src/armnn/layers/Convolution2dLayer.cpp

   1 //
   2 // Copyright © 2017 Arm Ltd. All rights reserved.
   3 // See LICENSE file in the project root for full license information.
   4 //
   5 #include "Convolution2dLayer.hpp"
   6
   7 #include "LayerCloneBase.hpp"
   8
   9 #include <armnn/TypesUtils.hpp>
  10 #include <backends/CpuTensorHandle.hpp>
  11 #include <backends/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     if (m_Param.m_BiasEnabled)
  30     {
  31         BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
  32         descriptor.m_Bias = m_Bias.get();
  33     }
  34     return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
  35 }
  36
  37 Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
  38 {
  39     auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
  40
  41     layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
  42
  43     if (layer->m_Param.m_BiasEnabled)
  44     {
  45         layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
  46     }
  47
  48     return std::move(layer);
  49 }
  50
  51 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
  52 {
  53     BOOST_ASSERT(inputShapes.size() == 2);
  54     const TensorShape& inputShape = inputShapes[0];
  55     const TensorShape filterShape = inputShapes[1];
  56
  57     // If we support multiple batch dimensions in the future, then this assert will need to change.
  58     BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
  59
  60     unsigned int inWidth = inputShape[3];
  61     unsigned int inHeight = inputShape[2];
  62     unsigned int inBatchSize = inputShape[0];
  63
  64     unsigned int filterWidth = filterShape[3];
  65     unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth);
  66     unsigned int outWidth =  1+(readWidth / m_Param.m_StrideX);
  67
  68     unsigned int filterHeight = filterShape[2];
  69     unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight);
  70     unsigned int outHeight = 1+(readHeight / m_Param.m_StrideY);
  71
  72     unsigned int outChannels = filterShape[0];
  73     unsigned int outBatchSize = inBatchSize;
  74
  75     return std::vector<TensorShape>({ TensorShape({outBatchSize, outChannels, outHeight, outWidth})});
  76 }
  77
  78 void Convolution2dLayer::ValidateTensorShapesFromInputs()
  79 {
  80     VerifyLayerConnections(1, CHECK_LOCATION());
  81
  82     // check if we m_Weight data is not nullptr
  83     BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
  84
  85     auto inferredShapes = InferOutputShapes({
  86         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
  87         m_Weight->GetTensorInfo().GetShape() });
  88
  89     BOOST_ASSERT(inferredShapes.size() == 1);
  90
  91     ConditionalThrowIfNotEqual<LayerValidationException>(
  92         "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
  93         GetOutputSlot(0).GetTensorInfo().GetShape(),
  94         inferredShapes[0]);
  95 }
  96
  97 Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef()
  98 {
  99     return {m_Weight, m_Bias};
 100 }
 101
 102 } // namespace armnn