedVector<T>(quantScale, quantOffset, testData.m_InputData1); + std::vector<T> inputData0 = armnnUtils::QuantizedVector<T>(testData.m_InputData0, quantScale, quantOffset); + std::vector<T> inputData1 = armnnUtils::QuantizedVector<T>(testData.m_InputData1, quantScale, quantOffset); return ComparisonTestImpl<4, ArmnnInType>( workloadFactory, diff --git a/src/backends/backendsCommon/test/layerTests/ConcatTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ConcatTestImpl.cpp index 29476e522a..e9932c81ef 100644 --- a/src/backends/backendsCommon/test/layerTests/ConcatTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ConcatTestImpl.cpp @@ -6,6 +6,7 @@ #include "ConcatTestImpl.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -15,22 +16,25 @@ #include <test/TensorHelpers.hpp> +using namespace armnn; +using namespace armnnUtils; + // // Helper functions and templates // -armnn::OriginsDescriptor CreateDescriptorForConcat( - const std::vector<armnn::TensorInfo> & inputTensorInfos, +OriginsDescriptor CreateDescriptorForConcat( + const std::vector<TensorInfo> & inputTensorInfos, unsigned int concatDim) { - std::vector<armnn::TensorShape> shapes; + std::vector<TensorShape> shapes; shapes.reserve(inputTensorInfos.size()); - for (const armnn::TensorInfo& it: inputTensorInfos) + for (const TensorInfo& it: inputTensorInfos) { shapes.push_back(it.GetShape()); } - return armnn::CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), concatDim); + return CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), concatDim); } // @@ -40,7 +44,7 @@ armnn::OriginsDescriptor CreateDescriptorForConcat( // bool NeedPermuteForConcat( - const std::vector<armnn::TensorInfo> & inputTensorInfos, + const std::vector<TensorInfo> & inputTensorInfos, unsigned int concatDim) { // See note above. Additionally we expect the input shapes to have the @@ -65,7 +69,7 @@ bool NeedPermuteForConcat( return (nDimensions < 3 || (nDimensions == 3 && (nDimensions-concatDim) < 3 && (nDimensions-concatDim) != 1)); } -armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) +TensorShape ExpandTensorShapeTo3dForPermute(const TensorShape & inputShape) { unsigned int numDims = inputShape.GetNumDimensions(); if (numDims >= 3) @@ -80,13 +84,13 @@ armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & in { newDims[expandedBy+i] = inputShape[i]; } - return armnn::TensorShape(3u, &newDims[0]); + return TensorShape(3u, &newDims[0]); } void Generate3dPermuteVectorForConcat( unsigned int numDimensions, unsigned int & concatDim, - std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations) + std::pair<PermutationVector, PermutationVector> & permutations) { BOOST_ASSERT_MSG(numDimensions <= 3, "Only dimensions 1,2 and 3 are supported by this helper"); @@ -96,15 +100,15 @@ void Generate3dPermuteVectorForConcat( if (expandedConcatAxis == 2) { concatDim = 0; - armnn::PermutationVector forwardPermutation({1, 2, 0}); - armnn::PermutationVector reversePermutation({2, 0, 1}); + PermutationVector forwardPermutation({1, 2, 0}); + 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}); + PermutationVector forwardPermutation({2, 0, 1}); + PermutationVector reversePermutation({1, 2, 0}); permutations = std::make_pair(forwardPermutation, reversePermutation); } else @@ -115,10 +119,10 @@ void Generate3dPermuteVectorForConcat( } template<typename T> void PermuteTensorData( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::PermutationVector& mappings, - armnn::TensorInfo & inputTensorInfo, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const PermutationVector& mappings, + TensorInfo & inputTensorInfo, const T * inputData, std::vector<T>& outputData) { @@ -131,18 +135,18 @@ template<typename T> void PermuteTensorData( return; } - armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); + TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - armnn::PermuteQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; - armnn::WorkloadInfo workloadInfo; + PermuteQueueDescriptor queueDescriptor; + queueDescriptor.m_Parameters = PermuteDescriptor{mappings}; + WorkloadInfo workloadInfo; AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); + std::unique_ptr<IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); inputHandle->Allocate(); outputHandle->Allocate(); @@ -164,23 +168,23 @@ template<typename T> void PermuteTensorData( // of the permuted concatenated tensor is going to be. // template<typename T> void PermuteInputsForConcat( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - std::vector<armnn::TensorInfo> & inputTensorInfos, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + std::vector<TensorInfo> & inputTensorInfos, std::vector<T *> & inputData, std::vector<std::vector<T>> & inputDataStorage, - armnn::PermutationVector & permuteVector, + PermutationVector & permuteVector, unsigned int & concatDim, - armnn::TensorInfo & outputTensorInfo) + 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}); + const PermutationVector identity({0, 1, 2}); - std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations = + std::pair<PermutationVector, PermutationVector> permutations = std::make_pair(identity, identity); inputDataStorage.resize(inputData.size()); @@ -203,7 +207,7 @@ template<typename T> void PermuteInputsForConcat( "All inputs must have the same number of dimensions"); } - armnn::TensorInfo newTensorInfo = tensorInfo; + TensorInfo newTensorInfo = tensorInfo; newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); PermuteTensorData<T>(workloadFactory, @@ -231,11 +235,11 @@ template<typename T> void PermuteInputsForConcat( // output. // template <typename T> void PermuteOutputForConcat( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TensorInfo & tensorInfo, - const armnn::PermutationVector & permuteVector, - std::unique_ptr<armnn::ITensorHandle> && inputDataHandle, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const TensorInfo & tensorInfo, + const PermutationVector & permuteVector, + std::unique_ptr<ITensorHandle> && inputDataHandle, T * data) { BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); @@ -247,7 +251,7 @@ template <typename T> void PermuteOutputForConcat( return; } - armnn::TensorInfo resultTensorInfo = tensorInfo; + TensorInfo resultTensorInfo = tensorInfo; std::vector<T> inputData(tensorInfo.GetNumElements()); std::vector<T> outputData; @@ -264,11 +268,11 @@ template <typename T> void PermuteOutputForConcat( } template<typename T> void Concatenate( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + std::initializer_list<const TensorInfo> inputTensorInfosOrig, std::initializer_list<T *> inputsOrig, - const armnn::TensorInfo& outputTensorInfoOrig, + const TensorInfo& outputTensorInfoOrig, T * output, unsigned int concatDim, bool useSubtensor) @@ -283,11 +287,11 @@ template<typename T> void Concatenate( } // Saves a copy of the parameters which we might need to change. - std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); + std::vector<TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); std::vector<T *> inputs = inputsOrig; - armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; + TensorInfo outputTensorInfo = outputTensorInfoOrig; - armnn::PermutationVector permuteVector{0, 1, 2}; + PermutationVector permuteVector{0, 1, 2}; // Holds and automatically releases memory for the reshaped input data. std::vector<std::vector<T>> tmpInputDataStorage; @@ -312,15 +316,15 @@ template<typename T> void Concatenate( outputTensorInfo); } - armnn::WorkloadInfo workloadInfo; + WorkloadInfo workloadInfo; - std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles; + std::vector<std::unique_ptr<ITensorHandle>> inputHandles; inputHandles.reserve(inputCount); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - armnn::ConcatQueueDescriptor queueDescriptor; - armnn::OriginsDescriptor viewsDescriptor = CreateDescriptorForConcat(inputTensorInfos, concatDim); + ConcatQueueDescriptor queueDescriptor; + OriginsDescriptor viewsDescriptor = CreateDescriptorForConcat(inputTensorInfos, concatDim); queueDescriptor.m_Parameters = viewsDescriptor; if (useSubtensor) @@ -337,8 +341,8 @@ template<typename T> void Concatenate( 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 = + const TensorInfo& inputTensorInfo = inputTensorInfos[i]; + std::unique_ptr<ITensorHandle> inputHandle = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(), @@ -353,7 +357,7 @@ template<typename T> void Concatenate( { for (unsigned int i = 0; i < inputCount; ++i) { - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfos[i]); + std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfos[i]); inputHandles.emplace_back(std::move(inputHandle)); } } @@ -365,7 +369,7 @@ template<typename T> void Concatenate( AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(queueDescriptor, workloadInfo); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(queueDescriptor, workloadInfo); for (auto& inputHandle : inputHandles) { @@ -403,20 +407,20 @@ template<typename T> void Concatenate( // Implementation templates // -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 1> Concat1dTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset); + TensorInfo inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset); - 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 })); + auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 1.0f, 2.0f, 3.0f }, qScale, qOffset)); + auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 4.0f, 5.0f, 6.0f }, qScale, qOffset)); + auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 7.0f, 8.0f, 9.0f }, qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 1> result(outputTensorInfo); @@ -430,48 +434,56 @@ LayerTestResult<T, 1> Concat1dTestImpl( 0, true); - 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 - })); + result.output = MakeTensor<T, 1>(outputTensorInfo, output); + result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 2> Concat2dTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TensorInfo& outputTensorInfo, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const TensorInfo& outputTensorInfo, unsigned int dimension, const float qScale, const int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); + TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, + auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0 + 1.0f, 2.0f, 3.0f, - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); + // Batch 1 + 10.0f, 11.0f, 12.0f, + }, + qScale, qOffset)); - auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, + auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0 + 4.0f, 5.0f, 6.0f, - // Batch 1 - 13.0f, 14.0f, 15.0f, - })); + // Batch 1 + 13.0f, 14.0f, 15.0f, + }, + qScale, qOffset)); - auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 7.0f, 8.0f, 9.0f, + auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0 + 7.0f, 8.0f, 9.0f, - // Batch 1 - 16.0f, 17.0f, 18.0f, - })); + // Batch 1 + 16.0f, 17.0f, 18.0f, + }, + qScale, qOffset)); LayerTestResult<T, 2> result(outputTensorInfo); @@ -489,99 +501,109 @@ LayerTestResult<T, 2> Concat2dTestImpl( return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 2> Concat2dDim0TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0 + 1.0f, 2.0f, 3.0f, - // Batch 1 - 10.0f, 11.0f, 12.0f, + // Batch 1 + 10.0f, 11.0f, 12.0f, - // Batch 2 - 4.0f, 5.0f, 6.0f, + // Batch 2 + 4.0f, 5.0f, 6.0f, - // Batch 3 - 13.0f, 14.0f, 15.0f, + // Batch 3 + 13.0f, 14.0f, 15.0f, - // Batch 4 - 7.0f, 8.0f, 9.0f, + // Batch 4 + 7.0f, 8.0f, 9.0f, - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); + // Batch 5 + 16.0f, 17.0f, 18.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 2> Concat2dDim1TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( workloadFactory, memoryManager, 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, + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( + { + // 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 - })); + // Batch 1 + 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 2> Concat2dDim0DiffInputDimsTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, + TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( + { + // Batch 0 + 1.0f, 2.0f, 3.0f, - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); + // Batch 1 + 10.0f, 11.0f, 12.0f, + }, + qScale, qOffset)); - armnn::TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset); - auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, + TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( + { + // Batch 0 + 4.0f, 5.0f, 6.0f, - // Batch 1 - 13.0f, 14.0f, 15.0f, + // Batch 1 + 13.0f, 14.0f, 15.0f, - // Batch 0 - 7.0f, 8.0f, 9.0f, - })); + // Batch 0 + 7.0f, 8.0f, 9.0f, + }, + qScale, qOffset)); - armnn::TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset); - auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 1 - 16.0f, 17.0f, 18.0f, - })); + TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset); + auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( + { + // Batch 1 + 16.0f, 17.0f, 18.0f, + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 2> result(outputTensorInfo); std::vector<T> output; @@ -595,64 +617,72 @@ LayerTestResult<T, 2> Concat2dDim0DiffInputDimsTestImpl( true); 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, + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0 + 1.0f, 2.0f, 3.0f, - // Batch 1 - 10.0f, 11.0f, 12.0f, + // Batch 1 + 10.0f, 11.0f, 12.0f, - // Batch 2 - 4.0f, 5.0f, 6.0f, + // Batch 2 + 4.0f, 5.0f, 6.0f, - // Batch 3 - 13.0f, 14.0f, 15.0f, + // Batch 3 + 13.0f, 14.0f, 15.0f, - // Batch 4 - 7.0f, 8.0f, 9.0f, + // Batch 4 + 7.0f, 8.0f, 9.0f, - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); + // Batch 5 + 16.0f, 17.0f, 18.