diff options
Diffstat (limited to 'src/backends/RefWorkloads')
92 files changed, 0 insertions, 3806 deletions
diff --git a/src/backends/RefWorkloads/Activation.cpp b/src/backends/RefWorkloads/Activation.cpp deleted file mode 100644 index ef4903074b..0000000000 --- a/src/backends/RefWorkloads/Activation.cpp +++ /dev/null @@ -1,91 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "Activation.hpp" - -#include <boost/log/trivial.hpp> - -#include <cmath> - -namespace armnn -{ - -void Activation(const float* in, - float* out, - const TensorInfo& tensorInfo, - ActivationFunction function, - float a, - float b) -{ - for (size_t i = 0; i<tensorInfo.GetNumElements(); i++) - { - float input = in[i]; - float output; - - // Compute the result of the activation function. - switch (function) - { - case ActivationFunction::Linear: - { - output = a * input + b; - break; - } - case ActivationFunction::Sigmoid: - { - output = 1.f / (1.f + expf(-input)); - break; - } - case ActivationFunction::ReLu: - { - output = std::max(0.f, input); - break; - } - case ActivationFunction::BoundedReLu: - { - output = std::min(a, std::max(b, input)); - break; - } - case ActivationFunction::SoftReLu: - { - output = logf(1.0f + expf(input)); - break; - } - case ActivationFunction::LeakyReLu: - { - output = input > 0.0f ? input : (input * a); - break; - } - case ActivationFunction::Abs: - { - output = input < 0 ? -input : input; - break; - } - case ActivationFunction::Sqrt: - { - output = sqrtf(input); - break; - } - case ActivationFunction::Square: - { - output = input * input; - break; - } - case ActivationFunction::TanH: - { - output = a * tanhf(b * input); - break; - } - default: - { - BOOST_LOG_TRIVIAL(error) << "Unsupported activation function"; - return; - } - } - - out[i] = output; - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Activation.hpp b/src/backends/RefWorkloads/Activation.hpp deleted file mode 100644 index c8a23114f0..0000000000 --- a/src/backends/RefWorkloads/Activation.hpp +++ /dev/null @@ -1,20 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <armnn/Tensor.hpp> -#include <armnn/Types.hpp> - -namespace armnn -{ - -/// Performs the ActivationFunction elementwise on the inputs to give the outputs. -void Activation(const float* in, - float* out, - const TensorInfo& tensorInfo, - ActivationFunction function, - float a, - float b); - -} //namespace armnn diff --git a/src/backends/RefWorkloads/ArithmeticFunction.cpp b/src/backends/RefWorkloads/ArithmeticFunction.cpp deleted file mode 100644 index fede138253..0000000000 --- a/src/backends/RefWorkloads/ArithmeticFunction.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ArithmeticFunction.hpp" -#include "Broadcast.hpp" -#include <functional> - -namespace armnn -{ - -template <typename Functor> -ArithmeticFunction<Functor>::ArithmeticFunction(const TensorShape& inShape0, - const TensorShape& inShape1, - const TensorShape& outShape, - const float* inData0, - const float* inData1, - float* outData) -{ - BroadcastLoop(inShape0, inShape1, outShape).Unroll(Functor(), 0, inData0, inData1, outData); -} - -} //namespace armnn - -template struct armnn::ArithmeticFunction<std::plus<float>>; -template struct armnn::ArithmeticFunction<std::minus<float>>; -template struct armnn::ArithmeticFunction<std::multiplies<float>>; -template struct armnn::ArithmeticFunction<std::divides<float>>; diff --git a/src/backends/RefWorkloads/ArithmeticFunction.hpp b/src/backends/RefWorkloads/ArithmeticFunction.hpp deleted file mode 100644 index eafb6444f6..0000000000 --- a/src/backends/RefWorkloads/ArithmeticFunction.hpp +++ /dev/null @@ -1,24 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -template <typename Functor> -struct ArithmeticFunction -{ - ArithmeticFunction(const TensorShape& inShape0, - const TensorShape& inShape1, - const TensorShape& outShape, - const float* inData0, - const float* inData1, - float* outData); -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/BatchNormImpl.hpp b/src/backends/RefWorkloads/BatchNormImpl.hpp deleted file mode 100644 index a7579c8373..0000000000 --- a/src/backends/RefWorkloads/BatchNormImpl.hpp +++ /dev/null @@ -1,56 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefWorkloadUtils.hpp" - -#include <armnn/Tensor.hpp> - -#include <cmath> - -namespace armnn -{ - -template<typename NormData> -static void BatchNormImpl(NormData data, - const float* varIn, - const float* meanIn, - const float* gammaIn, - const float* betaIn, - float * outputData, - const float * inputData) -{ - const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]); - for (unsigned int c = 0; c < inputInfo0.GetShape()[1]; c++) - { - float var = varIn[c]; - float mean = meanIn[c]; - float gamma = gammaIn[c]; - float beta = betaIn[c]; - - float mult = gamma / sqrtf(var + data.m_Parameters.m_Eps); - float add = beta - mult * mean; - - for (unsigned int n = 0; n < inputInfo0.GetShape()[0]; n++) - { - for (unsigned int j = 0; j < inputInfo0.GetShape()[2]; j++) - { - for (unsigned int i = 0; i < inputInfo0.GetShape()[3]; i++) - { - unsigned int index = i + - j*inputInfo0.GetShape()[3] + - c*inputInfo0.GetShape()[3] * inputInfo0.GetShape()[2] + - n*inputInfo0.GetShape()[3] * inputInfo0.GetShape()[2] - * inputInfo0.GetShape()[1]; - - outputData[index] = mult * inputData[index] + add; - } - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Broadcast.cpp b/src/backends/RefWorkloads/Broadcast.cpp deleted file mode 100644 index 8421a0a7ed..0000000000 --- a/src/backends/RefWorkloads/Broadcast.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "Broadcast.hpp" - -namespace armnn -{ - -BroadcastLoop::BroadcastLoop(const TensorShape& inShape0, const TensorShape& inShape1, const TensorShape& outShape) -: m_DimData(outShape.GetNumDimensions()) -{ - const unsigned int numDims = GetNumDimensions(); - - unsigned int sIn0 = 1; - unsigned int sIn1 = 1; - unsigned int sOut = 1; - - for (unsigned int j = numDims - 1, k = 0; k < numDims ; k++, j--) - { - m_DimData[j].m_DimSize = outShape[j]; - m_DimData[j].m_Stride1 = (inShape0[j] > 1) ? sIn0 : 0; - m_DimData[j].m_Stride2 = (inShape1[j] > 1) ? sIn1 : 0; - m_DimData[j].m_StrideOut = sOut; - - sIn0 *= inShape0[j]; - sIn1 *= inShape1[j]; - sOut *= outShape[j]; - } -} - -} // namespace armnn diff --git a/src/backends/RefWorkloads/Broadcast.hpp b/src/backends/RefWorkloads/Broadcast.hpp deleted file mode 100644 index e92ed0598d..0000000000 --- a/src/backends/RefWorkloads/Broadcast.hpp +++ /dev/null @@ -1,58 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <armnn/Tensor.hpp> - -#include <functional> - -namespace armnn -{ - -struct BroadcastLoop -{ - BroadcastLoop(const TensorShape& inShape0, const TensorShape& inShape1, const TensorShape& outShape); - - unsigned int GetNumDimensions() - { - return static_cast<unsigned int>(m_DimData.size()); - } - - template <typename T0, typename T1, typename U, typename Func> - void Unroll(Func operationFunc, - unsigned int dimension, - const T0* inData0, - const T1* inData1, - U* outData) - { - if (dimension >= GetNumDimensions()) - { - *outData = operationFunc(*inData0, *inData1); - return; - } - - for (unsigned int i = 0; i < m_DimData[dimension].m_DimSize; i++) - { - Unroll(operationFunc, dimension + 1, inData0, inData1, outData); - - inData0 += m_DimData[dimension].m_Stride1; - inData1 += m_DimData[dimension].m_Stride2; - outData += m_DimData[dimension].m_StrideOut; - } - } - -private: - // Struct to hold the dimension data. - struct BroadcastDimensionData - { - unsigned int m_DimSize; - unsigned int m_StrideOut; - unsigned int m_Stride1; - unsigned int m_Stride2; - }; - - std::vector<BroadcastDimensionData> m_DimData; -}; - -} //namespace armnn
\ No newline at end of file diff --git a/src/backends/RefWorkloads/CMakeLists.txt b/src/backends/RefWorkloads/CMakeLists.txt deleted file mode 100644 index 0f4bfe129f..0000000000 --- a/src/backends/RefWorkloads/CMakeLists.txt +++ /dev/null @@ -1,101 +0,0 @@ -# -# Copyright © 2017 Arm Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -list(APPEND armnnRefBackend_sources - Activation.cpp - Activation.hpp - ArithmeticFunction.cpp - ArithmeticFunction.hpp - BatchNormImpl.hpp - Broadcast.cpp - Broadcast.hpp - ConvImpl.cpp - ConvImpl.hpp - FullyConnected.cpp - FullyConnected.hpp - Merger.hpp - Pooling2d.cpp - Pooling2d.hpp - RefActivationFloat32Workload.cpp - RefActivationFloat32Workload.hpp - RefActivationUint8Workload.cpp - RefActivationUint8Workload.hpp - RefArithmeticWorkload.cpp - RefArithmeticWorkload.hpp - RefBaseConstantWorkload.cpp - RefBaseConstantWorkload.hpp - RefBatchNormalizationFloat32Workload.cpp - RefBatchNormalizationFloat32Workload.hpp - RefBatchNormalizationUint8Workload.cpp - RefBatchNormalizationUint8Workload.hpp - RefConstantFloat32Workload.cpp - RefConstantFloat32Workload.hpp - RefConstantUint8Workload.cpp - RefConstantUint8Workload.hpp - RefConvertFp16ToFp32Workload.cpp - RefConvertFp16ToFp32Workload.hpp - RefConvertFp32ToFp16Workload.cpp - RefConvertFp32ToFp16Workload.hpp - RefConvolution2dFloat32Workload.cpp - RefConvolution2dFloat32Workload.hpp - RefConvolution2dUint8Workload.cpp - RefConvolution2dUint8Workload.hpp - RefDepthwiseConvolution2dFloat32Workload.cpp - RefDepthwiseConvolution2dFloat32Workload.hpp - RefDepthwiseConvolution2dUint8Workload.cpp - RefDepthwiseConvolution2dUint8Workload.hpp - RefFakeQuantizationFloat32Workload.cpp - RefFakeQuantizationFloat32Workload.hpp - RefFloorFloat32Workload.cpp - RefFloorFloat32Workload.hpp - RefFullyConnectedFloat32Workload.cpp - RefFullyConnectedFloat32Workload.hpp - RefFullyConnectedUint8Workload.cpp - RefFullyConnectedUint8Workload.hpp - RefL2NormalizationFloat32Workload.cpp - RefL2NormalizationFloat32Workload.hpp - RefLstmFloat32Workload.cpp - RefLstmFloat32Workload.hpp - RefMergerFloat32Workload.cpp - RefMergerFloat32Workload.hpp - RefMergerUint8Workload.cpp - RefMergerUint8Workload.hpp - RefNormalizationFloat32Workload.cpp - RefNormalizationFloat32Workload.hpp - RefPermuteWorkload.cpp - RefPermuteWorkload.hpp - RefPooling2dFloat32Workload.cpp - RefPooling2dFloat32Workload.hpp - RefPooling2dUint8Workload.cpp - RefPooling2dUint8Workload.hpp - RefReshapeFloat32Workload.cpp - RefReshapeFloat32Workload.hpp - RefReshapeUint8Workload.cpp - RefReshapeUint8Workload.hpp - RefResizeBilinearFloat32Workload.cpp - RefResizeBilinearFloat32Workload.hpp - RefResizeBilinearUint8Workload.cpp - RefResizeBilinearUint8Workload.hpp - RefSoftmaxFloat32Workload.cpp - RefSoftmaxFloat32Workload.hpp - RefSoftmaxUint8Workload.cpp - RefSoftmaxUint8Workload.hpp - RefSplitterFloat32Workload.cpp - RefSplitterFloat32Workload.hpp - RefSplitterUint8Workload.cpp - RefSplitterUint8Workload.hpp - RefWorkloadUtils.hpp - ResizeBilinear.cpp - ResizeBilinear.hpp - Softmax.cpp - Softmax.hpp - Splitter.hpp - TensorBufferArrayView.hpp -) - -add_library(armnnRefBackend STATIC ${armnnRefBackend_sources}) -target_include_directories(armnnRefBackend PRIVATE ${PROJECT_SOURCE_DIR}/src) -target_include_directories(armnnRefBackend PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn) -target_include_directories(armnnRefBackend PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils) diff --git a/src/backends/RefWorkloads/ConvImpl.cpp b/src/backends/RefWorkloads/ConvImpl.cpp deleted file mode 100644 index 8743a2bd0d..0000000000 --- a/src/backends/RefWorkloads/ConvImpl.cpp +++ /dev/null @@ -1,71 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvImpl.hpp" - -#include <boost/assert.hpp> - -#include <cmath> -#include <limits> - -namespace armnn -{ - -QuantizedMultiplierSmallerThanOne::QuantizedMultiplierSmallerThanOne(float multiplier) -{ - BOOST_ASSERT(multiplier >= 0.0f && multiplier < 1.0f); - if (multiplier == 0.0f) - { - m_Multiplier = 0; - m_RightShift = 0; - } - else - { - const double q = std::frexp(multiplier, &m_RightShift); - m_RightShift = -m_RightShift; - int64_t qFixed = static_cast<int64_t>(std::round(q * (1ll << 31))); - BOOST_ASSERT(qFixed <= (1ll << 31)); - if (qFixed == (1ll << 31)) - { - qFixed /= 2; - --m_RightShift; - } - BOOST_ASSERT(m_RightShift >= 0); - BOOST_ASSERT(qFixed <= std::numeric_limits<int32_t>::max()); - m_Multiplier = static_cast<int32_t>(qFixed); - } -} - -int32_t QuantizedMultiplierSmallerThanOne::operator*(int32_t rhs) const -{ - int32_t x = SaturatingRoundingDoublingHighMul(rhs, m_Multiplier); - return RoundingDivideByPOT(x, m_RightShift); -} - -int32_t QuantizedMultiplierSmallerThanOne::SaturatingRoundingDoublingHighMul(int32_t a, int32_t b) -{ - // Check for overflow. - if (a == b && a == std::numeric_limits<int32_t>::min()) - { - return std::numeric_limits<int32_t>::max(); - } - int64_t a_64(a); - int64_t b_64(b); - int64_t ab_64 = a_64 * b_64; - int32_t nudge = ab_64 >= 0 ? (1 << 30) : (1 - (1 << 30)); - int32_t ab_x2_high32 = static_cast<std::int32_t>((ab_64 + nudge) / (1ll << 31)); - return ab_x2_high32; -} - -int32_t QuantizedMultiplierSmallerThanOne::RoundingDivideByPOT(int32_t x, int exponent) -{ - BOOST_ASSERT(exponent >= 0 && exponent <= 31); - int32_t mask = (1 << exponent) - 1; - int32_t remainder = x & mask; - int32_t threshold = (mask >> 1) + (x < 0 ? 1 : 0); - return (x >> exponent) + (remainder > threshold ? 