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Diffstat (limited to 'src/armnn/Layers.cpp')
| -rw-r--r-- | src/armnn/Layers.cpp | 1029 |
1 files changed, 0 insertions, 1029 deletions
diff --git a/src/armnn/Layers.cpp b/src/armnn/Layers.cpp deleted file mode 100644 index 48a02aba9c..0000000000 --- a/src/armnn/Layers.cpp +++ /dev/null @@ -1,1029 +0,0 @@ -// -// 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" - -#include <queue> - - -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); - layer->SetGuid(GetGuid()); - - 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 of size 1."); - } - } -#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()) - { - std::queue<MergerLayer*> m_MergerLayers; - - m_MergerLayers.push(this); - while (!m_MergerLayers.empty()) - { - MergerLayer* currentLayer = m_MergerLayers.front(); - ITensorHandle* parentTensor = currentLayer->GetOutputHandler(0).GetData(); - - m_MergerLayers.pop(); - - const unsigned int numInputSlots = currentLayer->GetNumInputSlots(); - for (unsigned int i = 0; i < numInputSlots; ++i) - { - OutputSlot* slot = currentLayer->GetInputSlot(i).GetConnectedOutputSlot(); - OutputHandler& outputHandler = slot->GetOutputHandler(); - outputHandler.SetData(factory.CreateSubTensorHandle(*parentTensor, - outputHandler.GetTensorInfo().GetShape(), - currentLayer->m_Param.GetViewOrigin(i))); - - Layer& inputLayer = slot->GetOwningLayer(); - if (inputLayer.GetType() == LayerType::Merger) - { - m_MergerLayers.push(boost::polymorphic_downcast<MergerLayer*>(&inputLayer)); - } - } - } - } -} - -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() -{ - 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 of size 1."); - } - } -#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), - "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."); -} - -} |