From 5d7b0a314b3e354a6cbcf15f5dd78b50f1e02774 Mon Sep 17 00:00:00 2001 From: Matthew Sloyan Date: Mon, 18 Oct 2021 13:07:49 +0100 Subject: Add ConstTensorsAsInput support for Conv3d * Constant weights and biases are now stored as Constant layers. * Updated Serializer, Deserializer and unit tests to reflect this. * Updated TfLiteParser. * Updated Ref backend to handle constant weights and bias as inputs rather than reading from member variables. * Added Conv3d EndToEnd test. * Added NCDHW DataLayout and unit tests. Signed-off-by: Matthew Sloyan Change-Id: I10cdd354ca5f1c748730f92ffdb36bf810f83c8e --- include/armnn/Descriptors.hpp | 5 +- include/armnn/INetwork.hpp | 4 - include/armnn/Types.hpp | 3 +- include/armnn/TypesUtils.hpp | 1 + src/armnn/Descriptors.cpp | 11 ++ src/armnn/Graph.cpp | 11 +- src/armnn/Network.cpp | 22 +-- src/armnn/Network.hpp | 2 - src/armnn/layers/Convolution3dLayer.cpp | 52 ++----- src/armnn/layers/Convolution3dLayer.hpp | 10 -- src/armnnDeserializer/Deserializer.cpp | 21 +-- .../test/DeserializeConvolution3d.cpp | 92 ++++++++---- src/armnnSerializer/ArmnnSchema.fbs | 5 +- src/armnnSerializer/ArmnnSchema_generated.h | 39 ++--- src/armnnSerializer/Serializer.cpp | 22 +-- src/armnnSerializer/Serializer.hpp | 1 - src/armnnSerializer/SerializerUtils.cpp | 2 + src/armnnSerializer/test/SerializerTestUtils.hpp | 1 + src/armnnSerializer/test/SerializerTests.cpp | 17 ++- src/armnnTfLiteParser/TfLiteParser.cpp | 27 ++-- src/armnnUtils/DataLayoutIndexed.cpp | 6 + src/backends/backendsCommon/WorkloadData.cpp | 15 +- src/backends/backendsCommon/WorkloadData.hpp | 11 +- src/backends/backendsCommon/WorkloadFactory.cpp | 11 +- src/backends/backendsCommon/test/CMakeLists.txt | 1 + .../test/Convolution3dEndToEndTestImpl.hpp | 167 +++++++++++++++++++++ .../backendsCommon/test/DataLayoutUtils.hpp | 24 +++ .../test/FullyConnectedEndToEndTestImpl.hpp | 6 +- .../test/layerTests/Conv3dTestImpl.cpp | 143 +++++++++++------- .../test/layerTests/Conv3dTestImpl.hpp | 60 +++++--- src/backends/reference/test/RefEndToEndTests.cpp | 31 ++++ src/backends/reference/test/RefLayerTests.cpp | 130 +++++++++++++--- src/backends/reference/workloads/Conv3dImpl.cpp | 47 ++++-- .../workloads/RefConvolution3dWorkload.cpp | 33 ++-- .../workloads/RefConvolution3dWorkload.hpp | 4 +- 35 files changed, 687 insertions(+), 350 deletions(-) create mode 100644 src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp index b412bbdcc9..39ea824045 100644 --- a/include/armnn/Descriptors.hpp +++ b/include/armnn/Descriptors.hpp @@ -498,6 +498,9 @@ struct Convolution3dDescriptor : BaseDescriptor m_DataLayout == rhs.m_DataLayout; } + /// Get the number of views/inputs. + uint32_t GetNumInputs() const; + /// Padding left value in the width dimension. uint32_t m_PadLeft; /// Padding right value in the width dimension. @@ -524,7 +527,7 @@ struct Convolution3dDescriptor : BaseDescriptor uint32_t m_DilationZ; /// Enable/disable bias. bool m_BiasEnabled; - /// The data layout to be used (NDHWC). + /// The data layout to be used (NDHWC, NCDHW). DataLayout m_DataLayout; }; diff --git a/include/armnn/INetwork.hpp b/include/armnn/INetwork.hpp index ab92f05112..707ae00bb3 100644 --- a/include/armnn/INetwork.hpp +++ b/include/armnn/INetwork.hpp @@ -258,13 +258,9 @@ public: /// Adds a 3D convolution layer to the network. /// @param convolution3dDescriptor - Description of the 3D convolution layer. - /// @param weights - Tensor for the weights data. - /// @param biases - Optional tensor for the bias data. If specified, must match the output tensor shape. /// @param name - Optional name for the layer. /// @return - Interface for configuring the layer. IConnectableLayer* AddConvolution3dLayer(const Convolution3dDescriptor& convolution3dDescriptor, - const ConstTensor& weights, - const Optional& biases, const char* name = nullptr); /// Adds a depth to space layer to the network. diff --git a/include/armnn/Types.hpp b/include/armnn/Types.hpp index 7f2e192102..4f39ebe16a 100644 --- a/include/armnn/Types.hpp +++ b/include/armnn/Types.hpp @@ -50,7 +50,8 @@ enum class DataLayout { NCHW = 1, NHWC = 2, - NDHWC = 3 + NDHWC = 3, + NCDHW = 4 }; /// Define the behaviour of the internal profiler when outputting network details diff --git a/include/armnn/TypesUtils.hpp b/include/armnn/TypesUtils.hpp index d08f592d86..a1c11b74df 100644 --- a/include/armnn/TypesUtils.hpp +++ b/include/armnn/TypesUtils.hpp @@ -215,6 +215,7 @@ constexpr const char* GetDataLayoutName(DataLayout dataLayout) case DataLayout::NCHW: return "NCHW"; case DataLayout::NHWC: return "NHWC"; case DataLayout::NDHWC: return "NDHWC"; + case DataLayout::NCDHW: return "NCDHW"; default: return "Unknown"; } } diff --git a/src/armnn/Descriptors.cpp b/src/armnn/Descriptors.cpp index ab68097247..ef55ee7bb5 100644 --- a/src/armnn/Descriptors.cpp +++ b/src/armnn/Descriptors.cpp @@ -441,4 +441,15 @@ uint32_t FullyConnectedDescriptor::GetNumInputs() const return numInputs; } +uint32_t Convolution3dDescriptor::GetNumInputs() const +{ + // Return 2 otherwise check if bias is enabled + unsigned int numInputs = 2; + if (m_BiasEnabled) + { + numInputs = 3; + } + return numInputs; +} + } diff --git a/src/armnn/Graph.cpp b/src/armnn/Graph.cpp index 30639b12e8..0591bea99a 100644 --- a/src/armnn/Graph.cpp +++ b/src/armnn/Graph.cpp @@ -588,7 +588,7 @@ void Graph::InferTensorInfos() } /// Throws exception due to a layer input not being connected to an output slot. -/// Verifies weights and bias are set for FullyConnected layers on input slots 1 +/// Verifies weights and bias are set for layers on input slots 1 /// and 2 respectively. Method checks if bias is enabled before ensuring it is set. /// /// @param layer constant pointer to a Layer object @@ -600,7 +600,8 @@ void Graph::ConstructErrorMessageForUnconnectedInputs(Layer* const layer, std::ostringstream message; bool noWeightsAndBias = false; - if (layer->GetType() == armnn::LayerType::FullyConnected && slotIndex > 0) + if ((layer->GetType() == armnn::LayerType::FullyConnected || + layer->GetType() == armnn::LayerType::Convolution3d) && slotIndex > 0) { // If weights are not set and is bias enabled, also check if bias is set if (slotIndex == 1 && layer->GetNumInputSlots() == 3) @@ -608,7 +609,7 @@ void Graph::ConstructErrorMessageForUnconnectedInputs(Layer* const layer, const IOutputSlot* biasSource = layer->GetInputSlot(2).GetConnectedOutputSlot(); if (biasSource == NULL) { - message << "FullyConnected layer weights and bias not set: "; + message << layer->GetName() << " layer weights and bias not set: "; noWeightsAndBias = true; } } @@ -618,11 +619,11 @@ void Graph::ConstructErrorMessageForUnconnectedInputs(Layer* const layer, { if (slotIndex == 1) { - message << "FullyConnected layer weights not set: "; + message << layer->GetName() << " layer weights not set: "; } else { - message << "FullyConnected layer bias not set: "; + message << layer->GetName() << " layer bias not set: "; } } } diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 99d7b96ec2..b516d519d5 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -114,11 +114,9 @@ IConnectableLayer* INetwork::AddConvolution2dLayer(const Convolution2dDescriptor IConnectableLayer* INetwork::AddConvolution3dLayer(const Convolution3dDescriptor& convolution3dDescriptor, - const ConstTensor& weights, - const Optional& biases, const char* name) { - return