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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Convolution3dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <backendsCommon/TensorHandle.hpp>
using namespace armnnUtils;
namespace armnn
{
Convolution3dLayer::Convolution3dLayer(const Convolution3dDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::Convolution3d, param, name)
{
}
void Convolution3dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const
{
const std::vector<TensorShape>& inputShapes =
{
GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
m_Weight->GetTensorInfo().GetShape()
};
// Conv3d Filter Layout: [D,H,W,I,O]
const TensorShape filterShape = inputShapes[1];
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int filterDepth = filterShape[0];
unsigned int filterHeight = filterShape[1];
unsigned int filterWidth = filterShape[2];
unsigned int inChannels = filterShape[3];
unsigned int outChannels = filterShape[4];
fn("FilterDepth",std::to_string(filterDepth));
fn("FilterHeight",std::to_string(filterHeight));
fn("FilterWidth",std::to_string(filterWidth));
fn("InputChannels",std::to_string(inChannels));
fn("OutputChannels",std::to_string(outChannels));
LayerWithParameters<Convolution3dDescriptor>::SerializeLayerParameters(fn);
}
std::unique_ptr<IWorkload> Convolution3dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
// At this level constant data should not be released.
ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution3dLayer: Weights data should not be null.");
Convolution3dQueueDescriptor descriptor;
descriptor.m_Weight = m_Weight.get();
if (m_Param.m_BiasEnabled)
{
ARMNN_ASSERT_MSG(m_Bias != nullptr, "Convolution3dLayer: Bias data should not be null.");
descriptor.m_Bias = m_Bias.get();
}
SetAdditionalInfo(descriptor);
return factory.CreateConvolution3d(descriptor, PrepInfoAndDesc(descriptor));
}
Convolution3dLayer* Convolution3dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<Convolution3dLayer>(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> Convolution3dLayer::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() == 5, "Convolutions will always have 5D input.");
ARMNN_ASSERT( m_Param.m_StrideX > 0);
ARMNN_ASSERT( m_Param.m_StrideY > 0);
ARMNN_ASSERT( m_Param.m_StrideZ > 0);
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
unsigned int inDepth = inputShape[dataLayoutIndex.GetDepthIndex()];
unsigned int inBatchSize = inputShape[0];
// Conv3d Filter Layout: [D,H,W,I,O]
unsigned int filterDepth = filterShape[0];
unsigned int dilatedFilterDepth = filterDepth + (m_Param.m_DilationZ - 1) * (filterDepth - 1);
unsigned int readDepth = (inDepth + m_Param.m_PadFront + m_Param.m_PadBack) - dilatedFilterDepth;
unsigned int outDepth = 1 + (readDepth / m_Param.m_StrideZ);
unsigned int filterHeight = filterShape[1];
unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
unsigned int outHeight = 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 = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX);
unsigned int outChannels = filterShape[4];
unsigned int outBatchSize = inBatchSize;
TensorShape tensorShape = TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } );
return std::vector<TensorShape>({ tensorShape });
}
void Convolution3dLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(1, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
// check if we m_Weight data is not nullptr
ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution3dLayer: Weights data should not be null.");
auto inferredShapes = InferOutputShapes({
GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
m_Weight->GetTensorInfo().GetShape() });
ARMNN_ASSERT(inferredShapes.size() == 1);
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution3dLayer");
}
Layer::ConstantTensors Convolution3dLayer::GetConstantTensorsByRef()
{
return {m_Weight, m_Bias};
}
ARMNN_NO_DEPRECATE_WARN_BEGIN
void Convolution3dLayer::Accept(ILayerVisitor& visitor) const
{
IgnoreUnused(visitor);
throw armnn::Exception("Convolution3dLayer: VisitConvolution3dLayer is not implemented");
}
ARMNN_NO_DEPRECATE_WARN_END
void Convolution3dLayer::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
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