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//
// Copyright © 2019-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "TransposeConvolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
using namespace armnnUtils;
namespace armnn
{
TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param,
const char* name)
: LayerWithParameters(1, 1, LayerType::TransposeConvolution2d, param, name)
{
}
std::unique_ptr<IWorkload> TransposeConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
if (!m_Weight)
{
throw armnn::NullPointerException("TransposeConvolution2dLayer: Weights data should not be null.");
}
TransposeConvolution2dQueueDescriptor descriptor;
descriptor.m_Weight = m_Weight.get();
if (m_Param.m_BiasEnabled)
{
if (!m_Bias)
{
throw armnn::NullPointerException("TransposeConvolution2dLayer: Bias data should not be null.");
}
descriptor.m_Bias = m_Bias.get();
}
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::TransposeConvolution2d, descriptor, PrepInfoAndDesc(descriptor));
}
TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<TransposeConvolution2dLayer>(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> TransposeConvolution2dLayer::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& kernelShape = inputShapes[1];
if (inputShape.GetNumDimensions() != 4)
{
throw armnn::Exception("Transpose convolutions will always have 4D input");
}
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
const unsigned int batches = inputShape[0];
const unsigned int wInput = inputShape[dataLayoutIndex.GetWidthIndex()];
const unsigned int hInput = inputShape[dataLayoutIndex.GetHeightIndex()];
const unsigned int wKernel = kernelShape[dataLayoutIndex.GetWidthIndex()];
const unsigned int hKernel = kernelShape[dataLayoutIndex.GetHeightIndex()];
unsigned int wPadding = m_Param.m_PadLeft + m_Param.m_PadRight;
unsigned int hPadding = m_Param.m_PadTop + m_Param.m_PadBottom;
unsigned int wOutput = (wInput - 1) * m_Param.m_StrideX + wKernel - wPadding;
unsigned int hOutput = (hInput - 1) * m_Param.m_StrideY + hKernel - hPadding;
unsigned int cOutput = kernelShape[0];
TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
TensorShape( { batches, hOutput, wOutput, cOutput } ) :
TensorShape( { batches, cOutput, hOutput, wOutput });
return std::vector<TensorShape>({ tensorShape });
}
void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(1, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
if (!m_Weight)
{
throw armnn::LayerValidationException("TransposeConvolution2dLayer: Weight data cannot be null.");
}
std::vector<TensorShape> expectedOutputShape;
std::vector<TensorShape> outputShapeGivenAsInput;
expectedOutputShape = InferOutputShapes({GetInputSlot(0).GetTensorInfo().GetShape(),
m_Weight->GetTensorInfo().GetShape() });
if (expectedOutputShape.size() != 1)
{
throw armnn::LayerValidationException("expectedOutputShape' size is "
+ std::to_string(expectedOutputShape.size()) +
" - should be \"1\".");
}
// If output_shape was specified then use it rather than calculate an inferred output shape.
if (m_Param.m_OutputShapeEnabled)
{
TensorShape shapeAsTensorShape(static_cast<unsigned int>(m_Param.m_OutputShape.size()),
m_Param.m_OutputShape.data());
outputShapeGivenAsInput.push_back(shapeAsTensorShape);
if (outputShapeGivenAsInput.size() != 1)
{
throw armnn::LayerValidationException("outputShapeGivenAsInput' size is "
+ std::to_string(outputShapeGivenAsInput.size()) +
" - should be \"1\".");
}
if (expectedOutputShape != outputShapeGivenAsInput)
{
throw armnn::LayerValidationException("TransposeConvolution2dLayer: "
"output calculated by InferOutputShapes and the output given "
"as an input parameter to the layer are not matching");
}
}
ValidateAndCopyShape(outputShape, expectedOutputShape[0], m_ShapeInferenceMethod, "TransposeConvolution2dLayer");
}
Layer::ImmutableConstantTensors TransposeConvolution2dLayer::GetConstantTensorsByRef() const
{
// For API stability DO NOT ALTER order and add new members to the end of vector
return {m_Weight, m_Bias};
}
void TransposeConvolution2dLayer::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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