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 2> Concat2dDim1DiffInputDimsTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, + TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( + { + // Batch 0 + 1.0f, 2.0f, 3.0f, - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); + // Batch 1 + 10.0f, 11.0f, 12.0f, + }, + qScale, qOffset)); - armnn::TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset); - auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, + TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( + { + // Batch 0 + 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - // Batch 1 - 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, - })); + // Batch 1 + 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, + }, + qScale, qOffset)); - armnn::TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset); - auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 9.0f, + TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset); + auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( + { + // Batch 0 + 9.0f, - // Batch 1 - 18.0f - })); + // Batch 1 + 18.0f + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 2> result(outputTensorInfo); std::vector<T> output; @@ -666,88 +696,96 @@ LayerTestResult<T, 2> Concat2dDim1DiffInputDimsTestImpl( true); 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, + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( + { + // 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, - })); + // Batch 1 + 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TensorInfo& outputTensorInfo, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const TensorInfo& outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); + TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, + auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f - })); + // Batch 1, Channel 2 + 23.0f, 24.0f + }, + qScale, qOffset)); - auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, + auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 7.0f, 8.0f, - // Batch 0, Channel 1 - 9.0f, 10.0f, + // Batch 0, Channel 1 + 9.0f, 10.0f, - // Batch 0, Channel 2 - 11.0f, 12.0f, + // Batch 0, Channel 2 + 11.0f, 12.0f, - // Batch 1, Channel 0 - 25.0f, 26.0f, + // Batch 1, Channel 0 + 25.0f, 26.0f, - // Batch 1, Channel 1 - 27.0f, 28.0f, + // Batch 1, Channel 1 + 27.0f, 28.0f, - // Batch 1, Channel 2 - 29.0f, 30.0f - })); + // Batch 1, Channel 2 + 29.0f, 30.0f + }, + qScale, qOffset)); - auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, + auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 13.0f, 14.0f, - // Batch 0, Channel 1 - 15.0f, 16.0f, + // Batch 0, Channel 1 + 15.0f, 16.0f, - // Batch 0, Channel 2 - 17.0f, 18.0f, + // Batch 0, Channel 2 + 17.0f, 18.0f, - // Batch 1, Channel 0 - 31.0f, 32.0f, + // Batch 1, Channel 0 + 31.0f, 32.0f, - // Batch 1, Channel 1 - 33.0f, 34.0f, + // Batch 1, Channel 1 + 33.0f, 34.0f, - // Batch 1, Channel 2 - 35.0f, 36.0f - })); + // Batch 1, Channel 2 + 35.0f, 36.0f + }, + qScale, qOffset)); LayerTestResult<T, 3> result(outputTensorInfo); @@ -765,193 +803,200 @@ LayerTestResult<T, 3> Concat3dTestImpl( return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim0TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f, + // Batch 1, Channel 2 + 23.0f, 24.0f, - // Batch 2, Channel 0 - 7.0f, 8.0f, + // Batch 2, Channel 0 + 7.0f, 8.0f, - // Batch 2, Channel 1 - 9.0f, 10.0f, + // Batch 2, Channel 1 + 9.0f, 10.0f, - // Batch 2, Channel 2 - 11.0f, 12.0f, + // Batch 2, Channel 2 + 11.0f, 12.0f, - // Batch 3, Channel 0 - 25.0f, 26.0f, + // Batch 3, Channel 0 + 25.0f, 26.0f, - // Batch 3, Channel 1 - 27.0f, 28.0f, + // Batch 3, Channel 1 + 27.0f, 28.0f, - // Batch 3, Channel 2 - 29.0f, 30.0f, + // Batch 3, Channel 2 + 29.0f, 30.0f, - // Batch 4, Channel 0 - 13.0f, 14.0f, + // Batch 4, Channel 0 + 13.0f, 14.0f, - // Batch 4, Channel 1 - 15.0f, 16.0f, + // Batch 4, Channel 1 + 15.0f, 16.0f, - // Batch 4, Channel 2 - 17.0f, 18.0f, + // Batch 4, Channel 2 + 17.0f, 18.0f, - // Batch 5, Channel 0 - 31.0f, 32.0f, + // Batch 5, Channel 0 + 31.0f, 32.0f, - // Batch 5, Channel 1 - 33.0f, 34.0f, + // Batch 5, Channel 1 + 33.0f, 34.0f, - // Batch 5, Channel 2 - 35.0f, 36.0f - })); + // Batch 5, Channel 2 + 35.0f, 36.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim1TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 0, Channel 3 - 7.0f, 8.0f, + // Batch 0, Channel 3 + 7.0f, 8.0f, - // Batch 0, Channel 4 - 9.0f, 10.0f, + // Batch 0, Channel 4 + 9.0f, 10.0f, - // Batch 0, Channel 5 - 11.0f, 12.0f, + // Batch 0, Channel 5 + 11.0f, 12.0f, - // Batch 0, Channel 6 - 13.0f, 14.0f, + // Batch 0, Channel 6 + 13.0f, 14.0f, - // Batch 0, Channel 7 - 15.0f, 16.0f, + // Batch 0, Channel 7 + 15.0f, 16.0f, - // Batch 0, Channel 8 - 17.0f, 18.0f, + // Batch 0, Channel 8 + 17.0f, 18.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f, + // Batch 1, Channel 2 + 23.0f, 24.0f, - // Batch 1, Channel 3 - 25.0f, 26.0f, + // Batch 1, Channel 3 + 25.0f, 26.0f, - // Batch 1, Channel 4 - 27.0f, 28.0f, + // Batch 1, Channel 4 + 27.0f, 28.0f, - // Batch 1, Channel 5 - 29.0f, 30.0f, + // Batch 1, Channel 5 + 29.0f, 30.0f, - // Batch 1, Channel 6 - 31.0f, 32.0f, + // Batch 1, Channel 6 + 31.0f, 32.0f, - // Batch 1, Channel 7 - 33.0f, 34.0f, + // Batch 1, Channel 7 + 33.0f, 34.0f, - // Batch 1, Channel 8 - 35.0f, 36.0f - })); + // Batch 1, Channel 8 + 35.0f, 36.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim2TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, 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, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // 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 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 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 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 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, - })); + // Batch 1, Channel 2 + 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { + TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); + auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( + { // Batch 0, Channel 0 1.0f, 2.0f, @@ -969,10 +1014,12 @@ LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( // Batch 1, Channel 2 23.0f, 24.0f - })); + }, + qScale, qOffset)); - armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { + TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); + auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( + { // Batch 0, Channel 0 7.0f, 8.0f, @@ -981,10 +1028,12 @@ LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( // Batch 0, Channel 2 11.0f, 12.0f, - })); + }, + qScale, qOffset)); - armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { + TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); + auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( + { // Batch 0, Channel 0 25.0f, 26.0f, @@ -1011,9 +1060,10 @@ LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( // Batch 2, Channel 2 35.0f, 36.0f - })); + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); + TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -1027,130 +1077,138 @@ LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( true); 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, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f, + // Batch 1, Channel 2 + 23.0f, 24.0f, - // Batch 2, Channel 0 - 7.0f, 8.0f, + // Batch 2, Channel 0 + 7.0f, 8.0f, - // Batch 2, Channel 1 - 9.0f, 10.0f, + // Batch 2, Channel 1 + 9.0f, 10.0f, - // Batch 2, Channel 2 - 11.0f, 12.0f, + // Batch 2, Channel 2 + 11.0f, 12.0f, - // Batch 3, Channel 0 - 25.0f, 26.0f, + // Batch 3, Channel 0 + 25.0f, 26.0f, - // Batch 3, Channel 1 - 27.0f, 28.0f, + // Batch 3, Channel 1 + 27.0f, 28.0f, - // Batch 3, Channel 2 - 29.0f, 30.0f, + // Batch 3, Channel 2 + 29.0f, 30.0f, - // Batch 4, Channel 0 - 13.0f, 14.0f, + // Batch 4, Channel 0 + 13.0f, 14.0f, - // Batch 4, Channel 1 - 15.0f, 16.0f, + // Batch 4, Channel 1 + 15.0f, 16.0f, - // Batch 4, Channel 2 - 17.0f, 18.0f, + // Batch 4, Channel 2 + 17.0f, 18.0f, - // Batch 5, Channel 0 - 31.0f, 32.0f, + // Batch 5, Channel 0 + 31.0f, 32.0f, - // Batch 5, Channel 1 - 33.0f, 34.0f, + // Batch 5, Channel 1 + 33.0f, 34.0f, - // Batch 5, Channel 2 - 35.0f, 36.0f - })); + // Batch 5, Channel 2 + 35.0f, 36.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim1DiffInputDimsTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, + TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f - })); + // Batch 1, Channel 2 + 23.0f, 24.0f + }, + qScale, qOffset)); - armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, + TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 7.0f, 8.0f, - // Batch 0, Channel 1 - 9.0f, 10.0f, + // Batch 0, Channel 1 + 9.0f, 10.0f, - // Batch 0, Channel 2 - 11.0f, 12.0f, + // Batch 0, Channel 2 + 11.0f, 12.0f, - // Batch 0, Channel 3 - 25.0f, 26.0f, + // Batch 0, Channel 3 + 25.0f, 26.0f, - // Batch 1, Channel 0 - 27.0f, 28.0f, + // Batch 1, Channel 0 + 27.0f, 28.0f, - // Batch 1, Channel 1 - 29.0f, 30.0f, + // Batch 1, Channel 1 + 29.0f, 30.0f, - // Batch 1, Channel 2 - 13.0f, 14.0f, + // Batch 1, Channel 2 + 13.0f, 14.0f, - // Batch 1, Channel 3 - 15.0f, 16.0f, - })); + // Batch 1, Channel 3 + 15.0f, 16.0f, + }, + qScale, qOffset)); - armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 17.0f, 18.0f, + TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset); + auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 17.0f, 18.0f, - // Batch 1, Channel 0 - 31.0f, 32.0f, - })); + // Batch 1, Channel 0 + 31.0f, 32.0f, + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -1164,131 +1222,139 @@ LayerTestResult<T, 3> Concat3dDim1DiffInputDimsTestImpl( true); 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, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 0, Channel 3 - 7.0f, 8.0f, + // Batch 0, Channel 3 + 7.0f, 8.0f, - // Batch 0, Channel 4 - 9.0f, 10.0f, + // Batch 0, Channel 4 + 9.0f, 10.0f, - // Batch 0, Channel 5 - 11.0f, 12.0f, + // Batch 0, Channel 5 + 11.0f, 12.0f, - // Batch 0, Channel 6 - 25.0f, 26.0f, + // Batch 0, Channel 6 + 25.0f, 26.0f, - // Batch 0, Channel 7 - 17.0f, 18.0f, + // Batch 0, Channel 7 + 17.0f, 18.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f, + // Batch 1, Channel 2 + 23.0f, 24.0f, - // Batch 1, Channel 3 - 27.0f, 28.0f, + // Batch 1, Channel 3 + 27.0f, 28.0f, - // Batch 1, Channel 4 - 29.0f, 30.0f, + // Batch 1, Channel 4 + 29.0f, 30.0f, - // Batch 1, Channel 5 - 13.0f, 14.0f, + // Batch 1, Channel 5 + 13.0f, 14.0f, - // Batch 1, Channel 6 - 15.0f, 16.0f, + // Batch 1, Channel 6 + 15.0f, 16.0f, - // Batch 1, Channel 7 - 31.0f, 32.0f, - })); + // Batch 1, Channel 7 + 31.0f, 32.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 3> Concat3dDim2DiffInputDimsTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, + TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 1.0f, 2.0f, - // Batch 0, Channel 1 - 3.0f, 4.0f, + // Batch 0, Channel 1 + 3.0f, 4.0f, - // Batch 0, Channel 2 - 5.0f, 6.0f, + // Batch 0, Channel 2 + 5.0f, 6.0f, - // Batch 1, Channel 0 - 19.0f, 20.0f, + // Batch 1, Channel 0 + 19.0f, 20.0f, - // Batch 1, Channel 1 - 21.0f, 22.0f, + // Batch 1, Channel 1 + 21.0f, 22.0f, - // Batch 1, Channel 2 - 23.0f, 24.0f - })); + // Batch 1, Channel 2 + 23.0f, 24.0f + }, + qScale, qOffset)); - armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, + TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 7.0f, - // Batch 0, Channel 1 - 9.0f, + // Batch 0, Channel 1 + 9.0f, - // Batch 0, Channel 2 - 11.0f, + // Batch 0, Channel 2 + 11.0f, - // Batch 1, Channel 0 - 25.0f, + // Batch 1, Channel 0 + 25.0f, - // Batch 1, Channel 1 - 27.0f, + // Batch 1, Channel 1 + 27.0f, - // Batch 1, Channel 2 - 29.0f - })); + // Batch 1, Channel 2 + 29.0f + }, + qScale, qOffset)); - armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, 50.0f, + TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset); + auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( + { + // Batch 0, Channel 0 + 13.0f, 14.0f, 50.0f, - // Batch 0, Channel 1 - 15.0f, 16.0f, 51.0f, + // Batch 0, Channel 1 + 15.0f, 16.0f, 51.0f, - // Batch 0, Channel 2 - 17.0f, 18.0f, 52.0f, + // Batch 0, Channel 2 + 17.0f, 18.0f, 52.0f, - // Batch 1, Channel 0 - 31.0f, 32.0f, 53.0f, + // Batch 1, Channel 0 + 31.0f, 32.0f, 53.0f, - // Batch 1, Channel 1 - 33.0f, 34.0f, 54.0f, + // Batch 1, Channel 1 + 33.0f, 34.0f, 54.0f, - // Batch 1, Channel 2 - 35.0f, 36.0f, 55.0f, - })); + // Batch 1, Channel 2 + 35.0f, 36.0f, 55.0f, + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -1302,67 +1368,75 @@ LayerTestResult<T, 3> Concat3dDim2DiffInputDimsTestImpl( useSubtensor); 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, + result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( + { + // 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 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 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 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 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, - })); + // Batch 1, Channel 2 + 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dTestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TensorInfo& outputTensorInfo, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const TensorInfo& outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); - auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f - })); - - auto input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f - })); + auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f + }, + qScale, qOffset)); + + auto input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( + { + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f + }, + qScale, qOffset)); - auto input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 21.0f, 22.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - 29.0f, 30.0f, - 31.0f, 32.0f - })); + auto input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( + { + 21.0f, 22.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); LayerTestResult<T, 4> result(outputTensorInfo); @@ -1382,197 +1456,209 @@ LayerTestResult<T, 4> Concat4dTestImpl( return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDim0TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f, - - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - - 21.0f, 22.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - 29.0f, 30.0f, - 31.0f, 32.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f, + + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, + + 21.0f, 22.