diff options
| author | Matthew Sloyan <matthew.sloyan@arm.com> | 2020-09-09 09:07:37 +0100 |
|---|---|---|
| committer | Jan Eilers <jan.eilers@arm.com> | 2020-09-17 08:31:09 +0000 |
| commit | 171214c8ff275c90cd4f7fc23a34ec2c83b5ea39 (patch) | |
| tree | 23fd3ee288d631c8c94bede71f89f0f1e12da862 /src/backends | |
| parent | a25886e0966a6b9433cd23595688fadb88a161b2 (diff) | |
| download | armnn-171214c8ff275c90cd4f7fc23a34ec2c83b5ea39.tar.gz | |
IVGCVSW-5300 Remove some boost::numeric_cast from armnn/backends
* Replaced with armnn/utility/NumericCast.hpp
* Some exclusions in reference backend
* Excluded as requires float implementation in NumericCast.hpp
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I9e4e9cd502c865452128fa04415fd6f250baa855
Diffstat (limited to 'src/backends')
45 files changed, 262 insertions, 228 deletions
diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.hpp b/src/backends/aclCommon/ArmComputeTensorUtils.hpp index 67676784ef..011f44dc69 100644 --- a/src/backends/aclCommon/ArmComputeTensorUtils.hpp +++ b/src/backends/aclCommon/ArmComputeTensorUtils.hpp @@ -7,6 +7,8 @@ #include <armnn/Tensor.hpp> #include <armnn/DescriptorsFwd.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <arm_compute/core/ITensor.h> #include <arm_compute/core/TensorInfo.h> #include <arm_compute/core/Types.h> @@ -14,8 +16,6 @@ #include <Half.hpp> -#include <boost/cast.hpp> - namespace armnn { class ITensorHandle; @@ -126,7 +126,7 @@ inline size_t GetTensorOffset(const arm_compute::ITensorInfo& info, coords.set(2, static_cast<int>(channelIndex)); coords.set(1, static_cast<int>(y)); coords.set(0, static_cast<int>(x)); - return boost::numeric_cast<size_t>(info.offset_element_in_bytes(coords)); + return armnn::numeric_cast<size_t>(info.offset_element_in_bytes(coords)); } // Helper function to obtain element offset into data buffer representing tensor data (assuming no strides). @@ -229,9 +229,9 @@ TensorShape GetTensorShape(const ArmComputeType& shapelike, T initial) std::vector<unsigned int> s(MaxNumOfTensorDimensions, initial); for (unsigned int i=0; i < shapelike.num_dimensions(); ++i) { - s[(shapelike.num_dimensions()-1)-i] = boost::numeric_cast<unsigned int>(shapelike[i]); + s[(shapelike.num_dimensions()-1)-i] = armnn::numeric_cast<unsigned int>(shapelike[i]); } - return TensorShape(boost::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data()); + return TensorShape(armnn::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data()); }; /// Get the strides from an ACL strides object diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index c7650dc58d..07ce14b763 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -7,6 +7,7 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> #include <armnnUtils/TensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <algorithm> #include <iomanip> @@ -14,7 +15,6 @@ #include <sstream> #include <boost/format.hpp> -#include <boost/numeric/conversion/cast.hpp> using namespace armnnUtils; @@ -306,7 +306,7 @@ void ValidateBroadcastTensorShapesMatch(const TensorInfo& first, } outputDims[i] = std::max(first.GetShape()[i], second.GetShape()[i]); } - TensorShape broadcastShape = TensorShape(boost::numeric_cast<unsigned int>(outputDims.size()), outputDims.data()); + TensorShape broadcastShape = TensorShape(armnn::numeric_cast<unsigned int>(outputDims.size()), outputDims.data()); if (broadcastShape != output.GetShape()) { throw InvalidArgumentException(descName + ": The tensor shape resulting from adding " @@ -2306,7 +2306,7 @@ void MeanQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const else { unsigned int outputDim = - inputTensorInfo.GetNumDimensions() - boost::numeric_cast<unsigned int>(m_Parameters.m_Axis.size()); + inputTensorInfo.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Parameters.m_Axis.size()); ValidateTensorNumDimensions(outputTensorInfo, descriptorName, outputDim > 0 ? outputDim : 1, diff --git a/src/backends/backendsCommon/WorkloadUtils.cpp b/src/backends/backendsCommon/WorkloadUtils.cpp index 37915cfc4d..5886630cd9 100644 --- a/src/backends/backendsCommon/WorkloadUtils.cpp +++ b/src/backends/backendsCommon/WorkloadUtils.cpp @@ -6,8 +6,7 @@ #include <backendsCommon/WorkloadUtils.hpp> #include <armnn/Utils.hpp> - -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> namespace armnn { @@ -194,12 +193,12 @@ armnn::ConstTensor ConvertWeightTensorFromArmnnToAcl(const ConstCpuTensorHandle* int32_t ConvertMaskToACLFormat(int32_t mask, int32_t numDim) { int32_t reversedMask = 0; - for (unsigned int i = 0; i < boost::numeric_cast<unsigned int>(numDim); ++i) + for (unsigned int i = 0; i < armnn::numeric_cast<unsigned int>(numDim); ++i) { // Check if bit set in mask for each dimension int32_t bit = (mask & 1 << i) != 0; // Increment the new mask with the bits reversed - reversedMask += (bit << std::max(numDim-(boost::numeric_cast<int>(i)+1), 0)); + reversedMask += (bit << std::max(numDim-(armnn::numeric_cast<int>(i)+1), 0)); } return reversedMask; diff --git a/src/backends/backendsCommon/test/ActivationFixture.hpp b/src/backends/backendsCommon/test/ActivationFixture.hpp index 8ff77f6c5d..d28174d6a6 100644 --- a/src/backends/backendsCommon/test/ActivationFixture.hpp +++ b/src/backends/backendsCommon/test/ActivationFixture.hpp @@ -7,9 +7,10 @@ #include "TensorCopyUtils.hpp" #include "WorkloadTestUtils.hpp" +#include <armnn/utility/NumericCast.hpp> + #include <test/TensorHelpers.hpp> -#include <boost/numeric/conversion/cast.hpp> #include <boost/multi_array.hpp> struct ActivationFixture @@ -17,10 +18,10 @@ struct ActivationFixture ActivationFixture() { auto boostArrayExtents = boost::extents - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; output.resize(boostArrayExtents); outputExpected.resize(boostArrayExtents); input.resize(boostArrayExtents); diff --git a/src/backends/backendsCommon/test/ComparisonEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ComparisonEndToEndTestImpl.hpp index dc53b7b246..c705f87b85 100644 --- a/src/backends/backendsCommon/test/ComparisonEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/ComparisonEndToEndTestImpl.hpp @@ -10,6 +10,8 @@ #include <armnn/INetwork.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <boost/test/unit_test.hpp> #include <vector> @@ -34,7 +36,7 @@ INetworkPtr CreateComparisonNetwork(const std::vector<TensorShape>& inputShapes, for (unsigned int i = 0; i < inputShapes.size(); ++i) { TensorInfo inputTensorInfo(inputShapes[i], ArmnnTypeInput, qScale, qOffset); - IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i)); + IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(i)); Connect(input, comparisonLayer, inputTensorInfo, 0, i); } diff --git a/src/backends/backendsCommon/test/ConcatEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ConcatEndToEndTestImpl.hpp index ded3857282..58a1f39a78 100644 --- a/src/backends/backendsCommon/test/ConcatEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/ConcatEndToEndTestImpl.hpp @@ -10,6 +10,8 @@ #include <armnn/INetwork.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <boost/test/unit_test.hpp> #include <vector> @@ -38,7 +40,7 @@ INetworkPtr CreateConcatNetwork(const std::vector<TensorShape>& inputShapes, for (unsigned int i = 0; i < inputShapes.size(); ++i) { TensorInfo inputTensorInfo(inputShapes[i], DataType, qScale, qOffset); - IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i)); + IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(i)); Connect(input, concat, inputTensorInfo, 0, i); } diff --git a/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp index 4c93735bc8..5fedaa2171 100644 --- a/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp @@ -10,6 +10,8 @@ #include <armnn/INetwork.