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Diffstat (limited to 'src/backends/test/SoftmaxTestImpl.hpp')
-rw-r--r-- | src/backends/test/SoftmaxTestImpl.hpp | 152 |
1 files changed, 0 insertions, 152 deletions
diff --git a/src/backends/test/SoftmaxTestImpl.hpp b/src/backends/test/SoftmaxTestImpl.hpp deleted file mode 100644 index 0bca8be49d..0000000000 --- a/src/backends/test/SoftmaxTestImpl.hpp +++ /dev/null @@ -1,152 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/ArmNN.hpp> -#include <armnn/Tensor.hpp> -#include <armnn/TypesUtils.hpp> - -#include <test/TensorHelpers.hpp> -#include "QuantizeHelper.hpp" - -#include <backends/CpuTensorHandle.hpp> -#include <backends/WorkloadFactory.hpp> - -#include <algorithm> - -template<typename T> -LayerTestResult<T, 2> SimpleSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - using std::exp; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { 2, 4 }; - - inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>()); - float qScale = 1.f / 256.f; - int qOffset = 0; - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - - outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>()); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - - LayerTestResult<T, 2> ret(outputTensorInfo); - - // Each row is independently softmax'd. - auto input = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(qScale, 0, { - 0.f, 1.f, 0.f, 0.f, - .5f, 0.f, 0.f, 0.f, - }))); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SoftmaxQueueDescriptor data; - data.m_Parameters.m_Beta = beta; - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta), - exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) }; - float sum0 = x0[0] + x0[1] + x0[2] + x0[3]; - float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta), - exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) }; - float sum1 = x1[0] + x1[1] + x1[2] + x1[3]; - - ret.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0, - x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 - }))); - - return ret; -} - -template<typename T> -LayerTestResult<T, 2> CompareSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - - const int batchSize = 20; - const int channels = 30; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { batchSize, channels }; - - inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>()); - outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>()); - float qScale = 1.f / 256.f; - int qOffset = 0; - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - - - LayerTestResult<T, 2> ret(outputTensorInfo); - auto input = MakeRandomTensor<T, 2>(inputTensorInfo, 0xF00D, 0.0f, 1.0f); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SoftmaxQueueDescriptor data; - data.m_Parameters.m_Beta = beta; - - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - - armnn::SoftmaxQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get()); - - return ret; -} |