1 : 0); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/ConvImpl.hpp b/src/backends/RefWorkloads/ConvImpl.hpp deleted file mode 100644 index 4c9ab2a644..0000000000 --- a/src/backends/RefWorkloads/ConvImpl.hpp +++ /dev/null @@ -1,187 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefWorkloadUtils.hpp" - -#include <armnn/Tensor.hpp> - -#include <boost/assert.hpp> -#include <boost/numeric/conversion/cast.hpp> - -#include <cmath> -#include <limits> - -namespace armnn -{ - -/// Performs multiplication of an integer with a multiplier which is less than one, -/// using quantized integer arithmetic which is consistent with AndroidNN's CPU executor. -struct QuantizedMultiplierSmallerThanOne -{ -public: - /// Constructs a QuantizedMultiplierSmallerThanOne which will multiply by the given multiplier. - /// This stores the appropriate integer quantities (derived from the given multiplier) for later use. - /// The implementation of this function is adapted from Android NN's QuantizeMultiplierSmallerThanOne(). - QuantizedMultiplierSmallerThanOne(float multiplier); - - /// The implementation of this function is adapted from Android NN's MultiplyByQuantizedMultiplierSmallerThanOne(). - int32_t operator*(int32_t rhs) const; - -private: - /// The implementation of this function is adapted from gemmlowp's SaturatingRoundingDoublingHighMul(). - static int32_t SaturatingRoundingDoublingHighMul(int32_t a, int32_t b); - - /// The implementation of this function is adapted from gemmlowp's RoundingDivideByPOT(). - static int32_t RoundingDivideByPOT(int32_t x, int exponent); - - int32_t m_Multiplier; - int32_t m_RightShift; -}; - -/// An implementation shared by normal and depthwise convolution. -template<typename ConvData, typename InputType, typename BiasType, typename AccumulatorType> -static void ConvImpl(ConvData data, - const InputType* inputData, - float inputScale, - int32_t inputOffset, - const InputType* filterData, - float filterScale, - int32_t filterOffset, - const BiasType* biasData, - InputType* outputData, - float outputScale, - int32_t outputOffset, - const TensorInfo& filterInfo, - bool depthwise = false) -{ - if (data.m_Parameters.m_BiasEnabled && !biasData) - { - throw InvalidArgumentException("Bias is enabled but the bias data is invalid"); - } - - const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]); - const TensorInfo& outputInfo0 = GetTensorInfo(data.m_Outputs[0]); - - unsigned int depthMult = depthwise ? filterInfo.GetShape()[0] : 1; - unsigned int channelsInput = filterInfo.GetShape()[1]; - unsigned int channelsOutput = depthwise ? channelsInput * depthMult : filterInfo.GetShape()[0]; - - unsigned int batchSize = outputInfo0.GetShape()[0]; - unsigned int heightOutput = outputInfo0.GetShape()[2]; - unsigned int widthOutput = outputInfo0.GetShape()[3]; - unsigned int heightInput = inputInfo0.GetShape()[2]; - unsigned int widthInput = inputInfo0.GetShape()[3]; - - unsigned int heightFilter = filterInfo.GetShape()[2]; - unsigned int widthFilter = filterInfo.GetShape()[3]; - - unsigned int paddingTop = data.m_Parameters.m_PadTop; - unsigned int paddingLeft = data.m_Parameters.m_PadLeft; - unsigned int hStride = data.m_Parameters.m_StrideY; - unsigned int xStride = data.m_Parameters.m_StrideX; - - // The world's least efficient convolution. - for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++) - { - for (unsigned int cOutput = 0; cOutput < channelsOutput; cOutput++) - { - for (unsigned int yOutput = 0; yOutput < heightOutput; yOutput++) - { - for (unsigned int xOutput = 0; xOutput < widthOutput; xOutput++) - { - // This loop goes over each output element. - AccumulatorType sum = AccumulatorType(); - - // For depthwise, each output channel corresponds to exactly one input channel. - // For normal, must loop over each input channel. - for (unsigned int cInput = 0; cInput < (depthwise ? 1 : channelsInput); cInput++) - { - unsigned int depthwiseMultiplierIdx = 0; - if (depthwise) - { - cInput = cOutput / depthMult; - depthwiseMultiplierIdx = cOutput % depthMult; - } - - for (unsigned int yFilter = 0; yFilter < heightFilter; yFilter++) - { - for (unsigned int xFilter = 0; xFilter < widthFilter; xFilter++) - { - // This loop goes over each input element for each output element. - - unsigned int filterIndex; - - // Since dimensionality of kernel depends on depthwiseness, so does index. - if (depthwise) - { - filterIndex = depthwiseMultiplierIdx * widthFilter * heightFilter * channelsInput + - cInput * widthFilter * heightFilter + - yFilter * widthFilter + - xFilter; - } - else - { - filterIndex = cOutput * widthFilter * heightFilter * channelsInput + - cInput * widthFilter * heightFilter + - yFilter * widthFilter + - xFilter; - } - AccumulatorType filterValue = filterData[filterIndex] - - boost::numeric_cast<AccumulatorType>(filterOffset); - - unsigned int yInput = yOutput * hStride + yFilter; - unsigned int xInput = xOutput * xStride + xFilter; - - AccumulatorType inputValue; - - // Check if we're in the padding. - if (yInput < paddingTop || yInput >= heightInput + paddingTop || - xInput < paddingLeft || xInput >= widthInput + paddingLeft ) - { - inputValue = AccumulatorType(); - } - else - { - inputValue = inputData[batchIdx * widthInput * heightInput * channelsInput + - widthInput * heightInput * cInput + - widthInput * (yInput - paddingTop) + - xInput - paddingLeft] - - boost::numeric_cast<AccumulatorType>(inputOffset); - } - sum += filterValue * inputValue; - } - } - } - - if (data.m_Parameters.m_BiasEnabled) - { - sum += biasData[cOutput]; - } - - if (outputScale != 0.0f) - { - float multiplier = (inputScale * filterScale) / outputScale; - // Apply the multiplier to sum, but do so using some quantized arithmetic which is consistent - // with the AndroidNN CPU implementation. This should be (roughly) equivalent to: - // sum = std::round(multiplier * sum + outputOffset); - sum = boost::numeric_cast<AccumulatorType>( - QuantizedMultiplierSmallerThanOne(multiplier) * boost::numeric_cast<int32_t>(sum)) - + boost::numeric_cast<AccumulatorType>(outputOffset); - sum = std::min<AccumulatorType>(std::max<AccumulatorType>(sum, 0), 255); - } - - outputData[batchIdx * widthOutput * heightOutput * channelsOutput + - widthOutput * heightOutput * cOutput + - widthOutput * yOutput + - xOutput] = boost::numeric_cast<InputType>(sum); - } - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/FullyConnected.cpp b/src/backends/RefWorkloads/FullyConnected.cpp deleted file mode 100644 index bf5814d2ad..0000000000 --- a/src/backends/RefWorkloads/FullyConnected.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "FullyConnected.hpp" - -#include <boost/assert.hpp> - -namespace armnn -{ - -void FullyConnected(const float* inputData, - float* outputData, - const TensorInfo& inputTensorInfo, - const TensorInfo& outputTensorInfo, - const float* weightData, - const float* biasData, - bool transposeWeights) -{ - unsigned int N = outputTensorInfo.GetShape()[1]; // Outputs Vector Size. - - BOOST_ASSERT(inputTensorInfo.GetNumDimensions() > 1); // Needs some data. - - unsigned int K = 1; // Total number of activations in the input. - for (unsigned int i = 1; i < inputTensorInfo.GetNumDimensions(); i++) - { - K *= inputTensorInfo.GetShape()[i]; - } - - for (unsigned int n = 0; n < inputTensorInfo.GetShape()[0]; n++) - { - for (unsigned int channelOutput = 0; channelOutput < N; channelOutput++) - { - float outval = 0.f; - - for (unsigned int channelInput = 0; channelInput < K; channelInput++) - { - float weight; - if (transposeWeights) - { - weight = weightData[channelOutput * K + channelInput]; - } - else - { - weight = weightData[channelInput * N + channelOutput]; - } - - outval += weight * inputData[n * K + channelInput]; - } - - if (biasData) - { - outval += biasData[channelOutput]; - } - - outputData[n * N + channelOutput] = outval; - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/FullyConnected.hpp b/src/backends/RefWorkloads/FullyConnected.hpp deleted file mode 100644 index 623259f8f8..0000000000 --- a/src/backends/RefWorkloads/FullyConnected.hpp +++ /dev/null @@ -1,22 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -/// Performs a matrix multiplication and optionally adds a bias. -void FullyConnected(const float* inputData, - float* outputData, - const TensorInfo& inputTensorInfo, - const TensorInfo& outputTensorInfo, - const float* weightData, - const float* biasData, - bool transposeWeights); - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Merger.hpp b/src/backends/RefWorkloads/Merger.hpp deleted file mode 100644 index 867925faa2..0000000000 --- a/src/backends/RefWorkloads/Merger.hpp +++ /dev/null @@ -1,82 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefWorkloadUtils.hpp" - -#include "backends/WorkloadData.hpp" - -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -template <typename DataType> -void Merger(const MergerQueueDescriptor& data) -{ - const TensorInfo& outputInfo0 = GetTensorInfo(data.m_Outputs[0]); - - for (unsigned int index = 0 ; index < outputInfo0.GetNumElements(); ++index) - { - unsigned int indices[MaxNumOfTensorDimensions] = { 0 }; - - unsigned int indexRemainder = index; - unsigned int dimensionStride = outputInfo0.GetNumElements(); - - for (unsigned int i=0; i<outputInfo0.GetNumDimensions(); i++) - { - dimensionStride /= outputInfo0.GetShape()[i]; - indices[i] = indexRemainder / dimensionStride; // Use integer division to round down. - indexRemainder -= indices[i] * dimensionStride; - } - - for (unsigned int viewIdx = 0; viewIdx < data.m_ViewOrigins.size(); ++viewIdx) - { - MergerQueueDescriptor::ViewOrigin const& view = data.m_ViewOrigins[viewIdx]; - - //Split view extents are defined by the size of (the corresponding) input tensor. - const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[viewIdx]); - BOOST_ASSERT(inputInfo.GetNumDimensions() == outputInfo0.GetNumDimensions()); - - // Check all dimensions to see if this element is inside the given input view. - bool insideView = true; - for (unsigned int i=0; i<inputInfo.GetNumDimensions(); i++) - { - if (indices[i] < view.m_Origin[i]) - { - insideView = false; - } - if (indices[i] >= view.m_Origin[i] + inputInfo.GetShape()[i]) - { - insideView = false; - } - } - - if (insideView) - { - unsigned int inIndex = 0; - unsigned int dimensionStride = 1; - - for (unsigned int i = inputInfo.GetNumDimensions(); i-- > 0;) - { - inIndex += dimensionStride * (indices[i] - view.m_Origin[i]); - dimensionStride *= inputInfo.GetShape()[i]; - } - - //We are within the view, copy input data to the output corresponding to this view. - (GetOutputTensorData<DataType>(0, data))[index] = - (GetInputTensorData<DataType>(viewIdx, data))[inIndex]; - - //What should we do if input views overlap on the output tensor? - //We could error, take the average, or shm else... - //For now just stop after finding first view (input) that matches. - break; - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Pooling2d.cpp b/src/backends/RefWorkloads/Pooling2d.cpp deleted file mode 100644 index 5812a290e7..0000000000 --- a/src/backends/RefWorkloads/Pooling2d.cpp +++ /dev/null @@ -1,241 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "Pooling2d.hpp" - -#include <armnn/Exceptions.hpp> -#include <armnn/Types.hpp> - -#include <boost/numeric/conversion/cast.hpp> - -#include <limits> -#include <algorithm> -#include <functional> - -namespace -{ - using PoolingAlgorithm = armnn::PoolingAlgorithm; - - float DefaultInitializer(PoolingAlgorithm algorithm) - { - switch (algorithm) - { - case PoolingAlgorithm::Max: - { - return std::numeric_limits<float>::lowest(); - } - case PoolingAlgorithm::Average: - case PoolingAlgorithm::L2: - { - return 0.0f; - } - default: - { - throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); - } - } - } - - using Accumulator = std::function<void(float & accu, float value)>; - - Accumulator GetAccumulator(PoolingAlgorithm algorithm) - { - switch (algorithm) - { - case PoolingAlgorithm::Max: - { - return [](float & accu, float value) { - if (value > accu) { - accu = value; - } - }; - } - - case PoolingAlgorithm::Average: - { - return [](float & accu, float value) { - accu += value; - }; - } - - case PoolingAlgorithm::L2: - { - return [](float & accu, float value) { - accu += (value*value); - }; - } - - default: - { - throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); - } - } - } - - using Executor = std::function<void(float & accumulated, float kernelSize)>; - - Executor GetExecutor(PoolingAlgorithm algorithm) - { - switch (algorithm) - { - case PoolingAlgorithm::Max: - { - return [](float & accumulated, float kernelSize) {}; - } - - case PoolingAlgorithm::Average: - { - return [](float & accumulated, float kernelSize) { - accumulated /= kernelSize; - }; - } - - case PoolingAlgorithm::L2: - { - return [](float & accumulated, float kernelSize) { - accumulated = sqrtf(accumulated / kernelSize); - }; - } - - default: - { - throw armnn::InvalidArgumentException("Unsupported pooling algorithm"); - } - } - } - - bool OnPaddingOnly(int start, int end, int maxRange, int padding) - { - if (end <= 0 || start > (maxRange - padding)) - { - return true; - } - else - { - return false; - } - } - - - bool ClampRange(int & start, int & end, int maxRange) - { - if (start < 0 || end > maxRange) - { - start = std::min(std::max(start, 0), maxRange); - end = std::min(std::max(end, 0), maxRange); - return true; - } - else - { - return false; - } - } -} - -namespace armnn -{ - -void Pooling2d(const float* in, - float* out, - const TensorInfo& inputInfo, - const TensorInfo& outputInfo, - const Pooling2dDescriptor& params) -{ - const int batchSize = boost::numeric_cast<int>(outputInfo.GetShape()[0]); - const int channels = boost::numeric_cast<int>(outputInfo.GetShape()[1]); - const int heightOutput = boost::numeric_cast<int>(outputInfo.GetShape()[2]); - const int widthOutput = boost::numeric_cast<int>(outputInfo.GetShape()[3]); - const int heightInput = boost::numeric_cast<int>(inputInfo.GetShape()[2]); - const int widthInput = boost::numeric_cast<int>(inputInfo.GetShape()[3]); - const int padLeft = boost::numeric_cast<int>(params.m_PadLeft); - const int padRight = boost::numeric_cast<int>(params.m_PadRight); - const int padTop = boost::numeric_cast<int>(params.m_PadTop); - const int padBottom = boost::numeric_cast<int>(params.m_PadBottom); - const int strideX = boost::numeric_cast<int>(params.m_StrideX); - const int strideY = boost::numeric_cast<int>(params.m_StrideY); - const int poolHeight = boost::numeric_cast<int>(params.m_PoolHeight); - const int poolWidth = boost::numeric_cast<int>(params.m_PoolWidth); - - float defaultInitializer = DefaultInitializer(params.m_PoolType); - - Accumulator accumulate = GetAccumulator(params.m_PoolType); - Executor execute = GetExecutor(params.m_PoolType); - - // Check supported padding methods outside the loop to simplify - // the inner loop. - if (params.m_PaddingMethod != PaddingMethod::Exclude && - params.m_PaddingMethod != PaddingMethod::IgnoreValue) - { - throw armnn::InvalidArgumentException("Unsupported padding type"); - } - - for (int n = 0; n < batchSize; n++) - { - for (int c = 0; c < channels; c++) - { - for (int yOutput = 0; yOutput < heightOutput; yOutput++) - { - for (int xOutput = 0; xOutput < widthOutput; xOutput++) - { - int hstart = (yOutput * strideY) - padTop; - int wstart = (xOutput * strideX) - padLeft; - int hend = hstart + poolHeight; - int wend = wstart + poolWidth; - - // Clamp the pooling region inside the valid input area (which includes the padding). - // This is necessary because the final pooling in a row may overlap beyond the padding. - hend = std::min(hend, heightInput + padBottom); - wend = std::min(wend, widthInput + padRight); - - float result = defaultInitializer; - float poolAreaSize = boost::numeric_cast<float>((hend - hstart) * (wend - wstart)); - - // Special case: when the pooling kernel is over a padding region and the padding - // size is larger or equal to the kernel and the kernel only covers - // padding and no real values, then we initialize the result as zero - // by convention. This is because we need to choose a value here and - // all values we have are padding, which we ignore. - if (OnPaddingOnly(hstart, hend, heightInput, padBottom) || - OnPaddingOnly(wstart, wend, widthInput, padRight)) - { - result = 0.0f; - } - - bool clamped = ClampRange(wstart, wend, widthInput); - clamped |= ClampRange(hstart, hend, heightInput); - - if (clamped && params.m_PaddingMethod == PaddingMethod::Exclude) - { - // When we exclude the padding, it means we calculate with a smaller - // kernel size, so I changed the divisor here. - poolAreaSize = boost::numeric_cast<float>((hend - hstart) * (wend - wstart)); - } - - for (auto yInput = hstart; yInput < hend; yInput++) - { - for (auto xInput = wstart; xInput < wend; xInput++) - { - float inval = in[n * widthInput * heightInput * channels + - c * widthInput * heightInput + - yInput * widthInput + - xInput]; - - accumulate(result, inval); - } - } - - execute(result, poolAreaSize); - - out[n * widthOutput * heightOutput * channels + - c * widthOutput * heightOutput + - yOutput * widthOutput + - xOutput] = result; - } - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Pooling2d.hpp b/src/backends/RefWorkloads/Pooling2d.hpp deleted file mode 100644 index da56b25c4e..0000000000 --- a/src/backends/RefWorkloads/Pooling2d.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Descriptors.hpp> -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -/// Computes the Pooling2d operation. -void Pooling2d(const float* in, - float* out, - const TensorInfo& inputInfo, - const TensorInfo& outputInfo, - const Pooling2dDescriptor& params); - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefActivationFloat32Workload.cpp b/src/backends/RefWorkloads/RefActivationFloat32Workload.cpp deleted file mode 100644 index 3cc59be7a4..0000000000 --- a/src/backends/RefWorkloads/RefActivationFloat32Workload.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefActivationFloat32Workload.hpp" - -#include "Activation.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefActivationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefActivationFloat32Workload_Execute"); - - Activation(GetInputTensorDataFloat(0, m_Data), - GetOutputTensorDataFloat(0, m_Data), - GetTensorInfo(m_Data.m_Inputs[0]), - m_Data.m_Parameters.m_Function, - m_Data.m_Parameters.m_A, - m_Data.m_Parameters.m_B); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefActivationFloat32Workload.hpp b/src/backends/RefWorkloads/RefActivationFloat32Workload.hpp deleted file mode 100644 index 0de33f02ff..0000000000 --- a/src/backends/RefWorkloads/RefActivationFloat32Workload.hpp +++ /dev/null @@ -1,20 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" - -namespace armnn -{ - -class RefActivationFloat32Workload : public Float32Workload<ActivationQueueDescriptor> -{ -public: - using Float32Workload<ActivationQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefActivationUint8Workload.cpp b/src/backends/RefWorkloads/RefActivationUint8Workload.cpp deleted file mode 100644 index b95c2e22a8..0000000000 --- a/src/backends/RefWorkloads/RefActivationUint8Workload.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefActivationUint8Workload.hpp" - -#include "Activation.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ - -void RefActivationUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefActivationUint8Workload_Execute"); - - const TensorInfo& tensorInfo = GetTensorInfo(m_Data.m_Inputs[0]); - - auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), tensorInfo); - - std::vector<float> results(tensorInfo.GetNumElements()); - - Activation(dequant.data(), - results.data(), - tensorInfo, - m_Data.m_Parameters.m_Function, - m_Data.m_Parameters.m_A, - m_Data.m_Parameters.m_B); - - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), GetTensorInfo(m_Data.m_Outputs[0])); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefActivationUint8Workload.hpp b/src/backends/RefWorkloads/RefActivationUint8Workload.hpp deleted file mode 100644 index f38888a9f7..0000000000 --- a/src/backends/RefWorkloads/RefActivationUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefActivationUint8Workload : public Uint8Workload<ActivationQueueDescriptor> -{ -public: - using Uint8Workload<ActivationQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefArithmeticWorkload.cpp b/src/backends/RefWorkloads/RefArithmeticWorkload.cpp deleted file mode 100644 index 6c39fa1186..0000000000 --- a/src/backends/RefWorkloads/RefArithmeticWorkload.cpp +++ /dev/null @@ -1,69 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefArithmeticWorkload.hpp" -#include "ArithmeticFunction.hpp" -#include "RefWorkloadUtils.hpp" -#include "Profiling.hpp" -#include <vector> - -namespace armnn -{ - -template <typename ParentDescriptor, typename Functor> -void BaseFloat32ArithmeticWorkload<ParentDescriptor, Functor>::ExecuteImpl(const char * debugString) const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, debugString); - - auto data = Float32Workload<ParentDescriptor>::GetData(); - const TensorShape& inShape0 = GetTensorInfo(data.m_Inputs[0]).GetShape(); - const TensorShape& inShape1 = GetTensorInfo(data.m_Inputs[1]).GetShape(); - const TensorShape& outShape = GetTensorInfo(data.m_Outputs[0]).GetShape(); - - const float* inData0 = GetInputTensorDataFloat(0, data); - const float* inData1 = GetInputTensorDataFloat(1, data); - float* outData = GetOutputTensorDataFloat(0, data); - - ArithmeticFunction<Functor>(inShape0, inShape1, outShape, inData0, inData1, outData); -} - -template <typename ParentDescriptor, typename Functor> -void BaseUint8ArithmeticWorkload<ParentDescriptor, Functor>::ExecuteImpl(const char * debugString) const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, debugString); - - auto data = Uint8Workload<ParentDescriptor>::GetData(); - const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]); - const TensorInfo& inputInfo1 = GetTensorInfo(data.m_Inputs[1]); - const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]); - - auto dequant0 = Dequantize(GetInputTensorDataU8(0, data), inputInfo0); - auto dequant1 = Dequantize(GetInputTensorDataU8(1, data), inputInfo1); - - std::vector<float> results(outputInfo.GetNumElements()); - - ArithmeticFunction<Functor>(inputInfo0.GetShape(), - inputInfo1.GetShape(), - outputInfo.GetShape(), - dequant0.data(), - dequant1.data(), - results.data()); - - Quantize(GetOutputTensorDataU8(0, data), results.data(), outputInfo); -} - -} - -template class armnn::BaseFloat32ArithmeticWorkload<armnn::AdditionQueueDescriptor, std::plus<float>>; -template class armnn::BaseUint8ArithmeticWorkload<armnn::AdditionQueueDescriptor, std::plus<float>>; - -template class armnn::BaseFloat32ArithmeticWorkload<armnn::SubtractionQueueDescriptor, std::minus<float>>; -template class armnn::BaseUint8ArithmeticWorkload<armnn::SubtractionQueueDescriptor, std::minus<float>>; - -template class armnn::BaseFloat32ArithmeticWorkload<armnn::MultiplicationQueueDescriptor, std::multiplies<float>>; -template class armnn::BaseUint8ArithmeticWorkload<armnn::MultiplicationQueueDescriptor, std::multiplies<float>>; - -template class armnn::BaseFloat32ArithmeticWorkload<armnn::DivisionQueueDescriptor, std::divides<float>>; -template class armnn::BaseUint8ArithmeticWorkload<armnn::DivisionQueueDescriptor, std::divides<float>>; diff --git a/src/backends/RefWorkloads/RefArithmeticWorkload.hpp b/src/backends/RefWorkloads/RefArithmeticWorkload.hpp deleted file mode 100644 index 7197b7a883..0000000000 --- a/src/backends/RefWorkloads/RefArithmeticWorkload.hpp +++ /dev/null @@ -1,122 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Types.hpp> -#include "backends/StringMapping.hpp" -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -template <typename Functor, - typename armnn::DataType DataType, - typename ParentDescriptor, - typename armnn::StringMapping::Id DebugString> -class RefArithmeticWorkload -{ - // Needs specialization. The default is empty on purpose. -}; - -template <typename ParentDescriptor, typename Functor> -class BaseFloat32ArithmeticWorkload : public Float32Workload<ParentDescriptor> -{ -public: - using Float32Workload<ParentDescriptor>::Float32Workload; - void ExecuteImpl(const char * debugString) const; -}; - -template <typename Functor, - typename ParentDescriptor, - typename armnn::StringMapping::Id DebugString> -class RefArithmeticWorkload<Functor, armnn::DataType::Float32, ParentDescriptor, DebugString> - : public BaseFloat32ArithmeticWorkload<ParentDescriptor, Functor> -{ -public: - using BaseFloat32ArithmeticWorkload<ParentDescriptor, Functor>::BaseFloat32ArithmeticWorkload; - - virtual void Execute() const override - { - using Parent = BaseFloat32ArithmeticWorkload<ParentDescriptor, Functor>; - Parent::ExecuteImpl(StringMapping::Instance().Get(DebugString)); - } -}; - -template <typename ParentDescriptor, typename Functor> -class BaseUint8ArithmeticWorkload : public Uint8Workload<ParentDescriptor> -{ -public: - using Uint8Workload<ParentDescriptor>::Uint8Workload; - void ExecuteImpl(const char * debugString) const; -}; - -template <typename Functor, - typename ParentDescriptor, - typename armnn::StringMapping::Id DebugString> -class RefArithmeticWorkload<Functor, armnn::DataType::QuantisedAsymm8, ParentDescriptor, DebugString> - : public BaseUint8ArithmeticWorkload<ParentDescriptor, Functor> -{ -public: - using BaseUint8ArithmeticWorkload<ParentDescriptor, Functor>::BaseUint8ArithmeticWorkload; - - virtual void Execute() const override - { - using Parent = BaseUint8ArithmeticWorkload<ParentDescriptor, Functor>; - Parent::ExecuteImpl(StringMapping::Instance().Get(DebugString)); - } -}; - -using RefAdditionFloat32Workload = - RefArithmeticWorkload<std::plus<float>, - DataType::Float32, - AdditionQueueDescriptor, - StringMapping::RefAdditionWorkload_Execute>; - -using RefAdditionUint8Workload = - RefArithmeticWorkload<std::plus<float>, - DataType::QuantisedAsymm8, - AdditionQueueDescriptor, - StringMapping::RefAdditionWorkload_Execute>; - - -using RefSubtractionFloat32Workload = - RefArithmeticWorkload<std::minus<float>, - DataType::Float32, - SubtractionQueueDescriptor, - StringMapping::RefSubtractionWorkload_Execute>; - -using RefSubtractionUint8Workload = - RefArithmeticWorkload<std::minus<float>, - DataType::QuantisedAsymm8, - SubtractionQueueDescriptor, - StringMapping::RefSubtractionWorkload_Execute>; - -using RefMultiplicationFloat32Workload = - RefArithmeticWorkload<std::multiplies<float>, - DataType::Float32, - MultiplicationQueueDescriptor, - StringMapping::RefMultiplicationWorkload_Execute>; - -using RefMultiplicationUint8Workload = - RefArithmeticWorkload<std::multiplies<float>, - DataType::QuantisedAsymm8, - MultiplicationQueueDescriptor, - StringMapping::RefMultiplicationWorkload_Execute>; - -using RefDivisionFloat32Workload = - RefArithmeticWorkload<std::divides<float>, - DataType::Float32, - DivisionQueueDescriptor, - StringMapping::RefDivisionWorkload_Execute>; - -using RefDivisionUint8Workload = - RefArithmeticWorkload<std::divides<float>, - DataType::QuantisedAsymm8, - DivisionQueueDescriptor, - StringMapping::RefDivisionWorkload_Execute>; - -} // armnn diff --git a/src/backends/RefWorkloads/RefBaseConstantWorkload.cpp b/src/backends/RefWorkloads/RefBaseConstantWorkload.cpp deleted file mode 100644 index 647677b4fb..0000000000 --- a/src/backends/RefWorkloads/RefBaseConstantWorkload.cpp +++ /dev/null @@ -1,49 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefBaseConstantWorkload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include <armnn/Types.hpp> - -#include <boost/assert.hpp> - -#include <cstring> - -namespace armnn -{ - -template <armnn::DataType DataType> -void RefBaseConstantWorkload<DataType>::Execute() const -{ - // Considering the reference backend independently, it could be possible to initialise the intermediate tensor - // created by the layer output handler at workload construction time, rather than at workload execution time. - // However, this is not an option for other backends (e.g. CL). For consistency, we prefer to align all - // implementations. - // A similar argument can be made about performing the memory copy in the first place (the layer output handler - // could have a non-owning reference to the layer output tensor managed by the const input layer); again, this is - // not an option for other backends, and the extra complexity required to make this work for the reference backend - // may not be worth the effort (skipping a memory copy in the first inference). - if (!m_RanOnce) - { - const ConstantQueueDescriptor& data = this->m_Data; - - BOOST_ASSERT(data.m_LayerOutput != nullptr); - - const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]); - BOOST_ASSERT(data.m_LayerOutput->GetTensorInfo().GetNumBytes() == outputInfo.GetNumBytes()); - - memcpy(GetOutputTensorData<void>(0, data), data.m_LayerOutput->GetConstTensor<void>(), - outputInfo.GetNumBytes()); - - m_RanOnce = true; - } -} - -template class RefBaseConstantWorkload<DataType::Float32>; -template class RefBaseConstantWorkload<DataType::QuantisedAsymm8>; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefBaseConstantWorkload.hpp b/src/backends/RefWorkloads/RefBaseConstantWorkload.hpp deleted file mode 100644 index 8dc9fd6104..0000000000 --- a/src/backends/RefWorkloads/RefBaseConstantWorkload.hpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -#include <armnn/Types.hpp> - -namespace armnn -{ - -// Base class template providing an implementation of the Constant layer common to all data types. -template <armnn::DataType DataType> -class RefBaseConstantWorkload : public TypedWorkload<ConstantQueueDescriptor, DataType> -{ -public: - RefBaseConstantWorkload(const ConstantQueueDescriptor& descriptor, const WorkloadInfo& info) - : TypedWorkload<ConstantQueueDescriptor, DataType>(descriptor, info) - , m_RanOnce(false) - { - } - - virtual void Execute() const override; - -private: - mutable bool m_RanOnce; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.cpp b/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.cpp deleted file mode 100644 index 313af9c438..0000000000 --- a/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefBatchNormalizationFloat32Workload.hpp" - -#include "BatchNormImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ -RefBatchNormalizationFloat32Workload::RefBatchNormalizationFloat32Workload( - const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info) - : Float32Workload<BatchNormalizationQueueDescriptor>(descriptor, info), - m_Mean(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Mean))), - m_Variance(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Variance))), - m_Beta(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Beta))), - m_Gamma(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Gamma))) {} - -void RefBatchNormalizationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefBatchNormalizationFloat32Workload_Execute"); - - const float* var = m_Variance->GetConstTensor<float>(); - const float* mean = m_Mean->GetConstTensor<float>(); - const float* gamma = m_Gamma->GetConstTensor<float>(); - const float* beta = m_Beta->GetConstTensor<float>(); - - auto inputData = GetInputTensorDataFloat(0, m_Data); - auto outputData = GetOutputTensorDataFloat(0, m_Data); - - BatchNormImpl(m_Data, var, mean, gamma, beta, outputData, inputData); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.hpp b/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.hpp deleted file mode 100644 index 15c843c2ca..0000000000 --- a/src/backends/RefWorkloads/RefBatchNormalizationFloat32Workload.hpp +++ /dev/null @@ -1,28 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefBatchNormalizationFloat32Workload : public Float32Workload<BatchNormalizationQueueDescriptor> -{ -public: - explicit RefBatchNormalizationFloat32Workload(const BatchNormalizationQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Mean; - std::unique_ptr<ScopedCpuTensorHandle> m_Variance; - std::unique_ptr<ScopedCpuTensorHandle> m_Beta; - std::unique_ptr<ScopedCpuTensorHandle> m_Gamma; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.cpp b/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.cpp deleted file mode 100644 index e248ad4b9d..0000000000 --- a/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.cpp +++ /dev/null @@ -1,47 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefBatchNormalizationUint8Workload.hpp" - -#include "BatchNormImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ -RefBatchNormalizationUint8Workload::RefBatchNormalizationUint8Workload( - const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info) - : Uint8Workload<BatchNormalizationQueueDescriptor>(descriptor, info), - m_Mean(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Mean))), - m_Variance(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Variance))), - m_Beta(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Beta))), - m_Gamma(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Gamma))) {} - -void RefBatchNormalizationUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefBatchNormalizationUint8Workload_Execute"); - - const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& varInfo = GetTensorInfo(m_Variance.get()); - const TensorInfo& meanInfo = GetTensorInfo(m_Mean.get()); - const TensorInfo& gammaInfo = GetTensorInfo(m_Gamma.get()); - const TensorInfo& betaInfo = GetTensorInfo(m_Beta.get()); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - auto input = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo0); - auto var = Dequantize(m_Variance->GetConstTensor<uint8_t>(), varInfo); - auto mean = Dequantize(m_Mean->GetConstTensor<uint8_t>(), meanInfo); - auto gamma = Dequantize(m_Gamma->GetConstTensor<uint8_t>(), gammaInfo); - auto beta = Dequantize(m_Beta->GetConstTensor<uint8_t>(), betaInfo); - - std::vector<float> results(outputInfo.GetNumElements()); - BatchNormImpl(m_Data, var.data(), mean.data(), gamma.data(), beta.data(), results.data(), input.data()); - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.hpp b/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.hpp deleted file mode 100644 index d3e8e0a120..0000000000 --- a/src/backends/RefWorkloads/RefBatchNormalizationUint8Workload.hpp +++ /dev/null @@ -1,28 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefBatchNormalizationUint8Workload : public Uint8Workload<BatchNormalizationQueueDescriptor> -{ -public: - explicit RefBatchNormalizationUint8Workload(const BatchNormalizationQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Mean; - std::unique_ptr<ScopedCpuTensorHandle> m_Variance; - std::unique_ptr<ScopedCpuTensorHandle> m_Beta; - std::unique_ptr<ScopedCpuTensorHandle> m_Gamma; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConstantFloat32Workload.cpp b/src/backends/RefWorkloads/RefConstantFloat32Workload.cpp deleted file mode 100644 index 074e8ccaae..0000000000 --- a/src/backends/RefWorkloads/RefConstantFloat32Workload.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConstantFloat32Workload.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefConstantFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConstantFloat32Workload_Execute"); - RefBaseConstantWorkload::Execute(); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConstantFloat32Workload.hpp b/src/backends/RefWorkloads/RefConstantFloat32Workload.hpp deleted file mode 100644 index 76e3a42026..0000000000 --- a/src/backends/RefWorkloads/RefConstantFloat32Workload.hpp +++ /dev/null @@ -1,20 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefBaseConstantWorkload.hpp" - -namespace armnn -{ - -class RefConstantFloat32Workload : public RefBaseConstantWorkload<DataType::Float32> -{ -public: - using RefBaseConstantWorkload<DataType::Float32>::RefBaseConstantWorkload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConstantUint8Workload.cpp b/src/backends/RefWorkloads/RefConstantUint8Workload.cpp deleted file mode 100644 index 07e4719d54..0000000000 --- a/src/backends/RefWorkloads/RefConstantUint8Workload.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConstantUint8Workload.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefConstantUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConstantUint8Workload_Execute"); - RefBaseConstantWorkload::Execute(); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConstantUint8Workload.hpp b/src/backends/RefWorkloads/RefConstantUint8Workload.hpp deleted file mode 100644 index 02552ac80b..0000000000 --- a/src/backends/RefWorkloads/RefConstantUint8Workload.hpp +++ /dev/null @@ -1,20 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefBaseConstantWorkload.hpp" - -namespace armnn -{ - -class RefConstantUint8Workload : public RefBaseConstantWorkload<DataType::QuantisedAsymm8> -{ -public: - using RefBaseConstantWorkload<DataType::QuantisedAsymm8>::RefBaseConstantWorkload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.cpp b/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.cpp deleted file mode 100644 index e148bf6a9d..0000000000 --- a/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvertFp16ToFp32Workload.hpp" -#include "Half.hpp" -#include "RefWorkloadUtils.hpp" -#include "FloatingPointConverter.hpp" - -namespace armnn -{ - -void RefConvertFp16ToFp32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvertFp16ToFp32Workload_Execute"); - - const Half* const input = GetInputTensorDataHalf(0, m_Data); - float* const output = GetOutputTensorDataFloat(0, m_Data); - - unsigned int numElements = GetTensorInfo(m_Data.m_Inputs[0]).GetNumElements(); - armnnUtils::FloatingPointConverter::ConvertFloat16To32(input, numElements, output); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.hpp b/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.hpp deleted file mode 100644 index 5e841ba34f..0000000000 --- a/src/backends/RefWorkloads/RefConvertFp16ToFp32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefConvertFp16ToFp32Workload : public Float16ToFloat32Workload<ConvertFp16ToFp32QueueDescriptor> -{ -public: - using Float16ToFloat32Workload<ConvertFp16ToFp32QueueDescriptor>::Float16ToFloat32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.cpp b/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.cpp deleted file mode 100644 index efaaf8e1ad..0000000000 --- a/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvertFp32ToFp16Workload.hpp" - -#include "Half.hpp" -#include "FloatingPointConverter.