IVGCVSW-3839 Add support of per-axis quantization to reference TransposeConvolution2d
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Ie0dc1204eee925adfb1e59aba3f1137178302184
diff --git a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp
index a2b477c..438828f 100644
--- a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp
+++ b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp
@@ -5,553 +5,41 @@
#pragma once
-#include <armnn/ArmNN.hpp>
+#include "LayerTestResult.hpp"
-#include <Permute.hpp>
-#include <QuantizeHelper.hpp>
#include <ResolveType.hpp>
-#include <backendsCommon/CpuTensorHandle.hpp>
-
-#include <backendsCommon/test/DataLayoutUtils.hpp>
-#include <backendsCommon/test/TensorCopyUtils.hpp>
-#include <backendsCommon/test/WorkloadTestUtils.hpp>
-
-#include <reference/RefWorkloadFactory.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace
-{
-
-template<typename T>
-using TensorData = std::pair<armnn::TensorInfo, std::vector<T>>;
-
-template<typename T>
-void VerifyInputTensorData(const TensorData<T>& data, const std::string& tensorName)
-{
- if (data.first.GetNumElements() > data.second.size())
- {
- throw armnn::InvalidArgumentException("Size of data too small for " + tensorName + ": expected " +
- std::to_string(data.first.GetNumElements()) + "but got " + std::to_string(data.second.size()));
- }
-}
-
-template<typename T, typename BT>
-void TransposeConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const armnn::TransposeConvolution2dDescriptor& descriptor,
- const TensorData<T>& input,
- TensorData<T>& output,
- const TensorData<T>& weights,
- const armnn::Optional<TensorData<BT>>& biases)
-{
- using namespace armnn;
-
- VerifyInputTensorData(input, "input");
- VerifyInputTensorData(weights, "biases");
-
- if (descriptor.m_BiasEnabled)
- {
- if (!biases.has_value())
- {
- throw InvalidArgumentException("Bias enabled but no bias data provided");
- }
- VerifyInputTensorData(biases.value(), "biases");
- }
-
- // set up weights
- ScopedCpuTensorHandle weightsTensor(weights.first);
-
- TransposeConvolution2dQueueDescriptor queueDescriptor;
- queueDescriptor.m_Parameters = descriptor;
- queueDescriptor.m_Weight = &weightsTensor;
-
- AllocateAndCopyDataToITensorHandle(&weightsTensor, weights.second.data());
-
- std::unique_ptr<ScopedCpuTensorHandle> biasesTensor;
- if (descriptor.m_BiasEnabled)
- {
- // set up biases
- biasesTensor = std::make_unique<ScopedCpuTensorHandle>(biases.value().first);
- queueDescriptor.m_Bias = biasesTensor.get();
-
- AllocateAndCopyDataToITensorHandle(biasesTensor.get(), biases.value().second.data());
- }
-
- // set up input and output handles
- std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(input.first);
- std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(output.first);
-
- // set up workload
- armnn::WorkloadInfo workloadInfo;
- AddInputToWorkload(queueDescriptor, workloadInfo, input.first, inputHandle.get());
- AddOutputToWorkload(queueDescriptor, workloadInfo, output.first, outputHandle.get());
-
- std::unique_ptr<armnn::IWorkload> workload =
- workloadFactory.CreateTransposeConvolution2d(queueDescriptor, workloadInfo);
-
- inputHandle->Allocate();
- outputHandle->Allocate();
-
- CopyDataToITensorHandle(inputHandle.get(), input.second.data());
-
- ExecuteWorkload(*workload, memoryManager);
-
- // copy output
- output.second = std::vector<T>(output.first.GetNumElements(), 0.0f);
- CopyDataFromITensorHandle(output.second.data(), outputHandle.get());
-}
-
-template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 4> TransposeConvolution2dTest(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const armnn::TransposeConvolution2dDescriptor& descriptor,
- armnn::TensorInfo& inputInfo,
