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Diffstat (limited to 'src/armnn/Layers.cpp')
| -rw-r--r-- | src/armnn/Layers.cpp | 986 |
1 files changed, 986 insertions, 0 deletions
diff --git a/src/armnn/Layers.cpp b/src/armnn/Layers.cpp new file mode 100644 index 0000000000..ddbc7d222c --- /dev/null +++ b/src/armnn/Layers.cpp @@ -0,0 +1,986 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#include "Layers.hpp" +#include "Graph.hpp" + +#include "backends/CpuTensorHandle.hpp" +#include "backends/Workload.hpp" +#include "backends/WorkloadFactory.hpp" + +#include "Permute.hpp" + + +namespace armnn +{ + +template <typename LayerType, typename ... Params> +LayerType* Layer::CloneBase(Graph& graph, Params&& ... params) const +{ + LayerType* const layer = graph.AddLayer<LayerType>(std::forward<Params>(params)...); + + layer->SetComputeDevice(m_ComputeDevice); + + return layer; +} + +ActivationLayer::ActivationLayer(const ActivationDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Activation, param, name) +{ +} + +std::unique_ptr<IWorkload> ActivationLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + ActivationQueueDescriptor descriptor; + return factory.CreateActivation(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +ActivationLayer* ActivationLayer::Clone(Graph& graph) const +{ + return CloneBase<ActivationLayer>(graph, m_Param, GetName()); +} + +void ActivationLayer::ValidateTensorShapesFromInputs() +{ + auto& info = GetInputSlot(0).GetConnection()->GetTensorInfo(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(info.GetShape()), + "ActivationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +AdditionLayer::AdditionLayer(const char* name) + : Layer(2, 1, LayerType::Addition, name) +{ +} + +std::unique_ptr<IWorkload> AdditionLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + AdditionQueueDescriptor descriptor; + return factory.CreateAddition(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +AdditionLayer* AdditionLayer::Clone(Graph& graph) const +{ + return CloneBase<AdditionLayer>(graph, GetName()); +} + +void AdditionLayer::ValidateTensorShapesFromInputs() +{ + auto& input0 = GetInputSlot(0).GetConnection()->GetTensorInfo(); + auto& input1 = GetInputSlot(1).GetConnection()->GetTensorInfo(); + + // Get the max of the inputs + BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions()); + unsigned int numDims = input0.GetNumDimensions(); + std::vector<unsigned int> dims(numDims); + + // validate inputs are broadcast compatible +#if !NDEBUG + for (unsigned int i = 0; i < numDims; i++) + { + unsigned int dim0 = input0.GetShape()[i]; + unsigned int dim1 = input1.GetShape()[i]; + if (dim0 != dim1) + { + BOOST_ASSERT_MSG(dim0 == 1 || dim1 == 1, "Dimensions should either match or one should be one length"); + } + } +#endif + + + for (unsigned int i = 0; i < numDims; i++) + { + unsigned int dim0 = input0.GetShape()[i]; + unsigned int dim1 = input1.GetShape()[i]; + dims[i] = std::max(dim0, dim1); + } + + TensorShape outShape(numDims, dims.data()); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "AdditionLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +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 +{ + 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() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "BatchNormalizationLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "BatchNormalizationLayer: TensorInfo must be set on connected OutputSlot."); + + auto& info = GetInputSlot(0).GetConnection()->GetTensorInfo(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(info.GetShape()), + "BatchNormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Convolution2d, param, name) +{ +} + +std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + Convolution2dQueueDescriptor descriptor; + + descriptor.m_Weight = m_Weight.get(); + if (m_Param.m_BiasEnabled) + { + descriptor.m_Bias = m_Bias.get(); + } + return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase<Convolution2dLayer>(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); +} + +void Convolution2dLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "Convolution2dLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "Convolution2dLayer: TensorInfo must be set on connected OutputSlot."); + + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + const TensorShape& inputShape = input->GetTensorInfo().GetShape(); + const TensorShape filterShape = m_Weight->GetTensorInfo().GetShape(); + + // If we support multiple batch dimensions in the future, then this assert will need to change. + BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input."); + + unsigned int inWidth = inputShape[3]; + unsigned int inHeight = inputShape[2]; + unsigned int inBatchSize = inputShape[0]; + + unsigned int filterWidth = filterShape[3]; + unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth); + unsigned int