| // |
| // Copyright © 2021-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "Pooling3dLayer.hpp" |
| |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| Pooling3dLayer::Pooling3dLayer(const Pooling3dDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::Pooling3d, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> Pooling3dLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| Pooling3dQueueDescriptor descriptor; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::Pooling3d, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| Pooling3dLayer* Pooling3dLayer::Clone(Graph& graph) const |
| { |
| return CloneBase<Pooling3dLayer>(graph, m_Param, GetName()); |
| } |
| |
| std::vector<TensorShape> Pooling3dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| if (inputShapes.size() != 1) |
| { |
| throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"1\"."); |
| } |
| |
| const TensorShape& inputShape = inputShapes[0]; |
| const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout; |
| |
| // If we support multiple batch dimensions in the future, then this assert will need to change. |
| if (inputShape.GetNumDimensions() != 5) |
| { |
| throw armnn::Exception("Pooling3dLayer will always have 5D input."); |
| } |
| |
| unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()]; |
| unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()]; |
| unsigned int inDepth = inputShape[dimensionIndices.GetDepthIndex()]; |
| unsigned int inChannels = inputShape[dimensionIndices.GetChannelsIndex()]; |
| unsigned int inBatchSize = inputShape[0]; |
| |
| bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0 && m_Param.m_StrideZ==0); |
| unsigned int outWidth = 1; |
| unsigned int outHeight = 1; |
| unsigned int outDepth = 1; |
| if (!isGlobalPooling) |
| { |
| if (!m_Param.m_StrideX || !m_Param.m_StrideY || !m_Param.m_StrideZ) |
| { |
| throw armnn::Exception("Stride can only be zero when performing global pooling"); |
| } |
| |
| auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding) |
| { |
| unsigned int readSize = inSize + lowPad + highPad - poolSize; |
| float div = static_cast<float>(readSize) / static_cast<float>(stride); |
| |
| unsigned int size = 0; |
| switch (outputShapeRounding) |
| { |
| case OutputShapeRounding::Ceiling: |
| size = static_cast<unsigned int>(ceil(div)) + 1; |
| break; |
| case OutputShapeRounding ::Floor: |
| size = static_cast<unsigned int>(floor(div)) + 1; |
| break; |
| default: |
| throw armnn::Exception("Unsupported Output Shape Rounding"); |
| } |
| |
| // Makes sure that border operations will start from inside the input and not the padded area. |
| // This is what CL does... |
| if ((size - 1)*stride >= inSize + lowPad) |
| { |
| --size; |
| } |
| |
| return size; |
| }; |
| |
| outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX, |
| m_Param.m_OutputShapeRounding); |
| outHeight = CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY, |
| m_Param.m_OutputShapeRounding); |
| outDepth = CalcSize(inDepth, m_Param.m_PadFront, m_Param.m_PadBack, m_Param.m_PoolDepth, m_Param.m_StrideZ, |
| m_Param.m_OutputShapeRounding); |
| } |
| unsigned int outChannels = inChannels; |
| unsigned int outBatchSize = inBatchSize; |
| |
| TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NDHWC ? |
| TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } ) : |
| TensorShape( { outBatchSize, outChannels, outDepth, outHeight, outWidth }); |
| |
| return std::vector<TensorShape>({ tensorShape }); |
| } |
| |
| void Pooling3dLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetTensorInfo().GetShape() }); |
| |
| if (inferredShapes.size() != 1) |
| { |
| throw armnn::LayerValidationException("inferredShapes has " |
| + std::to_string(inferredShapes.size()) + |
| " elements - should only have 1."); |
| } |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Pooling3dLayer"); |
| } |
| |
| void Pooling3dLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |