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-rw-r--r--src/backends/backendsCommon/test/SoftmaxTestImpl.hpp265
1 files changed, 0 insertions, 265 deletions
diff --git a/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp b/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp
deleted file mode 100644
index 983a53be9c..0000000000
--- a/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp
+++ /dev/null
@@ -1,265 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "QuantizeHelper.hpp"
-#include "WorkloadTestUtils.hpp"
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <backendsCommon/CpuTensorHandle.hpp>
-#include <backendsCommon/IBackendInternal.hpp>
-#include <backendsCommon/WorkloadFactory.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-#include <algorithm>
-
-template<armnn::DataType ArmnnType, std::size_t n, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta,
- const armnn::TensorShape& inputShape,
- const std::vector<float>& outputData,
- const std::vector<float>& inputData,
- int axis = 1)
-{
- using std::exp;
-
- const float qScale = 1.f / 256.f;
- const int qOffset = 0;
-
- armnn::TensorInfo inputTensorInfo;
- armnn::TensorInfo outputTensorInfo;
-
- inputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
- inputTensorInfo.SetQuantizationScale(qScale);
- inputTensorInfo.SetQuantizationOffset(qOffset);
-
- outputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
- outputTensorInfo.SetQuantizationScale(qScale);
- outputTensorInfo.SetQuantizationOffset(qOffset);
-
- 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)));
-
- 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;
- data.m_Parameters.m_Axis = axis;
-
- 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.origin());
-
- BOOST_ASSERT(workload);
-
- ExecuteWorkload(*workload, memoryManager);
-
- CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
-
- std::vector<T> expectedOutput = std::vector<T>(
- QuantizedVector<T>(qScale, qOffset, outputData));
- ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput);
-
- return ret;
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta)
-{
- using std::exp;
- const armnn::TensorShape inputShape{ 2, 4 };
-
- 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];
-
- const std::vector<float> outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
- x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 };
-
- const std::vector<float> inputData =
- {
- 0.f, 1.f, 0.f, 0.f,
- .5f, 0.f, 0.f, 0.f,
- };
-
- return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
- inputShape, outputData, inputData);
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta,
- int axis)
-{
- armnn::TensorShape inputShape;
- std::vector<float> inputData;
- std::vector<float> outputData;
- switch (axis)
- {
- case -2:
- case 0:
- {
- inputShape = {5, 2};
-
- inputData =
- {
- 17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f
- };
-
- outputData =
- {
- 0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
- 0.087144312427294f,
- 0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
- 7.246299848982885e-08f
- };
- break;
- }
- case -1:
- case 1:
- {
- inputShape = {2, 5};
-
- inputData =
- {
- 17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f
- };
-
- outputData =
- {
- 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
- 7.246299848982885e-08f,
- 0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
- 7.246299848982885e-08f
- };
- break;
- }
- }
- return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
- inputShape, outputData, inputData, axis);
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 3> Simple3dSoftmaxTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta,
- const armnn::TensorShape& inputShape,
- const std::vector<float>& outputData,
- const std::vector<float>& inputData,
- int axis = 1)
-{
- return SimpleSoftmaxBaseTestImpl<ArmnnType, 3>(workloadFactory, memoryManager, beta,
- inputShape, outputData, inputData, axis);
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 4> Simple4dSoftmaxTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta,
- const armnn::TensorShape& inputShape,
- const std::vector<float>& outputData,
- const std::vector<float>& inputData,
- int axis = 1)
-{
-
- return SimpleSoftmaxBaseTestImpl<ArmnnType, 4>(workloadFactory, memoryManager, beta,
- inputShape, outputData, inputData, axis);
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> CompareSoftmaxTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- 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, ArmnnType);
- outputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
- 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]);
-
- ExecuteWorkload(*workload, memoryManager);
-
- workloadRef->Execute();
-
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
- CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get());
-
- return ret;
-}
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-03 23:18:54 +0000