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); + return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDim1TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f, - - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - - 21.0f, 22.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - 29.0f, 30.0f, - 31.0f, 32.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f, + + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, + + 21.0f, 22.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDim2TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 2, true, qScale, qOffset); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 11.0f, 12.0f, - 13.0f, 14.0f, - 21.0f, 22.0f, - 23.0f, 24.0f, - - 5.0f, 6.0f, - 7.0f, 8.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - - 9.0f, 10.0f, - 11.0f, 12.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - 29.0f, 30.0f, - 31.0f, 32.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 11.0f, 12.0f, + 13.0f, 14.0f, + 21.0f, 22.0f, + 23.0f, 24.0f, + + 5.0f, 6.0f, + 7.0f, 8.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + + 9.0f, 10.0f, + 11.0f, 12.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDim3TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset, bool useSubtensor) { - armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 11.0f, 12.0f, - 21.0f, 22.0f, - 3.0f, 4.0f, - 13.0f, 14.0f, - 23.0f, 24.0f, - - 5.0f, 6.0f, - 15.0f, 16.0f, - 25.0f, 26.0f, - 7.0f, 8.0f, - 17.0f, 18.0f, - 27.0f, 28.0f, - - 9.0f, 10.0f, - 19.0f, 20.0f, - 29.0f, 30.0f, - 11.0f, 12.0f, - 21.0f, 22.0f, - 31.0f, 32.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 11.0f, 12.0f, + 21.0f, 22.0f, + 3.0f, 4.0f, + 13.0f, 14.0f, + 23.0f, 24.0f, + + 5.0f, 6.0f, + 15.0f, 16.0f, + 25.0f, 26.0f, + 7.0f, 8.0f, + 17.0f, 18.0f, + 27.0f, 28.0f, + + 9.0f, 10.0f, + 19.0f, 20.0f, + 29.0f, 30.0f, + 11.0f, 12.0f, + 21.0f, 22.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDiffShapeDim0TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - unsigned int dimension = 0; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + constexpr unsigned int dimension = 0u; - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f - })); - - armnn::TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset); - - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - - 21.0f, 22.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - 29.0f, 30.0f, - 31.0f, 32.0f + TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f + }, + qScale, qOffset)); + + TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset); + + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( + { + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, - })); + 21.0f, 22.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result(outputTensorInfo); @@ -1588,62 +1674,67 @@ LayerTestResult<T, 4> Concat4dDiffShapeDim0TestImpl( true); result.output = MakeTensor<T, 4>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f, - - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - - 21.0f, 22.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f, - 29.0f, 30.0f, - 31.0f, 32.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f, + + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, + + 21.0f, 22.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f, + 29.0f, 30.0f, + 31.0f, 32.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDiffShapeDim1TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - unsigned int dimension = 1; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f - })); - - armnn::TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset); + constexpr unsigned int dimension = 1u; - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f, - - })); + TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f + }, + qScale, qOffset)); + + TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset); + + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( + { + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f, + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result(outputTensorInfo); @@ -1659,57 +1750,61 @@ LayerTestResult<T, 4> Concat4dDiffShapeDim1TestImpl( true); result.output = MakeTensor<T, 4>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f, - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - 17.0f, 18.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f, + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + 17.0f, 18.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDiffShapeDim2TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - unsigned int dimension = 2; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + constexpr unsigned int dimension = 2u; - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f - })); - - armnn::TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset); - - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { - 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 - })); + TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset); + TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( + { + 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 + }, + qScale, qOffset)); + TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result(outputTensorInfo); std::vector<T> output; @@ -1723,64 +1818,69 @@ LayerTestResult<T, 4> Concat4dDiffShapeDim2TestImpl( dimension, true); - result.output = MakeTensor<T, 4>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 11.0f, 12.0f, - 13.0f, 14.0f, - 15.0f, 16.0f, - - 5.0f, 6.0f, - 7.0f, 8.0f, - 17.0f, 18.0f, - 19.0f, 20.0f, - 21.0f, 22.0f, - - 9.0f, 10.0f, - 11.0f, 12.0f, - 23.0f, 24.0f, - 25.0f, 26.0f, - 27.0f, 28.0f - })); + result.output = MakeTensor<T, 4>(outputTensorInfo, output); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 11.0f, 12.0f, + 13.0f, 14.0f, + 15.0f, 16.0f, + + 5.0f, 6.0f, + 7.0f, 8.0f, + 17.0f, 18.0f, + 19.0f, 20.0f, + 21.0f, 22.0f, + + 9.0f, 10.0f, + 11.0f, 12.0f, + 23.0f, 24.0f, + 25.0f, 26.0f, + 27.0f, 28.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> LayerTestResult<T, 4> Concat4dDiffShapeDim3TestImpl( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset, bool useSubtensor) { - unsigned int dimension = 3; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, - 9.0f, 10.0f, - 11.0f, 12.0f - })); + constexpr unsigned int dimension = 3u; - armnn::TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset); - - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { - 11.0f, 12.0f, 13.0f, - 14.0f, 15.0f, 16.0f, + TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); + auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( + { + 1.0f, 2.0f, + 3.0f, 4.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, + 9.0f, 10.0f, + 11.0f, 12.0f + }, + qScale, qOffset)); + + TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset); + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( + { + 11.0f, 12.0f, 13.0f, + 14.0f, 15.0f, 16.0f, - 17.0f, 18.0f, 19.0f, - 20.0f, 21.0f, 22.0f, + 17.0f, 18.0f, 19.0f, + 20.0f, 21.0f, 22.0f, - 23.0f, 24.0f, 25.0f, - 26.0f, 27.0f, 28.0f - })); + 23.0f, 24.0f, 25.0f, + 26.0f, 27.0f, 28.0f + }, + qScale, qOffset)); - armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset); + TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset); LayerTestResult<T, 4> result(outputTensorInfo); @@ -1796,30 +1896,32 @@ LayerTestResult<T, 4> Concat4dDiffShapeDim3TestImpl( useSubtensor); result.output = MakeTensor<T, 4>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, 11.0f, 12.0f, 13.0f, - 3.0f, 4.0f, 14.0f, 15.0f, 16.0f, - 5.0f, 6.0f, 17.0f, 18.0f, 19.0f, - 7.0f, 8.0f, 20.0f, 21.0f, 22.0f, - 9.0f, 10.0f, 23.0f, 24.0f, 25.0f, - 11.0f, 12.0f, 26.0f, 27.0f, 28.0f - })); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( + { + 1.0f, 2.0f, 11.0f, 12.0f, 13.0f, + 3.0f, 4.0f, 14.0f, 15.0f, 16.0f, + 5.0f, 6.0f, 17.0f, 18.0f, 19.0f, + 7.0f, 8.0f, 20.0f, 21.0f, 22.0f, + 9.0f, 10.0f, 23.0f, 24.0f, 25.0f, + 11.0f, 12.0f, 26.0f, 27.0f, 28.0f + }, + qScale, qOffset)); return result; } -template<armnn::DataType ArmnnType, typename T> +template<DataType ArmnnType, typename T> LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { // Defines the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType); - armnn::TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType); - armnn::TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType); + TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType); + TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType); + TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType); - std::vector<armnn::TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()}); + std::vector<TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()}); // Quantized input1 tensor. const float inputScale1 = 0.5f; @@ -1894,31 +1996,31 @@ LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( inputTensorInfo2.SetQuantizationOffset(inputOffset2); std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); + ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector<unsigned int> wOrigin2 = { 0, 0, 2 }; //Extent of the window is defined by size of input[1]. - armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); + ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = useSubtensor && workloadFactory.SupportsSubTensors(); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = + std::unique_ptr<ITensorHandle> inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = + std::unique_ptr<ITensorHandle> inputHandle2 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo2); - armnn::ConcatQueueDescriptor data; - armnn::OriginsDescriptor desc = armnn::CreateDescriptorForConcatenation( + ConcatQueueDescriptor data; + OriginsDescriptor desc = CreateDescriptorForConcatenation( inputTensorShapes.begin(),inputTensorShapes.end(), 2); data.m_Parameters = desc; - armnn::WorkloadInfo info; + WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); @@ -1926,7 +2028,7 @@ LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( data.m_ViewOrigins.push_back(window1); data.m_ViewOrigins.push_back(window2); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); inputHandle1->Allocate(); inputHandle2->Allocate(); @@ -1947,16 +2049,16 @@ LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( // Explicit template specializations // -template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 3> -ConcatDifferentInputOutputQParamTest<armnn::DataType::QuantisedAsymm8>( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, +template LayerTestResult<ResolveType<DataType::QuantisedAsymm8>, 3> +ConcatDifferentInputOutputQParamTest<DataType::QuantisedAsymm8>( + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor); -template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 3> -ConcatDifferentInputOutputQParamTest<armnn::DataType::QuantisedSymm16>( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, +template LayerTestResult<ResolveType<DataType::QuantisedSymm16>, 3> +ConcatDifferentInputOutputQParamTest<DataType::QuantisedSymm16>( + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor); // @@ -1964,8 +2066,8 @@ ConcatDifferentInputOutputQParamTest<armnn::DataType::QuantisedSymm16>( // LayerTestResult<float,3> ConcatTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { unsigned int outputWidth = 3; unsigned int outputHeight = 6; @@ -1980,9 +2082,9 @@ LayerTestResult<float,3> ConcatTest( 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); + TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::Float32); + TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::Float32); + TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::Float32); LayerTestResult<float,3> ret(outputTensorInfo); @@ -2041,27 +2143,27 @@ LayerTestResult<float,3> ConcatTest( ); std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. - armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); + ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. - armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); + ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = + std::unique_ptr<ITensorHandle> inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = + std::unique_ptr<ITensorHandle> inputHandle2 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo2); - armnn::ConcatQueueDescriptor data; - armnn::WorkloadInfo info; + ConcatQueueDescriptor data; + WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); @@ -2069,7 +2171,7 @@ LayerTestResult<float,3> ConcatTest( data.m_ViewOrigins.push_back(window1); data.m_ViewOrigins.push_back(window2); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); inputHandle1->Allocate(); inputHandle2->Allocate(); @@ -2087,156 +2189,156 @@ LayerTestResult<float,3> ConcatTest( } LayerTestResult<float, 1> Concat1dTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat1dTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat1dTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 2> Concat2dDim0Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat2dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 2> Concat2dDim1Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat2dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 2> Concat2dDim0DiffInputDimsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat2dDim0DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 2> Concat2dDim1DiffInputDimsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat2dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim0Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat3dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim1Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat3dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim2Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat3dDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); + return Concat3dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim0DiffInputDimsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>( + return Concat3dDim0DiffInputDimsTestImpl<DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim1DiffInputDimsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat3dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> Concat3dDim2DiffInputDimsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat3dDim2DiffInputDimsTestImpl<armnn::DataType::Float32>( + return