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-rw-r--r--src/armnn/InternalTypes.hpp3
-rw-r--r--src/armnn/LayersFwd.hpp2
-rw-r--r--src/armnn/Network.cpp22
-rw-r--r--src/armnn/Network.hpp5
-rw-r--r--src/armnn/layers/TransposeConvolution2dLayer.cpp130
-rw-r--r--src/armnn/layers/TransposeConvolution2dLayer.hpp59
6 files changed, 220 insertions, 1 deletions
diff --git a/src/armnn/InternalTypes.hpp b/src/armnn/InternalTypes.hpp
index a1434eae5e..dc3dc17c02 100644
--- a/src/armnn/InternalTypes.hpp
+++ b/src/armnn/InternalTypes.hpp
@@ -60,9 +60,10 @@ enum class LayerType
Splitter,
StridedSlice,
Subtraction,
+ Switch,
// Last layer goes here.
LastLayer,
- Switch = LastLayer
+ TransposeConvolution2d = LastLayer
};
const char* GetLayerTypeAsCString(LayerType type);
diff --git a/src/armnn/LayersFwd.hpp b/src/armnn/LayersFwd.hpp
index a801431f84..9837cd349d 100644
--- a/src/armnn/LayersFwd.hpp
+++ b/src/armnn/LayersFwd.hpp
@@ -53,6 +53,7 @@
#include "layers/StridedSliceLayer.hpp"
#include "layers/SubtractionLayer.hpp"
#include "layers/SwitchLayer.hpp"
+#include "layers/TransposeConvolution2dLayer.hpp"
namespace armnn
{
@@ -128,5 +129,6 @@ DECLARE_LAYER(Splitter)
DECLARE_LAYER(StridedSlice)
DECLARE_LAYER(Subtraction)
DECLARE_LAYER(Switch)
+DECLARE_LAYER(TransposeConvolution2d)
}
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index 75b63e49f6..9436fc6f9c 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -1008,6 +1008,28 @@ IConnectableLayer* Network::AddPreluLayer(const char* name)
return m_Graph->AddLayer<PreluLayer>(name);
}
+IConnectableLayer* Network::AddTransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& descriptor,
+ const ConstTensor& weights,
+ const Optional<ConstTensor>& biases,
+ const char* name)
+{
+ if (descriptor.m_BiasEnabled && !biases.has_value())
+ {
+ throw InvalidArgumentException("AddTransposeConvolution2dLayer: Biases cannot be empty");
+ }
+
+ const auto layer = m_Graph->AddLayer<TransposeConvolution2dLayer>(descriptor, name);
+
+ layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights);
+
+ if (descriptor.m_BiasEnabled)
+ {
+ layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(biases.value());
+ }
+
+ return layer;
+}
+
void Network::Accept(ILayerVisitor& visitor) const
{
for (auto layer : GetGraph())
diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp
index e1379d0014..b90e3c2f8d 100644
--- a/src/armnn/Network.hpp
+++ b/src/armnn/Network.hpp
@@ -187,6 +187,11 @@ public:
IConnectableLayer* AddPreluLayer(const char* name = nullptr) override;
+ IConnectableLayer* AddTransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& descriptor,
+ const ConstTensor& weights,
+ const Optional<ConstTensor>& biases,
+ const char* name = nullptr) override;
+
void Accept(ILayerVisitor& visitor) const override;
private:
diff --git a/src/armnn/layers/TransposeConvolution2dLayer.cpp b/src/armnn/layers/TransposeConvolution2dLayer.cpp
new file mode 100644
index 0000000000..69f598d288
--- /dev/null
+++ b/src/armnn/layers/TransposeConvolution2dLayer.cpp
@@ -0,0 +1,130 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "TransposeConvolution2dLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <DataLayoutIndexed.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 Graph& graph,
+ const IWorkloadFactory& factory) const
+{
+ BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null.");
+
+ TransposeConvolution2dQueueDescriptor descriptor;
+ descriptor.m_Weight = m_Weight.get();
+
+ if (m_Param.m_BiasEnabled)
+ {
+ BOOST_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null.");
+ descriptor.m_Bias = m_Bias.get();
+ }
+
+ return factory.CreateTransposeConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) const
+{
+ auto layer = CloneBase<TransposeConvolution2dLayer>(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> TransposeConvolution2dLayer::InferOutputShapes(
+ const std::vector<TensorShape>& inputShapes) const
+{
+ BOOST_ASSERT(inputShapes.size() == 2);
+ const TensorShape& inputShape = inputShapes[0];
+ const TensorShape& kernelShape = inputShapes[1];
+
+ BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input");
+
+ DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
+
+ unsigned int inBatchSize = inputShape[0];
+ unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
+ unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
+ unsigned int inChannels = inputShape[dataLayoutIndex.GetChannelsIndex()];
+