pNetworkImpl->AddConvolution3dLayer(convolution3dDescriptor, weights, biases, name); + return pNetworkImpl->AddConvolution3dLayer(convolution3dDescriptor, name); } @@ -1934,25 +1932,9 @@ IConnectableLayer* NetworkImpl::AddConvolution2dLayer(const Convolution2dDescrip } IConnectableLayer* NetworkImpl::AddConvolution3dLayer(const Convolution3dDescriptor& convolution3dDescriptor, - const ConstTensor& weights, - const Optional& biases, const char* name) { - if (convolution3dDescriptor.m_BiasEnabled && !biases.has_value()) - { - throw InvalidArgumentException("AddConvolution2dLayer: biases cannot be empty"); - } - - const auto layer = m_Graph->AddLayer(convolution3dDescriptor, name); - - layer->m_Weight = std::make_shared(weights); - - if (convolution3dDescriptor.m_BiasEnabled) - { - layer->m_Bias = std::make_shared(biases.value()); - } - - return layer; + return m_Graph->AddLayer(convolution3dDescriptor, name); } IConnectableLayer* NetworkImpl::AddDepthToSpaceLayer(const DepthToSpaceDescriptor& depthToSpaceDescriptor, diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp index eb1d39d2f6..818a765296 100644 --- a/src/armnn/Network.hpp +++ b/src/armnn/Network.hpp @@ -87,8 +87,6 @@ public: const char* name = nullptr); IConnectableLayer* AddConvolution3dLayer(const Convolution3dDescriptor& convolution3dDescriptor, - const ConstTensor& weights, - const Optional& biases, const char* name = nullptr); IConnectableLayer* AddConstantLayer(const ConstTensor& input, const char* name = nullptr); diff --git a/src/armnn/layers/Convolution3dLayer.cpp b/src/armnn/layers/Convolution3dLayer.cpp index 0e38c0b129..1c2d1b9872 100644 --- a/src/armnn/layers/Convolution3dLayer.cpp +++ b/src/armnn/layers/Convolution3dLayer.cpp @@ -16,7 +16,7 @@ namespace armnn { Convolution3dLayer::Convolution3dLayer(const Convolution3dDescriptor& param, const char* name) - : LayerWithParameters(1, 1, LayerType::Convolution3d, param, name) + : LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution3d, param, name) { } @@ -25,12 +25,11 @@ void Convolution3dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn const std::vector& inputShapes = { GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), - m_Weight->GetTensorInfo().GetShape() + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), }; // Conv3d Filter Layout: [D,H,W,I,O] const TensorShape filterShape = inputShapes[1]; - DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); unsigned int filterDepth = filterShape[0]; unsigned int filterHeight = filterShape[1]; unsigned int filterWidth = filterShape[2]; @@ -48,18 +47,7 @@ void Convolution3dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn std::unique_ptr Convolution3dLayer::CreateWorkload(const IWorkloadFactory& factory) const { - // At this level constant data should not be released. - ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution3dLayer: Weights data should not be null."); - Convolution3dQueueDescriptor descriptor; - descriptor.m_Weight = m_Weight.get(); - - if (m_Param.m_BiasEnabled) - { - ARMNN_ASSERT_MSG(m_Bias != nullptr, "Convolution3dLayer: Bias data should not be null."); - descriptor.m_Bias = m_Bias.get(); - } - SetAdditionalInfo(descriptor); return factory.CreateConvolution3d(descriptor, PrepInfoAndDesc(descriptor)); @@ -68,14 +56,6 @@ std::unique_ptr Convolution3dLayer::CreateWorkload(const IWorkloadFac Convolution3dLayer* Convolution3dLayer::Clone(Graph& graph) const { auto layer = CloneBase(graph, m_Param, GetName()); - - layer->m_Weight = m_Weight ? m_Weight : nullptr; - - if (layer->m_Param.m_BiasEnabled) - { - layer->m_Bias = m_Bias ? m_Bias : nullptr; - } - return std::move(layer); } @@ -117,36 +97,33 @@ std::vector Convolution3dLayer::InferOutputShapes(const std::vector unsigned int outChannels = filterShape[4]; unsigned int outBatchSize = inBatchSize; - TensorShape tensorShape = TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } ); + TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NDHWC ? + TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } ) : + TensorShape( { outBatchSize, outChannels, outDepth, outHeight, outWidth }); return std::vector({ tensorShape }); } void Convolution3dLayer::ValidateTensorShapesFromInputs() { - VerifyLayerConnections(1, CHECK_LOCATION()); + VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION()); const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); - // check if we m_Weight data is not nullptr - ARMNN_ASSERT_MSG(m_Weight != nullptr, "Convolution3dLayer: Weights data should not be null."); + ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(), + "Convolution3dLayer: Weights should be connected to input slot 1."); auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), - m_Weight->GetTensorInfo().GetShape() }); + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() }); ARMNN_ASSERT(inferredShapes.size() == 1); ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution3dLayer"); } -Layer::ConstantTensors Convolution3dLayer::GetConstantTensorsByRef() -{ - return {m_Weight, m_Bias}; -} - ARMNN_NO_DEPRECATE_WARN_BEGIN void Convolution3dLayer::Accept(ILayerVisitor& visitor) const { @@ -157,16 +134,7 @@ ARMNN_NO_DEPRECATE_WARN_END void Convolution3dLayer::ExecuteStrategy(IStrategy& strategy) const { - ManagedConstTensorHandle managedWeight(m_Weight); - std::vector constTensors { { managedWeight.GetTensorInfo(), managedWeight.Map() } }; - - ManagedConstTensorHandle managedBias(m_Bias); - if (GetParameters().m_BiasEnabled) - { - constTensors.emplace_back(ConstTensor(managedBias.GetTensorInfo(), managedBias.Map())); - } - - strategy.ExecuteStrategy(this, GetParameters(), constTensors, GetName()); + strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); } } // namespace armnn diff --git a/src/armnn/layers/Convolution3dLayer.hpp b/src/armnn/layers/Convolution3dLayer.hpp index bef5715098..7cbd6428dc 100644 --- a/src/armnn/layers/Convolution3dLayer.hpp +++ b/src/armnn/layers/Convolution3dLayer.hpp @@ -16,12 +16,6 @@ class ScopedTensorHandle; class Convolution3dLayer : public LayerWithParameters { public: - - /// A unique pointer to store Weight values. - std::shared_ptr m_Weight; - /// A unique pointer to store Bias values. - std::shared_ptr m_Bias; - /// Makes a workload for the Convolution3d type. /// @param [in] graph The graph where this layer can be found. /// @param [in] factory The workload factory which will create the workload. @@ -59,10 +53,6 @@ protected: /// Default destructor ~Convolution3dLayer() = default; - - /// Retrieve the handles to the constant values stored by the layer. - /// @return A vector of the constant tensors stored by this layer. - ConstantTensors GetConstantTensorsByRef() override; }; } // namespace diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp index 6b73946af2..c088ef7b54 100644 --- a/src/armnnDeserializer/Deserializer.cpp +++ b/src/armnnDeserializer/Deserializer.cpp @@ -449,6 +449,8 @@ armnn::DataLayout ToDataLayout(armnnSerializer::DataLayout dataLayout) return armnn::DataLayout::NHWC; case armnnSerializer::DataLayout::DataLayout_NDHWC: return armnn::DataLayout::NDHWC; + case armnnSerializer::DataLayout::DataLayout_NCDHW: + return armnn::DataLayout::NCDHW; case armnnSerializer::DataLayout::DataLayout_NCHW: default: return armnn::DataLayout::NCHW; @@ -1402,7 +1404,6 @@ void IDeserializer::DeserializerImpl::ParseConvolution3d(GraphPtr