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <boost/test/unit_test.hpp> #include <vector> @@ -32,7 +34,7 @@ INetworkPtr CreateElementwiseUnaryNetwork(const TensorShape& inputShape, IConnectableLayer* elementwiseUnaryLayer = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary"); TensorInfo inputTensorInfo(inputShape, ArmnnTypeInput, qScale, qOffset); - IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(0)); + IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(0)); Connect(input, elementwiseUnaryLayer, inputTensorInfo, 0, 0); TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset); diff --git a/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp b/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp index b06b30c935..404a412ca0 100644 --- a/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp +++ b/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp @@ -13,6 +13,8 @@ #include <armnn/INetwork.hpp> #include <armnn/QuantizedLstmParams.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <test/TensorHelpers.hpp> #include <boost/test/unit_test.hpp> @@ -27,9 +29,9 @@ using MultiArray = const boost::multi_array<uint8_t, 2>&; armnn::INetworkPtr CreateQuantizedLstmNetwork(MultiArray input, MultiArray expectedOutput) { - auto batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); - auto inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]); - auto outputSize = boost::numeric_cast<unsigned int>(expectedOutput.shape()[1]); + auto batchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]); + auto inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]); + auto outputSize = armnn::numeric_cast<unsigned int>(expectedOutput.shape()[1]); float inputOutputScale = 0.0078125f; int32_t inputOutputOffset = 128; diff --git a/src/backends/backendsCommon/test/SplitterEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/SplitterEndToEndTestImpl.hpp index 6c4c1772b6..257a81b50b 100644 --- a/src/backends/backendsCommon/test/SplitterEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/SplitterEndToEndTestImpl.hpp @@ -8,6 +8,8 @@ #include <armnn/INetwork.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <backendsCommon/test/CommonTestUtils.hpp> #include <boost/test/unit_test.hpp> @@ -63,7 +65,7 @@ INetworkPtr CreateSplitterNetwork(const TensorShape& inputShape, for (unsigned int i = 0; i < outputShapes.size(); ++i) { TensorInfo outputTensorInfo(outputShapes[i], DataType, qScale, qOffset); - IConnectableLayer* output = net->AddOutputLayer(boost::numeric_cast<LayerBindingId>(i)); + IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i)); Connect(splitter, output, outputTensorInfo, i, 0); } diff --git a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp index 543ea7716a..6d83b1ca99 100644 --- a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp @@ -13,6 +13,8 @@ #include <backendsCommon/test/WorkloadTestUtils.hpp> #include <reference/test/RefWorkloadFactoryHelper.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <test/TensorHelpers.hpp> #include <boost/multi_array.hpp> @@ -1261,10 +1263,10 @@ LayerTestResult<T,4> CompareActivationTestImpl( LayerTestResult<T,4> ret(outputTensorInfo); auto boostArrayExtents = boost::extents - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] - [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] + [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; ret.output.resize(boostArrayExtents); ret.outputExpected.resize(boostArrayExtents); diff --git a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp index e99a26e81e..690d1cd66f 100644 --- a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp @@ -9,6 +9,7 @@ #include <armnnUtils/TensorUtils.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> #include <armnnUtils/Permute.hpp> @@ -219,20 +220,20 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl( uint32_t dilationY = 1) { armnn::IgnoreUnused(memoryManager); - unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]); - unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]); + unsigned int inputHeight = armnn::numeric_cast<unsigned int>(originalInput.shape()[2]); + unsigned int inputWidth = armnn::numeric_cast<unsigned int>(originalInput.shape()[3]); + unsigned int inputChannels = armnn::numeric_cast<unsigned int>(originalInput.shape()[1]); + unsigned int inputNum = armnn::numeric_cast<unsigned int>(originalInput.shape()[0]); - unsigned int outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]); - unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]); + unsigned int outputHeight = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]); + unsigned int outputWidth = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]); + unsigned int outputChannels = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]); + unsigned int outputNum = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]); - unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]); - unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]); - unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]); + unsigned int kernelHeight = armnn::numeric_cast<unsigned int>(originalKernel.shape()[2]); + unsigned int kernelWidth = armnn::numeric_cast<unsigned int>(originalKernel.shape()[3]); + unsigned int kernelChannels = armnn::numeric_cast<unsigned int>(originalKernel.shape()[1]); + unsigned int kernelDepthMul = armnn::numeric_cast<unsigned int>(originalKernel.shape()[0]); bool biasEnabled = bias.size() > 0; @@ -385,20 +386,20 @@ LayerTestResult<O, 4> SimpleConvolution2dNhwcTestImpl( uint32_t strideY = 1) { armnn::IgnoreUnused(qScale, qOffset); - unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[3]); - unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[1]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]); + unsigned int inputNum = armnn::numeric_cast<unsigned int>(input.shape()[0]); + unsigned int inputChannels = armnn::numeric_cast<unsigned int>(input.shape()[3]); + unsigned int inputHeight = armnn::numeric_cast<unsigned int>(input.shape()[1]); + unsigned int inputWidth = armnn::numeric_cast<unsigned int>(input.shape()[2]); - unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]); - unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[3]); - unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[1]); - unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[2]); + unsigned int kernelChanMul = armnn::numeric_cast<unsigned int>(kernel.shape()[0]); + unsigned int kernelChannels = armnn::numeric_cast<unsigned int>(kernel.shape()[3]); + unsigned int kernelHeight = armnn::numeric_cast<unsigned int>(kernel.shape()[1]); + unsigned int kernelWidth = armnn::numeric_cast<unsigned int>(kernel.shape()[2]); - unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]); - unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); + unsigned int outputNum = armnn::numeric_cast<unsigned int>(outputExpected.shape()[0]); + unsigned int outputChannels = armnn::numeric_cast<unsigned int>(outputExpected.shape()[3]); + unsigned int outputHeight = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]); + unsigned int outputWidth = armnn::numeric_cast<unsigned int>(outputExpected.shape()[2]); bool biasEnabled = bias.size() > 0; @@ -1643,18 +1644,18 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl( uint32_t strideX = 1, uint32_t strideY = 1) { - unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]); - unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]); - unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]); - unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]); - unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]); - unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); - unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]); + unsigned int inputNum = armnn::numeric_cast<unsigned int>(input.shape()[0]); + unsigned int inputChannels = armnn::numeric_cast<unsigned int>(input.shape()[1]); + unsigned int inputHeight = armnn::numeric_cast<unsigned int>(input.shape()[2]); + unsigned int inputWidth = armnn::numeric_cast<unsigned int>(input.shape()[3]); + unsigned int kernelChanMul = armnn::numeric_cast<unsigned int>(kernel.shape()[0]); + unsigned int kernelChannels = armnn::numeric_cast<unsigned int>(kernel.shape()[1]); + unsigned int kernelHeight = armnn::numeric_cast<unsigned