Concat3dDim2DiffInputDimsTestImpl<DataType::Float32>( workloadFactory, memoryManager, useSubtensor, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDim0Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat4dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDim1Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat4dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDim2Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat4dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDim3Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat4dDim3TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); + return Concat4dDim3TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); } LayerTestResult<float, 4> Concat4dDiffShapeDim0Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat4dDiffShapeDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDiffShapeDim1Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim1TestImpl<armnn::DataType::Float32>( + return Concat4dDiffShapeDim1TestImpl<DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDiffShapeDim2Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); + return Concat4dDiffShapeDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> Concat4dDiffShapeDim3Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat4dDiffShapeDim3TestImpl<armnn::DataType::Float32>( + return Concat4dDiffShapeDim3TestImpl<DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0, useSubtensor); } -LayerTestResult<armnn::Half, 3> ConcatFloat16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +LayerTestResult<Half, 3> ConcatFloat16Test( + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim1TestImpl<armnn::DataType::Float16>(workloadFactory, memoryManager, 0.0f, 0); + return Concat3dDim1TestImpl<DataType::Float16>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { unsigned int outputWidth = 3; unsigned int outputHeight = 6; @@ -2251,9 +2353,9 @@ LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( 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); + TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedAsymm8); + TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedAsymm8); + TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedAsymm8); // Quantized input1 tensor. Range [-3, 1] const float inputScale1 = 0.015686f; @@ -2332,27 +2434,27 @@ LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( inputTensorInfo2.SetQuantizationOffset(inputOffset2); std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); + ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. - armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); + ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = + std::unique_ptr<ITensorHandle> inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = + std::unique_ptr<ITensorHandle> inputHandle2 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo2); - armnn::ConcatQueueDescriptor data; - armnn::WorkloadInfo info; + ConcatQueueDescriptor data; + WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); @@ -2360,7 +2462,7 @@ LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( data.m_ViewOrigins.push_back(window1); data.m_ViewOrigins.push_back(window2); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); inputHandle1->Allocate(); inputHandle2->Allocate(); @@ -2378,8 +2480,8 @@ LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( } LayerTestResult<uint8_t, 3> ConcatUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { unsigned int outputWidth = 3; unsigned int outputHeight = 6; @@ -2394,9 +2496,9 @@ LayerTestResult<uint8_t, 3> ConcatUint8Test( 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); + TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedAsymm8); + TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedAsymm8); + TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedAsymm8); // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. const float scale = 0.13497836f; @@ -2466,29 +2568,29 @@ LayerTestResult<uint8_t, 3> ConcatUint8Test( ); std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); + ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. - armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); + ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = + std::unique_ptr<ITensorHandle> inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = + std::unique_ptr<ITensorHandle> inputHandle2 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo2); - armnn::ConcatQueueDescriptor data; - armnn::WorkloadInfo info; + ConcatQueueDescriptor data; + WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); @@ -2496,7 +2598,7 @@ LayerTestResult<uint8_t, 3> ConcatUint8Test( data.m_ViewOrigins.push_back(window1); data.m_ViewOrigins.push_back(window2); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); inputHandle1->Allocate(); inputHandle2->Allocate(); @@ -2514,8 +2616,8 @@ LayerTestResult<uint8_t, 3> ConcatUint8Test( } LayerTestResult<uint16_t, 3> ConcatUint16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { unsigned int outputWidth = 3; unsigned int outputHeight = 6; @@ -2530,9 +2632,9 @@ LayerTestResult<uint16_t, 3> ConcatUint16Test( unsigned int inputChannels2 = 1; // Defines the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedSymm16); - armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedSymm16); - armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedSymm16); + TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedSymm16); + TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedSymm16); + TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedSymm16); // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. const float scale = 0.13497836f; @@ -2599,29 +2701,29 @@ LayerTestResult<uint16_t, 3> ConcatUint16Test( })); std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); + ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. - armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); + ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = + std::unique_ptr<ITensorHandle> inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = + std::unique_ptr<ITensorHandle> inputHandle2 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo2); - armnn::ConcatQueueDescriptor data; - armnn::WorkloadInfo info; + ConcatQueueDescriptor data; + WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); @@ -2629,7 +2731,7 @@ LayerTestResult<uint16_t, 3> ConcatUint16Test( data.m_ViewOrigins.push_back(window1); data.m_ViewOrigins.push_back(window2); - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); + std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); inputHandle1->Allocate(); inputHandle2->Allocate(); @@ -2647,147 +2749,147 @@ LayerTestResult<uint16_t, 3> ConcatUint16Test( } LayerTestResult<uint8_t, 1> Concat1dUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat1dTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat1dTestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concat2dDim0Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat2dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concat2dDim1Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat2dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concat2dDim0DiffInputDimsUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim0DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat2dDim0DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concat2dDim1DiffInputDimsUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat2dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat2dDim1DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim0Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat3dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim1Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat3dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim2Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat3dDim2TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat3dDim2TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, useSubtensor, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim0DiffInputDimsUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat3dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim1DiffInputDimsUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat3dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat3dDim1DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concat3dDim2DiffInputDimsUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat3dDim2DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat3dDim2DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, useSubtensor, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDim0Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat4dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDim1Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat4dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDim2Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDim2TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); + return Concat4dDim2TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDim3Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat4dDim3TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat4dDim3TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1, useSubtensor); } LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim0Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim0TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat4dDiffShapeDim0TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim1Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim1TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat4dDiffShapeDim1TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim2Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concat4dDiffShapeDim2TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat4dDiffShapeDim2TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim3Uint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + IWorkloadFactory& workloadFactory, + const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concat4dDiffShapeDim3TestImpl<armnn::DataType::QuantisedAsymm8>( + return Concat4dDiffShapeDim3TestImpl<DataType::QuantisedAsymm8>( workloadFactory, memoryManager, 0.5f, -1, useSubtensor); } diff --git a/src/backends/backendsCommon/test/layerTests/ConstantTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ConstantTestImpl.cpp index c3cacd5810..3f22c31c7d 100644 --- a/src/backends/backendsCommon/test/layerTests/ConstantTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ConstantTestImpl.cpp @@ -6,6 +6,7 @@ #include "ConstantTestImpl.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -53,43 +54,45 @@ LayerTestResult<T, 4> ConstantTestImpl( } 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, - }))); + armnnUtils::QuantizedVector<T>( + { + // 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, + }, + qScale, qOffset))); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = input; diff --git a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp index 01c1b1868e..198904e71d 100644 --- a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp @@ -7,13 +7,13 @@ #include <DataLayoutIndexed.hpp> #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <TensorUtils.hpp> #include <armnn/ArmNN.hpp> #include <backendsCommon/CpuTensorHandle.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -62,6 +62,8 @@ static std::vector<float> ConvInput3x8x16({ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }); +using namespace armnnUtils; + // // Helper templates // @@ -73,7 +75,7 @@ boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale) if(biasEnabled) { armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, ArmnnType); - boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias2)); + boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias2, qScale, 0.0f)); return bias; } else @@ -89,7 +91,7 @@ boost::multi_array<T, 1> GetBias4(bool biasEnabled, float qScale) if(biasEnabled) { armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType); - boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias4)); + boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias4, qScale, 0.0f)); return bias; } else @@ -105,7 +107,7 @@ boost::multi_array<T, 1> GetBias8(bool biasEnabled, float qScale) if(biasEnabled) { armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType); - boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias8)); + boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias8, qScale, 0.0f)); return bias; } else @@ -492,35 +494,39 @@ LayerTestResult<T,4> Convolution1dTestImpl( biasInfo.SetQuantizationOffset(0); } - std::vector<T> inputData( - QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), { - 5.0f, -2.0f, 2.5f, 0.0f, 1.0f, - -3.0f, 3.2f, 5.0f, 2.0f, 3.0f, - })); + std::vector<T> inputData = QuantizedVector<T>( + { + 5.0f, -2.0f, 2.5f, 0.0f, 1.0f, + -3.0f, 3.2f, 5.0f, 2.0f, 3.0f, + }, + inputInfo.GetQuantizationScale(), + inputInfo.GetQuantizationOffset()); - std::vector<T> kernelData( - QuantizedVector<T>(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f, - 0.0f, 2.0f, -1.5f, + std::vector<T> kernelData = QuantizedVector<T>( + { + 1.0f, 0.0f, 0.0f, + 0.0f, 2.0f, -1.5f, - 0.0f, 0.0f, 0.0f, - 0.2f, 0.2f, 0.2f, + 0.0f, 0.0f, 0.0f, + 0.2f, 0.2f, 0.2f, - 0.5f, 0.0f, 0.5f, - 0.0f, -1.0f, 0.0f - })); + 0.5f, 0.0f, 0.5f, + 0.0f, -1.0f, 0.0f + }, + kernelInfo.GetQuantizationScale(), + kernelInfo.GetQuantizationOffset()); - std::vector<B> biasData( - QuantizedVector<B>(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f - })); + std::vector<B> biasData = + QuantizedVector<B>({ 1.0f, 0.0f, 0.0f }, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset()); - std::vector<T> outputData( - QuantizedVector<T>(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), { - 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f, + std::vector<T> outputData = QuantizedVector<T>( + { + 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f, -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f, - 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f - })); + 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f + }, + outputInfo.GetQuantizationScale(), + outputInfo.GetQuantizationOffset()); // Optionally apply bias to output image. if(biasEnabled) @@ -698,54 +704,55 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( { // Use common single-batch 3-channel 16x8 image. armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType); - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); + boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset)); // Use a 2-element batch with 3-channel 3x5 kernels. armnn::TensorInfo kernelDesc({2, 3, 5, 3}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 1, 1, 1, + QuantizedVector<T>({ + 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 - }))); + 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 + }, + qScale, qOffset))); // Expected output is 2 batch elements of a 1-channel 14x4 image. armnn::TensorInfo outputDesc({1, 2, 4, 14}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -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, @@ -757,7 +764,8 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( 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 - }))); + }, + qScale, qOffset))); return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -785,42 +793,43 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( // Use common single-batch 3-channel 16x8 image. armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType); - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); + boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset)); // Use a 2-element batch of 3-channel 3x3 kernels. armnn::TensorInfo kernelDesc({2, 3, 3, 3}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 1, 1, 1, + QuantizedVector<T>({ + 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, + 0, 0, 0, - 2, 2, 2, - 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, + 0, 0, 0, - 1, 1, 1, - 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, + 0, 0, 0 + }, + qScale, qOffset))); // Expected output is 1 batch of a 2-channel 14x6 image. armnn::TensorInfo outputDesc({1, 2, 6, 14}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -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, @@ -834,7 +843,8 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( 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 - }))); + }, + qScale, qOffset))); return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -860,19 +870,21 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest // Use a single-batch 1-channel 3x3 image as input. armnn::TensorInfo inputDesc({1, 1, 3, 3}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 11,21,31, 12,22,32, 13,23,33 - }))); + }, + qScale, qOffset))); // Use 1 batch of a 1-channel 2x2 kernel. armnn::TensorInfo kernelDesc({1, 1, 2, 2}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -11,-21, -12,-22, - }))); + }, + qScale, qOffset))); // Expected output is 1 batch of a 1-channel 6x8 image. // Manually calculated like this: @@ -885,7 +897,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest //[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..] armnn::TensorInfo outputDesc({1, 1, 8, 6}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 0, 0, 0, 0, 0, 0, -242, -594, -934, -372, 0, 0, -495, -1190, -1850, -725, 0, 0, @@ -894,7 +906,8 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); + }, + qScale, qOffset))); return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -924,35 +937,37 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( // Use a single-batch 1-channel 5x5 image as input. armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 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, - }))); + }, qScale, qOffset))); // Use 1 batch of a 1-channel 4x4 kernel. armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -11,-21,-31,-41, -12,-22,-32,-42, -13,-23,-33,-43, -14,-24,-34,-44, - }))); + }, + qScale, qOffset))); // Expected output is 1 batch of a 1-channel 5x5 image. armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, ArmnnType); std::vector<T> myVec(outputDesc.GetNumElements(), 0); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -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, - }))); + }, + qScale, qOffset))); return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -1025,17 +1040,18 @@ LayerTestResult<T, 4> Convolution2d3x3DilationTestCommon( outputTensorInfo.SetQuantizationOffset(qOffset); auto input = MakeTensor<T, 4>(inputTensorInfo, - std::vector<T>(QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputNoQuantizedValues))); + std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset()))); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, - std::vector<T>(QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), - kernelTensorInfo.GetQuantizationOffset(), - kernelNoQuantizedValues))); - auto expectedOutput = MakeTensor<T, 4>(outputTensorInfo, - std::vector<T>(QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputExpectedNoQuantizedValues))); + std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues, + kernelTensorInfo.GetQuantizationScale(), + kernelTensorInfo.GetQuantizationOffset()))); + auto expectedOutput = + MakeTensor<T, 4>(outputTensorInfo, + std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()))); return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -1539,15 +1555,18 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl( biasDesc.SetQuantizationOffset(0); } std::vector<T> inputData = std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - })); + QuantizedVector<T>({ + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, + + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, + }, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); + // at this point if we require it permute the input data const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; if (layout == armnn::DataLayout::NHWC) @@ -1558,27 +1577,32 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl( } auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); - std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2})); + std::vector<B> biasV(QuantizedVector<B>({ 0, 2 }, + biasDesc.GetQuantizationScale(), + biasDesc.GetQuantizationOffset())); + auto bias = MakeTensor<B, 1>(biasDesc, biasV); std::vector<T> kernelData = std::vector<T>( - QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - })); + QuantizedVector<T>({ + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, + + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, + }, + kernelDesc.GetQuantizationScale(), + kernelDesc.GetQuantizationOffset())); + auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData); // Manually calculated. std::vector<T> outputImage( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - {0.f, 0.f}) + QuantizedVector<T>({ 0.f, 0.f }, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()) ); // Optionally apply bias to output image. @@ -1686,24 +1710,27 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl( // NOTE: originalInputData is in NCHW format std::vector<T> originalInputData = std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 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 - })); + QuantizedVector<T>({ + 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, + 0.0f, 0.0f, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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 + }, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); + std::vector<T> inputData = originalInputData; // at this point if we require it permute the input data const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; @@ -1714,70 +1741,76 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl( } auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); - std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2, 1, -1})); + std::vector<B> biasV = QuantizedVector<B>({ 0, 2, 1, -1 }, + biasDesc.GetQuantizationScale(), + biasDesc.GetQuantizationOffset()); + auto bias = MakeTensor<B, 1>(biasDesc, biasV); std::vector<T> kernelData = std::vector<T>( - QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - 0, 0, 0, - 0, -1, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 0, 0, 0, - 0, 0, 0, - 0, 1, 0, - 0, 0, 0, - 0, 0, 0 - - })); + QuantizedVector<T>({ + 1, 1, 1, + 1, -1, 1, + 1, 1, 1, + 1, 1, 1, + 1, 1, 1, + + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + 2, 2, 2, + + 0, 0, 0, + 0, -1, 0, + 0, 0, 0, + 0, 0, 0, + 0, 0, 0, + + 0, 0, 0, + 0, 0, 0, + 0, 1, 0, + 0, 0, 0, + 0, 0, 0 + }, + kernelDesc.GetQuantizationScale(), + kernelDesc.GetQuantizationOffset())); + auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData); // Manually calculated. std::vector<T> originalOutputImage = std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { - 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, - 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, + QuantizedVector<T>({ + 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, + 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, + 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, + 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, + 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, + 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, -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, -0.5f, -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, - 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 8.0f, 8.0f, 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, 0.0f, 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, 0.0f, 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 - })); + 8.0f, 8.0f, 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, 0.0f, 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, 0.0f, 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 + }, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); // Optionally apply bias to output image. if(biasEnabled) @@ -2016,8 +2049,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( // Use a single-batch 2-channel 5x5 image as input. armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, @@ -2029,13 +2061,14 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 - }))); + }, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset()))); // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( - QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, @@ -2045,14 +2078,15 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 - }))); + }, + kernelTensorInfo.GetQuantizationScale(), + kernelTensorInfo.GetQuantizationOffset()))); // 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 }, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 1062, 1580, 1850, 1530, 1117, 2140, 3108, 3500, 2842, 2042, 3580, 5068, 5460, 4342, 3062, @@ -2064,7 +2098,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( 3390, 4886, 5022, 4068, 2916, 3566, 5056, 5182, 4133, 2922, 3100, 4352, 4452, 3517, 2465 - }))); + }, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()))); return DepthwiseConvolution2dAsymmetricTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -2097,8 +2133,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5}, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, @@ -2110,12 +2145,13 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 - }))); + }, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset()))); armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( - QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, @@ -2125,12 +2161,13 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 - }))); + }, + kernelTensorInfo.GetQuantizationScale(), + kernelTensorInfo.GetQuantizationOffset()))); armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - { + QuantizedVector<T>({ 1062, 1580, 1850, 1530, 1117, 2140, 3108, 3500, 2842, 2042, 3580, 5068, 5460, 4342, 3062, @@ -2142,7 +2179,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 3390, 4886, 5022, 4068, 2916, 3566, 5056, 5182, 4133, 2922, 3100, 4352, 4452, 3517, 2465 - }))); + }, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()))); return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -2175,27 +2214,29 @@ LayerTestResult<T, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9}, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), - { - 0, 0, 0, 0, 0, 0, 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, 1, 1, 0, 0, 0, - 0, 0, 0, 1, 1, 1, 0, 0, 0, - 0, 0, 0, 1, 1, 1, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); + QuantizedVector<T>({ + 0, 0, 0, 0, 0, 0, 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, 1, 1, 0, 0, 0, + 0, 0, 0, 1, 1, 1, 0, 0, 0, + 0, 0, 0, 1, 1, 1, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0 + }, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset()))); armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( - QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9 - }))); + QuantizedVector<T>({ + 1, 2, 3, + 4, 5, 6, + 7, 8, 9 + }, + kernelTensorInfo.GetQuantizationScale(), + kernelTensorInfo.GetQuantizationOffset()))); uint32_t padLeft = 0; uint32_t padTop = 0; @@ -2209,12 +2250,13 @@ LayerTestResult<T, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon( // Since the dilation rate is 3 this will reduce the size of the output from 9x9 to 3x3 of all 5s. armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - { - 5, 5, 5, - 5, 5, 5, - 5, 5, 5 - }))); + QuantizedVector<T>({ + 5, 5, 5, + 5, 5, 5, + 5, 5, 5 + }, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()))); return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>( workloadFactory, @@ -2284,17 +2326,18 @@ LayerTestResult<T, 4> DepthwiseConvolution2d3x3DilationTestCommon( outputTensorInfo.SetQuantizationOffset(qOffset); auto input = MakeTensor<T, 4>(inputTensorInfo, - std::vector<T>(QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputNoQuantizedValues))); + std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset()))); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, - std::vector<T>(QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), - kernelTensorInfo.GetQuantizationOffset(), - kernelNoQuantizedValues))); - auto expectedOutput = MakeTensor<T, 