1 files changed, 172 insertions, 0 deletions
diff --git a/src/armnn/layers/Convolution3dLayer.cpp b/src/armnn/layers/Convolution3dLayer.cpp
new file mode 100644
index 0000000000..0e38c0b129
--- /dev/null
+++ b/src/armnn/layers/Convolution3dLayer.cpp
@@ -0,0 +1,172 @@
+//
+// 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