int>(kernel.shape()[2]); + unsigned int kernelWidth = armnn::numeric_cast<unsigned int>(kernel.shape()[3]); + unsigned int outputNum = armnn::numeric_cast<unsigned int>(outputExpected.shape()[0]); + unsigned int outputChannels = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]); + unsigned int outputHeight = armnn::numeric_cast<unsigned int>(outputExpected.shape()[2]); + unsigned int outputWidth = armnn::numeric_cast<unsigned int>(outputExpected.shape()[3]); // If a bias is used, its size must equal the number of output channels. bool biasEnabled = bias.size() > 0; @@ -2151,20 +2152,20 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl( uint32_t dilationX = 1, uint32_t dilationY = 1) { - unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]); - unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]); - - unsigned int outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]); - unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]); - - unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]); - unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]); - unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]); + unsigned int inputHeight = armnn::numeric_cast<unsigned int>(originalInput.shape()[2]); + unsigned int inputWidth = armnn::numeric_cast<unsigned int>(originalInput.shape()[3]); + unsigned int inputChannels = armnn::numeric_cast<unsigned int>(originalInput.shape()[1]); + unsigned int inputNum = armnn::numeric_cast<unsigned int>(originalInput.shape()[0]); + + unsigned int outputHeight = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]); + unsigned int outputWidth = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]); + unsigned int outputChannels = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]); + unsigned int outputNum = armnn::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]); + + unsigned int kernelHeight = armnn::numeric_cast<unsigned int>(originalKernel.shape()[2]); + unsigned int kernelWidth = armnn::numeric_cast<unsigned int>(originalKernel.shape()[3]); + unsigned int kernelChannels = armnn::numeric_cast<unsigned int>(originalKernel.shape()[1]); + unsigned int kernelDepthMul = armnn::numeric_cast<unsigned int>(originalKernel.shape()[0]); bool biasEnabled = bias.size() > 0; diff --git a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp index 8f39f42452..088ca3b4c2 100644 --- a/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/LstmTestImpl.cpp @@ -7,6 +7,7 @@ #include <QuantizeHelper.hpp> +#include <armnn/utility/NumericCast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> @@ -144,9 +145,9 @@ LstmNoCifgNoPeepholeNoProjectionTestImpl( armnn::DataType constantDataType = armnn::DataType::Float32) { IgnoreUnused(memoryManager); - unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); - unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]); - unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); + unsigned int batchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]); + unsigned int inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]); + unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]); // cellSize and outputSize have the same size when there is no projection. unsigned numUnits = outputSize; @@ -1069,10 +1070,10 @@ LayerTestResult<T, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl( bool peepholeEnabled = true; bool projectionEnabled = false; // These are not the input and the output of Lstm yet - unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); - unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]); + unsigned int batchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]); + unsigned int inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]); - unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); + unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]); const unsigned int cellSize = outputSize; @@ -1560,9 +1561,9 @@ LayerTestResult<uint8_t, 2> QuantizedLstmTestImpl( const boost::multi_array<uint8_t, 2>& outputExpected) { IgnoreUnused(memoryManager); - auto numBatches = boost::numeric_cast<unsigned int>(input.shape()[0]); - auto inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]); - auto outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); + auto numBatches = armnn::numeric_cast<unsigned int>(input.shape()[0]); + auto inputSize = armnn::numeric_cast<unsigned int>(input.shape()[1]); + auto outputSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[1]); // Scale/Offset for input/output, cellState In/Out, weights, bias float inputOutputScale = 0.0078125f; diff --git a/src/backends/backendsCommon/test/layerTests/NormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/NormalizationTestImpl.cpp index b42b180dc9..2e8e16f0c2 100644 --- a/src/backends/backendsCommon/test/layerTests/NormalizationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/NormalizationTestImpl.cpp @@ -8,6 +8,8 @@ #include <armnn/Exceptions.hpp> #include <armnn/LayerSupport.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/test/TensorCopyUtils.hpp> @@ -102,7 +104,7 @@ LayerTestResult<float,4> SimpleNormalizationTestImpl( // pow((kappa + (accumulatedScale * alpha)), beta) // ...where accumulatedScale is the sum of every element squared. float divisor[inputNum]; - for(int i = 0; i < boost::numeric_cast<int>(inputNum); i++) + for(int i = 0; i < armnn::numeric_cast<int>(inputNum); i++) { float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] + input[i][0][0][1]*input[i][0][0][1] + @@ -129,11 +131,11 @@ LayerTestResult<float,4> SimpleNormalizationTestImpl( // ...where adjacent channels means within half the normSize for the channel // The test data has only one channel, so this is simplified below. std::vector<float> outputVector; - for (int n = 0; n < boost::numeric_cast<int>(inputNum); ++n) + for (int n = 0; n < armnn::numeric_cast<int>(inputNum); ++n) { - for (int h = 0; h < boost::numeric_cast<int>(inputHeight); ++h) + for (int h = 0; h < armnn::numeric_cast<int>(inputHeight); ++h) { - for (int w = 0; w < boost::numeric_cast<int>(inputWidth); ++w) + for (int w = 0; w < armnn::numeric_cast<int>(inputWidth); ++w) { float accumulatedScale = input[n][0][h][w]*input[n][0][h][w]; float scale = powf((kappa + accumulatedScale * alpha), -beta); diff --git a/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp index a4f87ff3ed..70e2e61475 100644 --- a/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Pooling2dTestImpl.cpp @@ -15,6 +15,7 @@ #include <armnnUtils/Permute.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <backendsCommon/WorkloadInfo.hpp> @@ -48,15 +49,15 @@ LayerTestResult<T, 4> SimplePooling2dTestImpl( auto widthIndex = dimensionIndices.GetWidthIndex(); auto channelsIndex = dimensionIndices.GetChannelsIndex(); - unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]); - unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); + unsigned int inputHeight = armnn::numeric_cast<unsigned int>(input.shape()[heightIndex]); + unsigned int inputWidth = armnn::numeric_cast<unsigned int>(input.shape()[widthIndex]); + unsigned int inputChannels = armnn::numeric_cast<unsigned int>(input.shape()[channelsIndex]); + unsigned int inputBatchSize = armnn::numeric_cast<unsigned int>(input.shape()[0]); - unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); - unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); + unsigned int outputHeight = armnn::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); + unsigned int outputWidth = armnn::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); + unsigned int outputChannels = armnn::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); + unsigned int outputBatchSize = armnn::numeric_cast<unsigned int>(outputExpected.shape()[0]); armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( inputBatchSize, inputChannels, inputHeight, inputWidth, dataLayout, ArmnnType); diff --git a/src/backends/cl/ClTensorHandleFactory.cpp b/src/backends/cl/ClTensorHandleFactory.cpp index e92913f196..33995f7b34 100644 --- a/src/backends/cl/ClTensorHandleFactory.cpp +++ b/src/backends/cl/ClTensorHandleFactory.cpp @@ -7,6 +7,7 @@ #include "ClTensorHandleFactory.hpp" #include "ClTensorHandle.hpp" +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <arm_compute/runtime/CL/CLTensor.h> @@ -31,7 +32,7 @@ std::unique_ptr<ITensorHandle> ClTensorHandleFactory::CreateSubTensorHandle(ITen { // Arm compute indexes tensor coords in reverse order. unsigned int revertedIndex = subTensorShape.GetNumDimensions() - i - 1; - coords.set(i, boost::numeric_cast<int>(subTensorOrigin[revertedIndex])); + coords.set(i, armnn::numeric_cast<int>(subTensorOrigin[revertedIndex])); } const arm_compute::TensorShape parentShape = armcomputetensorutils::BuildArmComputeTensorShape( diff --git a/src/backends/cl/ClWorkloadFactory.cpp b/src/backends/cl/ClWorkloadFactory.cpp index 4acfa570f2..f6650dc2d0 100644 --- a/src/backends/cl/ClWorkloadFactory.cpp +++ b/src/backends/cl/ClWorkloadFactory.cpp @@ -11,6 +11,7 @@ #include <armnn/Exceptions.hpp> #include <armnn/Utils.