DepthwiseConvolution2dLayer.cpp

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//
// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "DepthwiseConvolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
 
#include <armnn/TypesUtils.hpp>
 
#include <armnnUtils/DataLayoutIndexed.hpp>
 
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
#include <string>
 
using namespace armnnUtils;
 
namespace armnn
{
 
DepthwiseConvolution2dLayer::DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& param,
                                                         const char* name)
    : LayerWithParameters(param.GetNumInputs(), 1, LayerType::DepthwiseConvolution2d, param, name)
{
}
 
void DepthwiseConvolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const
{
    const std::vector<TensorShape>& inputShapes =
    {
        GetInputSlot(0).GetTensorInfo().GetShape(),
        GetInputSlot(1).GetTensorInfo().GetShape()
    };
    const TensorShape filterShape = inputShapes[1];
    unsigned int inputChannels = filterShape[1];
    unsigned int filterWidth = filterShape[3];
    unsigned int filterHeight = filterShape[2];
    unsigned int depthMultiplier = filterShape[0];
 
    fn("FilterWidth",std::to_string(filterWidth));
    fn("FilterHeight",std::to_string(filterHeight));
    fn("DepthMultiplier",std::to_string(depthMultiplier));
    fn("InputChannels",std::to_string(inputChannels));
 
    LayerWithParameters<DepthwiseConvolution2dDescriptor>::SerializeLayerParameters(fn);
}
 
std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    DepthwiseConvolution2dQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::DepthwiseConvolution2d, descriptor, PrepInfoAndDesc(descriptor));
}
 
DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const
{
    auto layer      = CloneBase<DepthwiseConvolution2dLayer>(graph, m_Param, GetName());
    return std::move(layer);
}
 
std::vector<TensorShape>
DepthwiseConvolution2dLayer::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& filterShape = inputShapes[1];
 
    if (inputShape.GetNumDimensions() != 4)
    {
        throw armnn::Exception("Convolutions will always have 4D input.");
    }
 
    if (m_Param.m_StrideX == 0)
    {
        throw armnn::Exception("m_StrideX cannot be 0.");
    }
 
    if (m_Param.m_StrideY == 0)
    {
        throw armnn::Exception("m_StrideY cannot be 0.");
    }
 
 
    DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
    unsigned int inputBatchSize = inputShape[0];
    unsigned int inputHeight    = inputShape[dataLayoutIndex.GetHeightIndex()];
    unsigned int inputWidth     = inputShape[dataLayoutIndex.GetWidthIndex()];
 
    // Expected filter shape: [ 1, H, W, O ] - This shape does NOT depend on the data layout
    // Namely: [ 1, filter height, filter width, output channels ]
 
    unsigned int filterHeight = filterShape[1];
    unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
    unsigned int readHeight   = (inputHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
    unsigned int outputHeight = 1 + (readHeight / m_Param.m_StrideY);
 
    unsigned int filterWidth = filterShape[2];
    unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1);
    unsigned int readWidth   = (inputWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
    unsigned int outputWidth = 1 + (readWidth / m_Param.m_StrideX);
 
    unsigned int outputChannels  = filterShape[3];
    unsigned int outputBatchSize = inputBatchSize;
 
    TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
                              TensorShape{ outputBatchSize, outputHeight, outputWidth, outputChannels } :
                              TensorShape{ outputBatchSize, outputChannels, outputHeight, outputWidth };
 
    return std::vector<TensorShape>{ tensorShape };
}
 
void DepthwiseConvolution2dLayer::ValidateTensorShapesFromInputs()
{
    VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    if (!GetInputSlot(1).GetConnection())
    {
        throw armnn::LayerValidationException("DepthwiseConvolution2dLayer: Weights data should not be null.");
    }
 
    auto inferredShapes = InferOutputShapes({
        GetInputSlot(0).GetTensorInfo().GetShape(),
        GetInputSlot(1).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, "DepthwiseConvolution2dLayer");
}
 
Layer::ImmutableConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef() const
{
    Layer::ImmutableConstantTensors tensors = GetConnectedConstantAsInputTensors();
    return tensors;
}
 
void DepthwiseConvolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace armnn