- const std::vector<float>& inputData,
- armnn::TensorInfo& outputInfo,
- const std::vector<float>& expectedOutputData,
- armnn::TensorInfo& weightsInfo,
- const std::vector<float>& weightsData,
- armnn::TensorInfo& biasesInfo,
- const std::vector<float>& biasesData)
-{
- using namespace armnn;
-
- // set up quantization parameters
- if (armnn::IsQuantizedType<T>())
- {
- constexpr float qScale = 0.50f;
- constexpr int32_t qOffset = 10;
-
- inputInfo.SetQuantizationScale(qScale);
- inputInfo.SetQuantizationOffset(qOffset);
-
- outputInfo.SetQuantizationScale(qScale);
- outputInfo.SetQuantizationOffset(qOffset);
-
- weightsInfo.SetQuantizationScale(qScale);
- weightsInfo.SetQuantizationOffset(qOffset);
-
- biasesInfo.SetQuantizationScale(qScale * qScale);
- biasesInfo.SetQuantizationOffset(0);
- }
-
- // set up input
- TensorData<T> input =
- {
- inputInfo,
- armnnUtils::QuantizedVector<T>(inputData, inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset())
- };
-
- // set up weights
- TensorData<T> weights =
- {
- weightsInfo,
- armnnUtils::QuantizedVector<T>(weightsData,
- weightsInfo.GetQuantizationScale(),
- weightsInfo.GetQuantizationOffset())
- };
-
- // set up biases
- using BT = armnn::ResolveType<ArmnnBType>;
- Optional<TensorData<BT>> optionalBiases;
- if (descriptor.m_BiasEnabled)
- {
- TensorData<BT> biases =
- {
- biasesInfo,
- armnnUtils::QuantizedVector<BT>(biasesData,
- biasesInfo.GetQuantizationScale(),
- biasesInfo.GetQuantizationOffset())
- };
-
- optionalBiases = Optional<TensorData<BT>>(biases);
- }
-
- // set up output
- TensorData<T> output = { outputInfo, {} };
-
- // execute test
- TransposeConvolution2dTestImpl(workloadFactory,
- memoryManager,
- descriptor,
- input,
- output,
- weights,
- optionalBiases);
-
- // construct result object
- LayerTestResult<T, 4> testResult(outputInfo);
- testResult.output = MakeTensor<T, 4>(outputInfo, output.second);
- testResult.outputExpected = MakeTensor<T, 4>(outputInfo,
- armnnUtils::QuantizedVector<T>(expectedOutputData,
- outputInfo.GetQuantizationScale(),
- outputInfo.GetQuantizationOffset()));
-
- return testResult;
-}
-
-template<typename T>
-void SwizzleData(armnn::TensorInfo& inputInfo,
- std::vector<T>& inputData,
- armnn::TensorInfo& outputInfo,
- std::vector<T>& outputData,
- armnn::TensorInfo& weightsInfo,
- std::vector<T>& weightsData)
-{
- PermuteTensorNchwToNhwc<T>(inputInfo, inputData);
- PermuteTensorNchwToNhwc<T>(outputInfo, outputData);
- PermuteTensorNchwToNhwc<T>(weightsInfo, weightsData);
-}
-
-} // anonymous namespace
+#include <backendsCommon/IBackendInternal.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleTransposeConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
- const armnn::DataLayout layout)
-{
- using namespace armnn;
-
- constexpr unsigned int batches = 1u;
- constexpr unsigned int channels = 1u;
-
- constexpr unsigned int wInput = 3u;
- constexpr unsigned int hInput = wInput;
-
- constexpr unsigned int wOutput = 5u;
- constexpr unsigned int hOutput = wOutput;
-
- constexpr unsigned int wWeights = 3u;
- constexpr unsigned int hWeights = wWeights;
-
- TensorShape inputShape = { batches, channels, hInput, wInput };
- TensorShape outputShape = { batches, channels, hOutput, wOutput };
- TensorShape weightsShape = { batches, channels, hWeights, wWeights };
-
- TensorInfo inputInfo(inputShape, ArmnnType);
- TensorInfo outputInfo(outputShape, ArmnnType);
- TensorInfo weightsInfo(weightsShape, ArmnnType);
- TensorInfo biasesInfo({ channels }, ArmnnBType);
-
- std::vector<float> inputData =
- {
- 1.f, 1.f, 1.f,
- 1.f, 1.f, 1.f,
- 1.f, 1.f, 1.f
- };
-
- std::vector<float> weightsData =
- {
- 1.f, 2.f, 3.f,
- 4.f, 5.f, 6.f,
- 7.f, 8.f, 9.f
- };
-
- std::vector<float> biasesData = { 1.f };