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> @@ -130,7 +131,7 @@ std::unique_ptr<ITensorHandle> ClWorkloadFactory::CreateSubTensorHandle(ITensorH { // Arm compute indexes tensor coords in reverse order. unsigned int revertedIndex = subTensorShape.GetNumDimensions() - i - 1; - coords.set(i, boost::numeric_cast<int>(subTensorOrigin[revertedIndex])); + coords.set(i, armnn::numeric_cast<int>(subTensorOrigin[revertedIndex])); } const arm_compute::TensorShape parentShape = armcomputetensorutils::BuildArmComputeTensorShape(parent.GetShape()); diff --git a/src/backends/cl/workloads/ClArgMinMaxWorkload.cpp b/src/backends/cl/workloads/ClArgMinMaxWorkload.cpp index a79a7b286d..5910080859 100644 --- a/src/backends/cl/workloads/ClArgMinMaxWorkload.cpp +++ b/src/backends/cl/workloads/ClArgMinMaxWorkload.cpp @@ -11,6 +11,7 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <armnnUtils/TensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <cl/ClTensorHandle.hpp> #include <cl/ClLayerSupport.hpp> @@ -36,7 +37,7 @@ arm_compute::Status ClArgMinMaxWorkloadValidate(const TensorInfo& input, auto numDims = input.GetNumDimensions(); auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, descriptor.m_Axis); - int aclAxis = boost::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); + int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); if (descriptor.m_Function == ArgMinMaxFunction::Max) { @@ -60,7 +61,7 @@ ClArgMinMaxWorkload::ClArgMinMaxWorkload(const ArgMinMaxQueueDescriptor& descrip auto numDims = info.m_InputTensorInfos[0].GetNumDimensions(); auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, m_Data.m_Parameters.m_Axis); - int aclAxis = boost::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); + int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); if (m_Data.m_Parameters.m_Function == ArgMinMaxFunction::Max) { diff --git a/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp b/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp index a714e031e4..1a7a8dca81 100644 --- a/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp +++ b/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp @@ -9,6 +9,8 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> + #include "ClWorkloadUtils.hpp" namespace armnn @@ -27,8 +29,8 @@ ClBatchToSpaceNdWorkload::ClBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDesc input.info()->set_data_layout(aclDataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[1]); arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); output.info()->set_data_layout(aclDataLayout); @@ -49,8 +51,8 @@ arm_compute::Status ClBatchToSpaceNdWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(desc.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(desc.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_BlockShape[1]); const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout); diff --git a/src/backends/cl/workloads/ClDepthToSpaceWorkload.cpp b/src/backends/cl/workloads/ClDepthToSpaceWorkload.cpp index 04885b18aa..43c81dc209 100644 --- a/src/backends/cl/workloads/ClDepthToSpaceWorkload.cpp +++ b/src/backends/cl/workloads/ClDepthToSpaceWorkload.cpp @@ -8,12 +8,12 @@ #include "ClWorkloadUtils.hpp" #include <aclCommon/ArmComputeTensorUtils.hpp> + +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <cl/ClTensorHandle.hpp> -#include <boost/numeric/conversion/cast.hpp> - namespace armnn { @@ -26,7 +26,7 @@ arm_compute::Status ClDepthToSpaceWorkloadValidate(const TensorInfo& input, DataLayout dataLayout = desc.m_DataLayout; const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_BlockSize); const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout); @@ -48,7 +48,7 @@ ClDepthToSpaceWorkload::ClDepthToSpaceWorkload(const DepthToSpaceQueueDescriptor PolymorphicPointerDowncast<IClTensorHandle>(m_Data.m_Inputs[0])->GetTensor(); input.info()->set_data_layout(aclDataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); arm_compute::ICLTensor& output = PolymorphicPointerDowncast<IClTensorHandle>(m_Data.m_Outputs[0])->GetTensor(); diff --git a/src/backends/cl/workloads/ClLstmFloatWorkload.cpp b/src/backends/cl/workloads/ClLstmFloatWorkload.cpp index 9d06428902..fe9b45e054 100644 --- a/src/backends/cl/workloads/ClLstmFloatWorkload.cpp +++ b/src/backends/cl/workloads/ClLstmFloatWorkload.cpp @@ -9,6 +9,8 @@ #include <cl/ClLayerSupport.hpp> #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <arm_compute/runtime/CL/functions/CLLSTMLayer.h> #include "ClWorkloadUtils.hpp" @@ -132,8 +134,8 @@ ClLstmFloatWorkload::ClLstmFloatWorkload(const LstmQueueDescriptor &descriptor, // Get the batch_size and the num_units from the cellStateIn dimensions const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2]; - const unsigned int batch_size = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]); - const unsigned int num_units = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]); + const unsigned int batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]); + const unsigned int num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]); m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>(); if (m_Data.m_Parameters.m_CifgEnabled) diff --git a/src/backends/cl/workloads/ClSpaceToBatchNdWorkload.cpp b/src/backends/cl/workloads/ClSpaceToBatchNdWorkload.cpp index b87658b3f9..443c56b7b5 100644 --- a/src/backends/cl/workloads/ClSpaceToBatchNdWorkload.cpp +++ b/src/backends/cl/workloads/ClSpaceToBatchNdWorkload.cpp @@ -9,6 +9,7 @@ #include <aclCommon/ArmComputeUtils.hpp> #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> #include <cl/ClLayerSupport.hpp> @@ -27,8 +28,8 @@ arm_compute::Status ClSpaceToBatchNdWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( descriptor.m_PadList[1].first, descriptor.m_PadList[0].first); @@ -55,8 +56,8 @@ ClSpaceToBatchNdWorkload::ClSpaceToBatchNdWorkload( armnn::PolymorphicPointerDowncast<IClTensorHandle>(m_Data.m_Outputs[0])->GetTensor(); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]); arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( m_Data.m_Parameters.m_PadList[1].first, m_Data.m_Parameters.m_PadList[0].first); diff --git a/src/backends/cl/workloads/ClSpaceToDepthWorkload.cpp b/src/backends/cl/workloads/ClSpaceToDepthWorkload.cpp index 1acb5c64e6..f35fe0e3c9 100644 --- a/src/backends/cl/workloads/ClSpaceToDepthWorkload.cpp +++ b/src/backends/cl/workloads/ClSpaceToDepthWorkload.cpp @@ -11,6 +11,8 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <cl/ClTensorHandle.hpp> +#include <armnn/utility/NumericCast.hpp> + namespace armnn { using namespace armcomputetensorutils; @@ -26,7 +28,7 @@ ClSpaceToDepthWorkload::ClSpaceToDepthWorkload(const SpaceToDepthQueueDescriptor arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); input.info()->set_data_layout(aclDataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); output.info()->set_data_layout(aclDataLayout); @@ -47,7 +49,7 @@ arm_compute::Status ClSpaceToDepthWorkloadValidate(const