+ unsigned int kernelWidth = kernelShape[dataLayoutIndex.GetWidthIndex()];
+ unsigned int kernelHeight = kernelShape[dataLayoutIndex.GetHeightIndex()];
+
+ unsigned int totalPaddingX = m_Param.m_PadLeft + m_Param.m_PadRight;
+ unsigned int totalPaddingY = m_Param.m_PadTop + m_Param.m_PadBottom;
+
+ unsigned int outWidth = m_Param.m_StrideX * (inWidth + 1) - totalPaddingX + kernelWidth;
+ unsigned int outHeight = m_Param.m_StrideY * (inHeight + 1) - totalPaddingY + kernelHeight;
+
+ unsigned int outChannels = inChannels;
+ unsigned int outBatchSize = inBatchSize;
+
+ TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
+ TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
+ TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
+
+ return std::vector<TensorShape>({ tensorShape });
+}
+
+void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs()
+{
+ VerifyLayerConnections(1, CHECK_LOCATION());
+
+ BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null.");
+
+ auto inferredShapes = InferOutputShapes({
+ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
+ m_Weight->GetTensorInfo().GetShape() });
+
+ BOOST_ASSERT(inferredShapes.size() == 1);
+
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "TransposeConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+ GetOutputSlot(0).GetTensorInfo().GetShape(),
+ inferredShapes[0]);
+}
+
+Layer::ConstantTensors TransposeConvolution2dLayer::GetConstantTensorsByRef()
+{
+ return {m_Weight, m_Bias};
+}
+
+void TransposeConvolution2dLayer::Accept(ILayerVisitor& visitor) const
+{
+ ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true)) ;
+ Optional<ConstTensor> optionalBiasTensor = EmptyOptional();
+
+ if (GetParameters().m_BiasEnabled)
+ {
+ ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true));
+ optionalBiasTensor = Optional<ConstTensor>(biasTensor);
+ }
+
+ visitor.VisitTransposeConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
+}
+
+} // namespace armnn
diff --git a/src/armnn/layers/TransposeConvolution2dLayer.hpp b/src/armnn/layers/TransposeConvolution2dLayer.hpp
new file mode 100644
index 0000000000..4dc4644a3c
--- /dev/null
+++ b/src/armnn/layers/TransposeConvolution2dLayer.hpp
@@ -0,0 +1,59 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "LayerWithParameters.hpp"
+
+namespace armnn
+{
+
+class ScopedCpuTensorHandle;
+
+/// This layer represents a 2D transpose convolution operation.
+class TransposeConvolution2dLayer : public LayerWithParameters<TransposeConvolution2dDescriptor>
+{
+public:
+ /// A unique pointer to store weight values.
+ std::unique_ptr<ScopedCpuTensorHandle> m_Weight;
+ /// A unique pointer to store bias values.
+ std::unique_ptr<ScopedCpuTensorHandle> m_Bias;
+
+ /// Makes a workload for the TransposeConvolution2d type.
+ /// @param [in] graph The graph where this layer can be found.
+ /// @param [in] factory The workload factory which will create the workload.
+ /// @return A pointer to the created workload, or nullptr if not created.
+ virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph& graph,
+ const IWorkloadFactory& factory) const override;
+
+ /// Creates a dynamically-allocated copy of this layer.
+ /// @param [in] graph The graph into which this layer is being cloned.
+ TransposeConvolution2dLayer* Clone(Graph& graph) const override;
+
+ /// Check if the input tensor shape(s)
+ /// will lead to a valid configuration of @ref TransposeConvolution2dLayer.
+ void ValidateTensorShapesFromInputs() override;
+
+ /// Infers the output shapes from given input shapes and layer properties.
+ /// @param [in] inputShapes The input shapes the layer has.
+ /// @return A vector of the inferred output shape.
+ std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
+ void Accept(ILayerVisitor& visitor) const override;
+
+protected:
+ /// Constructor to create a TransposeConvolution2dLayer.
+ /// @param [in] param TransposeConvolution2dDescriptor to configure the 2D transpose convolution operation.
+ /// @param [in] name Optional name for the layer.
+ TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param, const char* name);
+
+ /// Default destructor
+ ~TransposeConvolution2dLayer() = default;
+
+ /// Retrieve the handles to the constant values stored by the layer.
+ /// @return A vector of the constant tensors stored by this layer.
+ ConstantTensors GetConstantTensorsByRef() override;
+};
+
+} // namespace armnn
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-06 15:01:06 +0000