1 files changed, 0 insertions, 153 deletions
diff --git a/src/armnn/backends/test/SoftmaxTestImpl.hpp b/src/armnn/backends/test/SoftmaxTestImpl.hpp
deleted file mode 100644
index 5bc13fa21c..0000000000
--- a/src/armnn/backends/test/SoftmaxTestImpl.hpp
+++ /dev/null
@@ -1,153 +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 <backends/WorkloadInfo.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;
-}