TensorInfo& input, DataLayout dataLayout = desc.m_DataLayout; const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_BlockSize); const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout); diff --git a/src/backends/cl/workloads/ClStackWorkload.cpp b/src/backends/cl/workloads/ClStackWorkload.cpp index e434f9897f..c0b88b1193 100644 --- a/src/backends/cl/workloads/ClStackWorkload.cpp +++ b/src/backends/cl/workloads/ClStackWorkload.cpp @@ -5,6 +5,7 @@ #include "ClStackWorkload.hpp" #include "ClWorkloadUtils.hpp" #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> #include <cl/ClTensorHandle.hpp> @@ -12,8 +13,6 @@ #include <arm_compute/core/Types.h> -#include <boost/numeric/conversion/cast.hpp> - namespace armnn { using namespace armcomputetensorutils; @@ -22,8 +21,8 @@ namespace { int CalcAxis(const unsigned int axis, const unsigned int inputDimensions) { - const int intAxis = boost::numeric_cast<int>(axis); - return boost::numeric_cast<int>(inputDimensions) - intAxis; + const int intAxis = armnn::numeric_cast<int>(axis); + return armnn::numeric_cast<int>(inputDimensions) - intAxis; } } //namespace diff --git a/src/backends/cl/workloads/ClStridedSliceWorkload.cpp b/src/backends/cl/workloads/ClStridedSliceWorkload.cpp index 6b0a34d90e..b094a910f4 100644 --- a/src/backends/cl/workloads/ClStridedSliceWorkload.cpp +++ b/src/backends/cl/workloads/ClStridedSliceWorkload.cpp @@ -13,7 +13,8 @@ #include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/WorkloadUtils.hpp> -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <cl/ClLayerSupport.hpp> #include <cl/ClTensorHandle.hpp> #include <cl/ClLayerSupport.hpp> @@ -36,7 +37,7 @@ arm_compute::Status ClStridedSliceWorkloadValidate(const TensorInfo& input, std::tie(starts, ends, strides) = SetClStridedSliceData(descriptor.m_Begin, descriptor.m_End, descriptor.m_Stride); - auto numDimensions = boost::numeric_cast<int>(input.GetNumDimensions()); + auto numDimensions = armnn::numeric_cast<int>(input.GetNumDimensions()); int32_t begin_mask = ConvertMaskToACLFormat(descriptor.m_BeginMask, numDimensions); int32_t end_mask = ConvertMaskToACLFormat(descriptor.m_EndMask, numDimensions); int32_t shrink_axis_mask = ConvertMaskToACLFormat(descriptor.m_ShrinkAxisMask, numDimensions); @@ -68,7 +69,7 @@ ClStridedSliceWorkload::ClStridedSliceWorkload(const StridedSliceQueueDescriptor m_Data.m_Parameters.m_End, m_Data.m_Parameters.m_Stride); - auto numDimensions = boost::numeric_cast<int>(info.m_InputTensorInfos[0].GetNumDimensions()); + auto numDimensions = armnn::numeric_cast<int>(info.m_InputTensorInfos[0].GetNumDimensions()); int32_t begin_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_BeginMask, numDimensions); int32_t end_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_EndMask, numDimensions); int32_t shrink_axis_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_ShrinkAxisMask, numDimensions); diff --git a/src/backends/neon/NeonTensorHandleFactory.cpp b/src/backends/neon/NeonTensorHandleFactory.cpp index 1dd83950cd..ce3ce5c0d7 100644 --- a/src/backends/neon/NeonTensorHandleFactory.cpp +++ b/src/backends/neon/NeonTensorHandleFactory.cpp @@ -9,6 +9,7 @@ #include "Layer.hpp" #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> namespace armnn @@ -29,7 +30,7 @@ std::unique_ptr<ITensorHandle> NeonTensorHandleFactory::CreateSubTensorHandle(IT { // Arm compute indexes tensor coords in reverse order. unsigned int revertedIndex = subTensorShape.GetNumDimensions() - i - 1; - coords.set(i, boost::numeric_cast<int>(subTensorOrigin[revertedIndex])); + coords.set(i, armnn::numeric_cast<int>(subTensorOrigin[revertedIndex])); } const arm_compute::TensorShape parentShape = armcomputetensorutils::BuildArmComputeTensorShape(parent.GetShape()); diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp index 928989b1e4..709dd93e9b 100644 --- a/src/backends/neon/NeonWorkloadFactory.cpp +++ b/src/backends/neon/NeonWorkloadFactory.cpp @@ -12,6 +12,7 @@ #include <armnn/Utils.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> @@ -73,7 +74,7 @@ std::unique_ptr<ITensorHandle> NeonWorkloadFactory::CreateSubTensorHandle(ITenso { // Arm compute indexes tensor coords in reverse order. unsigned int revertedIndex = subTensorShape.GetNumDimensions() - i - 1; - coords.set(i, boost::numeric_cast<int>(subTensorOrigin[revertedIndex])); + coords.set(i, armnn::numeric_cast<int>(subTensorOrigin[revertedIndex])); } const arm_compute::TensorShape parentShape = armcomputetensorutils::BuildArmComputeTensorShape(parent.GetShape()); diff --git a/src/backends/neon/test/NeonTensorHandleTests.cpp b/src/backends/neon/test/NeonTensorHandleTests.cpp index 3cea29323a..e6d740280d 100644 --- a/src/backends/neon/test/NeonTensorHandleTests.cpp +++ b/src/backends/neon/test/NeonTensorHandleTests.cpp @@ -8,6 +8,7 @@ #include <neon/NeonTensorHandle.hpp> #include <neon/NeonTensorHandleFactory.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <test/GraphUtils.hpp> @@ -366,7 +367,7 @@ BOOST_AUTO_TEST_CASE(SplitteronXorYNoPaddingRequiredTest) for (unsigned int i = 0; i < outputShapes.size(); ++i) { TensorInfo outputTensorInfo(outputShapes[i], armnn::DataType::Float32, qScale, qOffset); - IConnectableLayer* output = net->AddOutputLayer(boost::numeric_cast<LayerBindingId>(i)); + IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i)); Connect(pooling2dLayers[i], output, outputTensorInfo, 0, 0); } @@ -541,7 +542,7 @@ BOOST_AUTO_TEST_CASE(SplitteronXorYPaddingRequiredTest) for (unsigned int i = 0; i < outputShapes.size(); ++i) { TensorInfo outputTensorInfo(outputShapes[i], armnn::DataType::Float32, qScale, qOffset); - IConnectableLayer* output = net->AddOutputLayer(boost::numeric_cast<LayerBindingId>(i)); + IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i)); Connect(pooling2dLayers[i], output, outputTensorInfo, 0, 0); } diff --git a/src/backends/neon/workloads/NeonArgMinMaxWorkload.cpp b/src/backends/neon/workloads/NeonArgMinMaxWorkload.cpp index 0fb819db0b..6290ecce17 100644 --- a/src/backends/neon/workloads/NeonArgMinMaxWorkload.cpp +++ b/src/backends/neon/workloads/NeonArgMinMaxWorkload.cpp @@ -10,6 +10,7 @@ #include <backendsCommon/CpuTensorHandle.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <armnnUtils/TensorUtils.hpp> @@ -36,7 +37,7 @@ arm_compute::Status NeonArgMinMaxWorkloadValidate(const TensorInfo& input, auto numDims = input.GetNumDimensions(); auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, descriptor.m_Axis); - int aclAxis = boost::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); + int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); if (descriptor.m_Function == ArgMinMaxFunction::Max) { @@ -60,7 +61,7 @@ NeonArgMinMaxWorkload::NeonArgMinMaxWorkload(const ArgMinMaxQueueDescriptor& des auto numDims = info.m_InputTensorInfos[0].GetNumDimensions(); auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, m_Data.m_Parameters.m_Axis); - int aclAxis = boost::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); + int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); auto layer = std::make_unique<arm_compute::NEArgMinMaxLayer>(); diff --git a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp index d2f538745c..3d479ff80d 100644 --- a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp +++ b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp @@ -7,7 +7,9 @@ #include "NeonWorkloadUtils.hpp" +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> + #include <ResolveType.hpp> namespace armnn @@ -23,8 +25,8 @@ arm_compute::Status NeonBatchToSpaceNdWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, desc.m_DataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(desc.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(desc.