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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Convolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
namespace armnn
{
Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::Convolution2d, param, name)
{
}
std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const
{
// on this level constant data should not be released..
BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
Convolution2dQueueDescriptor descriptor;
descriptor.m_Weight = m_Weight.get();
if (m_Param.m_BiasEnabled)
{
BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
descriptor.m_Bias = m_Bias.get();
}
return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
}
Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<Convolution2dLayer>(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> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
BOOST_ASSERT(inputShapes.size() == 2);
const TensorShape& inputShape = inputShapes[0];
const TensorShape filterShape = inputShapes[1];
// If we support multiple batch dimensions in the future, then this assert will need to change.
BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
unsigned int inWidth = inputShape[3];
unsigned int inHeight = inputShape[2];
unsigned int inBatchSize = inputShape[0];
unsigned int filterWidth = filterShape[3];
unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth);
unsigned int outWidth = 1+(readWidth / m_Param.m_StrideX);
unsigned int filterHeight = filterShape[2];
unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight);
unsigned int outHeight = 1+(readHeight / m_Param.m_StrideY);
unsigned int outChannels = filterShape[0];
unsigned int outBatchSize = inBatchSize;
return std::vector<TensorShape>({ TensorShape({outBatchSize, outChannels, outHeight, outWidth})});
}
void Convolution2dLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(1, CHECK_LOCATION());
// check if we m_Weight data is not nullptr
BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: 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>(
"Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
GetOutputSlot(0).GetTensorInfo().GetShape(),
inferredShapes[0]);
}
Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef()
{
return {m_Weight, m_Bias};
}
} // namespace armnn
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