-
- std::vector<float> expectedOutputData =
- {
- 1.f, 3.f, 6.f, 5.f, 3.f,
- 5.f, 12.f, 21.f, 16.f, 9.f,
- 12.f, 27.f, 45.f, 33.f, 18.f,
- 11.f, 24.f, 39.f, 28.f, 15.f,
- 7.f, 15.f, 24.f, 17.f, 9.f
- };
-
- if (biasEnabled)
- {
- // apply bias to expected output data
- std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
- [&](float f) -> float { return f + biasesData[0]; });
- }
-
- TransposeConvolution2dDescriptor descriptor;
- descriptor.m_StrideX = 1;
- descriptor.m_StrideY = 1;
- descriptor.m_BiasEnabled = biasEnabled;
- descriptor.m_DataLayout = layout;
-
- // swizzle data if needed
- if (layout == armnn::DataLayout::NHWC)
- {
- SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
- }
-
- return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
- memoryManager,
- descriptor,
- inputInfo,
- inputData,
- outputInfo,
- expectedOutputData,
- weightsInfo,
- weightsData,
- biasesInfo,
- biasesData);
-}
+ const armnn::DataLayout layout);
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> PaddedTransposeConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
- const armnn::DataLayout layout)
-{
- using namespace armnn;
-
- constexpr unsigned int batches = 1u;
- constexpr unsigned int channels = 1u;
-
- constexpr unsigned int wInput = 4u;
- constexpr unsigned int hInput = wInput;
-
- constexpr unsigned int wOutput = 2u;
- constexpr unsigned int hOutput = wOutput;
-
- constexpr unsigned int wWeights = 3u;
- constexpr unsigned int hWeights = wWeights;
-
- TensorShape inputShape = { batches, channels, hInput, wInput };
- TensorShape outputShape = { batches, channels, hOutput, wOutput };
- TensorShape weightsShape = { batches, channels, hWeights, wWeights };
-
- TensorInfo inputInfo(inputShape, ArmnnType);
- TensorInfo outputInfo(outputShape, ArmnnType);
- TensorInfo weightsInfo(weightsShape, ArmnnType);
- TensorInfo biasesInfo({ channels }, ArmnnBType);
-
- std::vector<float> inputData =
- {
- 1.f, 3.f, 2.f, 1.f,
- 1.f, 3.f, 3.f, 1.f,
- 2.f, 1.f, 1.f, 3.f,
- 3.f, 2.f, 3.f, 3.f
- };
-
- std::vector<float> weightsData =
- {
- 1.f, 2.f, 3.f,
- 0.f, 1.f, 0.f,
- 2.f, 1.f, 2.f
- };
-
- std::vector<float> biasesData = { 1.f };
-
- std::vector<float> expectedOutputData =
- {
- 21.f, 21.f,
- 28.f, 27.f
- };
-
- if (biasEnabled)
- {
- // apply bias to expected output data
- std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
- [&](float f) -> float { return f + biasesData[0]; });
- }
-
- TransposeConvolution2dDescriptor descriptor;
- descriptor.m_PadLeft = 2;
- descriptor.m_PadRight = 2;
- descriptor.m_PadTop = 2;
- descriptor.m_PadBottom = 2;
- descriptor.m_StrideX = 1;
- descriptor.m_StrideY = 1;
- descriptor.m_BiasEnabled = biasEnabled;
- descriptor.m_DataLayout = layout;
-
- // swizzle data if needed
- if (layout == armnn::DataLayout::NHWC)
- {
- SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
- }
-
- return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
- memoryManager,
- descriptor,
- inputInfo,
- inputData,
- outputInfo,
- expectedOutputData,
- weightsInfo,
- weightsData,
- biasesInfo,
- biasesData);
-}
+ const armnn::DataLayout layout);
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> StridedTransposeConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
- const armnn::DataLayout layout)
-{
- using namespace armnn;
-
- constexpr unsigned int batches = 1u;
- constexpr unsigned int channels = 1u;
-
- constexpr unsigned int wInput = 3u;
- constexpr unsigned int hInput = wInput;
-
- constexpr unsigned int wOutput = 7u;
- constexpr unsigned int hOutput = wOutput;
-
- constexpr unsigned int wWeights = 3u;
- constexpr unsigned int hWeights = wWeights;
-
- TensorShape inputShape = { batches, channels, hInput, wInput };
