| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // See LICENSE file in the project root for full license information. |
| // |
| #include "BatchNormalizationLayer.hpp" |
| |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| #include <backends/CpuTensorHandle.hpp> |
| #include <backends/WorkloadFactory.hpp> |
| |
| namespace armnn |
| { |
| |
| BatchNormalizationLayer::BatchNormalizationLayer(const armnn::BatchNormalizationDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::BatchNormalization, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> BatchNormalizationLayer::CreateWorkload(const Graph& graph, |
| const IWorkloadFactory& factory) const |
| { |
| // on this level constant data should not be released.. |
| BOOST_ASSERT_MSG(m_Mean != nullptr, "BatchNormalizationLayer: Mean data should not be null."); |
| BOOST_ASSERT_MSG(m_Variance != nullptr, "BatchNormalizationLayer: Variance data should not be null."); |
| BOOST_ASSERT_MSG(m_Beta != nullptr, "BatchNormalizationLayer: Beta data should not be null."); |
| BOOST_ASSERT_MSG(m_Gamma != nullptr, "BatchNormalizationLayer: Gamma data should not be null."); |
| |
| BatchNormalizationQueueDescriptor descriptor; |
| |
| descriptor.m_Mean = m_Mean.get(); |
| descriptor.m_Variance = m_Variance.get(); |
| descriptor.m_Beta = m_Beta.get(); |
| descriptor.m_Gamma = m_Gamma.get(); |
| |
| return factory.CreateBatchNormalization(descriptor, PrepInfoAndDesc(descriptor, graph)); |
| } |
| |
| BatchNormalizationLayer* BatchNormalizationLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<BatchNormalizationLayer>(graph, m_Param, GetName()); |
| |
| layer->m_Mean = m_Mean ? std::make_unique<ScopedCpuTensorHandle>(*m_Mean) : nullptr; |
| layer->m_Variance = m_Variance ? std::make_unique<ScopedCpuTensorHandle>(*m_Variance) : nullptr; |
| layer->m_Beta = m_Beta ? std::make_unique<ScopedCpuTensorHandle>(*m_Beta) : nullptr; |
| layer->m_Gamma = m_Gamma ? std::make_unique<ScopedCpuTensorHandle>(*m_Gamma) : nullptr; |
| |
| return std::move(layer); |
| } |
| |
| void BatchNormalizationLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() }); |
| |
| BOOST_ASSERT(inferredShapes.size() == 1); |
| |
| ConditionalThrowIfNotEqual<LayerValidationException>( |
| "BatchNormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |
| GetOutputSlot(0).GetTensorInfo().GetShape(), |
| inferredShapes[0]); |
| |
| } |
| |
| Layer::ConstantTensors BatchNormalizationLayer::GetConstantTensorsByRef() |
| { |
| return {m_Mean, m_Variance, m_Beta, m_Gamma}; |
| } |
| |
| } // namespace armnn |