| // |
| // Copyright © 2019-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "TransposeConvolution2dLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| #include <armnn/backends/TensorHandle.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param, |
| const char* name) |
| : LayerWithParameters(1, 1, LayerType::TransposeConvolution2d, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> TransposeConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| if (!m_Weight) |
| { |
| throw armnn::NullPointerException("TransposeConvolution2dLayer: Weights data should not be null."); |
| } |
| |
| TransposeConvolution2dQueueDescriptor descriptor; |
| descriptor.m_Weight = m_Weight.get(); |
| |
| if (m_Param.m_BiasEnabled) |
| { |
| if (!m_Bias) |
| { |
| throw armnn::NullPointerException("TransposeConvolution2dLayer: Bias data should not be null."); |
| } |
| descriptor.m_Bias = m_Bias.get(); |
| } |
| |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::TransposeConvolution2d, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<TransposeConvolution2dLayer>(graph, m_Param, GetName()); |
| |
| layer->m_Weight = m_Weight ? m_Weight : nullptr; |
| |
| if (layer->m_Param.m_BiasEnabled) |
| { |
| layer->m_Bias = m_Bias ? m_Bias : nullptr; |
| } |
| |
| return std::move(layer); |
| } |
| |
| std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes( |
| const std::vector<TensorShape>& inputShapes) const |
| { |
| if (inputShapes.size() != 2) |
| { |
| throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"2\"."); |
| } |
| |
| const TensorShape& inputShape = inputShapes[0]; |
| const TensorShape& kernelShape = inputShapes[1]; |
| |
| if (inputShape.GetNumDimensions() != 4) |
| { |
| throw armnn::Exception("Transpose convolutions will always have 4D input"); |
| } |
| |
| DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); |
| |
| const unsigned int batches = inputShape[0]; |
| |
| const unsigned int wInput = inputShape[dataLayoutIndex.GetWidthIndex()]; |
| const unsigned int hInput = inputShape[dataLayoutIndex.GetHeightIndex()]; |
| |
| const unsigned int wKernel = kernelShape[dataLayoutIndex.GetWidthIndex()]; |
| const unsigned int hKernel = kernelShape[dataLayoutIndex.GetHeightIndex()]; |
| |
| unsigned int wPadding = m_Param.m_PadLeft + m_Param.m_PadRight; |
| unsigned int hPadding = m_Param.m_PadTop + m_Param.m_PadBottom; |
| |
| unsigned int wOutput = (wInput - 1) * m_Param.m_StrideX + wKernel - wPadding; |
| unsigned int hOutput = (hInput - 1) * m_Param.m_StrideY + hKernel - hPadding; |
| unsigned int cOutput = kernelShape[0]; |
| |
| TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ? |
| TensorShape( { batches, hOutput, wOutput, cOutput } ) : |
| TensorShape( { batches, cOutput, hOutput, wOutput }); |
| |
| return std::vector<TensorShape>({ tensorShape }); |
| } |
| |
| void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| if (!m_Weight) |
| { |
| throw armnn::LayerValidationException("TransposeConvolution2dLayer: Weight data cannot be null."); |
| } |
| |
| std::vector<TensorShape> expectedOutputShape; |
| std::vector<TensorShape> outputShapeGivenAsInput; |
| |
| expectedOutputShape = InferOutputShapes({GetInputSlot(0).GetTensorInfo().GetShape(), |
| m_Weight->GetTensorInfo().GetShape() }); |
| |
| if (expectedOutputShape.size() != 1) |
| { |
| throw armnn::LayerValidationException("expectedOutputShape' size is " |
| + std::to_string(expectedOutputShape.size()) + |
| " - should be \"1\"."); |
| } |
| |
| // If output_shape was specified then use it rather than calculate an inferred output shape. |
| if (m_Param.m_OutputShapeEnabled) |
| { |
| TensorShape shapeAsTensorShape(static_cast<unsigned int>(m_Param.m_OutputShape.size()), |
| m_Param.m_OutputShape.data()); |
| outputShapeGivenAsInput.push_back(shapeAsTensorShape); |
| |
| if (outputShapeGivenAsInput.size() != 1) |
| { |
| throw armnn::LayerValidationException("outputShapeGivenAsInput' size is " |
| + std::to_string(outputShapeGivenAsInput.size()) + |
| " - should be \"1\"."); |
| } |
| |
| if (expectedOutputShape != outputShapeGivenAsInput) |
| { |
| throw armnn::LayerValidationException("TransposeConvolution2dLayer: " |
| "output calculated by InferOutputShapes and the output given " |
| "as an input parameter to the layer are not matching"); |
| } |
| } |
| |
| ValidateAndCopyShape(outputShape, expectedOutputShape[0], m_ShapeInferenceMethod, "TransposeConvolution2dLayer"); |
| } |
| |
| Layer::ImmutableConstantTensors TransposeConvolution2dLayer::GetConstantTensorsByRef() const |
| { |
| // For API stability DO NOT ALTER order and add new members to the end of vector |
| return {m_Weight, m_Bias}; |
| } |
| |
| void TransposeConvolution2dLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| ManagedConstTensorHandle managedWeight(m_Weight); |
| std::vector<armnn::ConstTensor> constTensors { { managedWeight.GetTensorInfo(), managedWeight.Map() } }; |
| |
| ManagedConstTensorHandle managedBias(m_Bias); |
| if (GetParameters().m_BiasEnabled) |
| { |
| constTensors.emplace_back(ConstTensor(managedBias.GetTensorInfo(), managedBias.Map())); |
| } |
| |
| strategy.ExecuteStrategy(this, GetParameters(), constTensors, GetName()); |
| } |
| |
| } // namespace armnn |