- TensorShape outputShape = { batches, channels, hOutput, wOutput };
- TensorShape weightsShape = { batches, channels, hWeights, wWeights };
-
- TensorInfo inputInfo(inputShape, ArmnnType);
- TensorInfo outputInfo(outputShape, ArmnnType);
- TensorInfo weightsInfo(weightsShape, ArmnnType);
- TensorInfo biasesInfo({ channels }, ArmnnBType);
-
- std::vector<float> inputData =
- {
- 1.f, 1.f, 1.f,
- 1.f, 1.f, 1.f,
- 1.f, 1.f, 1.f
- };
-
- std::vector<float> weightsData =
- {
- 1.f, 2.f, 3.f,
- 4.f, 5.f, 6.f,
- 7.f, 8.f, 9.f
- };
-
- std::vector<float> biasesData = { 1.f };
-
- std::vector<float> expectedOutputData =
- {
- 1.f, 2.f, 4.f, 2.f, 4.f, 2.f, 3.f,
- 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f,
- 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f,
- 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f,
- 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f,
- 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f,
- 7.f, 8.f, 16.f, 8.f, 16.f, 8.f, 9.f
- };
-
- if (biasEnabled)
- {
- // apply bias to expected output data
- std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
- [&](float f) -> float { return f + biasesData[0]; });
- }
-
- TransposeConvolution2dDescriptor descriptor;
- descriptor.m_StrideX = 2;
- descriptor.m_StrideY = 2;
- descriptor.m_BiasEnabled = biasEnabled;
- descriptor.m_DataLayout = layout;
-
- // swizzle data if needed
- if (layout == armnn::DataLayout::NHWC)
- {
- SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
- }
-
- return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
- memoryManager,
- descriptor,
- inputInfo,
- inputData,
- outputInfo,
- expectedOutputData,
- weightsInfo,
- weightsData,
- biasesInfo,
- biasesData);
-}
+ const armnn::DataLayout layout);
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> MultiChannelTransposeConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const armnn::DataLayout layout)
-{
- using namespace armnn;
+ const armnn::DataLayout layout);
- TensorShape inputShape = { 1, 1, 2, 2 };
- TensorShape outputShape = { 1, 2, 5, 5 };
-
- // OIHW for NCHW; OHWI for NHWC
- TensorShape weightsShape = { 2, 1, 3, 3 };
- TensorShape biasesShape = { 2 };
-
- TensorInfo inputInfo(inputShape, ArmnnType);
- TensorInfo outputInfo(outputShape, ArmnnType);
- TensorInfo weightsInfo(weightsShape, ArmnnType);
- TensorInfo biasesInfo(biasesShape, ArmnnBType);
-
- std::vector<float> inputData =
- {
- 1.f, 2.f,
- 3.f, 4.f,
- };
-
- std::vector<float> weightsData =
- {
- 1.f, 3.f, 5.f,
- 7.f, 9.f, 11.f,
- 13.f, 15.f, 17.f,
-
- 2.f, 4.f, 6.f,
- 8.f, 10.f, 12.f,
- 14.f, 16.f, 18.f
- };
-
- std::vector<float> biasesData = { -1.5f, -2.0f };
-
- std::vector<float> expectedOutputData =
- {
- -0.5f, 1.5f, 5.5f, 4.5f, 8.5f,
- 5.5f, 7.5f, 23.5f, 16.5f, 20.5f,
- 14.5f, 22.5f, 60.5f, 40.5f, 52.5f,
- 19.5f, 25.5f, 59.5f, 34.5f, 42.5f,
- 37.5f, 43.5f, 101.5f, 58.5f, 66.5f,
-
- 0.0f, 2.0f, 8.0f, 6.0f, 10.0f,
- 6.0f, 8.0f, 26.0f, 18.0f, 22.0f,
- 18.0f, 26.0f, 70.0f, 46.0f, 58.0f,
- 22.0f, 28.0f, 66.0f, 38.0f, 46.0f,
- 40.0f, 46.0f, 108.0f, 62.0f, 70.0f,
- };
-
- TransposeConvolution2dDescriptor descriptor;
- descriptor.m_StrideX = 2;
- descriptor.m_StrideY = 2;
- descriptor.m_BiasEnabled = true;
- descriptor.m_DataLayout = layout;
-
- // swizzle data if needed
- if (layout == armnn::DataLayout::NHWC)
- {
- SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
- }
-
- return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
- memoryManager,
- descriptor,
- inputInfo,
- inputData,
- outputInfo,
- expectedOutputData,
- weightsInfo,
- weightsData,
- biasesInfo,
- biasesData);
-}
+LayerTestResult<uint8_t, 4> TransposeConvolution2dPerAxisQuantTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ const armnn::DataLayout layout);