graph, unsigne CHECK_LAYERS(graph, 0, layerIndex); auto inputs = GetInputs(graph, layerIndex); CHECK_LOCATION(); - CHECK_VALID_SIZE(inputs.size(), 1); auto outputs = GetOutputs(graph, layerIndex); CHECK_VALID_SIZE(outputs.size(), 1); @@ -1424,22 +1425,14 @@ void IDeserializer::DeserializerImpl::ParseConvolution3d(GraphPtr graph, unsigne descriptor.m_DilationX = serializerDescriptor->dilationX(); descriptor.m_DilationY = serializerDescriptor->dilationY(); descriptor.m_DilationZ = serializerDescriptor->dilationZ(); - descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();; + descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled(); descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout()); - armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights()); - armnn::ConstTensor biases; + uint32_t numInputs = descriptor.GetNumInputs(); + CHECK_VALID_SIZE(inputs.size(), numInputs); + + IConnectableLayer* layer = m_Network->AddConvolution3dLayer(descriptor, layerName.c_str()); - armnn::Optional optionalBiases = armnn::EmptyOptional(); - if (descriptor.m_BiasEnabled) - { - biases = ToConstTensor(serializerLayer->biases()); - optionalBiases = armnn::Optional(biases); - } - IConnectableLayer* layer = m_Network->AddConvolution3dLayer(descriptor, - weights, - optionalBiases, - layerName.c_str()); armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); diff --git a/src/armnnDeserializer/test/DeserializeConvolution3d.cpp b/src/armnnDeserializer/test/DeserializeConvolution3d.cpp index 057ab6fbda..23fd811cdb 100644 --- a/src/armnnDeserializer/test/DeserializeConvolution3d.cpp +++ b/src/armnnDeserializer/test/DeserializeConvolution3d.cpp @@ -30,13 +30,11 @@ struct Convolution3dFixture : public ParserFlatbuffersSerializeFixture base: { layerName: "InputLayer", layerType: "Input", - inputSlots: [{ - index: 0, - connection: {sourceLayerIndex:0, outputSlotIndex:0 }, - }], + inputSlots: [ + + ], outputSlots: [ { - index: 0, tensorInfo: { dimensions: )" + inputShape + R"(, dataType: )" + dataType + R"(, @@ -56,26 +54,19 @@ struct Convolution3dFixture : public ParserFlatbuffersSerializeFixture } }, { - layer_type: "Convolution3dLayer", + layer_type: "ConstantLayer", layer: { base: { index: 1, - layerName: "convolution3d", - layerType: "Convolution2d", + layerName: "Weights", + layerType: "Constant", inputSlots: [ - { - index: 0, - connection: { - sourceLayerIndex: 0, - outputSlotIndex: 0 - } - } + ], outputSlots: [ { - index: 0, tensorInfo: { - dimensions: )" + outputShape + R"(, + dimensions: )" + weightsShape + R"(, dataType: )" + dataType + R"(, quantizationScale: 0.1, dimensionSpecificity: [ @@ -89,12 +80,7 @@ struct Convolution3dFixture : public ParserFlatbuffersSerializeFixture } ] }, - descriptor: { - strideX: 2, - strideY: 2, - strideZ: 2 - }, - weights: { + input: { info: { dimensions: )" + weightsShape + R"(, dataType: )" + dataType + R"(, @@ -126,30 +112,72 @@ struct Convolution3dFixture : public ParserFlatbuffersSerializeFixture } } }, + { + layer_type: "Convolution3dLayer", + layer: { + base: { + index: 2, + layerName: "convolution3d", + layerType: "Convolution3d", + inputSlots: [ + { + connection: { + sourceLayerIndex: 0, + outputSlotIndex: 0 + } + }, + { + index: 1, + connection: { + sourceLayerIndex: 1, + outputSlotIndex: 0 + } + } + ], + outputSlots: [ + { + tensorInfo: { + dimensions: )" + outputShape + R"(, + dataType: )" + dataType + R"(, + quantizationScale: 0.1, + dimensionSpecificity: [ + true, + true, + true, + true, + true + ] + } + } + ] + }, + descriptor: { + strideX: 2, + strideY: 2, + strideZ: 2 + } + } + }, { layer_type: "OutputLayer", layer: { base: { layerBindingId: 2, base: { - index: 2, + index: 3, layerName: "OutputLayer", layerType: "Output", inputSlots: [ { connection: { - sourceLayerIndex: 1, + sourceLayerIndex: 2, outputSlotIndex: 0 } } ], - outputSlots: [{ - index: 0, - tensorInfo: { - dimensions: )" + outputShape + R"(, - dataType: )" + dataType + R"( - }, - }] + outputSlots: [ + + ] } } } diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs index 77982888c8..c577a11a52 100644 --- a/src/armnnSerializer/ArmnnSchema.fbs +++ b/src/armnnSerializer/ArmnnSchema.fbs @@ -46,7 +46,8 @@ enum DataType : byte { enum DataLayout : byte { NHWC = 0, NCHW = 1, - NDHWC = 2 + NDHWC = 2, + NCDHW = 3 } enum ReduceOperation: byte { @@ -287,8 +288,6 @@ table Convolution2dDescriptor { table Convolution3dLayer { base:LayerBase; descriptor:Convolution3dDescriptor; - weights:ConstTensor; - biases:ConstTensor; } table Convolution3dDescriptor { diff --git a/src/armnnSerializer/ArmnnSchema_generated.h b/src/armnnSerializer/ArmnnSchema_generated.h index 8234aa9c47..712ad28574 100644 --- a/src/armnnSerializer/ArmnnSchema_generated.h +++ b/src/armnnSerializer/ArmnnSchema_generated.h @@ -540,31 +540,34 @@ enum DataLayout { DataLayout_NHWC = 0, DataLayout_NCHW = 1, DataLayout_NDHWC = 2, + DataLayout_NCDHW = 3, DataLayout_MIN = DataLayout_NHWC, - DataLayout_MAX = DataLayout_NDHWC + DataLayout_MAX = DataLayout_NCDHW }; -inline const DataLayout (&EnumValuesDataLayout())[3] { +inline const DataLayout (&EnumValuesDataLayout())[4] { static const DataLayout values[] = { DataLayout_NHWC, DataLayout_NCHW, - DataLayout_NDHWC + DataLayout_NDHWC, + DataLayout_NCDHW }; return values; } inline const char * const *EnumNamesDataLayout() { - static const char * const names[4] = { + static const char * const names[5] = { "NHWC", "NCHW", "NDHWC", + "NCDHW", nullptr }; return names; } inline const char *EnumNameDataLayout(DataLayout e) { - if (flatbuffers::IsOutRange(e, DataLayout_NHWC, DataLayout_NDHWC)) return ""; + if (flatbuffers::IsOutRange(e, DataLayout_NHWC, DataLayout_NCDHW)) return ""; const size_t index = static_cast(e); return EnumNamesDataLayout()[index]; } @@ -3250,9 +3253,7 @@ struct Convolution3dLayer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef Convolution3dLayerBuilder Builder; enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { VT_BASE = 4, - VT_DESCRIPTOR = 6, - VT_WEIGHTS = 8, - VT_BIASES = 10 + VT_DESCRIPTOR = 6 }; const armnnSerializer::LayerBase *base() const { return GetPointer(VT_BASE); @@ -3260,22 +3261,12 @@ struct Convolution3dLayer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const armnnSerializer::Convolution3dDescriptor *descriptor() const { return GetPointer(VT_DESCRIPTOR); } - const armnnSerializer::ConstTensor *weights() const { - return GetPointer(VT_WEIGHTS); - } - const armnnSerializer::ConstTensor *biases() const { - return GetPointer(VT_BIASES); - } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_BASE) && verifier.VerifyTable(base()) && VerifyOffset(verifier, VT_DESCRIPTOR) && verifier.VerifyTable(descriptor()) && - VerifyOffset(verifier, VT_WEIGHTS) && - verifier.VerifyTable(weights()) && - VerifyOffset(verifier, VT_BIASES) && - verifier.VerifyTable(biases()) && verifier.EndTable(); } }; @@ -3290,12 +3281,6 @@ struct Convolution3dLayerBuilder { void add_descriptor(flatbuffers::Offset descriptor) { fbb_.AddOffset(Convolution3dLayer::VT_DESCRIPTOR, descriptor); } - void add_weights(flatbuffers::Offset weights) { - fbb_.AddOffset(Convolution3dLayer::VT_WEIGHTS, weights); - } - void add_biases(flatbuffers::Offset biases) { - fbb_.AddOffset(Convolution3dLayer::VT_BIASES, biases); - } explicit Convolution3dLayerBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -3311,12 +3296,8 @@ struct Convolution3dLayerBuilder { inline flatbuffers::Offset CreateConvolution3dLayer( flatbuffers::FlatBufferBuilder &_fbb, flatbuffers::Offset base = 0, - flatbuffers::Offset descriptor = 0, - flatbuffers::Offset weights = 0, - flatbuffers::Offset biases = 0) { + flatbuffers::Offset descriptor = 0) { Convolution3dLayerBuilder builder_(_fbb); - builder_.add_biases(biases); - builder_.add_weights(weights); builder_.add_descriptor(descriptor); builder_.add_base(base); return builder_.Finish(); diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp index 7e1b74e10d..84a9d53b69 100644 --- a/src/armnnSerializer/Serializer.cpp +++ b/src/armnnSerializer/Serializer.cpp @@ -388,18 +388,15 @@ void SerializerStrategy::SerializeConvolution2dLayer(const armnn::IConnectableLa CreateAnyLayer(flatBufferLayer.o, serializer::Layer::Layer_Convolution2dLayer); } -// Build FlatBuffer for Convolution2dLayer +// Build FlatBuffer for Convolution3dLayer void SerializerStrategy::SerializeConvolution3dLayer(const armnn::IConnectableLayer* layer, const armnn::Convolution3dDescriptor& descriptor, - const std::vector& constants, const char* name) { IgnoreUnused(name); - const armnn::ConstTensor weights = constants[0]; - // Create FlatBuffer BaseLayer - auto flatBufferBaseLayer = CreateLayerBase(layer, serializer::LayerType::LayerType_Convolution2d); + auto flatBufferBaseLayer = CreateLayerBase(layer, serializer::LayerType::LayerType_Convolution3d); auto flatBufferDescriptor = CreateConvolution3dDescriptor(m_flatBufferBuilder, descriptor.m_PadLeft, @@ -416,21 +413,11 @@ void SerializerStrategy::SerializeConvolution3dLayer(const armnn::IConnectableLa descriptor.m_DilationZ, descriptor.m_BiasEnabled, GetFlatBufferDataLayout(descriptor.m_DataLayout)); - auto flatBufferWeightsConstTensorInfo = CreateConstTensorInfo(weights); - flatbuffers::Offset flatBufferBiasesConstTensorInfo; - - if (constants.size() > 1) - { - const armnn::ConstTensor biases = constants[1]; - flatBufferBiasesConstTensorInfo = CreateConstTensorInfo(biases); - } - // Create the FlatBuffer Convolution2dLayer + // Create the FlatBuffer Convolution3dLayer auto flatBufferLayer = CreateConvolution3dLayer(m_flatBufferBuilder, flatBufferBaseLayer, - flatBufferDescriptor, - flatBufferWeightsConstTensorInfo, - flatBufferBiasesConstTensorInfo); + flatBufferDescriptor); // Add the AnyLayer to the FlatBufferLayers CreateAnyLayer(flatBufferLayer.o, serializer::Layer::Layer_Convolution3dLayer); @@ -2038,7 +2025,6 @@ void SerializerStrategy::ExecuteStrategy(const armnn::IConnectableLayer* layer, static_cast(descriptor); SerializeConvolution3dLayer(layer, layerDescriptor, - constants, name); break; } diff --git a/src/armnnSerializer/Serializer.hpp b/src/armnnSerializer/Serializer.hpp index 2f827ac059..1c0a9a619f 100644 --- a/src/armnnSerializer/Serializer.hpp +++ b/src/armnnSerializer/Serializer.hpp @@ -150,7 +150,6 @@ private: void SerializeConvolution3dLayer(const armnn::IConnectableLayer* layer, const armnn::Convolution3dDescriptor& descriptor, - const std::vector& constants, const char* name = nullptr); void SerializeDepthToSpaceLayer(const armnn::IConnectableLayer* layer, diff --git a/src/armnnSerializer/SerializerUtils.cpp b/src/armnnSerializer/SerializerUtils.cpp index fca6db8449..5ad27715c4 100644 --- a/src/armnnSerializer/SerializerUtils.cpp +++ b/src/armnnSerializer/SerializerUtils.cpp @@ -99,6 +99,8 @@ armnnSerializer::DataLayout GetFlatBufferDataLayout(armnn::DataLayout dataLayout return armnnSerializer::DataLayout::DataLayout_NHWC; case armnn::DataLayout::NDHWC: return armnnSerializer::DataLayout::DataLayout_NDHWC; + case armnn::DataLayout::NCDHW: + return armnnSerializer::DataLayout::DataLayout_NCDHW; case armnn::DataLayout::NCHW: default: return armnnSerializer::DataLayout::DataLayout_NCHW; diff --git a/src/armnnSerializer/test/SerializerTestUtils.hpp b/src/armnnSerializer/test/SerializerTestUtils.hpp index c6f148b1a1..ce4d2cc330 100644 --- a/src/armnnSerializer/test/SerializerTestUtils.hpp +++ b/src/armnnSerializer/test/SerializerTestUtils.hpp @@ -69,6 +69,7 @@ public: { case armnn::LayerType::Input: break; case armnn::LayerType::Output: break; + case armnn::LayerType::Constant: break; default: { VerifyNameAndConnections(layer, name); diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp index f2c9852607..2bffe0b9fd 100644 --- a/src/armnnSerializer/test/SerializerTests.cpp +++ b/src/armnnSerializer/test/SerializerTests.cpp @@ -472,25 +472,26 @@ TEST_CASE("SerializeConvolution3d") armnn::INetworkPtr network = armnn::INetwork::Create(); armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0); - armnn::IConnectableLayer* const convLayer = - network->AddConvolution3dLayer(descriptor, - weights, - armnn::Optional(biases), - layerName.c_str()); + armnn::IConnectableLayer* const weightsLayer = network->AddConstantLayer(weights, "Weights"); + armnn::IConnectableLayer* const biasesLayer = network->AddConstantLayer(biases, "Biases"); + armnn::IConnectableLayer* const convLayer = network->AddConvolution3dLayer(descriptor, layerName.c_str()); armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0); inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + weightsLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1)); + biasesLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2)); convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); + biasesLayer->GetOutputSlot(0).SetTensorInfo(biasesInfo); convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); CHECK(deserializedNetwork); - const std::vector& constants {weights, biases}; - LayerVerifierBaseWithDescriptorAndConstants verifier( - layerName, {inputInfo}, {outputInfo}, descriptor, constants); + LayerVerifierBaseWithDescriptor verifier( + layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor); deserializedNetwork->ExecuteStrategy(verifier); } diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp index 81d491a1a1..7db5d85b13 100644 --- a/src/armnnTfLiteParser/TfLiteParser.cpp +++ b/src/armnnTfLiteParser/TfLiteParser.cpp @@ -1099,36 +1099,29 @@ void TfLiteParserImpl::ParseConv3D(size_t subgraphIndex, size_t operatorIndex) auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo); - armnn::IConnectableLayer* layer = nullptr; auto layerName = fmt::format("Conv3D:{}:{}", subgraphIndex, operatorIndex); + auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); + // Add the first input and weights tensor to the registration list. + // The constant weights will be added by SetupConstantLayers. + std::vector tensorIndexesToRegister = {inputTensorIndexes[0], inputTensorIndexes[1]}; + if (inputs.size() == 3) { desc.m_BiasEnabled = true; - armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); - auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo); - layer = m_Network->AddConvolution3dLayer(desc, - filterTensorAndData, - Optional(biasTensorAndData), - layerName.c_str()); - } - else - { - layer = m_Network->AddConvolution3dLayer(desc, - filterTensorAndData, - EmptyOptional(), - layerName.c_str()); + + // Add the biases input to the registration list, a constant layer will be added by SetupConstantLayers. + tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]); } + armnn::IConnectableLayer* layer = m_Network->AddConvolution3dLayer(desc, layerName.c_str()); ARMNN_ASSERT(layer != nullptr); armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true); layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); // Register the input connection slots for the layer, connections are made after all layers have been created - // only the tensors for the inputs are relevant, exclude the const tensors - auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); - RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); + RegisterInputSlots(subgraphIndex, operatorIndex, layer, tensorIndexesToRegister); layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); // Register the output connection slots for the layer, connections are made after all layers have been created diff --git a/src/armnnUtils/DataLayoutIndexed.cpp b/src/armnnUtils/DataLayoutIndexed.cpp index c1c98fc0fd..01505a0a31 100644 --- a/src/armnnUtils/DataLayoutIndexed.cpp +++ b/src/armnnUtils/DataLayoutIndexed.cpp @@ -31,6 +31,12 @@ DataLayoutIndexed::DataLayoutIndexed(armnn::DataLayout dataLayout) m_WidthIndex = 3; m_ChannelsIndex = 4; break; + case armnn::DataLayout::NCDHW: + m_ChannelsIndex = 1; + m_DepthIndex = 2; + m_HeightIndex = 3; + m_WidthIndex = 4; + break; default: throw armnn::InvalidArgumentException("Unknown DataLayout value: " + std::to_string(static_cast(dataLayout))); diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index 27b59ea3a6..2716c827af 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -1320,7 +1320,12 @@ void Convolution3dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) co { const std::string descriptorName{"Convolution3dQueueDescriptor"}; - ValidateNumInputs(workloadInfo, descriptorName, 1); + uint32_t numInputs = 2; + if (m_Parameters.m_BiasEnabled) + { + numInputs = 3; + } + ValidateNumInputs(workloadInfo, descriptorName, numInputs); ValidateNumOutputs(workloadInfo, descriptorName, 1); const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0]; @@ -1329,9 +1334,7 @@ void Convolution3dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) co ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 5, "input"); ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 5, "output"); - ValidatePointer(m_Weight, descriptorName, "weight"); - - const TensorInfo& weightTensorInfo = m_Weight->GetTensorInfo(); + const TensorInfo& weightTensorInfo = workloadInfo.m_InputTensorInfos[1]; ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 5, "weight"); ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName); @@ -1339,9 +1342,7 @@ void Convolution3dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) co Optional optionalBiasTensorInfo; if (m_Parameters.m_BiasEnabled) { - ValidatePointer(m_Bias, descriptorName, "bias"); - - optionalBiasTensorInfo = MakeOptional(m_Bias->GetTensorInfo()); + optionalBiasTensorInfo = MakeOptional(workloadInfo.m_InputTensorInfos[2]); const TensorInfo& biasTensorInfo = optionalBiasTensorInfo.value(); ValidateTensorDataType(biasTensorInfo, GetBiasDataType(inputTensorInfo.GetDataType()), descriptorName, "bias"); diff --git a/src/backends/backendsCommon/WorkloadData.hpp b/src/backends/backendsCommon/WorkloadData.hpp index 29d39d14a9..4e56aaf823 100644 --- a/src/backends/backendsCommon/WorkloadData.hpp +++ b/src/backends/backendsCommon/WorkloadData.hpp @@ -208,18 +208,9 @@ struct Convolution2dQueueDescriptor : QueueDescriptorWithParameters { - Convolution3dQueueDescriptor() - : m_Weight(nullptr) - , m_Bias(nullptr) - { - } - - const ConstTensorHandle* m_Weight; - const ConstTensorHandle* m_Bias; - void Validate(const WorkloadInfo& workloadInfo) const; }; diff --git a/src/backends/backendsCommon/WorkloadFactory.cpp b/src/backends/backendsCommon/WorkloadFactory.cpp index 3b7f3a0f1f..55ce3554f9 100644 --- a/src/backends/backendsCommon/WorkloadFactory.cpp +++ b/src/backends/backendsCommon/WorkloadFactory.cpp @@ -250,7 +250,11 @@ bool IWorkloadFactory::IsLayerConfigurationSupported(const BackendId& backendId, const TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(), dataType); const TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType); - ARMNN_ASSERT(cLayer->m_Weight.get() != nullptr); + + ARMNN_ASSERT_MSG(layer.GetInputSlot(1).GetConnection(), + "Convolution3dLayer: Weights should be connected as a Constant Layer."); + const TensorInfo weights = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(), + dataType); const Convolution3dDescriptor& descriptor = cLayer->GetParameters(); @@ -258,14 +262,15 @@ bool IWorkloadFactory::IsLayerConfigurationSupported(const BackendId& backendId, Optional biases; if (descriptor.m_BiasEnabled) { - biases = OverrideDataType(cLayer->m_Bias->GetTensorInfo(), GetBiasTypeFromWeightsType(dataType)); + biases = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(), + GetBiasTypeFromWeightsType(dataType)); } result = layerSupportObject.IsConvolution3dSupported( input, output, descriptor, - OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType), + weights, biases, reason); break; diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt index e3221c5ae4..b90407fd7c 100644 --- a/src/backends/backendsCommon/test/CMakeLists.txt +++ b/src/backends/backendsCommon/test/CMakeLists.txt @@ -13,6 +13,7 @@ list(APPEND armnnBackendsCommonUnitTests_sources ChannelShuffleEndToEndTestImpl.hpp ComparisonEndToEndTestImpl.hpp CompatibilityTests.cpp + Convolution3dEndToEndTestImpl.hpp CustomMemoryOptimizerStrategyTests.cpp DefaultAsyncExecuteTest.cpp DepthToSpaceEndToEndTestImpl.hpp diff --git a/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp new file mode 100644 index 0000000000..33bf9a180b --- /dev/null +++ b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp @@ -0,0 +1,167 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "EndToEndTestImpl.hpp" +#include "QuantizeHelper.hpp" + +#include + +#include +#include + +#include +#include + +namespace +{ + +armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor, + const armnn::TensorInfo& inputInfo, + const armnn::TensorInfo& weightsInfo, + const armnn::TensorInfo& biasInfo, + const armnn::TensorInfo& outputInfo, + const armnn::ConstTensor& weights, + const armnn::ConstTensor& biases) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + IConnectableLayer* input = network->AddInputLayer(0, "input"); + armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); + armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias"); + IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d"); + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + Connect(input, convolution3d, inputInfo, 0, 0); + Connect(weightsLayer, convolution3d, weightsInfo, 0, 1); + Connect(biasLayer, convolution3d, biasInfo, 0, 2); + Connect(convolution3d, output, outputInfo, 0, 0); + + return network; +} + +} // anonymous namespace + +template +void Convolution3dEndToEnd(const std::vector& backends, + armnn::DataLayout dataLayout) +{ + using namespace armnn; + using T = ResolveType; + using BT = ResolveType; + + const float qScale = IsQuantizedType() ? 0.25f : 1.0f; + const int32_t qOffset = IsQuantizedType() ? 