outWidth = 1+(readWidth / m_Param.m_StrideX); + + unsigned int filterHeight = filterShape[2]; + unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight); + unsigned int outHeight = 1+(readHeight / m_Param.m_StrideY); + + unsigned int outChannels = filterShape[0]; + unsigned int outBatchSize = inBatchSize; + + TensorShape shapeOut({outBatchSize, outChannels, outHeight, outWidth}); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(shapeOut), + "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + + +DepthwiseConvolution2dLayer::DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& param, + const char* name) + : LayerWithParameters(1, 1, LayerType::DepthwiseConvolution2d, param, name) +{ +} + +std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + DepthwiseConvolution2dQueueDescriptor descriptor; + + descriptor.m_Weight = m_Weight.get(); + if (m_Param.m_BiasEnabled) + { + descriptor.m_Bias = m_Bias.get(); + } + return factory.CreateDepthwiseConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase<DepthwiseConvolution2dLayer>(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); +} + +void DepthwiseConvolution2dLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "DepthwiseConvolution2dLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "DepthwiseConvolution2dLayer: TensorInfo must be set on connected OutputSlot."); + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + const TensorShape& inputShape = input->GetTensorInfo().GetShape(); + const TensorShape filterShape = m_Weight->GetTensorInfo().GetShape(); + + BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input."); + + unsigned int inWidth = inputShape[3]; + unsigned int inHeight = inputShape[2]; + unsigned int inBatchSize = inputShape[0]; + + unsigned int filterWidth = filterShape[3]; + unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth); + unsigned int outWidth = 1+(readWidth / m_Param.m_StrideX); + + unsigned int filterHeight = filterShape[2]; + unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight); + unsigned int outHeight = 1+(readHeight / m_Param.m_StrideY); + unsigned int depthMultiplier = filterShape[0]; + + unsigned int outChannels = filterShape[1]*depthMultiplier; + unsigned int outBatchSize = inBatchSize; + + TensorShape outShape({outBatchSize, outChannels, outHeight, outWidth}); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "DepthwiseConvolution2dLayer: " + "TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +FakeQuantizationLayer::FakeQuantizationLayer(const FakeQuantizationDescriptor& param, const char* name) +: LayerWithParameters(1, 1, LayerType::FakeQuantization, param, name) +{ +} + +std::unique_ptr<IWorkload> FakeQuantizationLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + FakeQuantizationQueueDescriptor descriptor; + return factory.CreateFakeQuantization(descriptor, PrepInfoAndDesc(descriptor, graph) ); +} + +FakeQuantizationLayer* FakeQuantizationLayer::Clone(Graph& graph) const +{ + return CloneBase<FakeQuantizationLayer>(graph, m_Param, GetName()); +} + +void FakeQuantizationLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "FakeQuantizationLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "FakeQuantizationLayer: TensorInfo must be set on connected OutputSlot."); + + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + + // input and output shapes are the same + TensorShape const& outShape = input->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "FakeQuantizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +FloorLayer::FloorLayer(const char* name) + : Layer(1, 1, LayerType::Floor, name) +{ +} + +std::unique_ptr<IWorkload> FloorLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + FloorQueueDescriptor descriptor; + return factory.CreateFloor(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +FloorLayer* FloorLayer::Clone(Graph& graph) const +{ + return CloneBase<FloorLayer>(graph, GetName()); +} + +void FloorLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "FloorLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "FloorLayer: TensorInfo must be set on connected OutputSlot."); + + // input and output shapes are the same + IOutputSlot* input = GetInputSlot(0).GetConnection(); + TensorShape const& outShape = input->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "FloorLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +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 +{ + FullyConnectedQueueDescriptor descriptor; + + descriptor.m_Weight = m_Weight.get(); + if (m_Param.m_BiasEnabled) + { + 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); +} + +void FullyConnectedLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "FullyConnectedLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "FullyConnectedLayer: TensorInfo must be set on connected OutputSlot."); + + + TensorShape const& weightShape = m_Weight->GetTensorInfo().GetShape(); + + // output for FC is [1, w[1]] + unsigned int batches = GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()[0]; + unsigned int dimIdx = m_Param.m_TransposeWeightMatrix ? 