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_BlockShape[1]); const arm_compute::Status aclStatus = arm_compute::NEBatchToSpaceLayer::validate(&aclInputInfo, blockWidth, @@ -49,8 +51,8 @@ NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueue output.info()->set_data_layout(aclDataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[1]); m_Layer.reset(new arm_compute::NEBatchToSpaceLayer()); m_Layer->configure(&input, blockWidth, blockHeight, &output); diff --git a/src/backends/neon/workloads/NeonDepthToSpaceWorkload.cpp b/src/backends/neon/workloads/NeonDepthToSpaceWorkload.cpp index 12e7d206bf..2c4a6517e7 100644 --- a/src/backends/neon/workloads/NeonDepthToSpaceWorkload.cpp +++ b/src/backends/neon/workloads/NeonDepthToSpaceWorkload.cpp @@ -8,10 +8,9 @@ #include "NeonWorkloadUtils.hpp" #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> -#include <boost/numeric/conversion/cast.hpp> - namespace armnn { @@ -25,7 +24,7 @@ arm_compute::Status NeonDepthToSpaceWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, dataLayout); const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, dataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(descriptor.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(descriptor.m_BlockSize); return arm_compute::NEDepthToSpaceLayer::validate(&aclInput, &aclOutput, blockSize); } @@ -42,7 +41,7 @@ NeonDepthToSpaceWorkload::NeonDepthToSpaceWorkload(const DepthToSpaceQueueDescri PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor(); input.info()->set_data_layout(aclDataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); arm_compute::ITensor& output = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor(); diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp index d5f3c5de34..175e908817 100644 --- a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp +++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp @@ -8,6 +8,8 @@ #include "aclCommon/ArmComputeTensorUtils.hpp" +#include <armnn/utility/NumericCast.hpp> + #include "neon/NeonTensorHandle.hpp" namespace armnn @@ -131,8 +133,8 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descript // Get the batch_size and the num_units from the cellStateIn dimensions const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2]; - const unsigned int batch_size = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]); - const unsigned int num_units = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]); + const unsigned int batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]); + const unsigned int num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]); m_ScratchBuffer = std::make_unique<arm_compute::Tensor>(); if (m_Data.m_Parameters.m_CifgEnabled) diff --git a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp index d68ab4c4ac..42dd49cdc1 100644 --- a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp +++ b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp @@ -7,7 +7,9 @@ #include "NeonWorkloadUtils.hpp" +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> + #include <ResolveType.hpp> namespace armnn @@ -23,8 +25,8 @@ arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( descriptor.m_PadList[1].first, descriptor.m_PadList[0].first); @@ -51,8 +53,8 @@ NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueue PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor(); // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = boost::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = boost::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]); arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( m_Data.m_Parameters.m_PadList[1].first, m_Data.m_Parameters.m_PadList[0].first); diff --git a/src/backends/neon/workloads/NeonSpaceToDepthWorkload.cpp b/src/backends/neon/workloads/NeonSpaceToDepthWorkload.cpp index 2982cd181d..43c991cfb2 100644 --- a/src/backends/neon/workloads/NeonSpaceToDepthWorkload.cpp +++ b/src/backends/neon/workloads/NeonSpaceToDepthWorkload.cpp @@ -6,7 +6,9 @@ #include "NeonSpaceToDepthWorkload.hpp" #include "NeonWorkloadUtils.hpp" +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> + #include <ResolveType.hpp> namespace armnn @@ -22,7 +24,7 @@ arm_compute::Status NeonSpaceToDepthWorkloadValidate(const TensorInfo& input, const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, dataLayout); const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, dataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(descriptor.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(descriptor.m_BlockSize); return arm_compute::NESpaceToDepthLayer::validate(&aclInput, &aclOutput, blockSize); } @@ -38,7 +40,7 @@ NeonSpaceToDepthWorkload::NeonSpaceToDepthWorkload(const SpaceToDepthQueueDescri arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); input.info()->set_data_layout(aclDataLayout); - int32_t blockSize = boost::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); + int32_t blockSize = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockSize); arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); output.info()->set_data_layout(aclDataLayout); diff --git a/src/backends/neon/workloads/NeonStackWorkload.cpp b/src/backends/neon/workloads/NeonStackWorkload.cpp index a3ba8d888d..696de65620 100644 --- a/src/backends/neon/workloads/NeonStackWorkload.cpp +++ b/src/backends/neon/workloads/NeonStackWorkload.cpp @@ -6,12 +6,11 @@ #include "NeonWorkloadUtils.hpp" #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/CpuTensorHandle.hpp> #include <neon/NeonTensorHandle.hpp> -#include <boost/numeric/conversion/cast.hpp> - namespace armnn { using namespace armcomputetensorutils; @@ -20,8 +19,8 @@ namespace { int CalcAxis(const unsigned int axis, const unsigned int inputDimensions) { - const int intAxis = boost::numeric_cast<int>(axis); - return boost::numeric_cast<int>(inputDimensions) - intAxis; + const int intAxis = armnn::numeric_cast<int>(axis); + return armnn::numeric_cast<int>(inputDimensions) - intAxis; } } //namespace diff --git a/src/backends/neon/workloads/NeonStridedSliceWorkload.cpp b/src/backends/neon/workloads/NeonStridedSliceWorkload.cpp index 282005c7cc..d0aee07f9b 100644 --- a/src/backends/neon/workloads/NeonStridedSliceWorkload.cpp +++ b/src/backends/neon/workloads/NeonStridedSliceWorkload.cpp @@ -9,6 +9,7 @@ #include <neon/NeonTensorHandle.hpp> #include <aclCommon/ArmComputeUtils.hpp> #include <aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> #include <backendsCommon/WorkloadUtils.hpp> @@ -30,7 +31,7 @@ arm_compute::Status NeonStridedSliceWorkloadValidate(const TensorInfo& input, descriptor.m_End, descriptor.m_Stride); - auto numDimensions = boost::numeric_cast<int>(input.GetNumDimensions()); + auto numDimensions = armnn::numeric_cast<int>(input.GetNumDimensions()); int32_t begin_mask = ConvertMaskToACLFormat(descriptor.m_BeginMask, numDimensions); int32_t end_mask = ConvertMaskToACLFormat(descriptor.m_EndMask, numDimensions); int32_t shrink_axis_mask = ConvertMaskToACLFormat(descriptor.m_ShrinkAxisMask, numDimensions); @@ -62,7 +63,7 @@ NeonStridedSliceWorkload::NeonStridedSliceWorkload(const StridedSliceQueueDescri m_Data.m_Parameters.m_End, m_Data.m_Parameters.m_Stride); - auto numDimensions = boost::numeric_cast<int>(info.m_InputTensorInfos[0].GetNumDimensions()); + auto numDimensions = armnn::numeric_cast<int>(info.m_InputTensorInfos[0].GetNumDimensions()); int32_t begin_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_BeginMask, numDimensions); int32_t end_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_EndMask, numDimensions); int32_t shrink_axis_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_ShrinkAxisMask, numDimensions); diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp index 5e3c96d31d..52c079fae4 100644 --- a/src/backends/reference/RefLayerSupport.cpp +++ b/src/backends/reference/RefLayerSupport.cpp @@ -9,17 +9,14 @@ #include <armnn/Types.hpp> #include <armnn/Descriptors.