4>(outputTensorInfo, - std::vector<T>(QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputExpectedNoQuantizedValues))); + std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues, + kernelTensorInfo.GetQuantizationScale(), + kernelTensorInfo.GetQuantizationOffset()))); + auto expectedOutput = + MakeTensor<T, 4>(outputTensorInfo, + std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset()))); uint32_t padLeft = 0; uint32_t padTop = 0; diff --git a/src/backends/backendsCommon/test/layerTests/DebugTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/DebugTestImpl.cpp index 1997c4bd11..023bbaedd1 100644 --- a/src/backends/backendsCommon/test/layerTests/DebugTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/DebugTestImpl.cpp @@ -5,6 +5,7 @@ #include "DebugTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -40,11 +41,11 @@ LayerTestResult<T, Dim> DebugTestImpl( } boost::multi_array<T, Dim> input = - MakeTensor<T, Dim>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + MakeTensor<T, Dim>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, Dim> ret(outputTensorInfo); ret.outputExpected = - MakeTensor<T, Dim>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); + MakeTensor<T, Dim>(outputTensorInfo, armnnUtils::QuantizedVector<T>(outputExpectedData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/DepthToSpaceTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/DepthToSpaceTestImpl.cpp index e21a4b64f8..4e8c938823 100644 --- a/src/backends/backendsCommon/test/layerTests/DepthToSpaceTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/DepthToSpaceTestImpl.cpp @@ -5,7 +5,7 @@ #include "DepthToSpaceTestImpl.hpp" -#include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <armnn/ArmNN.hpp> @@ -44,10 +44,12 @@ LayerTestResult<T, 4> DepthToSpaceTestImpl( outputInfo.SetQuantizationOffset(qOffset); } - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + boost::multi_array<T, 4> input = + MakeTensor<T, 4>(inputInfo, armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, 4> result(outputInfo); - result.outputExpected = MakeTensor<T, 4>(outputInfo, QuantizedVector<T>(qScale, qOffset, expectedOutputData)); + result.outputExpected = + MakeTensor<T, 4>(outputInfo, armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo); diff --git a/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp index c84b941e1f..cf101eeee1 100644 --- a/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/FullyConnectedTestImpl.cpp @@ -7,6 +7,8 @@ #include <armnn/ArmNN.hpp> +#include <QuantizeHelper.hpp> + #include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/test/DataTypeUtils.hpp> @@ -191,15 +193,17 @@ LayerTestResult<T, 2> FullyConnectedLargeTestCommon( LayerTestResult<T, 2> result(outputTensorInfo); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f, - }) + }, + qScale, qOffset) ); boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc, - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 2.0f, 3.0f, 4.0f, 5.0f, 6.0f - }) + }, + qScale, qOffset) ); std::vector<T> biasValues({900000.f}); @@ -215,10 +219,7 @@ LayerTestResult<T, 2> FullyConnectedLargeTestCommon( ); result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 965432.0f, - }) - ); + armnnUtils::QuantizedVector<T>({ 965432.0f }, qScale, qOffset)); return result; } diff --git a/src/backends/backendsCommon/test/layerTests/InstanceNormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/InstanceNormalizationTestImpl.cpp index 4e9cbbf40d..d25fcea66b 100644 --- a/src/backends/backendsCommon/test/layerTests/InstanceNormalizationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/InstanceNormalizationTestImpl.cpp @@ -5,6 +5,7 @@ #include "InstanceNormalizationTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -14,7 +15,6 @@ #include <backendsCommon/WorkloadFactory.hpp> #include <backendsCommon/test/DataLayoutUtils.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -35,12 +35,12 @@ LayerTestResult<T, 4> InstanceNormTestImpl( float qScale = 0.0f, int32_t qOffset = 0) { - auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputValues)); + auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputValues, qScale, qOffset)); LayerTestResult<T, 4> result(outputTensorInfo); - - result.outputExpected = - MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, expectedOutputValues)); + result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, + armnnUtils::QuantizedVector<T>(expectedOutputValues, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/L2NormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/L2NormalizationTestImpl.cpp index 5c75b6f540..569f5af227 100644 --- a/src/backends/backendsCommon/test/layerTests/L2NormalizationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/L2NormalizationTestImpl.cpp @@ -6,6 +6,7 @@ #include "L2NormalizationTestImpl.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <TensorUtils.hpp> @@ -44,10 +45,10 @@ LayerTestResult<T, 4> L2NormalizationTestImpl( inputData = tmp; } - auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); std::vector<float> expectedOutputData = expectedOutputValues; if (layout == armnn::DataLayout::NHWC) @@ -59,10 +60,11 @@ LayerTestResult<T, 4> L2NormalizationTestImpl( } LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - expectedOutputData)); + result.outputExpected = + MakeTensor<T, 4>(outputTensorInfo, + armnnUtils::QuantizedVector<T>(expectedOutputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -693,16 +695,10 @@ LayerTestResult<float, 2> L2Normalization2dShapeTest( const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32, 0.f, 0); const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32, 0.f, 0); - auto inputTensor = MakeTensor<float, 2>(inputTensorInfo, QuantizedVector<float>( - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto inputTensor = MakeTensor<float, 2>(inputTensorInfo, inputData); LayerTestResult<float, 2> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, QuantizedVector<float>( - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - expectedOutputData)); + result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, expectedOutputData); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/LogSoftmaxTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/LogSoftmaxTestImpl.cpp index 0b73d37305..4c340c8076 100644 --- a/src/backends/backendsCommon/test/layerTests/LogSoftmaxTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/LogSoftmaxTestImpl.cpp @@ -6,6 +6,7 @@ #include "LogSoftmaxTestImpl.hpp" #include <Half.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -14,7 +15,6 @@ #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/WorkloadFactory.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -39,7 +39,7 @@ LayerTestResult<T, NumDims> LogSoftmaxTestImpl( { LayerTestResult<T, NumDims> result(outputInfo); result.outputExpected = - MakeTensor<T, NumDims>(outputInfo, QuantizedVector<T>(qScale, qOffset, expectedOutputValues)); + MakeTensor<T, NumDims>(outputInfo, armnnUtils::QuantizedVector<T>(expectedOutputValues, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo); @@ -54,7 +54,7 @@ LayerTestResult<T, NumDims> LogSoftmaxTestImpl( inputHandle->Allocate(); outputHandle->Allocate(); - auto inputTensor = MakeTensor<T, NumDims>(inputInfo, QuantizedVector<T>(qScale, qOffset, inputValues)); + auto inputTensor = MakeTensor<T, NumDims>(inputInfo, armnnUtils::QuantizedVector<T>(inputValues, qScale, qOffset)); CopyDataToITensorHandle(inputHandle.get(), inputTensor.origin()); workload->Execute(); diff --git a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp index c07f6232fe..6cea777a25 100644 --- a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp @@ -5,11 +5,12 @@ #include "LstmTestImpl.hpp" +#include <QuantizeHelper.hpp> + #include <armnn/ArmNN.hpp> #include <backendsCommon/CpuTensorHandle.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -1963,13 +1964,19 @@ LayerTestResult<int16_t, 2> LstmLayerInt16NoCifgNoPeepholeNoProjectionTest( const armnn::DataType constantDatatype = armnn::DataType::QuantisedAsymm8; armnn::TensorInfo inputDesc({2, 2}, datatype); - boost::multi_array<int16_t , 2> input = MakeTensor<int16_t , 2>(inputDesc, QuantizedVector<int16_t>(qScale, qOffset, - std::vector<float>{2., 3., 3., 4.})); + boost::multi_array<int16_t , 2> input = MakeTensor<int16_t , 2>( + inputDesc, + armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset)); armnn::TensorInfo outputDesc({2, 4}, datatype); - boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(outputDesc, QuantizedVector<int16_t>(qScale, - qOffset, std::vector<float>({{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, - -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}))); + boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>( + outputDesc, + armnnUtils::QuantizedVector<int16_t>( + { + -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f, + -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f + }, + qScale, qOffset)); return LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>( workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype); @@ -1987,14 +1994,21 @@ LayerTestResult<int16_t, 2> LstmLayerInt16WithCifgWithPeepholeNoProjectionTest( const armnn::DataType constantDatatype = armnn::DataType::QuantisedAsymm8; armnn::TensorInfo inputDesc({ 2, 2 }, datatype); - boost::multi_array<int16_t, 2> input = MakeTensor<int16_t, 2>(inputDesc, QuantizedVector<int16_t>(qScale, qOffset, - std::vector<float>({ 2., 3., 3., 4. }))); + boost::multi_array<int16_t, 2> input = + MakeTensor<int16_t, 2>( + inputDesc, + armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset)); armnn::TensorInfo outputDesc({ 2, 4 }, datatype); - boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(outputDesc, QuantizedVector<int16_t>(qScale, - qOffset, std::vector<float>( - {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, - -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}))); + boost::multi_array<int16_t, 2> expectedOutput = + MakeTensor<int16_t, 2>( + outputDesc, + armnnUtils::QuantizedVector<int16_t>( + { + -0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f + }, + qScale, qOffset)); return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl<datatype>( workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype); @@ -2011,20 +2025,32 @@ LayerTestResult<int16_t, 2> LstmLayerInt16NoCifgWithPeepholeWithProjectionTest( const armnn::DataType constantDatatype = armnn::DataType::QuantisedAsymm8; armnn::TensorInfo inputDesc({ 2, 5 }, datatype); - boost::multi_array<int16_t, 2> input = MakeTensor<int16_t, 2>(inputDesc, QuantizedVector<int16_t>(qScale, - qOffset, std::vector<float>( - {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, - 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}))); + boost::multi_array<int16_t, 2> input = + MakeTensor<int16_t, 2>( + inputDesc, + armnnUtils::QuantizedVector<int16_t>( + { + 0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, + 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f + }, + qScale, qOffset)); armnn::TensorInfo outputDesc({ 2, 16 }, datatype); - boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(outputDesc, QuantizedVector<int16_t>(qScale, - qOffset, 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}))); + boost::multi_array<int16_t, 2> expectedOutput = + MakeTensor<int16_t, 2>( + outputDesc, + armnnUtils::QuantizedVector<int16_t>( + { + -0.00396806f, 0.02935200f, -0.00279226f, 0.01599770f, + -0.00835576f, -0.02117790f, 0.02835120f, -0.01145970f, + 0.00907307f, -0.02440040f, -0.01521910f, -0.02590630f, + 0.00914318f, 0.00415118f, 0.01714700f, 0.01342030f, + -0.01386900f, 0.02872680f, -0.00334693f, 0.00733398f, + -0.02879260f, -0.01869260f, 0.01936620f, -0.01154370f, + 0.00422612f, -0.03452320f, 0.00223253f, -0.00957321f, + 0.02106240f, 0.01333100f, 0.01509540f, 0.02168000f + }, + qScale, qOffset)); return LstmLayerNoCifgWithPeepholeWithProjectionTestImpl<datatype>( workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, constantDatatype); @@ -2040,13 +2066,20 @@ LayerTestResult<int16_t, 2> LstmLayerInt16NoCifgNoPeepholeNoProjectionInt16Const const armnn::DataType datatype = armnn::DataType::QuantisedSymm16; // datatype & constants set to QSymm16 armnn::TensorInfo inputDesc({2, 2}, datatype); - boost::multi_array<int16_t , 2> input = MakeTensor<int16_t , 2>(inputDesc, QuantizedVector<int16_t>(qScale, - qOffset, std::vector<float>{2., 3., 3., 4.