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefConvertFp32ToFp16Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvertFp32ToFp16Workload_Execute"); - - const float* const input = GetInputTensorDataFloat(0, m_Data); - Half* const output = GetOutputTensorDataHalf(0, m_Data); - - // convert Fp32 input to Fp16 output - unsigned int numElements = GetTensorInfo(m_Data.m_Inputs[0]).GetNumElements(); - armnnUtils::FloatingPointConverter::ConvertFloat32To16(input, numElements, output); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.hpp b/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.hpp deleted file mode 100644 index 0754fd5c79..0000000000 --- a/src/backends/RefWorkloads/RefConvertFp32ToFp16Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefConvertFp32ToFp16Workload : public Float32ToFloat16Workload<ConvertFp32ToFp16QueueDescriptor> -{ -public: - using Float32ToFloat16Workload<ConvertFp32ToFp16QueueDescriptor>::Float32ToFloat16Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.cpp b/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.cpp deleted file mode 100644 index 20905646d7..0000000000 --- a/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvolution2dFloat32Workload.hpp" - -#include "ConvImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ -RefConvolution2dFloat32Workload::RefConvolution2dFloat32Workload( - const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) - : Float32Workload<Convolution2dQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefConvolution2dFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvolution2dFloat32Workload_Execute"); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - const float* weightData = m_Weight->template GetConstTensor<float>(); - const float* biasData = m_Data.m_Parameters.m_BiasEnabled ? - m_Bias->template GetConstTensor<float>() : nullptr; - const TensorInfo& filterInfo = m_Weight->GetTensorInfo(); - - ConvImpl<armnn::Convolution2dQueueDescriptor, float, float, float>( - m_Data, inputData, 0.0f, 0, weightData, 0.0f, 0, biasData, outputData, 0.0f, 0, filterInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.hpp b/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.hpp deleted file mode 100644 index 34489e807c..0000000000 --- a/src/backends/RefWorkloads/RefConvolution2dFloat32Workload.hpp +++ /dev/null @@ -1,27 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefConvolution2dFloat32Workload : public Float32Workload<Convolution2dQueueDescriptor> -{ -public: - explicit RefConvolution2dFloat32Workload(const Convolution2dQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; - -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvolution2dUint8Workload.cpp b/src/backends/RefWorkloads/RefConvolution2dUint8Workload.cpp deleted file mode 100644 index 881e9bf6b0..0000000000 --- a/src/backends/RefWorkloads/RefConvolution2dUint8Workload.cpp +++ /dev/null @@ -1,45 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvolution2dUint8Workload.hpp" - -#include "ConvImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ -RefConvolution2dUint8Workload::RefConvolution2dUint8Workload( - const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) - : Uint8Workload<Convolution2dQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefConvolution2dUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvolution2dUint8Workload_Execute"); - - const uint8_t* inputData = GetInputTensorDataU8(0, m_Data); - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const uint8_t* weightsData = m_Weight->template GetConstTensor<uint8_t>(); - const TensorInfo& weightsInfo = GetTensorInfo(m_Weight.get()); - const int32_t* biasData = m_Data.m_Parameters.m_BiasEnabled ? - m_Bias->template GetConstTensor<int32_t>() : - nullptr; - uint8_t* outputData = GetOutputTensorDataU8(0, m_Data); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - const TensorInfo& filterInfo = m_Weight->GetTensorInfo(); - - ConvImpl<armnn::Convolution2dQueueDescriptor, uint8_t, int32_t, int32_t>( - m_Data, - inputData, inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), - weightsData, weightsInfo.GetQuantizationScale(), weightsInfo.GetQuantizationOffset(), - biasData, - outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), filterInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefConvolution2dUint8Workload.hpp b/src/backends/RefWorkloads/RefConvolution2dUint8Workload.hpp deleted file mode 100644 index 0e2dd6aada..0000000000 --- a/src/backends/RefWorkloads/RefConvolution2dUint8Workload.hpp +++ /dev/null @@ -1,28 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefConvolution2dUint8Workload : public Uint8Workload<Convolution2dQueueDescriptor> -{ -public: - explicit RefConvolution2dUint8Workload(const Convolution2dQueueDescriptor& descriptor, - const WorkloadInfo& info); - - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; - -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.cpp b/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.cpp deleted file mode 100644 index e89013b9bd..0000000000 --- a/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefDepthwiseConvolution2dFloat32Workload.hpp" - -#include "ConvImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ -RefDepthwiseConvolution2dFloat32Workload::RefDepthwiseConvolution2dFloat32Workload( - const DepthwiseConvolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) - : Float32Workload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefDepthwiseConvolution2dFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefDepthwiseConvolution2dFloat32Workload_Execute"); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - const float* weightData = m_Weight->template GetConstTensor<float>(); - const float* biasData = m_Data.m_Parameters.m_BiasEnabled ? - m_Bias->template GetConstTensor<float>() : nullptr; - const TensorInfo& filterInfo = m_Weight->GetTensorInfo(); - - ConvImpl<armnn::DepthwiseConvolution2dQueueDescriptor, float, float, float> - (m_Data, inputData, 0.0f, 0, weightData, 0.0f, 0, biasData, outputData, 0.0f, 0, filterInfo, true); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.hpp b/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.hpp deleted file mode 100644 index 8f1227e2de..0000000000 --- a/src/backends/RefWorkloads/RefDepthwiseConvolution2dFloat32Workload.hpp +++ /dev/null @@ -1,27 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefDepthwiseConvolution2dFloat32Workload : public Float32Workload<DepthwiseConvolution2dQueueDescriptor> -{ -public: - explicit RefDepthwiseConvolution2dFloat32Workload(const DepthwiseConvolution2dQueueDescriptor& descriptor, - const WorkloadInfo& info); - - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.cpp b/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.cpp deleted file mode 100644 index e8e501d6ae..0000000000 --- a/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefDepthwiseConvolution2dUint8Workload.hpp" - -#include "ConvImpl.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -RefDepthwiseConvolution2dUint8Workload::RefDepthwiseConvolution2dUint8Workload( - const DepthwiseConvolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) - : Uint8Workload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefDepthwiseConvolution2dUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefDepthwiseConvolution2dUint8Workload_Execute"); - - const uint8_t* inputData = GetInputTensorDataU8(0, m_Data); - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const uint8_t* weightsData = m_Weight->template GetConstTensor<uint8_t>(); - const TensorInfo& weightsInfo = GetTensorInfo(m_Weight.get()); - const int32_t* biasData = m_Data.m_Parameters.m_BiasEnabled ? - m_Bias->template GetConstTensor<int32_t>() : - nullptr; - uint8_t* outputData = GetOutputTensorDataU8(0, m_Data); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - const TensorInfo& filterInfo = m_Weight->GetTensorInfo(); - - ConvImpl<armnn::DepthwiseConvolution2dQueueDescriptor, uint8_t, int32_t, int32_t>( - m_Data, - inputData, inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), - weightsData, weightsInfo.GetQuantizationScale(), weightsInfo.GetQuantizationOffset(), - biasData, - outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), filterInfo, true); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.hpp b/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.hpp deleted file mode 100644 index c615cf7880..0000000000 --- a/src/backends/RefWorkloads/RefDepthwiseConvolution2dUint8Workload.hpp +++ /dev/null @@ -1,26 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefDepthwiseConvolution2dUint8Workload : public Uint8Workload<DepthwiseConvolution2dQueueDescriptor> -{ -public: - explicit RefDepthwiseConvolution2dUint8Workload(const DepthwiseConvolution2dQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.cpp b/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.cpp deleted file mode 100644 index 3e16f60b11..0000000000 --- a/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.cpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefFakeQuantizationFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <boost/numeric/conversion/cast.hpp> - -namespace armnn -{ - -void FakeQuantization(const float* inputData, float* outputData, uint32_t numElements, float min, float max) -{ - float scale = (max - min) / 255.f; - int32_t offset = boost::numeric_cast<int32_t>((-min * 255.f) / (max - min)); - - for (uint32_t i = 0; i < numElements; i++) - { - outputData[i] = static_cast<float>(armnn::Quantize<uint8_t>(inputData[i], scale, offset)); - } - -} - -void RefFakeQuantizationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFakeQuantizationFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - - const float* inputData = GetInputTensorDataFloat(0, m_Data); - float* outputData = GetOutputTensorDataFloat(0, m_Data); - FakeQuantization(inputData, outputData, inputInfo.GetNumElements(), - m_Data.m_Parameters.m_Min, - m_Data.m_Parameters.m_Max); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.hpp b/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.hpp deleted file mode 100644 index 523fdcff50..0000000000 --- a/src/backends/RefWorkloads/RefFakeQuantizationFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefFakeQuantizationFloat32Workload : public Float32Workload<FakeQuantizationQueueDescriptor> -{ -public: - using Float32Workload<FakeQuantizationQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFloorFloat32Workload.cpp b/src/backends/RefWorkloads/RefFloorFloat32Workload.cpp deleted file mode 100644 index cc1f8800dc..0000000000 --- a/src/backends/RefWorkloads/RefFloorFloat32Workload.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefFloorFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefFloorFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFloorFloat32Workload_Execute"); - - const float* const input = GetInputTensorDataFloat(0, m_Data); - float* const output = GetOutputTensorDataFloat(0, m_Data); - - unsigned int numElements = GetTensorInfo(m_Data.m_Inputs[0]).GetNumElements(); - for (unsigned int i = 0; i < numElements; ++i) - { - output[i] = floorf(input[i]); - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFloorFloat32Workload.hpp b/src/backends/RefWorkloads/RefFloorFloat32Workload.hpp deleted file mode 100644 index d7cfa50365..0000000000 --- a/src/backends/RefWorkloads/RefFloorFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefFloorFloat32Workload : public Float32Workload<FloorQueueDescriptor> -{ -public: - using Float32Workload<FloorQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.cpp b/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.cpp deleted file mode 100644 index ccaf4cd87b..0000000000 --- a/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefFullyConnectedFloat32Workload.hpp" - -#include "FullyConnected.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ -RefFullyConnectedFloat32Workload::RefFullyConnectedFloat32Workload( - const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info) - : Float32Workload<FullyConnectedQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefFullyConnectedFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFullyConnectedFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - const float* weightData = m_Weight->GetConstTensor<float>(); - const float* biasData = m_Data.m_Parameters.m_BiasEnabled ? m_Bias->GetConstTensor<float>() : nullptr; - - FullyConnected(inputData, - outputData, - inputInfo, - outputInfo, - weightData, - biasData, - m_Data.m_Parameters.m_TransposeWeightMatrix); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.hpp b/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.hpp deleted file mode 100644 index ce058690ac..0000000000 --- a/src/backends/RefWorkloads/RefFullyConnectedFloat32Workload.hpp +++ /dev/null @@ -1,26 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefFullyConnectedFloat32Workload : public Float32Workload<FullyConnectedQueueDescriptor> -{ -public: - explicit RefFullyConnectedFloat32Workload(const FullyConnectedQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.cpp b/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.cpp deleted file mode 100644 index cd785d786c..0000000000 --- a/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.cpp +++ /dev/null @@ -1,66 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefFullyConnectedUint8Workload.hpp" - -#include "FullyConnected.