0 : 1; + TensorShape outShape({batches, weightShape[dimIdx]}); + + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "FullyConnectedLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +InputLayer::InputLayer(LayerBindingId id, const char* name) + : BindableLayer(0, 1, LayerType::Input, name, id) +{ +} + +std::unique_ptr<IWorkload> InputLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + return nullptr; +} + +InputLayer* InputLayer::Clone(Graph& graph) const +{ + return CloneBase<InputLayer>(graph, GetBindingId(), GetName()); +} + +void InputLayer::ValidateTensorShapesFromInputs() +{ + //The input layer should already have it's inputs set during graph building phase in the driver/parser. + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).IsTensorInfoSet(), + "InputLayer should already have the TensorInfo set."); +} + + +MergerLayer::MergerLayer(const OriginsDescriptor& param, const char* name) + : LayerWithParameters(param.GetNumViews(), 1, LayerType::Merger, param, name) +{ +} + +std::unique_ptr<IWorkload> MergerLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + MergerQueueDescriptor descriptor; + + // copy the view origins to the descriptor + descriptor.m_ViewOrigins.reserve(m_Param.GetNumViews()); + for (unsigned int i = 0; i < m_Param.GetNumViews(); ++i) + { + descriptor.m_ViewOrigins.emplace_back( + std::vector<unsigned int>(m_Param.GetViewOrigin(i), m_Param.GetViewOrigin(i) + m_Param.GetNumDimensions())); + } + + return factory.CreateMerger(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +void MergerLayer::CreateTensorHandles(Graph& graph, const IWorkloadFactory& factory) +{ + //if sub tensors are supported than the merger + //just needs to make sure that the outputs of the prev layer + //are made subtensors of the output of the merger layer + m_OutputHandlers[0].CreateTensorHandles(factory); + if (factory.SupportsSubTensors()) + { + const unsigned int numInputSlots = GetNumInputSlots(); + for (unsigned int i = 0; i < numInputSlots; ++i) + { + OutputHandler& outputHandler = GetInputSlot(i).GetConnectedOutputSlot()->GetOutputHandler(); + + outputHandler.SetData(factory.CreateSubTensorHandle(*m_OutputHandlers[0].GetData(), + outputHandler.GetTensorInfo().GetShape(), + m_Param.GetViewOrigin(i))); + } + } +} + +MergerLayer* MergerLayer::Clone(Graph& graph) const +{ + return CloneBase<MergerLayer>(graph, m_Param, GetName()); +} + +void MergerLayer::ValidateTensorShapesFromInputs() +{ + // Validate Merger layer + ConditionalThrow<LayerValidationException>(m_Param.GetNumViews() == GetNumInputSlots(), + "MergerLayer: Num Inputs must match num views."); + + unsigned int numDims = m_Param.GetNumDimensions(); + for (unsigned int i=0; i<GetNumInputSlots(); i++) + { + auto& inputInfo = GetInputSlot(i).GetConnection()->GetTensorInfo(); + + boost::ignore_unused(inputInfo); + ConditionalThrow<LayerValidationException>(numDims == inputInfo.GetNumDimensions(), + "MergerLayer: Num Dimensions must match all inputs."); + } + + // Find the bounding box (extents) of all the views + std::vector<unsigned int> extentMin(numDims); + std::vector<unsigned int> extentMax(numDims); + for (unsigned int i = 0; i < GetNumInputSlots(); i++) + { + const uint32_t* origin = m_Param.GetViewOrigin(i); + const armnn::TensorShape& shape = GetInputSlot(i).GetConnection()->GetTensorInfo().GetShape(); + for (unsigned int d = 0; d < numDims; d++) + { + extentMin[d] = std::min(extentMin[d], origin[d]); + extentMax[d] = std::max(extentMax[d], origin[d] + shape[d]); + } + } + + // Check that the bounding box starts at the origin + if (!std::all_of(extentMin.begin(), extentMin.end(), [](unsigned int s) { return s == 0; })) + { + throw LayerValidationException("MergerLayer: there is no view that starts at the origin"); + } + + // Check that there are no overlaps of views (this would lead to undefined output at those locations). + // Check each pair of views against each other + // (and don't bother to check against self, or check the same pair both ways round) + for (unsigned int a = 0; a < GetNumInputSlots(); a++) + { + const uint32_t* aOrigin = m_Param.GetViewOrigin(a); + const armnn::TensorShape& aShape = GetInputSlot(a).GetConnection()->GetTensorInfo().GetShape(); + for (unsigned int b = 0; b < a; b++) + { + const uint32_t* bOrigin = m_Param.GetViewOrigin(b); + const armnn::TensorShape& bShape = GetInputSlot(b).GetConnection()->GetTensorInfo().GetShape(); + + bool allAxesOverlap = true; + for (unsigned int d = 0; d < numDims && allAxesOverlap; d++) + { + unsigned int a1 = aOrigin[d]; + unsigned int a2 = aOrigin[d] + aShape[d]; + + unsigned int b1 = bOrigin[d]; + unsigned int b2 = bOrigin[d] + bShape[d]; + + if (a2 <= b1 || b2 <= a1) + { + allAxesOverlap = false; + } + } + if (allAxesOverlap) + { + throw LayerValidationException("MergerLayer: Some views overlap."); + } + } + } + + // Check that there are no "holes", i.e. regions of the output which is not covered by a view. + // Because we already checked that there are no overlaps, this can be done simply by checking that + // the total 'volume' of the views is the same as the output. + unsigned int totalViewsVolume = 0; + for (unsigned int i = 0; i < GetNumInputSlots(); i++) + { + totalViewsVolume += GetInputSlot(i).GetConnection()->GetTensorInfo().GetNumElements(); + } + unsigned int outputVolume = 1; + for (unsigned int d = 0; d < numDims; d++) + { + outputVolume *= (extentMax[d] - extentMin[d]); + } + if (totalViewsVolume != outputVolume) + { + throw LayerValidationException("MergerLayer: there are some gaps between views"); + } + + TensorShape outShape(numDims, extentMax.data()); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "MergerLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +MultiplicationLayer::MultiplicationLayer(const char* name) + : Layer(2, 1, LayerType::Multiplication, name) +{ +} + +std::unique_ptr<IWorkload> MultiplicationLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + MultiplicationQueueDescriptor descriptor; + + return factory.CreateMultiplication(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +MultiplicationLayer* MultiplicationLayer::Clone(Graph& graph) const +{ + return CloneBase<MultiplicationLayer>(graph, GetName()); +} + +void MultiplicationLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() == + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), + "MultiplicationLayer: Inputs must match"); + + TensorInfo infoOut(GetInputSlot(0).GetConnection()->GetTensorInfo()); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(infoOut.GetShape()), + "MultiplicationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +NormalizationLayer::NormalizationLayer(const NormalizationDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Normalization, param, name) +{ +} + +std::unique_ptr<IWorkload> NormalizationLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + NormalizationQueueDescriptor descriptor; + return factory.CreateNormalization(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +NormalizationLayer* NormalizationLayer::Clone(Graph& graph) const +{ + return CloneBase<NormalizationLayer>(graph, m_Param, GetName()); +} + +void NormalizationLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "NormalizationLayer: Input slot must be connected."); + + const TensorShape& outShape = GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "NormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +OutputLayer::OutputLayer(LayerBindingId id, const char* name) + : BindableLayer(1, 0, LayerType::Output, name, id) +{ +} + +std::unique_ptr<IWorkload> OutputLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + return nullptr; +} + +OutputLayer* OutputLayer::Clone(Graph& graph) const +{ + return CloneBase<OutputLayer>(graph, GetBindingId(), GetName()); +} + +void OutputLayer::ValidateTensorShapesFromInputs() +{ + // Just validate the input is connected + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "OutputLayer: Input slot must be connected."); +} + +PermuteLayer::PermuteLayer(const PermuteDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Permute, param, name) +{ +} + +std::unique_ptr<IWorkload> PermuteLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + PermuteQueueDescriptor descriptor; + return factory.CreatePermute(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +PermuteLayer* PermuteLayer::Clone(Graph& graph) const +{ + return CloneBase<PermuteLayer>(graph, m_Param, GetName()); +} + +void PermuteLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "PermuteLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "PermuteLayer: TensorInfo must be set on connected InputSlot."); + + const TensorInfo& infoIn = GetInputSlot(0).GetConnection()->GetTensorInfo(); + TensorShape shapeOut = armnnUtils::Permuted(infoIn.GetShape(), m_Param.m_DimMappings); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(shapeOut), + "PermuteLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +Pooling2dLayer::Pooling2dLayer(const Pooling2dDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Pooling2d, param, name) +{ +} + +std::unique_ptr<IWorkload> Pooling2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + Pooling2dQueueDescriptor descriptor; + return factory.CreatePooling2d(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const +{ + return CloneBase<Pooling2dLayer>(graph, m_Param, GetName()); +} + +void Pooling2dLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "Pooling2dLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "Pooling2dLayer: TensorInfo must be set on connected InputSlot."); + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + