| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // See LICENSE file in the project root for full license information. |
| // |
| #include "FullyConnectedLayer.hpp" |
| |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| #include <backends/CpuTensorHandle.hpp> |
| #include <backends/WorkloadData.hpp> |
| #include <backends/WorkloadFactory.hpp> |
| |
| namespace armnn |
| { |
| |
| FullyConnectedLayer::FullyConnectedLayer(const FullyConnectedDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::FullyConnected, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> FullyConnectedLayer::CreateWorkload(const Graph& graph, |
| const IWorkloadFactory& factory) const |
| { |
| // on this level constant data should not be released.. |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null."); |
| |
| FullyConnectedQueueDescriptor descriptor; |
| |
| descriptor.m_Weight = m_Weight.get(); |
| if (m_Param.m_BiasEnabled) |
| { |
| BOOST_ASSERT_MSG(m_Bias != nullptr, "FullyConnectedLayer: Bias data should not be null."); |
| descriptor.m_Bias = m_Bias.get(); |
| } |
| return factory.CreateFullyConnected(descriptor, PrepInfoAndDesc(descriptor, graph)); |
| } |
| |
| FullyConnectedLayer* FullyConnectedLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<FullyConnectedLayer>(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> FullyConnectedLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| BOOST_ASSERT(inputShapes.size() == 2); |
| const TensorShape& inputShape = inputShapes[0]; |
| const TensorShape weightShape = inputShapes[1]; |
| |
| // Output for FC is [1, w[1]]. |
| unsigned int batches = inputShape[0]; |
| unsigned int dimIdx = m_Param.m_TransposeWeightMatrix ? 0 : 1; |
| |
| return std::vector<TensorShape>({ TensorShape({batches, weightShape[dimIdx]})}); |
| } |
| |
| void FullyConnectedLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| // check if we m_Weight data is not nullptr |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null."); |
| |
| auto inferredShapes = InferOutputShapes({ |
| GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), |
| m_Weight->GetTensorInfo().GetShape() }); |
| |
| BOOST_ASSERT(inferredShapes.size() == 1); |
| |
| ConditionalThrowIfNotEqual<LayerValidationException>( |
| "FullyConnectedLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |
| GetOutputSlot(0).GetTensorInfo().GetShape(), |
| inferredShapes[0]); |
| } |
| |
| Layer::ConstantTensors FullyConnectedLayer::GetConstantTensorsByRef() |
| { |
| return {m_Weight, m_Bias}; |
| } |
| |
| } // namespace armnn |