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ -RefFullyConnectedUint8Workload::RefFullyConnectedUint8Workload( - const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info) - : Uint8Workload<FullyConnectedQueueDescriptor>(descriptor, info), - m_Weight(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Weight))), - m_Bias(descriptor.m_Parameters.m_BiasEnabled - ? std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Bias)) : nullptr) {} - -void RefFullyConnectedUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFullyConnectedUint8Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - const uint8_t* weightData = m_Weight->GetConstTensor<uint8_t>(); - - auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo); - - auto weight = Dequantize(weightData, m_Weight->GetTensorInfo()); - - std::vector<float> results(outputInfo.GetNumElements()); - - if (m_Data.m_Parameters.m_BiasEnabled) - { - const int32_t* biasData = m_Bias->GetConstTensor<int32_t>(); - auto bias = Dequantize(biasData, m_Bias->GetTensorInfo()); - - FullyConnected(dequant.data(), - results.data(), - inputInfo, - outputInfo, - weight.data(), - bias.data(), - m_Data.m_Parameters.m_TransposeWeightMatrix); - } - else - { - FullyConnected(dequant.data(), - results.data(), - inputInfo, - outputInfo, - weight.data(), - nullptr, - m_Data.m_Parameters.m_TransposeWeightMatrix); - } - - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.hpp b/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.hpp deleted file mode 100644 index e489cc7d81..0000000000 --- a/src/backends/RefWorkloads/RefFullyConnectedUint8Workload.hpp +++ /dev/null @@ -1,26 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefFullyConnectedUint8Workload : public Uint8Workload<FullyConnectedQueueDescriptor> -{ -public: - explicit RefFullyConnectedUint8Workload(const FullyConnectedQueueDescriptor& descriptor, - const WorkloadInfo& info); - virtual void Execute() const override; - -private: - std::unique_ptr<ScopedCpuTensorHandle> m_Weight; - std::unique_ptr<ScopedCpuTensorHandle> m_Bias; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.cpp b/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.cpp deleted file mode 100644 index 973c87b009..0000000000 --- a/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.cpp +++ /dev/null @@ -1,61 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefL2NormalizationFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" -#include "TensorBufferArrayView.hpp" - -#include "Profiling.hpp" - -#include <cmath> - -namespace armnn -{ - -void RefL2NormalizationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - TensorBufferArrayView<const float> input(inputInfo.GetShape(), GetInputTensorDataFloat(0, m_Data)); - TensorBufferArrayView<float> output(outputInfo.GetShape(), GetOutputTensorDataFloat(0, m_Data)); - - const unsigned int batchSize = inputInfo.GetShape()[0]; - const unsigned int depth = inputInfo.GetShape()[1]; - const unsigned int rows = inputInfo.GetShape()[2]; - const unsigned int cols = inputInfo.GetShape()[3]; - - for (unsigned int n = 0; n < batchSize; ++n) - { - for (unsigned int d = 0; d < depth; ++d) - { - for (unsigned int h = 0; h < rows; ++h) - { - for (unsigned int w = 0; w < cols; ++w) - { - float reduction = 0.0; - for (unsigned int c = 0; c < depth; ++c) - { - const float value = input.Get(n, c, h, w); - reduction += value * value; - } - - // Using std::max(reduction, epsilon) below would prevent against division by 0. - // However, at the time of writing: - // - This is not supported by the ACL functions used to implement L2Normalization in the CL - // backend. - // - The reference semantics for this operator do not include this parameter. - const float scale = 1.0f / sqrtf(reduction); - output.Get(n, d, h, w) = input.Get(n, d, h, w) * scale; - } - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.hpp b/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.hpp deleted file mode 100644 index a3f03f3060..0000000000 --- a/src/backends/RefWorkloads/RefL2NormalizationFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefL2NormalizationFloat32Workload : public Float32Workload<L2NormalizationQueueDescriptor> -{ -public: - using Float32Workload<L2NormalizationQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefLstmFloat32Workload.cpp b/src/backends/RefWorkloads/RefLstmFloat32Workload.cpp deleted file mode 100644 index 50ff605701..0000000000 --- a/src/backends/RefWorkloads/RefLstmFloat32Workload.cpp +++ /dev/null @@ -1,16 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefLstmFloat32Workload.hpp" - -namespace armnn -{ - -void RefLstmFloat32Workload::Execute() const -{ - throw armnn::Exception("No implementation of Lstm in the Ref backend!"); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefLstmFloat32Workload.hpp b/src/backends/RefWorkloads/RefLstmFloat32Workload.hpp deleted file mode 100644 index fc4f7776c6..0000000000 --- a/src/backends/RefWorkloads/RefLstmFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefLstmFloat32Workload : public Float32Workload<LstmQueueDescriptor> -{ -public: - using Float32Workload<LstmQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefMergerFloat32Workload.cpp b/src/backends/RefWorkloads/RefMergerFloat32Workload.cpp deleted file mode 100644 index b1f8a32ee7..0000000000 --- a/src/backends/RefWorkloads/RefMergerFloat32Workload.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefMergerFloat32Workload.hpp" - -#include "Merger.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefMergerFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefMergerFloat32Workload_Execute"); - Merger<float>(m_Data); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefMergerFloat32Workload.hpp b/src/backends/RefWorkloads/RefMergerFloat32Workload.hpp deleted file mode 100644 index 23a523c852..0000000000 --- a/src/backends/RefWorkloads/RefMergerFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefMergerFloat32Workload : public Float32Workload<MergerQueueDescriptor> -{ -public: - using Float32Workload<MergerQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefMergerUint8Workload.cpp b/src/backends/RefWorkloads/RefMergerUint8Workload.cpp deleted file mode 100644 index 47ce1cf731..0000000000 --- a/src/backends/RefWorkloads/RefMergerUint8Workload.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefMergerUint8Workload.hpp" - -#include "Merger.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefMergerUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefMergerUint8Workload_Execute"); - Merger<uint8_t>(m_Data); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefMergerUint8Workload.hpp b/src/backends/RefWorkloads/RefMergerUint8Workload.hpp deleted file mode 100644 index 65dc42120a..0000000000 --- a/src/backends/RefWorkloads/RefMergerUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefMergerUint8Workload : public Uint8Workload<MergerQueueDescriptor> -{ -public: - using Uint8Workload<MergerQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp b/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp deleted file mode 100644 index 5c24416624..0000000000 --- a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp +++ /dev/null @@ -1,185 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefNormalizationFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <armnn/Tensor.hpp> - -#include <boost/log/trivial.hpp> -#include <boost/numeric/conversion/cast.hpp> - -namespace armnn -{ - -// Helper function to compute "Within" normalization using Krichevsky 2012: Local Brightness Normalization. -static void NormalizeWithinUingLbr(const float* inputData, - float* outputData, - const TensorShape& tensorShape, - uint32_t norm_size, - float alpha, - float beta, - float kappa) -{ - const unsigned int batchSize = tensorShape[0]; - const unsigned int depth = tensorShape[1]; - const unsigned int rows = tensorShape[2]; - const unsigned int cols = tensorShape[3]; - - int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ - - for (unsigned int n = 0; n < batchSize; n++) - { - for (unsigned int c = 0; c < depth; c++) - { - for (unsigned int h = 0; h < rows; h++) - { - for (unsigned int w = 0; w < cols; w++) - { - float accumulated_scale = 0.0; - for (int y = -radius; y <= radius; y++) - { - for (int x = -radius; x <= radius; x++) - { - int i = boost::numeric_cast<int>(w) + x; - int j = boost::numeric_cast<int>(h) + y; - - if ((i < 0) || (i >= boost::numeric_cast<int>(cols))) - { - continue; - } - - if ((j < 0) || (j >= boost::numeric_cast<int>(rows))) - { - continue; - } - - float inval = inputData[n * cols * rows * depth + - c * cols * rows + - boost::numeric_cast<unsigned int>(j) * cols + - boost::numeric_cast<unsigned int>(i)]; - - accumulated_scale += inval*inval; - } - } - outputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] = inputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] / (powf((kappa + (accumulated_scale * alpha)), beta)); - } - } - } - } -} - -// Helper function to compute "Across" normalization using Krichevsky 2012: Local Brightness Normalization. -void NormalizeAcrossUingLbr(const float* inputData, - float* outputData, - const TensorShape& tensorShape, - uint32_t norm_size, - float alpha, - float beta, - float kappa) -{ - const unsigned int batchSize = tensorShape[0]; - const unsigned int depth = tensorShape[1]; - const unsigned int rows = tensorShape[2]; - const unsigned int cols = tensorShape[3]; - - int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ - - for (unsigned int n = 0; n < batchSize; n++) - { - for (unsigned int c = 0; c < depth; c++) - { - for (unsigned int h = 0; h < rows; h++) - { - for (unsigned int w = 0; w < cols; w++) - { - float accumulated_scale = 0.0; - for (int z = -radius; z <= radius; z++) - { - int k = boost::numeric_cast<int>(c) + z; - - if ((k < 0) || (k >= boost::numeric_cast<int>(depth))) - { - continue; - } - - float inval = inputData[n * cols * rows * depth + - boost::numeric_cast<unsigned int>(k) * cols * rows + - h * cols + - w]; - - accumulated_scale += inval*inval; - } - float scale = kappa + (accumulated_scale * alpha); - scale = powf(scale, -beta); - outputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] = scale * - inputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w]; - } - } - } - } -} - -void RefNormalizationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefNormalizationFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - - - if (NormalizationAlgorithmMethod::LocalBrightness == m_Data.m_Parameters.m_NormMethodType) - { - if (NormalizationAlgorithmChannel::Within == m_Data.m_Parameters.m_NormChannelType) - { - NormalizeWithinUingLbr(inputData, - outputData, - inputInfo.GetShape(), - m_Data.m_Parameters.m_NormSize, - m_Data.m_Parameters.m_Alpha, - m_Data.m_Parameters.m_Beta, - m_Data.m_Parameters.m_K); - } - else if (NormalizationAlgorithmChannel::Across == m_Data.m_Parameters.m_NormChannelType) - { - NormalizeAcrossUingLbr(inputData, - outputData, - inputInfo.GetShape(), - m_Data.m_Parameters.m_NormSize, - m_Data.m_Parameters.m_Alpha, - m_Data.m_Parameters.m_Beta, - m_Data.m_Parameters.m_K); - } - else - { - BOOST_LOG_TRIVIAL(warning) << "Illegal NORMALIZATION mode in normalization_f32"; - return; - } - } - else - { - BOOST_LOG_TRIVIAL(warning) << "Lcr method (Jarret 2009: Local Contrast Normalization) not supported yet."; - return; - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.hpp b/src/backends/RefWorkloads/RefNormalizationFloat32Workload.hpp deleted file mode 100644 index e30356c422..0000000000 --- a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefNormalizationFloat32Workload : public Float32Workload<NormalizationQueueDescriptor> -{ -public: - using Float32Workload<NormalizationQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPermuteWorkload.cpp b/src/backends/RefWorkloads/RefPermuteWorkload.cpp deleted file mode 100644 index 4093ff38f4..0000000000 --- a/src/backends/RefWorkloads/RefPermuteWorkload.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefPermuteWorkload.hpp" -#include "RefWorkloadUtils.hpp" - -#include <Permute.hpp> -#include "TypeUtils.hpp" - -namespace armnn -{ - -template <armnn::DataType DataType> -void RefPermuteWorkload<DataType>::Execute() const -{ - using T = ResolveType<DataType>; - - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, GetName() + "_Execute"); - - const ITensorHandle* src = m_Data.m_Inputs[0]; - const ITensorHandle* dst = m_Data.m_Outputs[0]; - const PermutationVector& mappings = m_Data.m_Parameters.m_DimMappings; - - armnnUtils::Permute(GetTensorInfo(dst).GetShape(), mappings, GetConstCpuData<T>(src), GetCpuData<T>(dst)); -} - -template class RefPermuteWorkload<DataType::Float32>; -template class RefPermuteWorkload<DataType::QuantisedAsymm8>; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPermuteWorkload.hpp b/src/backends/RefWorkloads/RefPermuteWorkload.hpp deleted file mode 100644 index d72cf77e74..0000000000 --- a/src/backends/RefWorkloads/RefPermuteWorkload.hpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" - -#include <armnn/TypesUtils.hpp> - -namespace armnn -{ - -template <armnn::DataType DataType> -class RefPermuteWorkload : public TypedWorkload<PermuteQueueDescriptor, DataType> -{ -public: - static const std::string& GetName() - { - static const std::string name = std::string("RefPermute") + GetDataTypeName(DataType) + "Workload"; - return name; - } - - using TypedWorkload<PermuteQueueDescriptor, DataType>::m_Data; - using TypedWorkload<PermuteQueueDescriptor, DataType>::TypedWorkload; - void Execute() const override; -}; - -using RefPermuteFloat32Workload = RefPermuteWorkload<DataType::Float32>; -using RefPermuteUint8Workload = RefPermuteWorkload<DataType::QuantisedAsymm8>; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPooling2dFloat32Workload.cpp b/src/backends/RefWorkloads/RefPooling2dFloat32Workload.cpp deleted file mode 100644 index 2542756c26..0000000000 --- a/src/backends/RefWorkloads/RefPooling2dFloat32Workload.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefPooling2dFloat32Workload.hpp" - -#include "Pooling2d.