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <LayerSupportCommon.hpp> #include <backendsCommon/LayerSupportRules.hpp> -#include <boost/cast.hpp> - #include <vector> #include <array> -using namespace boost; - namespace armnn { @@ -1326,7 +1323,7 @@ bool RefLayerSupport::IsMeanSupported(const TensorInfo& input, } else { - auto outputDim = input.GetNumDimensions() - boost::numeric_cast<unsigned int>(descriptor.m_Axis.size()); + auto outputDim = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(descriptor.m_Axis.size()); if (outputDim > 0) { diff --git a/src/backends/reference/workloads/ArgMinMax.cpp b/src/backends/reference/workloads/ArgMinMax.cpp index 637aa17013..c455c52e5a 100644 --- a/src/backends/reference/workloads/ArgMinMax.cpp +++ b/src/backends/reference/workloads/ArgMinMax.cpp @@ -7,7 +7,7 @@ #include <armnnUtils/TensorUtils.hpp> -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> namespace armnn { @@ -39,7 +39,7 @@ void ArgMinMax(Decoder<float>& in, int32_t* out, const TensorInfo& inputTensorIn tmpIndex = i; } } - out[outer * innerElements + inner] = boost::numeric_cast<int32_t>(tmpIndex); + out[outer * innerElements + inner] = armnn::numeric_cast<int32_t>(tmpIndex); } } } diff --git a/src/backends/reference/workloads/DetectionPostProcess.cpp b/src/backends/reference/workloads/DetectionPostProcess.cpp index 61a504ec6b..ce07110da9 100644 --- a/src/backends/reference/workloads/DetectionPostProcess.cpp +++ b/src/backends/reference/workloads/DetectionPostProcess.cpp @@ -6,6 +6,7 @@ #include "DetectionPostProcess.hpp" #include <armnn/utility/Assert.hpp> +#include <armnn/utility/NumericCast.hpp> #include <boost/numeric/conversion/cast.hpp> @@ -67,7 +68,7 @@ std::vector<unsigned int> NonMaxSuppression(unsigned int numBoxes, } // Sort the indices based on scores. - unsigned int numAboveThreshold = boost::numeric_cast<unsigned int>(scoresAboveThreshold.size()); + unsigned int numAboveThreshold = armnn::numeric_cast<unsigned int>(scoresAboveThreshold.size()); std::vector<unsigned int> sortedIndices = GenerateRangeK(numAboveThreshold); TopKSort(numAboveThreshold, sortedIndices.data(), scoresAboveThreshold.data(), numAboveThreshold); @@ -267,7 +268,7 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo, } // Select max detection numbers of the highest score across all classes - unsigned int numSelected = boost::numeric_cast<unsigned int>(selectedBoxesAfterNms.size()); + unsigned int numSelected = armnn::numeric_cast<unsigned int>(selectedBoxesAfterNms.size()); unsigned int numOutput = std::min(desc.m_MaxDetections, numSelected); // Sort the max scores among the selected indices. @@ -311,7 +312,7 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo, desc.m_MaxDetections, desc.m_NmsIouThreshold); - unsigned int numSelected = boost::numeric_cast<unsigned int>(selectedIndices.size()); + unsigned int numSelected = armnn::numeric_cast<unsigned int>(selectedIndices.size()); unsigned int numOutput = std::min(desc.m_MaxDetections, numSelected); AllocateOutputData(detectionBoxesInfo.GetShape()[1], numOutput, boxCorners, selectedIndices, diff --git a/src/backends/reference/workloads/Gather.cpp b/src/backends/reference/workloads/Gather.cpp index 3e2190c81b..03aa2458f5 100644 --- a/src/backends/reference/workloads/Gather.cpp +++ b/src/backends/reference/workloads/Gather.cpp @@ -9,8 +9,7 @@ #include <backendsCommon/WorkloadData.hpp> #include <armnn/utility/IgnoreUnused.hpp> - -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> namespace armnn { @@ -37,7 +36,7 @@ void Gather(const TensorInfo& paramsInfo, unsigned int outIndex = 0; for (unsigned int i = 0; i < indicesInfo.GetNumElements(); ++i) { - unsigned int indx = boost::numeric_cast<unsigned int>(indices[i]); + unsigned int indx = armnn::numeric_cast<unsigned int>(indices[i]); ARMNN_ASSERT(indices[i] >= 0 && indx < paramsShape[0]); diff --git a/src/backends/reference/workloads/LogSoftmax.cpp b/src/backends/reference/workloads/LogSoftmax.cpp index 1998f50c87..2b6384913e 100644 --- a/src/backends/reference/workloads/LogSoftmax.cpp +++ b/src/backends/reference/workloads/LogSoftmax.cpp @@ -8,17 +8,16 @@ #include <armnnUtils/TensorUtils.hpp> #include <armnn/utility/Assert.hpp> #include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/NumericCast.hpp> #include <cmath> -#include <boost/numeric/conversion/cast.hpp> - namespace { inline bool ValidateAxis(int axis, unsigned int numDimensions) { - const int sNumDimensions = boost::numeric_cast<int>(numDimensions); + const int sNumDimensions = armnn::numeric_cast<int>(numDimensions); return axis < sNumDimensions && axis >= -sNumDimensions; } @@ -40,8 +39,8 @@ void LogSoftmax(Decoder<float>& input, IgnoreUnused(axisIsValid); unsigned int uAxis = descriptor.m_Axis < 0 ? - numDimensions - boost::numeric_cast<unsigned int>(std::abs(descriptor.m_Axis)) : - boost::numeric_cast<unsigned int>(descriptor.m_Axis); + numDimensions - armnn::numeric_cast<unsigned int>(std::abs(descriptor.m_Axis)) : + armnn::numeric_cast<unsigned int>(descriptor.m_Axis); const TensorShape& inputShape = inputInfo.GetShape(); const unsigned int outerSize = armnnUtils::GetNumElementsBetween(inputShape, 0, uAxis); diff --git a/src/backends/reference/workloads/Mean.cpp b/src/backends/reference/workloads/Mean.cpp index 72080ef042..e43a4d5312 100644 --- a/src/backends/reference/workloads/Mean.cpp +++ b/src/backends/reference/workloads/Mean.cpp @@ -6,6 +6,8 @@ #include "Mean.hpp" #include <backendsCommon/WorkloadData.hpp> +#include <armnn/utility/NumericCast.hpp> + #include <boost/numeric/conversion/cast.hpp> #include <cmath> @@ -111,7 +113,7 @@ void Mean(const armnn::TensorInfo& inputInfo, resolvedAxis.push_back(idx); } } - auto numResolvedAxis = boost::numeric_cast<unsigned int>(resolvedAxis.size()); + auto numResolvedAxis = armnn::numeric_cast<unsigned int>(resolvedAxis.size()); // Iterates through input_data and sum up the reduced axis. for (bool hasNext = true; hasNext; hasNext = NextIndex(inputNumDims, inputDims, tempIndex)) diff --git a/src/backends/reference/workloads/Pooling2d.cpp b/src/backends/reference/workloads/Pooling2d.cpp index 9b220619a4..435671ffad 100644 --- a/src/backends/reference/workloads/Pooling2d.cpp +++ b/src/backends/reference/workloads/Pooling2d.cpp @@ -9,6 +9,7 @@ #include <armnn/Types.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> +#include <armnn/utility/NumericCast.hpp> #include <boost/numeric/conversion/cast.hpp> @@ -151,20 +152,20 @@ void Pooling2d(Decoder<float>& rInputDecoder, auto heightIndex = dataLayout.GetHeightIndex(); auto widthIndex = dataLayout.GetWidthIndex(); - const int batchSize = boost::numeric_cast<int>(outputInfo.GetShape()[0]); - const int channels = boost::numeric_cast<int>(outputInfo.GetShape()[channelsIndex]); - const int heightOutput = boost::numeric_cast<int>(outputInfo.GetShape()[heightIndex]); - const int widthOutput = boost::numeric_cast<int>(outputInfo.GetShape()[widthIndex]); - const int heightInput = boost::numeric_cast<int>(inputInfo.GetShape()[heightIndex]); - const int widthInput = boost::numeric_cast<int>(inputInfo.GetShape()[widthIndex]); - 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); + const int batchSize = armnn::numeric_cast<int>(outputInfo.GetShape()[0]); + const int channels = armnn::numeric_cast<int>(outputInfo.GetShape()[channelsIndex]); + const int heightOutput = armnn::numeric_cast<int>(outputInfo.GetShape()[heightIndex]); + const int widthOutput = armnn::numeric_cast<int>(outputInfo.GetShape()[widthIndex]); + const int heightInput = armnn::numeric_cast<int>(inputInfo.GetShape()[heightIndex]); + const int widthInput = armnn::numeric_cast<int>(inputInfo.GetShape()[widthIndex]); + const int padLeft = armnn::numeric_cast<int>(params.m_PadLeft); + const int padRight = armnn::numeric_cast<int>(params.m_PadRight); + const int padTop = armnn::numeric_cast<int>(params.m_PadTop); + const int padBottom = armnn::numeric_cast<int>(params.m_PadBottom); + const int strideX = armnn::numeric_cast<int>(params.m_StrideX); + const int strideY = armnn::numeric_cast<int>(params.m_StrideY); + const int poolHeight = armnn::numeric_cast<int>(params.m_PoolHeight); + const int poolWidth = armnn::numeric_cast<int>(params.m_PoolWidth); float defaultInitializer = DefaultInitializer(params.m_PoolType); @@ -221,10 +222,10 @@ void Pooling2d(Decoder<float>& rInputDecoder, result = 0.0f; unsigned