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Diffstat (limited to 'src/backends/aclCommon/ArmComputeUtils.hpp')
| -rw-r--r-- | src/backends/aclCommon/ArmComputeUtils.hpp | 94 |
1 files changed, 94 insertions, 0 deletions
diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp index d9efab288f..624ce5df7a 100644 --- a/src/backends/aclCommon/ArmComputeUtils.hpp +++ b/src/backends/aclCommon/ArmComputeUtils.hpp @@ -7,10 +7,19 @@ #include <armnn/Descriptors.hpp> #include <armnn/Tensor.hpp> #include <armnn/utility/Assert.hpp> +#include <armnn/utility/NumericCast.hpp> #include <backendsCommon/WorkloadData.hpp> #include <arm_compute/core/Types.h> +#if defined(ARMCOMPUTENEON_ENABLED) +#include "neon/workloads/NeonReduceWorkload.hpp" +#endif + +#if defined(ARMCOMPUTECL_ENABLED) +#include "cl/workloads/ClReduceWorkload.hpp" +#endif + namespace armnn { @@ -267,4 +276,89 @@ inline arm_compute::ReductionOperation ConvertReductionOperationToAcl(const Redu } } +/// Function to compute the output tensor shape based on the axes and if keepDims is set. +inline const TensorInfo ComputeReductionTensorShape(const armnn::TensorInfo& input, + const std::vector<uint32_t>& vAxis, + const bool keepDims) +{ + auto reducedTensorInfo = input; + unsigned int rank = reducedTensorInfo.GetNumDimensions(); + unsigned int outputRank = 0; + // Calculate output dimension + if (keepDims) + { + outputRank = rank; + } + else if (vAxis.empty()) + { + outputRank = 1; + } + else if (vAxis.size() > reducedTensorInfo.GetNumDimensions()) + { + throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions"); + } + else + { + outputRank = reducedTensorInfo.GetNumDimensions() - armnn::numeric_cast<unsigned int>(vAxis.size()); + if (outputRank == 0) + { + outputRank = 1; + } + } + std::vector<unsigned int> dimSizes(outputRank, 1); + if (!vAxis.empty()) + { + // Skip the dimension that has been reduced unless keepDims is true. + unsigned int outputIndex = 0; + for (unsigned int i = 0; i < reducedTensorInfo.GetNumDimensions(); ++i) + { + if (std::find(vAxis.begin(), vAxis.end(), i) == vAxis.end()) + { + dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(reducedTensorInfo.GetShape()[i]); + ++outputIndex; + } + else if (keepDims) + { + dimSizes[outputIndex] = 1; + ++outputIndex; + } + } + } + const TensorShape inferredShape = TensorShape(outputRank, dimSizes.data()); + reducedTensorInfo.SetShape(inferredShape); + return reducedTensorInfo; +} + +/// Macro function check if layer with multiple axes is supported on each backend +#define IS_MULTI_AXES_REDUCE_SUPPORTED(func, input, desc, status) \ + armnn::TensorInfo inputTensorInfo = input; \ + unsigned int recalulatedAxis = 0; \ + std::vector<uint32_t> axes; \ + \ + for (unsigned int i = 0; i != desc.m_vAxis.size(); ++i) \ + { \ + axes.emplace_back(desc.m_vAxis[i]); \ + \ + const armnn::TensorInfo& reducedTensorInfo = \ + ComputeReductionTensorShape(input, axes, desc.m_KeepDims); \ + \ + std::vector<uint32_t> singleAxis(1, desc.m_vAxis[i] - recalulatedAxis); \ + \ + armnn::ReduceDescriptor newReduceDescriptor = desc; \ + newReduceDescriptor.m_vAxis.assign(singleAxis.begin(), singleAxis.end()); \ + \ + status = func(inputTensorInfo, reducedTensorInfo, newReduceDescriptor); \ + if (!status) \ + { \ + break; \ + } \ + \ + if (!desc.m_KeepDims) \ + { \ + recalulatedAxis++; \ + } \ + \ + inputTensorInfo = reducedTensorInfo; \ + } + } // namespace armnn |