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefPooling2dFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefPooling2dFloat32Workload_Execute"); - - const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo0 = GetTensorInfo(m_Data.m_Outputs[0]); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - - Pooling2d(inputData, - outputData, - inputInfo0, - outputInfo0, - m_Data.m_Parameters); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPooling2dFloat32Workload.hpp b/src/backends/RefWorkloads/RefPooling2dFloat32Workload.hpp deleted file mode 100644 index 501fb71aff..0000000000 --- a/src/backends/RefWorkloads/RefPooling2dFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefPooling2dFloat32Workload : public Float32Workload<Pooling2dQueueDescriptor> -{ -public: - using Float32Workload<Pooling2dQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPooling2dUint8Workload.cpp b/src/backends/RefWorkloads/RefPooling2dUint8Workload.cpp deleted file mode 100644 index 91fdf291ee..0000000000 --- a/src/backends/RefWorkloads/RefPooling2dUint8Workload.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefPooling2dUint8Workload.hpp" - -#include "Pooling2d.hpp" -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ - -void RefPooling2dUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefPooling2dUint8Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo); - - std::vector<float> results(outputInfo.GetNumElements()); - Pooling2d(dequant.data(), - results.data(), - inputInfo, - outputInfo, - m_Data.m_Parameters); - - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefPooling2dUint8Workload.hpp b/src/backends/RefWorkloads/RefPooling2dUint8Workload.hpp deleted file mode 100644 index 6544f9a785..0000000000 --- a/src/backends/RefWorkloads/RefPooling2dUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefPooling2dUint8Workload : public Uint8Workload<Pooling2dQueueDescriptor> -{ -public: - using Uint8Workload<Pooling2dQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefReshapeFloat32Workload.cpp b/src/backends/RefWorkloads/RefReshapeFloat32Workload.cpp deleted file mode 100644 index 99c94a49a1..0000000000 --- a/src/backends/RefWorkloads/RefReshapeFloat32Workload.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefReshapeFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <cstring> - -namespace armnn -{ - -void RefReshapeFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefReshapeFloat32Workload_Execute"); - - void* output = GetOutputTensorData<void>(0, m_Data); - const void* input = GetInputTensorData<void>(0, m_Data); - unsigned int numBytes = GetTensorInfo(m_Data.m_Inputs[0]).GetNumBytes(); - memcpy(output, input, numBytes); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefReshapeFloat32Workload.hpp b/src/backends/RefWorkloads/RefReshapeFloat32Workload.hpp deleted file mode 100644 index 9281e89cf7..0000000000 --- a/src/backends/RefWorkloads/RefReshapeFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefReshapeFloat32Workload : public Float32Workload<ReshapeQueueDescriptor> -{ -public: - using Float32Workload<ReshapeQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefReshapeUint8Workload.cpp b/src/backends/RefWorkloads/RefReshapeUint8Workload.cpp deleted file mode 100644 index 8f475f3db3..0000000000 --- a/src/backends/RefWorkloads/RefReshapeUint8Workload.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefReshapeUint8Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <cstring> - -namespace armnn -{ - -void RefReshapeUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefReshapeUint8Workload_Execute"); - - void* output = GetOutputTensorData<void>(0, m_Data); - const void* input = GetInputTensorData<void>(0, m_Data); - unsigned int numBytes = GetTensorInfo(m_Data.m_Inputs[0]).GetNumBytes(); - memcpy(output, input, numBytes); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefReshapeUint8Workload.hpp b/src/backends/RefWorkloads/RefReshapeUint8Workload.hpp deleted file mode 100644 index b37fb4bdeb..0000000000 --- a/src/backends/RefWorkloads/RefReshapeUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefReshapeUint8Workload : public Uint8Workload<ReshapeQueueDescriptor> -{ -public: - using Uint8Workload<ReshapeQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.cpp b/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.cpp deleted file mode 100644 index 50ee7a218a..0000000000 --- a/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefResizeBilinearFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" -#include "ResizeBilinear.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefResizeBilinearFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefResizeBilinearFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - ResizeBilinear(GetInputTensorDataFloat(0, m_Data), - inputInfo, - GetOutputTensorDataFloat(0, m_Data), - outputInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.hpp b/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.hpp deleted file mode 100644 index 0fff7ee695..0000000000 --- a/src/backends/RefWorkloads/RefResizeBilinearFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefResizeBilinearFloat32Workload : public Float32Workload<ResizeBilinearQueueDescriptor> -{ -public: - using Float32Workload<ResizeBilinearQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.cpp b/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.cpp deleted file mode 100644 index 67ab039ef3..0000000000 --- a/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefResizeBilinearUint8Workload.hpp" - -#include "RefWorkloadUtils.hpp" -#include "ResizeBilinear.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ - -void RefResizeBilinearUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefResizeBilinearUint8Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - - auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo); - - std::vector<float> results(outputInfo.GetNumElements()); - ResizeBilinear(dequant.data(), inputInfo, results.data(), outputInfo); - - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.hpp b/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.hpp deleted file mode 100644 index bbaf899ca6..0000000000 --- a/src/backends/RefWorkloads/RefResizeBilinearUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefResizeBilinearUint8Workload : public Uint8Workload<ResizeBilinearQueueDescriptor> -{ -public: - using Uint8Workload<ResizeBilinearQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.cpp b/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.cpp deleted file mode 100644 index 1f519bda10..0000000000 --- a/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefSoftmaxFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" -#include "Softmax.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefSoftmaxFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefSoftmaxFloat32Workload_Execute"); - - Softmax(GetInputTensorDataFloat(0, m_Data), - GetOutputTensorDataFloat(0, m_Data), - GetTensorInfo(m_Data.m_Inputs[0]), - m_Data.m_Parameters.m_Beta); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.hpp b/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.hpp deleted file mode 100644 index d37f2b5990..0000000000 --- a/src/backends/RefWorkloads/RefSoftmaxFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefSoftmaxFloat32Workload : public Float32Workload<SoftmaxQueueDescriptor> -{ -public: - using Float32Workload<SoftmaxQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSoftmaxUint8Workload.cpp b/src/backends/RefWorkloads/RefSoftmaxUint8Workload.cpp deleted file mode 100644 index 17114ec83a..0000000000 --- a/src/backends/RefWorkloads/RefSoftmaxUint8Workload.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefSoftmaxUint8Workload.hpp" - -#include "RefWorkloadUtils.hpp" -#include "Softmax.hpp" - -#include "Profiling.hpp" - -#include <vector> - -namespace armnn -{ - -void RefSoftmaxUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefSoftmaxUint8Workload_Execute"); - - const TensorInfo& tensorInfo = GetTensorInfo(m_Data.m_Inputs[0]); - - auto dequant = Dequantize(GetInputTensorDataU8(0, m_Data), tensorInfo); - - std::vector<float> results(tensorInfo.GetNumElements()); - - Softmax(dequant.data(), - results.data(), - tensorInfo, - m_Data.m_Parameters.m_Beta); - - Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), GetTensorInfo(m_Data.m_Outputs[0])); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSoftmaxUint8Workload.hpp b/src/backends/RefWorkloads/RefSoftmaxUint8Workload.hpp deleted file mode 100644 index b179d529da..0000000000 --- a/src/backends/RefWorkloads/RefSoftmaxUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefSoftmaxUint8Workload : public Uint8Workload<SoftmaxQueueDescriptor> -{ -public: - using Uint8Workload<SoftmaxQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSplitterFloat32Workload.cpp b/src/backends/RefWorkloads/RefSplitterFloat32Workload.cpp deleted file mode 100644 index 75611dacf3..0000000000 --- a/src/backends/RefWorkloads/RefSplitterFloat32Workload.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefSplitterFloat32Workload.hpp" - -#include "Splitter.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefSplitterFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefSplitterFloat32Workload_Execute"); - Splitter<float>(m_Data); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSplitterFloat32Workload.hpp b/src/backends/RefWorkloads/RefSplitterFloat32Workload.hpp deleted file mode 100644 index 12176dd277..0000000000 --- a/src/backends/RefWorkloads/RefSplitterFloat32Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefSplitterFloat32Workload : public Float32Workload<SplitterQueueDescriptor> -{ -public: - using Float32Workload<SplitterQueueDescriptor>::Float32Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSplitterUint8Workload.cpp b/src/backends/RefWorkloads/RefSplitterUint8Workload.cpp deleted file mode 100644 index ca9f5db850..0000000000 --- a/src/backends/RefWorkloads/RefSplitterUint8Workload.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefSplitterUint8Workload.hpp" - -#include "Splitter.hpp" - -#include "Profiling.hpp" - -namespace armnn -{ - -void RefSplitterUint8Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefSplitterUint8Workload_Execute"); - Splitter<uint8_t>(m_Data); -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefSplitterUint8Workload.hpp b/src/backends/RefWorkloads/RefSplitterUint8Workload.hpp deleted file mode 100644 index e80cb1a654..0000000000 --- a/src/backends/RefWorkloads/RefSplitterUint8Workload.hpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/Workload.hpp" -#include "backends/WorkloadData.hpp" - -namespace armnn -{ - -class RefSplitterUint8Workload : public Uint8Workload<SplitterQueueDescriptor> -{ -public: - using Uint8Workload<SplitterQueueDescriptor>::Uint8Workload; - virtual void Execute() const override; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/RefWorkloadUtils.hpp b/src/backends/RefWorkloads/RefWorkloadUtils.hpp deleted file mode 100644 index 616a875028..0000000000 --- a/src/backends/RefWorkloads/RefWorkloadUtils.hpp +++ /dev/null @@ -1,138 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "backends/CpuTensorHandle.hpp" - -#include <armnn/Tensor.hpp> -#include <armnn/Types.hpp> -#include <Half.hpp> - -#include <boost/polymorphic_cast.hpp> - -namespace armnn -{ - -//////////////////////////////////////////// -/// float32 helpers -//////////////////////////////////////////// - -inline const TensorInfo& GetTensorInfo(const ITensorHandle* tensorHandle) -{ - // We know that reference workloads use CpuTensorHandles only, so this cast is legitimate. - const ConstCpuTensorHandle* cpuTensorHandle = - boost::polymorphic_downcast<const ConstCpuTensorHandle*>(tensorHandle); - return cpuTensorHandle->GetTensorInfo(); -} - -template <typename DataType> -inline const DataType* GetConstCpuData(const ITensorHandle* tensorHandle) -{ - // We know that reference workloads use (Const)CpuTensorHandles only, so this cast is legitimate. - const ConstCpuTensorHandle* cpuTensorHandle = - boost::polymorphic_downcast<const ConstCpuTensorHandle*>(tensorHandle); - return cpuTensorHandle->GetConstTensor<DataType>(); -} - -template <typename DataType> -inline DataType* GetCpuData(const ITensorHandle* tensorHandle) -{ - // We know that reference workloads use CpuTensorHandles only, so this cast is legitimate. - const CpuTensorHandle* cpuTensorHandle = boost::polymorphic_downcast<const CpuTensorHandle*>(tensorHandle); - return cpuTensorHandle->GetTensor<DataType>(); -}; - -template <typename DataType, typename PayloadType> -const DataType* GetInputTensorData(unsigned int idx, const PayloadType& data) -{ - const ITensorHandle* tensorHandle = data.m_Inputs[idx]; - return GetConstCpuData<DataType>(tensorHandle); -} - -template <typename DataType, typename PayloadType> -DataType* GetOutputTensorData(unsigned int idx, const PayloadType& data) -{ - const ITensorHandle* tensorHandle = data.m_Outputs[idx]; - return GetCpuData<DataType>(tensorHandle); -} - -template <typename PayloadType> -const float* GetInputTensorDataFloat(unsigned int idx, const PayloadType& data) -{ - return GetInputTensorData<float>(idx, data); -} - -template <typename PayloadType> -float* GetOutputTensorDataFloat(unsigned int idx, const PayloadType& data) -{ - return GetOutputTensorData<float>(idx, data); -} - -template <typename PayloadType> -const Half* GetInputTensorDataHalf(unsigned int idx, const PayloadType& data) -{ - return GetInputTensorData<Half>(idx, data); -} - -template <typename PayloadType> -Half* GetOutputTensorDataHalf(unsigned int idx, const PayloadType& data) -{ - return GetOutputTensorData<Half>(idx, data); -} - -//////////////////////////////////////////// -/// u8 helpers -//////////////////////////////////////////// - -inline const uint8_t* GetConstCpuU8Data(const ITensorHandle* tensorHandle) -{ - // We know that reference workloads use (Const)CpuTensorHandles only, so this cast is legitimate. - const ConstCpuTensorHandle* cpuTensorHandle = - boost::polymorphic_downcast<const ConstCpuTensorHandle*>(tensorHandle); - return cpuTensorHandle->GetConstTensor<uint8_t>(); -}; - -inline uint8_t* GetCpuU8Data(const ITensorHandle* tensorHandle) -{ - // We know that reference workloads use CpuTensorHandles only, so this cast is legitimate. - const CpuTensorHandle* cpuTensorHandle = boost::polymorphic_downcast<const CpuTensorHandle*>(tensorHandle); - return cpuTensorHandle->GetTensor<uint8_t>(); -}; - -template <typename PayloadType> -const uint8_t* GetInputTensorDataU8(unsigned int idx, const PayloadType& data) -{ - const ITensorHandle* tensorHandle = data.m_Inputs[idx]; - return GetConstCpuU8Data(tensorHandle); -} - -template <typename PayloadType> -uint8_t* GetOutputTensorDataU8(unsigned int idx, const PayloadType& data) -{ - const ITensorHandle* tensorHandle = data.m_Outputs[idx]; - return GetCpuU8Data(tensorHandle); -} - -template<typename T> -std::vector<float> Dequantize(const T* quant, const TensorInfo& info) -{ - std::vector<float> ret(info.GetNumElements()); - for (size_t i = 0; i < info.GetNumElements(); i++) - { - ret[i] = armnn::Dequantize(quant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); - } - return ret; -} - -inline void Quantize(uint8_t* quant, const float* dequant, const TensorInfo& info) -{ - for (size_t i = 0; i < info.GetNumElements(); i++) - { - quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/ResizeBilinear.cpp b/src/backends/RefWorkloads/ResizeBilinear.cpp deleted file mode 100644 index 0bce3c7ed8..0000000000 --- a/src/backends/RefWorkloads/ResizeBilinear.cpp +++ /dev/null @@ -1,92 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ResizeBilinear.hpp" - -#include "TensorBufferArrayView.hpp" - -#include <boost/numeric/conversion/cast.hpp> - -#include <cmath> -#include <algorithm> - -namespace armnn -{ - -namespace -{ - -inline float Lerp(float a, float b, float w) -{ - return w * b + (1.f - w) * a; -} - -} - -void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo) -{ - // We follow the definition of TensorFlow and AndroidNN: the top-left corner of a texel in the output - // image is projected into the input image to figure out the interpolants and weights. Note that this - // will yield different results than if projecting the centre of output texels. - - const unsigned int batchSize = inputInfo.GetShape()[0]; - const unsigned int channelCount = inputInfo.GetShape()[1]; - - const unsigned int inputHeight = inputInfo.GetShape()[2]; - const unsigned int inputWidth = inputInfo.GetShape()[3]; - const unsigned int outputHeight = outputInfo.GetShape()[2]; - const unsigned int outputWidth = outputInfo.GetShape()[3]; - - // How much to scale pixel coordinates in the output image, to get the corresponding pixel coordinates - // in the input image. - const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight); - const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth); - - TensorBufferArrayView<const float> input(inputInfo.GetShape(), in); - TensorBufferArrayView<float> output(outputInfo.GetShape(), out); - - for (unsigned int n = 0; n < batchSize; ++n) - { - for (unsigned int c = 0; c < channelCount; ++c) - { - for (unsigned int y = 0; y < outputHeight; ++y) - { - // Corresponding real-valued height coordinate in input image. - const float iy = boost::numeric_cast<float>(y) * scaleY; - - // Discrete height coordinate of top-left texel (in the 2x2 texel area used for interpolation). - const float fiy = floorf(iy); - const unsigned int y0 = boost::numeric_cast<unsigned int>(fiy); - - // Interpolation weight (range [0,1]). - const float yw = iy - fiy; - - for (unsigned int x = 0; x < outputWidth; ++x) - { - // Real-valued and discrete width coordinates in input image. - const float ix = boost::numeric_cast<float>(x) * scaleX; - const float fix = floorf(ix); - const unsigned int x0 = boost::numeric_cast<unsigned int>(fix); - - // Interpolation weight (range [0,1]). - const float xw = ix - fix; - - // Discrete width/height coordinates of texels below and to the right of (x0, y0). - const unsigned int x1 = std::min(x0 + 1, inputWidth - 1u); - const unsigned int y1 = std::min(y0 + 1, inputHeight - 1u); - - // Interpolation - const float ly0 = Lerp(input.Get(n, c, y0, x0), input.Get(n, c, y0, x1), xw); // lerp along row y0. - const float ly1 = Lerp(input.Get(n, c, y1, x0), input.Get(n, c, y1, x1), xw); // lerp along row y1. - const float l = Lerp(ly0, ly1, yw); - - output.Get(n, c, y, x) = l; - } - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/ResizeBilinear.hpp b/src/backends/RefWorkloads/ResizeBilinear.hpp deleted file mode 100644 index 847b8e8bef..0000000000 --- a/src/backends/RefWorkloads/ResizeBilinear.hpp +++ /dev/null @@ -1,15 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo); - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Softmax.cpp b/src/backends/RefWorkloads/Softmax.cpp deleted file mode 100644 index 4f1016e86c..0000000000 --- a/src/backends/RefWorkloads/Softmax.cpp +++ /dev/null @@ -1,49 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "Softmax.hpp" - -#include <cmath> -#include <vector> - -namespace armnn -{ - -/// Computes the softmax function on some inputs, into outputs, with a shape given by tensorInfo. -void Softmax(const float* in, float* out, const TensorInfo& tensorInfo, float beta) -{ - unsigned int numChannels = tensorInfo.GetShape()[1]; - for (unsigned int n = 0; n < tensorInfo.GetShape()[0]; n++) - { - // Find maximum channel. - float max = in[n * numChannels]; - for (unsigned int c = 1; c < numChannels; c++) - { - float val = in[n * numChannels + c]; - if (val > max) - { - max = val; - } - } - - // Exponentiate all values and sum. - std::vector<float> exponentials(numChannels); - float sum = 0.0f; - for (unsigned int c = 0; c < numChannels; c++) - { - float val = in[n * numChannels + c]; - exponentials[c] = expf((val - max) * beta); - sum += exponentials[c]; - } - - // Divide exponentials by sum to give outputs. - for (unsigned int c = 0; c < numChannels; c++) - { - out[n * numChannels + c] = exponentials[c] / sum; - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Softmax.hpp b/src/backends/RefWorkloads/Softmax.hpp deleted file mode 100644 index 3b974f9e9e..0000000000 --- a/src/backends/RefWorkloads/Softmax.hpp +++ /dev/null @@ -1,16 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/Tensor.hpp> - -namespace armnn -{ - -/// Computes the softmax function on some inputs, into outputs, with a shape given by tensorInfo. -void Softmax(const float* in, float* out, const TensorInfo& tensorInfo, float beta); - -} //namespace armnn diff --git a/src/backends/RefWorkloads/Splitter.hpp b/src/backends/RefWorkloads/Splitter.hpp deleted file mode 100644 index e9c0379c9e..0000000000 --- a/src/backends/RefWorkloads/Splitter.hpp +++ /dev/null @@ -1,84 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefWorkloadUtils.hpp" - -#include "backends/WorkloadData.hpp" - -#include <armnn/Tensor.hpp> - -#include <boost/assert.hpp> - -namespace armnn -{ - -template <typename DataType> -void Splitter(const SplitterQueueDescriptor& data) -{ - const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]); - - for (unsigned int index = 0; index < inputInfo0.GetNumElements(); ++index) - { - unsigned int indices[MaxNumOfTensorDimensions] = { 0 }; - - unsigned int indexRemainder = index; - unsigned int dimensionStride = inputInfo0.GetNumElements(); - - for (unsigned int i = 0; i<inputInfo0.GetNumDimensions(); i++) - { - dimensionStride /= inputInfo0.GetShape()[i]; - indices[i] = indexRemainder / dimensionStride; // Use integer division to round down. - indexRemainder -= indices[i] * dimensionStride; - } - - for (unsigned int viewIdx = 0; viewIdx < data.m_ViewOrigins.size(); ++viewIdx) - { - SplitterQueueDescriptor::ViewOrigin const& view = data.m_ViewOrigins[viewIdx]; - - //Split view extents are defined by the size of (the corresponding) input tensor. - const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[viewIdx]); - BOOST_ASSERT(outputInfo.GetNumDimensions() == inputInfo0.GetNumDimensions()); - - // Check all dimensions to see if this element is inside the given input view. - bool insideView = true; - for (unsigned int i = 0; i<outputInfo.GetNumDimensions(); i++) - { - if (indices[i] < view.m_Origin[i]) - { - insideView = false; - } - if (indices[i] >= view.m_Origin[i] + outputInfo.GetShape()[i]) - { - insideView = false; - } - } - - if (insideView) - { - unsigned int outIndex = 0; - unsigned int dimensionStride = 1; - - for (unsigned int i = outputInfo.GetNumDimensions(); i-- > 0;) - { - outIndex += dimensionStride * (indices[i] - view.m_Origin[i]); - dimensionStride *= outputInfo.GetShape()[i]; - } - - //We are within the view, to copy input data to the output corresponding to this view. - DataType* outputData = GetOutputTensorData<DataType>(viewIdx, data); - BOOST_ASSERT(outputData); - - const DataType* inputData = GetInputTensorData<DataType>(0, data); - BOOST_ASSERT(inputData); - - outputData[outIndex] = inputData[index]; - } - } - } -} - -} //namespace armnn diff --git a/src/backends/RefWorkloads/TensorBufferArrayView.hpp b/src/backends/RefWorkloads/TensorBufferArrayView.hpp deleted file mode 100644 index e19810ca87..0000000000 --- a/src/backends/RefWorkloads/TensorBufferArrayView.hpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <armnn/Tensor.hpp> - -#include <boost/assert.hpp> - -namespace armnn -{ - -// Utility class providing access to raw tensor memory based on indices along each dimension. -template <typename DataType> -class TensorBufferArrayView -{ -public: - TensorBufferArrayView(const TensorShape& shape, DataType* data) - : m_Shape(shape) - , m_Data(data) - { - } - - DataType& Get(unsigned int b, unsigned int c, unsigned int h, unsigned int w) const - { - BOOST_ASSERT( b < m_Shape[0] || (m_Shape[0] == 0 && b == 0) ); - BOOST_ASSERT( c < m_Shape[1] || (m_Shape[1] == 0 && c == 0) ); - BOOST_ASSERT( h < m_Shape[2] || (m_Shape[2] == 0 && h == 0) ); - BOOST_ASSERT( w < m_Shape[3] || (m_Shape[3] == 0 && w == 0) ); - - return m_Data[b * m_Shape[1] * m_Shape[2] * m_Shape[3] - + c * m_Shape[2] * m_Shape[3] - + h * m_Shape[3] - + w]; - } - -private: - const TensorShape m_Shape; - DataType* m_Data; -}; - -} //namespace armnn diff --git a/src/backends/RefWorkloads/backend.cmake b/src/backends/RefWorkloads/backend.cmake deleted file mode 100644 index 6fd09eba6c..0000000000 --- a/src/backends/RefWorkloads/backend.cmake +++ /dev/null @@ -1,7 +0,0 @@ -# -# Copyright © 2017 Arm Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -add_subdirectory(${PROJECT_SOURCE_DIR}/src/backends/RefWorkloads) -list(APPEND armnnLibraries armnnRefBackend) diff --git a/src/backends/RefWorkloads/backend.mk b/src/backends/RefWorkloads/backend.mk deleted file mode 100644 index 8cde8c95dc..0000000000 --- a/src/backends/RefWorkloads/backend.mk +++ /dev/null @@ -1,49 +0,0 @@ -# -# Copyright © 2017 ARM Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -BACKEND_SOURCES := \ - FullyConnected.cpp \ - RefMergerUint8Workload.cpp \ - RefResizeBilinearUint8Workload.cpp \ - RefFullyConnectedFloat32Workload.cpp \ - RefSoftmaxFloat32Workload.cpp \ - RefActivationFloat32Workload.cpp \ - RefBatchNormalizationUint8Workload.cpp \ - RefBaseConstantWorkload.cpp \ - RefResizeBilinearFloat32Workload.cpp \ - RefBatchNormalizationFloat32Workload.cpp \ - Broadcast.cpp \ - ArithmeticFunction.cpp \ - RefArithmeticWorkload.cpp \ - RefFakeQuantizationFloat32Workload.cpp \ - ResizeBilinear.cpp \ - RefSoftmaxUint8Workload.cpp \ - RefDepthwiseConvolution2dFloat32Workload.cpp \ - RefPooling2dUint8Workload.cpp \ - RefFloorFloat32Workload.cpp \ - ConvImpl.cpp \ - Activation.cpp \ - RefReshapeUint8Workload.cpp \ - RefL2NormalizationFloat32Workload.cpp \ - RefLstmFloat32Workload.cpp \ - RefConvolution2dFloat32Workload.cpp \ - RefConvolution2dUint8Workload.cpp \ - RefSplitterFloat32Workload.cpp \ - RefActivationUint8Workload.cpp \ - RefSplitterUint8Workload.cpp \ - RefPooling2dFloat32Workload.cpp \ - RefReshapeFloat32Workload.cpp \ - RefNormalizationFloat32Workload.cpp \ - Softmax.cpp \ - RefDepthwiseConvolution2dUint8Workload.cpp \ - RefConstantUint8Workload.cpp \ - RefConstantFloat32Workload.cpp \ - Pooling2d.cpp \ - RefMergerFloat32Workload.cpp \ - RefFullyConnectedUint8Workload.cpp \ - RefPermuteWorkload.cpp \ - RefConvertFp16ToFp32Workload.cpp \ - RefConvertFp32ToFp16Workload.cpp - |