int outputIndex = dataLayout.GetIndex(outputShape, - boost::numeric_cast<unsigned int>(n), - boost::numeric_cast<unsigned int>(c), - boost::numeric_cast<unsigned int>(yOutput), - boost::numeric_cast<unsigned int>(xOutput)); + armnn::numeric_cast<unsigned int>(n), + armnn::numeric_cast<unsigned int>(c), + armnn::numeric_cast<unsigned int>(yOutput), + armnn::numeric_cast<unsigned int>(xOutput)); rOutputEncoder[outputIndex]; rOutputEncoder.Set(result); continue; @@ -244,10 +245,10 @@ void Pooling2d(Decoder<float>& rInputDecoder, for (auto xInput = wstart; xInput < wend; xInput++) { unsigned int inputIndex = dataLayout.GetIndex(inputShape, - boost::numeric_cast<unsigned int>(n), - boost::numeric_cast<unsigned int>(c), - boost::numeric_cast<unsigned int>(yInput), - boost::numeric_cast<unsigned int>(xInput)); + armnn::numeric_cast<unsigned int>(n), + armnn::numeric_cast<unsigned int>(c), + armnn::numeric_cast<unsigned int>(yInput), + armnn::numeric_cast<unsigned int>(xInput)); rInputDecoder[inputIndex]; float inval = rInputDecoder.Get(); @@ -259,10 +260,10 @@ void Pooling2d(Decoder<float>& rInputDecoder, execute(result, poolAreaSize); unsigned int outputIndex = dataLayout.GetIndex(outputShape, - boost::numeric_cast<unsigned int>(n), - boost::numeric_cast<unsigned int>(c), - boost::numeric_cast<unsigned int>(yOutput), - boost::numeric_cast<unsigned int>(xOutput)); + armnn::numeric_cast<unsigned int>(n), + armnn::numeric_cast<unsigned int>(c), + armnn::numeric_cast<unsigned int>(yOutput), + armnn::numeric_cast<unsigned int>(xOutput)); rOutputEncoder[outputIndex]; rOutputEncoder.Set(result); diff --git a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp index 6fec1abe6f..f80901edc9 100644 --- a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp +++ b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp @@ -11,8 +11,7 @@ #include <Profiling.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> - -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> #include <cmath> @@ -39,26 +38,26 @@ void RefL2NormalizationWorkload::Execute() const const TensorShape& shape = inputInfo.GetShape(); unsigned int paddedShapeArray[4]; - const int idxShift = 4 - boost::numeric_cast<int>(shape.GetNumDimensions()); + const int idxShift = 4 - armnn::numeric_cast<int>(shape.GetNumDimensions()); const unsigned int batches = (idxShift == 0) ? shape[0] : 1; paddedShapeArray[0] = batches; - const int channelsIdx = boost::numeric_cast<int>(dataLayout.GetChannelsIndex()); + const int channelsIdx = armnn::numeric_cast<int>(dataLayout.GetChannelsIndex()); const unsigned int channels = (channelsIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)] + ? shape[armnn::numeric_cast<unsigned int>(channelsIdx - idxShift)] : 1; paddedShapeArray[channelsIdx] = channels; - const int heightIdx = boost::numeric_cast<int>(dataLayout.GetHeightIndex()); + const int heightIdx = armnn::numeric_cast<int>(dataLayout.GetHeightIndex()); const unsigned int height = (heightIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)] + ? shape[armnn::numeric_cast<unsigned int>(heightIdx - idxShift)] : 1; paddedShapeArray[heightIdx] = height; - const int widthIdx = boost::numeric_cast<int>(dataLayout.GetWidthIndex()); + const int widthIdx = armnn::numeric_cast<int>(dataLayout.GetWidthIndex()); const unsigned int width = (widthIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)] + ? shape[armnn::numeric_cast<unsigned int>(widthIdx - idxShift)] : 1; paddedShapeArray[widthIdx] = width; diff --git a/src/backends/reference/workloads/RefNormalizationWorkload.cpp b/src/backends/reference/workloads/RefNormalizationWorkload.cpp index a41f68349a..d5d2104cba 100644 --- a/src/backends/reference/workloads/RefNormalizationWorkload.cpp +++ b/src/backends/reference/workloads/RefNormalizationWorkload.cpp @@ -8,11 +8,10 @@ #include <armnn/Logging.hpp> #include <armnn/Tensor.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> +#include <armnn/utility/NumericCast.hpp> #include <Profiling.hpp> -#include <boost/numeric/conversion/cast.hpp> - #include "RefWorkloadUtils.hpp" #include "Decoders.hpp" #include "Encoders.hpp" @@ -37,7 +36,7 @@ void NormalizeWithinUingLbr(Decoder<float>& inputData, 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 */ + int radius = armnn::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ for (unsigned int n = 0; n < batchSize; n++) { @@ -52,23 +51,23 @@ void NormalizeWithinUingLbr(Decoder<float>& inputData, { for (int x = -radius; x <= radius; x++) { - int i = boost::numeric_cast<int>(w) + x; - int j = boost::numeric_cast<int>(h) + y; + int i = armnn::numeric_cast<int>(w) + x; + int j = armnn::numeric_cast<int>(h) + y; - if ((i < 0) || (i >= boost::numeric_cast<int>(cols))) + if ((i < 0) || (i >= armnn::numeric_cast<int>(cols))) { continue; } - if ((j < 0) || (j >= boost::numeric_cast<int>(rows))) + if ((j < 0) || (j >= armnn::numeric_cast<int>(rows))) { continue; } unsigned int inputIndex = n * cols * rows * depth + c * cols * rows + - boost::numeric_cast<unsigned int>(j) * cols + - boost::numeric_cast<unsigned int>(i); + armnn::numeric_cast<unsigned int>(j) * cols + + armnn::numeric_cast<unsigned int>(i); inputData[inputIndex]; float inval = inputData.Get(); @@ -106,7 +105,7 @@ void NormalizeAcrossUingLbr(Decoder<float>& inputData, const unsigned int rows = tensorShape[dataLayoutIndexed.GetHeightIndex()]; const unsigned int cols = tensorShape[dataLayoutIndexed.GetWidthIndex()]; - int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ + int radius = armnn::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ for (unsigned int n = 0; n < batchSize; n++) { @@ -119,16 +118,16 @@ void NormalizeAcrossUingLbr(Decoder<float>& inputData, float accumulated_scale = 0.0; for (int z = -radius; z <= radius; z++) { - int k = boost::numeric_cast<int>(c) + z; + int k = armnn::numeric_cast<int>(c) + z; - if ((k < 0) || (k >= boost::numeric_cast<int>(depth))) + if ((k < 0) || (k >= armnn::numeric_cast<int>(depth))) { continue; } unsigned inputIndex = dataLayoutIndexed.GetIndex(tensorShape, n, - boost::numeric_cast<unsigned int>(k), + armnn::numeric_cast<unsigned int>(k), h, w); diff --git a/src/backends/reference/workloads/StridedSlice.cpp b/src/backends/reference/workloads/StridedSlice.cpp index b00b049ff6..c5fb121cb3 100644 --- a/src/backends/reference/workloads/StridedSlice.cpp +++ b/src/backends/reference/workloads/StridedSlice.cpp @@ -8,8 +8,7 @@ #include <ResolveType.hpp> #include <armnn/utility/Assert.hpp> - -#include <boost/numeric/conversion/cast.hpp> +#include <armnn/utility/NumericCast.hpp> #include <cstring> @@ -24,7 +23,7 @@ void PadParams(StridedSliceDescriptor& p, unsigned int dimCount) ARMNN_ASSERT_MSG(dimCount <= 4, "Expected input with at most 4 dimensions"); const unsigned int beginIndicesCount = - boost::numeric_cast<unsigned int>(p.m_Begin.size()); + armnn::numeric_cast<unsigned int>(p.m_Begin.size()); ARMNN_ASSERT(dimCount >= beginIndicesCount); const unsigned int padCount = dimCount - beginIndicesCount; @@ -116,7 +115,7 @@ void StridedSlice(const TensorInfo& inputInfo, const int start3 = paddedParams.GetStartForAxis(inputShape, 3); const int stop3 = paddedParams.GetStopForAxis (inputShape, 3, start3); - const int step = boost::numeric_cast<int>(dataTypeSize); + const int step = armnn::numeric_cast<int>(dataTypeSize); for (int in0 = start0; !LoopCondition(in0, stop0, paddedParams.m_Stride[0]); @@ -134,9 +133,9 @@ void StridedSlice(const TensorInfo& inputInfo, !LoopCondition(in3, stop3, paddedParams.m_Stride[3]); in3 += paddedParams.m_Stride[3]) { - int dim1 = boost::numeric_cast<int>(inputShape[1]); - int dim2 = boost::numeric_cast<int>(inputShape[2]); - int dim3 = boost::numeric_cast<int>(inputShape[3]); + int dim1 = armnn::numeric_cast<int>(inputShape[1]); + int dim2 = armnn::numeric_cast<int>(inputShape[2]); + int dim3 = armnn::numeric_cast<int>(inputShape[3]); int inputOffset = (((in0 * dim1 + in1) * dim2 + in2) * dim3 + in3) * step; ::memcpy(output, input + inputOffset, dataTypeSize); |