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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "DepthwiseConvolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
#include <DataLayoutIndexed.hpp>
using namespace armnnUtils;
namespace armnn
{
DepthwiseConvolution2dLayer::DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& param,
const char* name)
: LayerWithParameters(1, 1, LayerType::DepthwiseConvolution2d, param, name)
{
}
std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const Graph& graph,
const IWorkloadFactory& factory) const
{
// on this level constant data should not be released..
BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
DepthwiseConvolution2dQueueDescriptor descriptor;
descriptor.m_Weight = m_Weight.get();
if (m_Param.m_BiasEnabled)
{
BOOST_ASSERT_MSG(m_Bias != nullptr, "DepthwiseConvolution2dLayer: Bias data should not be null.");
descriptor.m_Bias = m_Bias.get();
}
return factory.CreateDepthwiseConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
}
DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<DepthwiseConvolution2dLayer>(graph, m_Param, GetName());
layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
if (layer->m_Param.m_BiasEnabled)
{
layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
}
return std::move(layer);
}
std::vector<TensorShape>
DepthwiseConvolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
BOOST_ASSERT(inputShapes.size() == 2);
const TensorShape& inputShape = inputShapes[0];
const TensorShape& filterShape = inputShapes[1];
BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int inputBatchSize = inputShape[0];
unsigned int inputHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
unsigned int inputWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
unsigned int inputChannels = inputShape[dataLayoutIndex.GetChannelsIndex()];
// Expected filter shape: [ M, I, H, W ] - This shape does NOT depend on the data layout
// Namely: [ depth multiplier, input channels, filter height, filter width ]
// Output channels = input channels * depthMultiplier
unsigned int depthMultiplier = filterShape[0];
unsigned int filterHeight = filterShape[2];
unsigned int readHeight = (inputHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - filterHeight;
unsigned int outputHeight = 1 + (readHeight / m_Param.m_StrideY);
unsigned int filterWidth = filterShape[3];
unsigned int readWidth = (inputWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - filterWidth;
unsigned int outputWidth = 1 + (readWidth / m_Param.m_StrideX);
unsigned int outputChannels = inputChannels * depthMultiplier;
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(1, CHECK_LOCATION());
// on this level constant data should not be released..
BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null.");
auto inferredShapes = InferOutputShapes({
GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
m_Weight->GetTensorInfo().GetShape()
});
BOOST_ASSERT(inferredShapes.size() == 1);
ConditionalThrowIfNotEqual<LayerValidationException>(
"DepthwiseConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
GetOutputSlot(0).GetTensorInfo().GetShape(),
inferredShapes[0]);
}
Layer::ConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef()
{
return {m_Weight, m_Bias};
}
} // namespace armnn
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