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//
// Copyright © 2017 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).GetConnection()->GetTensorInfo().GetShape(),
GetInputSlot(1).GetConnection()->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;
if (m_Weight)
{
descriptor.m_Weight = m_Weight.get();
}
if (m_Param.m_BiasEnabled && m_Bias)
{
descriptor.m_Bias = m_Bias.get();
}
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::DepthwiseConvolution2d, descriptor, PrepInfoAndDesc(descriptor));
}
DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<DepthwiseConvolution2dLayer>(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>
DepthwiseConvolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
ARMNN_ASSERT(inputShapes.size() == 2);
const TensorShape& inputShape = inputShapes[0];
const TensorShape& filterShape = inputShapes[1];
ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
ARMNN_ASSERT( m_Param.m_StrideX > 0);
ARMNN_ASSERT( m_Param.m_StrideY > 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);
ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(),
"DepthwiseConvolution2dLayer: Weights data should not be null.");
auto inferredShapes = InferOutputShapes({
GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
});
ARMNN_ASSERT(inferredShapes.size() == 1);
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "DepthwiseConvolution2dLayer");
}
Layer::ConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef()
{
// For API stability DO NOT ALTER order and add new members to the end of vector
return {m_Weight, m_Bias};
}
ARMNN_NO_DEPRECATE_WARN_BEGIN
void DepthwiseConvolution2dLayer::Accept(ILayerVisitor& visitor) const
{
visitor.VisitDepthwiseConvolution2dLayer(this, GetParameters(), GetName());
}
ARMNN_NO_DEPRECATE_WARN_END
void DepthwiseConvolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
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