})); + boost::multi_array<int16_t , 2> input = + MakeTensor<int16_t , 2>(inputDesc, + armnnUtils::QuantizedVector<int16_t>({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset)); armnn::TensorInfo outputDesc({2, 4}, datatype); - boost::multi_array<int16_t, 2> expectedOutput = MakeTensor<int16_t, 2>(outputDesc, QuantizedVector<int16_t>(qScale, - qOffset, std::vector<float>({{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, - -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}))); + boost::multi_array<int16_t, 2> expectedOutput = + MakeTensor<int16_t, 2>( + outputDesc, + armnnUtils::QuantizedVector<int16_t>( + { + -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f, + -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f + }, + qScale, qOffset)); return LstmNoCifgNoPeepholeNoProjectionTestImpl<datatype>( workloadFactory, memoryManager, input, expectedOutput, qScale, qOffset, datatype); diff --git a/src/backends/backendsCommon/test/layerTests/PadTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/PadTestImpl.cpp index 82b772ec98..0f9a30e76a 100644 --- a/src/backends/backendsCommon/test/layerTests/PadTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/PadTestImpl.cpp @@ -5,6 +5,8 @@ #include "PadTestImpl.hpp" +#include <QuantizeHelper.hpp> + #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -28,28 +30,27 @@ LayerTestResult<T, 2> Pad2dTestCommon( const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType, qScale, qOffset); const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType, qScale, qOffset); - 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> inputValues = armnnUtils::QuantizedVector<T>( + { + // Height (3) x Width (3) + 4, 8, 6, + 7, 4, 4, + 3, 2, 4 + }, + qScale, qOffset); auto p = customPaddingValue; - std::vector<T> expectedOutputValues; - expectedOutputValues = ( - QuantizedVector<T>(qScale, qOffset, - { - p, p, p, p, p, p, p, - p, p, p, p, p, p, p, - p, p, 4, 8, 6, p, p, - p, p, 7, 4, 4, p, p, - p, p, 3, 2, 4, p, p, - p, p, p, p, p, p, p, - p, p, p, p, p, p, p - })); + std::vector<T> expectedOutputValues = armnnUtils::QuantizedVector<T>( + { + p, p, p, p, p, p, p, + p, p, p, p, p, p, p, + p, p, 4, 8, 6, p, p, + p, p, 7, 4, 4, p, p, + p, p, 3, 2, 4, p, p, + p, p, p, p, p, p, p, + p, p, p, p, p, p, p + }, + qScale, qOffset); auto inputTensor = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(inputValues)); @@ -100,41 +101,39 @@ LayerTestResult<T, 3> Pad3dTestCommon( const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType, qScale, qOffset); const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType, qScale, qOffset); - 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 - - })); + std::vector<T> inputValues = armnnUtils::QuantizedVector<T>( + { + // Channel 0, Height (2) x Width (2) + 0, 4, + 2, 5, + + // Channel 1, Height (2) x Width (2) + 6, 1, + 5, 2 + }, + qScale, qOffset); + + std::vector<T> expectedOutputValues = armnnUtils::QuantizedVector<T>( + { + 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 + }, + qScale, qOffset); auto inputTensor = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(inputValues)); @@ -185,193 +184,193 @@ LayerTestResult<T, 4> Pad4dTestCommon( const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType, qScale, qOffset); const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType, qScale, qOffset); - 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 - })); + std::vector<T> inputValues = armnnUtils::QuantizedVector<T>( + { + // 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 + }, + qScale, qOffset); + + std::vector<T> expectedOutputValues = armnnUtils::QuantizedVector<T>( + { + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 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 + }, + qScale, qOffset); auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(inputValues)); diff --git a/src/backends/backendsCommon/test/layerTests/PermuteTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/PermuteTestImpl.hpp index ef48c9726f..fe0d07684a 100644 --- a/src/backends/backendsCommon/test/layerTests/PermuteTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/PermuteTestImpl.hpp @@ -12,7 +12,6 @@ #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/WorkloadFactory.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> #include <test/TensorHelpers.hpp> diff --git a/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp index f250fa5ac0..fcc898026a 100644 --- a/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp @@ -9,12 +9,12 @@ #include <DataLayoutIndexed.hpp> #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <TensorUtils.hpp> #include <backendsCommon/WorkloadInfo.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -25,6 +25,8 @@ namespace { +using namespace armnnUtils; + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimplePooling2dTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -187,7 +189,7 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset)); // These were calculated manually. auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); @@ -195,7 +197,7 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( if (forceNoPadding) { outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 8.0f, 8.0f, 8.0f, 9.0f, 7.0f, 9.0f, 9.0f, 9.0f, 9.0f, @@ -211,12 +213,13 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( 0.0f, 0.0f, -3.0f, -1.0f, 0.0f, 0.0f, -1.0f, -1.0f, -1.0f - })); + }, + qScale, qOffset)); } else { outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, @@ -232,7 +235,8 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f - })); + }, + qScale, qOffset)); } return SimplePooling2dTestImpl<ArmnnType>( @@ -267,7 +271,7 @@ LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon( } std::vector<T> inputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 2.0f, 5.0f, 6.0f, 3.0f, 4.0f, 7.0f, 8.0f, 9.0f, 10.0f, 13.0f, 14.0f, @@ -277,16 +281,18 @@ LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon( 19.0f, 20.0f, 23.0f, 24.0f, 25.0f, 26.0f, 29.0f, 30.0f, 27.0f, 28.0f, 31.0f, 32.0f, - })); + }, + qScale, qOffset)); std::vector<T> outputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 4.0f, 8.0f, 12.0f, 16.0f, 20.0f, 24.0f, 28.0f, 32.0f, - })); + }, + qScale, qOffset)); const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; if (dataLayout == armnn::DataLayout::NHWC) @@ -336,7 +342,7 @@ LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon( } std::vector<T> inputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 2.0f, 6.0f, 6.0f, 4.0f, 4.0f, 8.0f, 8.0f, 10.0f, 12.0f, 14.0f, 16.0f, @@ -346,16 +352,18 @@ LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon( 20.0f, 18.0f, 22.0f, 24.0f, 26.0f, 28.0f, 0.0f, 0.0f, 26.0f, 28.0f, 0.0f, 0.0f, - })); + }, + qScale, qOffset)); std::vector<T> outputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, 7.0f, 11.0f, 15.0f, 19.0f, 23.0f, 27.0f, 0.0f, - })); + }, + qScale, qOffset)); const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; if (dataLayout == armnn::DataLayout::NHWC) @@ -447,7 +455,7 @@ LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon( armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType); std::vector<T> inputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 7.0f, 5.0f, 5.0f, 1.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, 1.0f, 1.0f, @@ -457,16 +465,18 @@ LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon( 1.0f, 7.0f, 2.0f, 0.0f, 0.0f, 2.0f, 1.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, - })); + }, + qScale, qOffset)); std::vector<T> outputData( - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 5.0f, 5.0f, 3.0f, 1.0f, 5.0f, 1.0f, 1.0f, 1.0f, - })); + }, + qScale, qOffset)); const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; if (dataLayout == armnn::DataLayout::NHWC) @@ -503,19 +513,21 @@ LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, - })); + }, + qScale, qOffset)); armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, 3.0f, 3.0f, 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -536,7 +548,7 @@ LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, @@ -546,15 +558,17 @@ LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon( 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); + }, + qScale, qOffset)); armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, ArmnnType); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -575,7 +589,7 @@ LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, @@ -583,14 +597,16 @@ LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon( 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, - })); + }, + qScale, qOffset)); armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, ArmnnType); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, 3.0f, 3.0f, 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -611,7 +627,7 @@ LayerTestResult<T, 4> L2Pooling2dSize7TestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, @@ -619,13 +635,15 @@ LayerTestResult<T, 4> L2Pooling2dSize7TestCommon( 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, 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, - })); + }, + qScale, qOffset)); armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, ArmnnType); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -646,7 +664,7 @@ LayerTestResult<T, 4> L2Pooling2dSize9TestCommon( armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, @@ -656,13 +674,15 @@ LayerTestResult<T, 4> L2Pooling2dSize9TestCommon( 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); + }, + qScale, qOffset)); armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, ArmnnType); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -693,15 +713,17 @@ LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon( // Construct input data. auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 3.0f, 4.0f, - })); + }, + qScale, qOffset)); // These were calculated manually. auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 0.0f, 3.0f, 0.0f, 3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -883,11 +905,11 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon( outputTensorInfo.SetQuantizationOffset(qOffset); } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); + forceNoPadding ? QuantizedVector<T>(expectedOutputDataNoPadding, qScale, qOffset) : + QuantizedVector<T>(expectedOutputDataWithPadding, qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -961,11 +983,11 @@ LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( outputTensorInfo.SetQuantizationOffset(qOffset); } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputData, qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); + forceNoPadding ? QuantizedVector<T>(expectedOutputDataNoPadding, qScale, qOffset) : + QuantizedVector<T>(expectedOutputDataWithPadding, qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1002,19 +1024,21 @@ LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -1.0f, -2.0f, 3.0f, 4.0f, -1.0f, -2.0f, 3.0f, 4.0f, 1.0f, 2.0f, -3.0f, -4.0f, 1.0f, 2.0f, -3.0f, -4.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -1.0f, 3.0f, 4.0f, 1.0f, 3.0f, 4.0f, 1.0f, 2.0f, -4.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1050,20 +1074,22 @@ LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -1.0f, -2.0f, 3.0f, 4.0f, -1.0f, -2.0f, 3.0f, 4.0f, 1.0f, 2.0f, -3.0f, -4.0f, 1.0f, 2.0f, -3.0f, -4.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ -1.0f, 3.0f, 4.0f, 4.0f, 2.0f, 3.0f, 4.0f, 4.0f, 2.0f, 3.0f, 4.0f, 4.0f, 2.0f, 2.0f, 2.0f, -3.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1099,19 +1125,21 @@ LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 12.0f, 20.0f, 32.0f, 40.0f, 12.0f, 20.0f, 32.0f, 40.0f, 12.0f, 20.0f, 32.0f, 40.0f, 12.0f, 20.0f, 32.0f, 40.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 3.0f, 13.0f, 10.0f, 6.0f, 26.0f, 20.0f, 3.0f, 13.0f, 10.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1148,18 +1176,20 @@ LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 3.5f, 2.0f, 3.5f - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1195,20 +1225,22 @@ LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 9.0f, 27.0f, 18.0f, 36.0f, 18.0f, 9.0f, 18.0f, 9.0f, 27.0f, 18.0f, 9.0f, 27.0f, 9.0f, 27.0f, 9.0f, 18.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 7.0f, 11.0f, 13.0f, 9.0f, 12.0f, 17.0f, 19.0f, 13.0f, 12.0f, 16.0f, 16.0f, 10.0f, 9.0f, 11.0f, 12.0f, 7.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1244,19 +1276,21 @@ LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 2.0f, 4.0f, 8.0f, 16.0f, 4.0f, 2.0f, 2.0f, 4.0f, 8.0f, 2.0f, 4.0f, 2.0f, 16.0f, 2.0f, 2.0f, 8.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 4.4721f, 8.0f, 4.4721f, 2.6457f, 2.236f, 8.0f, 1.4142f, 4.0f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); @@ -1292,20 +1326,22 @@ LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon( } auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f, - })); + }, + qScale, qOffset)); auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { + QuantizedVector<T>({ 1.0540f, 1.7638f, 2.5385f, 2.3570f, 1.2909f, 2.1602f, 3.1091f, 2.8867f, 1.2909f, 2.1602f, 3.1091f, 2.8867f, 1.0540f, 1.7638f, 2.5385f, 2.3570f, - })); + }, + qScale, qOffset)); return SimplePooling2dTestImpl<ArmnnType>( workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); diff --git a/src/backends/backendsCommon/test/layerTests/PreluTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/PreluTestImpl.hpp index 18a5bd035c..dc9b908b12 100644 --- a/src/backends/backendsCommon/test/layerTests/PreluTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/PreluTestImpl.hpp @@ -7,6 +7,7 @@ #include "LayerTestResult.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -57,18 +58,22 @@ LayerTestResult<T, 4> PreluTest( 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, -1.0f, -2.0f, 0.0f, -2.0f, -4.0f }; - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); - auto alpha = MakeTensor<T, 4>(alphaTensorInfo, QuantizedVector<T>(alphaTensorInfo.GetQuantizationScale(), - alphaTensorInfo.GetQuantizationOffset(), - alphaData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); + + auto alpha = MakeTensor<T, 4>(alphaTensorInfo, + armnnUtils::QuantizedVector<T>(alphaData, + alphaTensorInfo.GetQuantizationScale(), + alphaTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputExpectedData)); + result.outputExpected = + MakeTensor<T, 4>(outputTensorInfo, + armnnUtils::QuantizedVector<T>(outputExpectedData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> alphaHandle = workloadFactory.CreateTensorHandle(alphaTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp index bb2392ff01..56ce51a844 100644 --- a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp @@ -8,6 +8,7 @@ #include "LayerTestResult.