const TensorShape& inputShape = input->GetTensorInfo().GetShape(); + + // If we support multiple batch dimensions in the future, then this assert will need to change. + BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input."); + + + unsigned int inWidth = inputShape[3]; + unsigned int inHeight = inputShape[2]; + unsigned int inChannels = inputShape[1]; + unsigned int inBatchSize = inputShape[0]; + + bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0); + unsigned int outWidth = 1; + unsigned int outHeight = 1; + if (!isGlobalPooling) + { + BOOST_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0, + "Stride can only be zero when performing global pooling"); + + auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto padMethod, + auto outputShapeRounding) + { + unsigned int readSize = inSize + lowPad + highPad - poolSize; + float div = static_cast<float>(readSize) / static_cast<float>(stride); + + unsigned int size = 0; + switch (outputShapeRounding) + { + case OutputShapeRounding::Ceiling: + size = static_cast<unsigned int>(ceil(div)) + 1; + break; + case OutputShapeRounding ::Floor: + size = static_cast<unsigned int>(floor(div)) + 1; + break; + default: + BOOST_ASSERT_MSG(false, "Unsupported Output Shape Rounding"); + } + + // Make sure that border operations will start from inside the input and not the padded area + // This is what both Caffe and CL does... + if ((size - 1)*stride >= inSize + lowPad) + { + --size; + } + + return size; + }; + + outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX, + m_Param.m_PaddingMethod, m_Param.m_OutputShapeRounding); + outHeight= CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY, + m_Param.m_PaddingMethod, m_Param.m_OutputShapeRounding); + + + } + unsigned int outChannels = inChannels; + unsigned int outBatchSize = inBatchSize; + + TensorShape shapeOut({outBatchSize, outChannels, outHeight, outWidth}); + + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(shapeOut), + "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +SoftmaxLayer::SoftmaxLayer(const SoftmaxDescriptor ¶m, const char* name) + : LayerWithParameters(1, 1, LayerType::Softmax, param, name) +{ +} + +std::unique_ptr<IWorkload> SoftmaxLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + SoftmaxQueueDescriptor descriptor; + return factory.CreateSoftmax(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +SoftmaxLayer* SoftmaxLayer::Clone(Graph& graph) const +{ + return CloneBase<SoftmaxLayer>(graph, m_Param, GetName()); +} + +void SoftmaxLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "SoftmaxLayer: Input slot must be connected."); + const TensorShape& outShape = GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "SoftmaxLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +SplitterLayer::SplitterLayer(const ViewsDescriptor& param, const char* name) + : LayerWithParameters(1, param.GetNumViews(), LayerType::Splitter, param, name) +{ +} + +std::unique_ptr<IWorkload> SplitterLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + SplitterQueueDescriptor descriptor; + + // copy the window origins to the descriptor + for (unsigned int i = 0; i < m_Param.GetNumViews(); ++i) + { + descriptor.m_ViewOrigins.emplace_back( + std::vector<unsigned int>(m_Param.GetViewOrigin(i), m_Param.GetViewOrigin(i) + m_Param.GetNumDimensions())); + } + + return factory.CreateSplitter(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +void SplitterLayer::CreateTensorHandles(Graph& graph, const IWorkloadFactory& factory) +{ + //if sub tensors are supported than all the "splitter" need to do is to + //set the outputs to be appropriate sub tensors of the input. + if (factory.SupportsSubTensors()) + { + const OutputHandler& outputHandler = GetInputSlots()[0].GetConnectedOutputSlot()->GetOutputHandler(); + + ITensorHandle* inputData = outputHandler.GetData(); + //create the outputs as subtensors of the input + for (unsigned int i = 0; i < m_Param.GetNumViews(); ++i) + { + m_OutputHandlers[i].SetData(factory.CreateSubTensorHandle(*inputData, + m_OutputHandlers[i].GetTensorInfo().GetShape(), + m_Param.GetViewOrigin(i))); + } + } + else + { + for (unsigned int i = 0; i < m_Param.GetNumViews(); ++i) + { + m_OutputHandlers[i].CreateTensorHandles(factory); + } + } +} + +SplitterLayer* SplitterLayer::Clone(Graph& graph) const +{ + return CloneBase<SplitterLayer>(graph, m_Param, GetName()); +} + +void SplitterLayer::ValidateTensorShapesFromInputs() +{ + //Output shapes must match View shapes. + for (unsigned int viewIdx = 0; viewIdx < m_Param.GetNumViews(); viewIdx++) + { + const uint32_t* sizes = m_Param.GetViewSizes(viewIdx); + + TensorShape outShape(m_Param.GetNumDimensions(), sizes); + ConditionalThrow<LayerValidationException>(GetOutputSlot(viewIdx).ValidateTensorShape(outShape), + "SplitterLayer: View sizes must match output tensor shapes."); + } +} + +MemCopyLayer::MemCopyLayer(const