src/armnn/layers/Convolution2dLayer.cpp

   1 //
   2 // Copyright © 2017 Arm Ltd. All rights reserved.
   3 // SPDX-License-Identifier: MIT
   4 //
   5 #include "Convolution2dLayer.hpp"
   6
   7 #include "LayerCloneBase.hpp"
   8
   9 #include <armnn/TypesUtils.hpp>
  10 #include <backendsCommon/CpuTensorHandle.hpp>
  11 #include <backendsCommon/WorkloadFactory.hpp>
  12
  13 namespace armnn
  14 {
  15
  16 Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name)
  17     : LayerWithParameters(1, 1, LayerType::Convolution2d, param, name)
  18 {
  19 }
  20
  21 std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const
  22 {
  23     // on this level constant data should not be released..
  24     BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
  25
  26     Convolution2dQueueDescriptor descriptor;
  27
  28     descriptor.m_Weight = m_Weight.get();
  29
  30     if (m_Param.m_BiasEnabled)
  31     {
  32         BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
  33         descriptor.m_Bias = m_Bias.get();
  34     }
  35     return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
  36 }
  37
  38 Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
  39 {
  40     auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
  41
  42     layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
  43
  44     if (layer->m_Param.m_BiasEnabled)
  45     {
  46         layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
  47     }
  48
  49     return std::move(layer);
  50 }
  51
  52 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
  53 {
  54     BOOST_ASSERT(inputShapes.size() == 2);
  55     const TensorShape& inputShape = inputShapes[0];
  56     const TensorShape filterShape = inputShapes[1];
  57
  58     // If we support multiple batch dimensions in the future, then this assert will need to change.
  59     BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
  60
  61     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
  62
  63     unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
  64     unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
  65     unsigned int inBatchSize = inputShape[0];
  66
  67     unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
  68     unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth);
  69     unsigned int outWidth =  1 + (readWidth / m_Param.m_StrideX);
  70
  71     unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
  72     unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight);
  73     unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY);
  74
  75     unsigned int outChannels = filterShape[0];
  76     unsigned int outBatchSize = inBatchSize;
  77
  78     TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
  79         TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
  80         TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
  81
  82     return std::vector<TensorShape>({ tensorShape });
  83 }
  84
  85 void Convolution2dLayer::ValidateTensorShapesFromInputs()
  86 {
  87     VerifyLayerConnections(1, CHECK_LOCATION());
  88
  89     // check if we m_Weight data is not nullptr
  90     BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
  91
  92     auto inferredShapes = InferOutputShapes({
  93         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
  94         m_Weight->GetTensorInfo().GetShape() });
  95
  96     BOOST_ASSERT(inferredShapes.size() == 1);
  97
  98     ConditionalThrowIfNotEqual<LayerValidationException>(
  99         "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
 100         GetOutputSlot(0).GetTensorInfo().GetShape(),
 101         inferredShapes[0]);
 102 }
 103
 104 Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef()
 105 {
 106     return {m_Weight, m_Bias};
 107 }
 108
 109 } // namespace armnn