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <TensorUtils.hpp> @@ -76,9 +77,10 @@ LayerTestResult<T, 4> ResizeBilinearNopTest( inputData = tmp; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = input; @@ -174,15 +176,16 @@ LayerTestResult<T, 4> SimpleResizeBilinearTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -278,15 +281,16 @@ LayerTestResult<T, 4> ResizeBilinearSqMinTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -377,15 +381,16 @@ LayerTestResult<T, 4> ResizeBilinearMinTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -484,15 +489,16 @@ LayerTestResult<T, 4> ResizeBilinearMagTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -573,9 +579,10 @@ LayerTestResult<T, 4> ResizeNearestNeighborNopTest( inputData = tmp; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = input; @@ -670,15 +677,16 @@ LayerTestResult<T, 4> SimpleResizeNearestNeighborTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -773,15 +781,16 @@ LayerTestResult<T, 4> ResizeNearestNeighborSqMinTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -871,15 +880,16 @@ LayerTestResult<T, 4> ResizeNearestNeighborMinTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -978,15 +988,16 @@ LayerTestResult<T, 4> ResizeNearestNeighborMagTest( outputData = tmp1; } - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset(), - inputData)); + auto input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, + inputTensorInfo.GetQuantizationScale(), + inputTensorInfo.GetQuantizationOffset())); LayerTestResult<T, 4> result(outputTensorInfo); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - outputData)); + armnnUtils::QuantizedVector<T>(outputData, + outputTensorInfo.GetQuantizationScale(), + outputTensorInfo.GetQuantizationOffset())); std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/SliceTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SliceTestImpl.cpp index f0479c8ea9..a60b1898ad 100644 --- a/src/backends/backendsCommon/test/layerTests/SliceTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/SliceTestImpl.cpp @@ -5,6 +5,7 @@ #include "SliceTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -39,11 +40,11 @@ LayerTestResult<T, NumDims> SliceTestImpl( } boost::multi_array<T, NumDims> input = - MakeTensor<T, NumDims>(inputInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + MakeTensor<T, NumDims>(inputInfo, armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, NumDims> result(outputInfo); result.outputExpected = - MakeTensor<T, NumDims>(outputInfo, QuantizedVector<T>(qScale, qOffset, expectedOutputData)); + MakeTensor<T, NumDims>(outputInfo, armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo); diff --git a/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp index c0b62aa640..a5f6477a28 100644 --- a/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp @@ -5,13 +5,13 @@ #include "SoftmaxTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> #include <backendsCommon/CpuTensorHandle.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -85,8 +85,7 @@ LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl( LayerTestResult<T, n> ret(outputTensorInfo); // Each row is independently softmax'd. - auto input = MakeTensor<T, n>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, inputData))); + auto input = MakeTensor<T, n>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -111,8 +110,7 @@ LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl( CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); - std::vector<T> expectedOutput = std::vector<T>( - QuantizedVector<T>(qScale, qOffset, outputData)); + std::vector<T> expectedOutput = armnnUtils::QuantizedVector<T>(outputData, qScale, qOffset); ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput); return ret; diff --git a/src/backends/backendsCommon/test/layerTests/SpaceToBatchNdTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SpaceToBatchNdTestImpl.cpp index 094ed23893..f815604aaa 100644 --- a/src/backends/backendsCommon/test/layerTests/SpaceToBatchNdTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/SpaceToBatchNdTestImpl.cpp @@ -6,6 +6,7 @@ #include "SpaceToBatchNdTestImpl.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -55,10 +56,12 @@ LayerTestResult<T, 4> SpaceToBatchNdTestImpl( outputTensorInfo.SetQuantizationOffset(qOffset); } - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, + armnnUtils::QuantizedVector<T>(outputExpectedData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/SpaceToDepthTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SpaceToDepthTestImpl.cpp index 48e157dd8d..05413230dd 100644 --- a/src/backends/backendsCommon/test/layerTests/SpaceToDepthTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/SpaceToDepthTestImpl.cpp @@ -6,6 +6,7 @@ #include "SpaceToDepthTestImpl.hpp" #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -56,10 +57,12 @@ LayerTestResult<T, 4> SpaceToDepthTestImpl( outputTensorInfo.SetQuantizationOffset(qOffset); } - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, + armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, + armnnUtils::QuantizedVector<T>(outputExpectedData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp index 1716091cb9..7aebdd0596 100644 --- a/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp @@ -5,11 +5,11 @@ #include "SplitterTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -80,7 +80,7 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon( LayerTestResult<T,3> ret4(outputTensorInfo4); auto input = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 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, @@ -101,24 +101,26 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon( 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, - }) + }, + qScale, qOffset) )); // Channel 0 of the original input. ret1.outputExpected = MakeTensor<T, 3>(outputTensorInfo1, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 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, - }) + }, + qScale, qOffset) )); // Channel 1 & 2 of the original input. ret2.outputExpected = MakeTensor<T, 3>(outputTensorInfo2, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 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, @@ -132,31 +134,34 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon( 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, - }) + }, + qScale, qOffset) )); // Channel 0 of return 2 (i.e. channels 1 and 2 of the original input). ret3.outputExpected = MakeTensor<T, 3>(outputTensorInfo3, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 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, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, - }) + }, + qScale, qOffset) )); // Channel 1 of return 2. ret4.outputExpected = MakeTensor<T, 3>(outputTensorInfo4, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { + armnnUtils::QuantizedVector<T>({ 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, - }) + }, + qScale, qOffset) )); // NOTE: as a corollary of the splitting of x and y restriction the x and y values of the view origins @@ -253,29 +258,31 @@ LayerTestResult<T, 3> CopyViaSplitterTestImpl( float qScale, int32_t qOffset) { const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, ArmnnType, qScale, qOffset); - auto input = MakeTensor<T, 3>(tensorInfo, QuantizedVector<T>(qScale, qOffset, - { - 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, 55.0f, - 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, - - 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, - 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, - 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, - 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, - 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, - 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, - })); + auto input = MakeTensor<T, 3>( + tensorInfo, + armnnUtils::QuantizedVector<T>({ + 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, 55.0f, + 56.0f, 57.0f, 58.0f, 59.0f, 60.0f, + + 61.0f, 62.0f, 63.0f, 64.0f, 65.0f, + 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, + 71.0f, 72.0f, 73.0f, 74.0f, 75.0f, + 76.0f, 77.0f, 78.0f, 79.0f, 80.0f, + 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, + 86.0f, 87.0f, 88.0f, 89.0f, 90.0f, + }, + qScale, qOffset)); std::vector<unsigned int> origin = { 0, 0, 0 }; armnn::SplitterQueueDescriptor::ViewOrigin window(origin); diff --git a/src/backends/backendsCommon/test/layerTests/StridedSliceTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/StridedSliceTestImpl.cpp index b32e6226b8..515b5a026c 100644 --- a/src/backends/backendsCommon/test/layerTests/StridedSliceTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/StridedSliceTestImpl.cpp @@ -5,6 +5,7 @@ #include "StridedSliceTestImpl.hpp" +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <armnn/ArmNN.hpp> @@ -39,11 +40,11 @@ LayerTestResult<T, OutDim> StridedSliceTestImpl( } boost::multi_array<T, InDim> input = - MakeTensor<T, InDim>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + MakeTensor<T, InDim>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset)); LayerTestResult<T, OutDim> ret(outputTensorInfo); ret.outputExpected = - MakeTensor<T, OutDim>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); + MakeTensor<T, OutDim>(outputTensorInfo, armnnUtils::QuantizedVector<T>(outputExpectedData, qScale, qOffset)); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); diff --git a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp index 7391f9c198..a2b477cc2d 100644 --- a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp @@ -8,12 +8,12 @@ #include <armnn/ArmNN.hpp> #include <Permute.hpp> +#include <QuantizeHelper.hpp> #include <ResolveType.hpp> #include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/test/DataLayoutUtils.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> #include <backendsCommon/test/WorkloadTestUtils.hpp> @@ -146,14 +146,16 @@ LayerTestResult<T, 4> TransposeConvolution2dTest( TensorData<T> input = { inputInfo, - QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), inputData) + armnnUtils::QuantizedVector<T>(inputData, inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset()) }; // set up weights TensorData<T> weights = { weightsInfo, - QuantizedVector<T>(weightsInfo.GetQuantizationScale(), weightsInfo.GetQuantizationOffset(), weightsData) + armnnUtils::QuantizedVector<T>(weightsData, + weightsInfo.GetQuantizationScale(), + weightsInfo.GetQuantizationOffset()) }; // set up biases @@ -164,7 +166,9 @@ LayerTestResult<T, 4> TransposeConvolution2dTest( TensorData<BT> biases = { biasesInfo, - QuantizedVector<BT>(biasesInfo.GetQuantizationScale(), biasesInfo.GetQuantizationOffset(), biasesData) + armnnUtils::QuantizedVector<BT>(biasesData, + biasesInfo.GetQuantizationScale(), + biasesInfo.GetQuantizationOffset()) }; optionalBiases = Optional<TensorData<BT>>(biases); @@ -186,9 +190,9 @@ LayerTestResult<T, 4> TransposeConvolution2dTest( LayerTestResult<T, 4> testResult(outputInfo); testResult.output = MakeTensor<T, 4>(outputInfo, output.second); testResult.outputExpected = MakeTensor<T, 4>(outputInfo, - QuantizedVector<T>(outputInfo.GetQuantizationScale(), - outputInfo.GetQuantizationOffset(), - expectedOutputData)); + armnnUtils::QuantizedVector<T>(expectedOutputData, + outputInfo.GetQuantizationScale(), + outputInfo.GetQuantizationOffset())); return testResult; } diff --git a/src/backends/cl/test/Fp16SupportTest.cpp b/src/backends/cl/test/Fp16SupportTest.cpp index dac1ebcd02..f117c92e49 100644 --- a/src/backends/cl/test/Fp16SupportTest.cpp +++ b/src/backends/cl/test/Fp16SupportTest.cpp @@ -12,7 +12,6 @@ #include <Graph.hpp> #include <Optimizer.hpp> #include <backendsCommon/CpuTensorHandle.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <boost/core/ignore_unused.hpp> #include <boost/test/unit_test.hpp> diff --git a/src/backends/cl/test/OpenClTimerTest.cpp b/src/backends/cl/test/OpenClTimerTest.cpp index 1eeb9ed98f..13620c4311 100644 --- a/src/backends/cl/test/OpenClTimerTest.cpp +++ b/src/backends/cl/test/OpenClTimerTest.cpp @@ -51,40 +51,27 @@ BOOST_AUTO_TEST_CASE(OpenClTimerBatchNorm) const unsigned int height = 3; const unsigned int channels = 2; const unsigned int num = 1; - int32_t qOffset = 0; - float qScale = 0.f; - TensorInfo inputTensorInfo({num, channels, height, width}, DataType::Float32); + TensorInfo inputTensorInfo( {num, channels, height, width}, DataType::Float32); TensorInfo outputTensorInfo({num, channels, height, width}, DataType::Float32); TensorInfo tensorInfo({channels}, DataType::Float32); - // Set quantization parameters if the requested type is a quantized type. - if(IsQuantizedType<float>()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - tensorInfo.SetQuantizationScale(qScale); - tensorInfo.SetQuantizationOffset(qOffset); - } - auto input = MakeTensor<float, 4>(inputTensorInfo, - QuantizedVector<float>(qScale, qOffset, - { - 1.f, 4.f, - 4.f, 2.f, - 1.f, 6.f, - - 1.f, 1.f, - 4.f, 1.f, - -2.f, 4.f - })); + { + 1.f, 4.f, + 4.f, 2.f, + 1.f, 6.f, + + 1.f, 1.f, + 4.f, 1.f, + -2.f, 4.f + }); + // these values are per-channel of the input - auto mean = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {3, -2})); - auto variance = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {4, 9})); - auto beta = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {3, 2})); - auto gamma = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {2, 1})); + auto mean = MakeTensor<float, 1>(tensorInfo, { 3.f, -2.f }); + auto variance = MakeTensor<float, 1>(tensorInfo, { 4.f, 9.f }); + auto beta = MakeTensor<float, 1>(tensorInfo, { 3.f, 2.f }); + auto gamma = MakeTensor<float, 1>(tensorInfo, { 2.f, 1.f }); std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); diff --git a/tests/DeepSpeechV1Database.hpp b/tests/DeepSpeechV1Database.hpp index 037c810122..182f373fc2 100644 --- a/tests/DeepSpeechV1Database.hpp +++ b/tests/DeepSpeechV1Database.hpp @@ -11,7 +11,6 @@ #include <vector> #include <armnn/TypesUtils.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <boost/log/trivial.hpp> #include <boost/numeric/conversion/cast.hpp> @@ -202,4 +201,3 @@ std::unique_ptr<DeepSpeechV1TestCaseData> DeepSpeechV1Database::GetTestCaseData( } } // anonymous namespace - diff --git a/tests/MobileNetSsdDatabase.hpp b/tests/MobileNetSsdDatabase.hpp index 349d1ad96a..1a99ed715a 100644 --- a/tests/MobileNetSsdDatabase.hpp +++ b/tests/MobileNetSsdDatabase.hpp @@ -2,24 +2,23 @@ // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // + #pragma once +#include "InferenceTestImage.hpp" #include "ObjectDetectionCommon.hpp" -#include <memory> -#include <string> -#include <vector> +#include <QuantizeHelper.hpp> #include <armnn/TypesUtils.hpp> -#include <backendsCommon/test/QuantizeHelper.hpp> #include <boost/log/trivial.hpp> #include <boost/numeric/conversion/cast.hpp> #include <array> +#include <memory> #include <string> - -#include "InferenceTestImage.hpp" +#include <vector> namespace { @@ -97,7 +96,7 @@ std::unique_ptr<MobileNetSsdTestCaseData> MobileNetSsdDatabase::GetTestCaseData( // Get image data as a vector of floats std::vector<float> floatImageData = GetImageDataAsNormalizedFloats(ImageChannelLayout::Rgb, image); - imageData = QuantizedVector<uint8_t>(m_Scale, m_Offset, floatImageData); + imageData = armnnUtils::QuantizedVector<uint8_t>(floatImageData, m_Scale, m_Offset); } catch (const InferenceTestImageException& e) { |