char* name) + : Layer(1, 1, LayerType::MemCopy, name) +{ +} + +MemCopyLayer* MemCopyLayer::Clone(Graph& graph) const +{ + return CloneBase<MemCopyLayer>(graph, GetName()); +} + +std::unique_ptr<IWorkload> MemCopyLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const +{ + MemCopyQueueDescriptor descriptor; + return factory.CreateMemCopy(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +void MemCopyLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "MemCopyLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "MemCopyLayer: TensorInfo must be set on connected OutputSlot."); + + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + + // input and output shapes are the same + TensorShape const& outShape = input->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "MemCopyLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +ResizeBilinearLayer::ResizeBilinearLayer(const ResizeBilinearDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::ResizeBilinear, param, name) +{ +} + +std::unique_ptr<IWorkload> ResizeBilinearLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + ResizeBilinearQueueDescriptor descriptor; + return factory.CreateResizeBilinear(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +ResizeBilinearLayer* ResizeBilinearLayer::Clone(Graph& graph) const +{ + return CloneBase<ResizeBilinearLayer>(graph, m_Param, GetName()); +} + +void ResizeBilinearLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "MemCopyLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "MemCopyLayer: TensorInfo must be set on connected OutputSlot."); + + const TensorShape& inputShape = GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(); + unsigned int outWidth = m_Param.m_TargetWidth; + unsigned int outHeight = m_Param.m_TargetHeight; + unsigned int outChannels = inputShape[1]; + unsigned int outBatch = inputShape[0]; + TensorShape outShape({outBatch, outChannels, outHeight, outWidth}); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "ResizeBilinearLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +L2NormalizationLayer::L2NormalizationLayer(const char* name) + : Layer(1, 1, LayerType::L2Normalization, name) +{ +} + +std::unique_ptr<IWorkload> L2NormalizationLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + L2NormalizationQueueDescriptor descriptor; + return factory.CreateL2Normalization(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +L2NormalizationLayer* L2NormalizationLayer::Clone(Graph& graph) const +{ + return CloneBase<L2NormalizationLayer>(graph, GetName()); +} + +void L2NormalizationLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "L2NormalizationLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "L2NormalizationLayer: TensorInfo must be set on connected OutputSlot."); + + IOutputSlot* input = GetInputSlot(0).GetConnection(); + + // input and output shapes are the same + TensorShape const& outShape = input->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "L2NormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +ConstantLayer::ConstantLayer(const std::shared_ptr<ScopedCpuTensorHandle>& input, const char* name) + : Layer(0, 1, LayerType::Constant, name) + , m_LayerOutput(input) +{ +} + +std::unique_ptr<IWorkload> ConstantLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + ConstantQueueDescriptor descriptor; + descriptor.m_LayerOutput = m_LayerOutput.get(); + return factory.CreateConstant(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +ConstantLayer* ConstantLayer::Clone(Graph& graph) const +{ + // Cloned layers share the same layer output object + return CloneBase<ConstantLayer>(graph, m_LayerOutput, GetName()); +} + +void ConstantLayer::ValidateTensorShapesFromInputs() +{ + // get the output shape from the value of the constant layer + TensorShape const& outShape = m_LayerOutput->GetTensorInfo().GetShape(); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), + "ConstantLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +ReshapeLayer::ReshapeLayer(const ReshapeDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Reshape, param, name) +{ +} + +std::unique_ptr<IWorkload> ReshapeLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + ReshapeQueueDescriptor descriptor; + return factory.CreateReshape(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +ReshapeLayer* ReshapeLayer::Clone(Graph& graph) const +{ + return CloneBase<ReshapeLayer>(graph, m_Param, GetName()); +} + +void ReshapeLayer::ValidateTensorShapesFromInputs() +{ + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr, + "ReshapeLayer: InputSlot must be connected to an OutputSlot"); + ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(), + "ReshapeLayer: TensorInfo must be set on connected OutputSlot."); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(m_Param.m_TargetShape), + "ReshapeLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); +} + +} |