50 : 0; + + TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset); + TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); + TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true); + TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true); + + std::vector inputData = + { + 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, + 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, + 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, + + 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, + 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, + 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, + 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, + + 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, + 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, + 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, + 60.0f, 61.0f, 62.0f, 63.0f, 64.0f, + 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, + + 70.0f, 71.0f, 72.0f, 73.0f, 74.0f, + 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, + 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, + 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, + 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, + 95.0f, 96.0f, 97.0f, 98.0f, 99.0f, + + 100.0f, 101.0f, 102.0f, 103.0f, 104.0f, + 105.0f, 106.0f, 107.0f, 108.0f, 109.0f, + 110.0f, 111.0f, 112.0f, 113.0f, 114.0f, + 115.0f, 116.0f, 117.0f, 118.0f, 119.0f, + 120.0f, 121.0f, 122.0f, 123.0f, 124.0f + }; + + std::vector weightsData = + { + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + + 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, + + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + }; + + std::vector biasesData = { 1.f }; + + std::vector expectedOutputData = + { + 559.0f, 595.0f, + + 739.0f, 775.0f, + + 1459.0f, 1495.0f, + + 1639.0f, 1675.0f, + }; + + Convolution3dDescriptor descriptor; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_PadFront = 0; + descriptor.m_PadBack = 0; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_StrideZ = 2; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = dataLayout; + + // Permute input and output if NCDHW. + if (dataLayout == DataLayout::NCDHW) + { + PermuteTensorNdhwcToNcdhw(inputInfo, inputData); + PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData); + } + + // Quantize data + std::vector qInputData = armnnUtils::QuantizedVector(inputData, qScale, qOffset); + std::vector qWeightsData = armnnUtils::QuantizedVector(weightsData, qScale, qOffset); + std::vector qExpectedOutputData = armnnUtils::QuantizedVector(expectedOutputData, qScale, qOffset); + + std::vector qBiasesData = armnnUtils::QuantizedVector(biasesData, qScale * qScale, 0); + + ConstTensor weights(weightsInfo, qWeightsData); + ConstTensor biases(biasesInfo, qBiasesData); + + INetworkPtr network = CreateConvolution3dNetwork(descriptor, + inputInfo, + weightsInfo, + biasesInfo, + outputInfo, + weights, + biases); + + EndToEndLayerTestImpl(std::move(network), + { { 0, qInputData } }, + { { 0, qExpectedOutputData } }, + backends); +} diff --git a/src/backends/backendsCommon/test/DataLayoutUtils.hpp b/src/backends/backendsCommon/test/DataLayoutUtils.hpp index 9411212f4f..89b3900979 100644 --- a/src/backends/backendsCommon/test/DataLayoutUtils.hpp +++ b/src/backends/backendsCommon/test/DataLayoutUtils.hpp @@ -34,3 +34,27 @@ void PermuteTensorNhwcToNchw(armnn::TensorInfo& tensorInfo, std::vector& tens tensorData = tmp; } + +template +void PermuteTensorNdhwcToNcdhw(armnn::TensorInfo& tensorInfo, std::vector& tensorData) +{ + const armnn::PermutationVector ndhwcToNcdhw = { 0, 2, 3, 4, 1 }; + + tensorInfo = armnnUtils::Permuted(tensorInfo, ndhwcToNcdhw); + + std::vector tmp(tensorData.size()); + armnnUtils::Permute(tensorInfo.GetShape(), ndhwcToNcdhw, tensorData.data(), tmp.data(), sizeof(T)); + tensorData = tmp; +} + +template +void PermuteTensorNcdhwToNdhwc(armnn::TensorInfo& tensorInfo, std::vector& tensorData) +{ + const armnn::PermutationVector ncdhwToNdhwc = { 0, 4, 1, 2, 3 }; + + tensorInfo = armnnUtils::Permuted(tensorInfo, ncdhwToNdhwc); + + std::vector tmp(tensorData.size()); + armnnUtils::Permute(tensorInfo.GetShape(), ncdhwToNdhwc, tensorData.data(), tmp.data(), sizeof(T)); + tensorData = tmp; +} diff --git a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp index c3a6aa1a3c..f9bdfde622 100644 --- a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp @@ -407,7 +407,7 @@ void FullyConnectedErrorChecking(const std::vector& backends, } catch (const LayerValidationException& exc) { - CHECK(strcmp(exc.what(), "FullyConnected layer weights not set: Input slot(s) 1 not connected " + CHECK(strcmp(exc.what(), "Fully_Connected layer weights not set: Input slot(s) 1 not connected " "to an output slot on FullyConnected layer \"Fully_Connected\"") == 0); } } @@ -434,7 +434,7 @@ void FullyConnectedErrorChecking(const std::vector& backends, } catch (const LayerValidationException& exc) { - CHECK(strcmp(exc.what(), "FullyConnected layer bias not set: Input slot(s) 2 not connected " + CHECK(strcmp(exc.what(), "Fully_Connected layer bias not set: Input slot(s) 2 not connected " "to an output slot on FullyConnected layer \"Fully_Connected\"") == 0); } } @@ -457,7 +457,7 @@ void FullyConnectedErrorChecking(const std::vector& backends, } catch (const LayerValidationException& exc) { - CHECK(strcmp(exc.what(), "FullyConnected layer weights and bias not set: Input slot(s) 1 & 2 not " + CHECK(strcmp(exc.what(), "Fully_Connected layer weights and bias not set: Input slot(s) 1 & 2 not " "connected to an output slot on FullyConnected layer \"Fully_Connected\"") == 0); } diff --git a/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.cpp index 259272d996..1406ab039b 100644 --- a/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.cpp @@ -11,6 +11,7 @@ #include +#include #include #include @@ -228,23 +229,20 @@ LayerTestResult SimpleConvolution3dTestImpl( biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset()); } + // Permute input and output if data layout is NCDHW. + if (dataLayout == armnn::DataLayout::NCDHW) + { + PermuteTensorNdhwcToNcdhw(inputTensorInfo, inputData); + PermuteTensorNdhwcToNcdhw(outputTensorInfo, outputData); + } + std::vector actualOutput(outputTensorInfo.GetNumElements()); - std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr input0Handle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr input1Handle = tensorHandleFactory.CreateTensorHandle(kernelDesc); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); - armnn::ScopedTensorHandle weightsTensor(kernelDesc); - AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data()); - - armnn::ScopedTensorHandle biasTensor(biasDesc); - if (biasEnabled) - { - AllocateAndCopyDataToITensorHandle(&biasTensor, bias.data()); - } - armnn::Convolution3dQueueDescriptor data; - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs. data.m_Parameters.m_StrideX = strideX; data.m_Parameters.m_StrideY = strideY; data.m_Parameters.m_StrideZ = strideZ; @@ -261,14 +259,29 @@ LayerTestResult SimpleConvolution3dTestImpl( data.m_Parameters.m_BiasEnabled = biasEnabled; armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, inputTensorInfo, input0Handle.get()); + AddInputToWorkload(data, info, kernelDesc, input1Handle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + std::unique_ptr input2Handle = nullptr; + if (biasEnabled) + { + input2Handle = tensorHandleFactory.CreateTensorHandle(biasDesc); + AddInputToWorkload(data, info, biasDesc, input2Handle.get()); + } + std::unique_ptr workload = workloadFactory.CreateConvolution3d(data, info); - inputHandle->Allocate(); + input0Handle->Allocate(); + input1Handle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), inputData.data()); + CopyDataToITensorHandle(input0Handle.get(), inputData.data()); + CopyDataToITensorHandle(input1Handle.get(), kernel.data()); + if (biasEnabled) + { + input2Handle->Allocate(); + CopyDataToITensorHandle(input2Handle.get(), bias.data()); + } ExecuteWorkload(*workload, memoryManager); @@ -840,40 +853,44 @@ LayerTestResult SimpleConvolution3d3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return SimpleConvolution3d3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult SimpleConvolution3d3x3x3Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return SimpleConvolution3d3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult SimpleConvolution3d3x3x3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return SimpleConvolution3d3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult SimpleConvolution3d3x3x3Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return SimpleConvolution3d3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } @@ -881,158 +898,174 @@ LayerTestResult Convolution3d2x2x2Strides3x5x5Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Strides3x5x5TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Strides3x5x5Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Strides3x5x5TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Strides3x5x5Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Strides3x5x5TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Strides3x5x5Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Strides3x5x5TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Dilation2x2x2Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Dilation2x2x2TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Dilation2x2x2Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Dilation2x2x2TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Dilation2x2x2Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Dilation2x2x2TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Dilation2x2x2Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Dilation2x2x2TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3dPaddingSame3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3dPaddingSame3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3dPaddingSame3x3x3Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3dPaddingSame3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3dPaddingSame3x3x3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3dPaddingSame3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3dPaddingSame3x3x3Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3dPaddingSame3x3x3TestCommon( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3dStrideDilationPadding3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3dStrideDilationPadding3x3x3TestCommonFloat32( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2Stride3x3x3SmallFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2Stride3x3x3SmallTestCommonFloat32( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x3x3Float16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x3x3TestCommonFloat16( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } LayerTestResult Convolution3d2x2x2SmallFloat16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled) + bool biasEnabled, + armnn::DataLayout dataLayout) { return Convolution3d2x2x2SmallTestCommonFloat16( - workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, armnn::DataLayout::NDHWC); + workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); } diff --git a/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.hpp index a07c183c76..c612e19c9b 100644 --- a/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/Conv3dTestImpl.hpp @@ -24,118 +24,138 @@ LayerTestResult SimpleConvolution3d3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult SimpleConvolution3d3x3x3Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult SimpleConvolution3d3x3x3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult SimpleConvolution3d3x3x3Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Strides3x5x5Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Strides3x5x5Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Strides3x5x5Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Strides3x5x5Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Dilation2x2x2Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Dilation2x2x2Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Dilation2x2x2Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Dilation2x2x2Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3dPaddingSame3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3dPaddingSame3x3x3Int8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3dPaddingSame3x3x3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3dPaddingSame3x3x3Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3dStrideDilationPadding3x3x3Float32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2Stride3x3x3SmallFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x3x3Float16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); LayerTestResult Convolution3d2x2x2SmallFloat16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - bool biasEnabled); + bool biasEnabled, + armnn::DataLayout dataLayout); diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp index 0cc8f4aa10..dc4dcecd81 100644 --- a/src/backends/reference/test/RefEndToEndTests.cpp +++ b/src/backends/reference/test/RefEndToEndTests.cpp @@ -11,6 +11,7 @@ #include #include #include +#include #include #include #include @@ -566,6 +567,36 @@ TEST_CASE("RefConcatEndToEndDim3Uint8Test") ConcatDim3EndToEnd(defaultBackends); } +TEST_CASE("RefConvolution3dFloat32Test") +{ + Convolution3dEndToEnd(defaultBackends, + armnn::DataLayout::NDHWC); +} + +TEST_CASE("RefConvolution3dNcdhwFloat32Test") +{ + Convolution3dEndToEnd(defaultBackends, + armnn::DataLayout::NCDHW); +} + +TEST_CASE("RefConvolution3dFloat16Test") +{ + Convolution3dEndToEnd(defaultBackends, + armnn::DataLayout::NDHWC); +} + +TEST_CASE("RefConvolution3dUint8Test") +{ + Convolution3dEndToEnd(defaultBackends, + armnn::DataLayout::NDHWC); +} + +TEST_CASE("RefConvolution3dInt8Test") +{ + Convolution3dEndToEnd(defaultBackends, + armnn::DataLayout::NDHWC); +} + TEST_CASE("RefEluEndToEndTestFloat32") { EluEndToEndTest(defaultBackends); diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp index f5d388d007..cb31b37161 100644 --- a/src/backends/reference/test/RefLayerTests.cpp +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -208,37 +208,119 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Stride2x2BFloat16SmallValue, false, DataLayout::NHWC); -// Convolution 3d -ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Float32, SimpleConvolution3d3x3x3Float32Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Int8, SimpleConvolution3d3x3x3Int8Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Uint8, SimpleConvolution3d3x3x3Uint8Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Int16, SimpleConvolution3d3x3x3Int16Test, false) - -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5Float32, Convolution3d2x2x2Strides3x5x5Float32Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestInt8, Convolution3d2x2x2Strides3x5x5Int8Test, true) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestUint8, Convolution3d2x2x2Strides3x5x5Uint8Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestInt16, Convolution3d2x2x2Strides3x5x5Int16Test, true) - -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3Float32, Convolution3dPaddingSame3x3x3Float32Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestInt8, Convolution3dPaddingSame3x3x3Int8Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestUint8, Convolution3dPaddingSame3x3x3Uint8Test, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestInt16, Convolution3dPaddingSame3x3x3Int16Test, false) - -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2Float32, Convolution3d2x2x2Dilation2x2x2Float32Test, true) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestInt8, Convolution3d2x2x2Dilation2x2x2Int8Test, true) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestUint8, Convolution3d2x2x2Dilation2x2x2Uint8Test, true) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestInt16, Convolution3d2x2x2Dilation2x2x2Int16Test, true) +// Convolution 3d - NDHWC +ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Float32, + SimpleConvolution3d3x3x3Float32Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Int8, + SimpleConvolution3d3x3x3Int8Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Uint8, + SimpleConvolution3d3x3x3Uint8Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Int16, + SimpleConvolution3d3x3x3Int16Test, + false, + DataLayout::NDHWC) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5Float32, + Convolution3d2x2x2Strides3x5x5Float32Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestInt8, + Convolution3d2x2x2Strides3x5x5Int8Test, + true, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestUint8, + Convolution3d2x2x2Strides3x5x5Uint8Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5TestInt16, + Convolution3d2x2x2Strides3x5x5Int16Test, + true, + DataLayout::NDHWC) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3Float32, + Convolution3dPaddingSame3x3x3Float32Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestInt8, + Convolution3dPaddingSame3x3x3Int8Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestUint8, + Convolution3dPaddingSame3x3x3Uint8Test, + false, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3TestInt16, + Convolution3dPaddingSame3x3x3Int16Test, + false, + DataLayout::NDHWC) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2Float32, + Convolution3d2x2x2Dilation2x2x2Float32Test, + true, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestInt8, + Convolution3d2x2x2Dilation2x2x2Int8Test, + true, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestUint8, + Convolution3d2x2x2Dilation2x2x2Uint8Test, + true, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2TestInt16, + Convolution3d2x2x2Dilation2x2x2Int16Test, + true, + DataLayout::NDHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dStrideDilationPadding3x3x3Float32, Convolution3dStrideDilationPadding3x3x3Float32Test, - true) + true, + DataLayout::NDHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Stride3x3x3SmallTestFloat32, Convolution3d2x2x2Stride3x3x3SmallFloat32Test, - false) + false, + DataLayout::NDHWC) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x3x3TestFloat16, Convolution3d2x3x3Float16Test, true) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2SmallTestFloat16, Convolution3d2x2x2SmallFloat16Test, false) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x3x3TestFloat16, + Convolution3d2x3x3Float16Test, + true, + DataLayout::NDHWC) +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2SmallTestFloat16, + Convolution3d2x2x2SmallFloat16Test, + false, + DataLayout::NDHWC) + +// Convolution 3d - NCDHW +ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3NcdhwFloat32, + SimpleConvolution3d3x3x3Float32Test, + false, + DataLayout::NCDHW) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x3x3TestNcdhwFloat16, + Convolution3d2x3x3Float16Test, + false, + DataLayout::NCDHW) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Strides3x5x5NcdhwTestInt8, + Convolution3d2x2x2Strides3x5x5Int8Test, + true, + DataLayout::NCDHW) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3dPaddingSame3x3x3NcdhwTestUint8, + Convolution3dPaddingSame3x3x3Uint8Test, + false, + DataLayout::NCDHW) + +ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution3d2x2x2Dilation2x2x2NcdhwTestInt16, + Convolution3d2x2x2Dilation2x2x2Int16Test, + true, + DataLayout::NCDHW) // Depthwise Convolution diff --git a/src/backends/reference/workloads/Conv3dImpl.cpp b/src/backends/reference/workloads/Conv3dImpl.cpp index 484d887cfc..1c06d624a8 100644 --- a/src/backends/reference/workloads/Conv3dImpl.cpp +++ b/src/backends/reference/workloads/Conv3dImpl.cpp @@ -113,11 +113,25 @@ void Convolve3d(const TensorShape& rInputShape, // Keep this implementation, as using DataLayoutIndexed::GetIndex // causes large performance regression. - inputIndex = batchIdx * inputDepth * inputHeight * inputWidth * inChannels + - (zInput-paddingFront) * inputHeight * inputWidth * inChannels + - (yInput-paddingTop) * inputWidth * inChannels + - (xInput-paddingLeft) * inChannels + - cInput; + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NDHWC) + { + inputIndex = + batchIdx * inputDepth * inputHeight * inputWidth * inChannels + + (zInput-paddingFront) * inputHeight * inputWidth * inChannels + + (yInput-paddingTop) * inputWidth * inChannels + + (xInput-paddingLeft) * inChannels + + cInput; + } + else + { + // NCDHW DataLayout + inputIndex = + batchIdx * inputDepth * inputHeight * inputWidth * inChannels + + inputDepth * inputHeight * inputWidth * cInput + + (zInput-paddingFront) * inputHeight * inputWidth + + (yInput-paddingTop) * inputWidth + + xInput-paddingLeft; + } inputValue = inputVec[inputIndex]; } @@ -133,11 +147,24 @@ void Convolve3d(const TensorShape& rInputShape, sum += biasVec[cOutput]; } - unsigned int outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels + - zOutput * outputHeight * outputWidth * outChannels + - yOutput * outputWidth * outChannels + - xOutput * outChannels + - cOutput; + unsigned int outIdx; + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NDHWC) + { + outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels + + zOutput * outputHeight * outputWidth * outChannels + + yOutput * outputWidth * outChannels + + xOutput * outChannels + + cOutput; + } + else + { + // NCDHW DataLayout + outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels + + cOutput * outputDepth * outputHeight * outputWidth + + zOutput * outputHeight * outputWidth + + yOutput * outputWidth + + xOutput; + } rOutputEncoder[outIdx]; rOutputEncoder.Set(sum); diff --git a/src/backends/reference/workloads/RefConvolution3dWorkload.cpp b/src/backends/reference/workloads/RefConvolution3dWorkload.cpp index ea425daec9..afab88f0a8 100644 --- a/src/backends/reference/workloads/RefConvolution3dWorkload.cpp +++ b/src/backends/reference/workloads/RefConvolution3dWorkload.cpp @@ -19,10 +19,10 @@ RefConvolution3dWorkload::RefConvolution3dWorkload( WorkloadInfo detailsInfo; detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos; detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos; - detailsInfo.m_WeightsTensorInfo = armnn::Optional(descriptor.m_Weight->GetTensorInfo()); + detailsInfo.m_WeightsTensorInfo = armnn::Optional(info.m_InputTensorInfos[1]); if (descriptor.m_Parameters.m_BiasEnabled) { - detailsInfo.m_BiasTensorInfo = armnn::Optional(descriptor.m_Bias->GetTensorInfo()); + detailsInfo.m_BiasTensorInfo = armnn::Optional(info.m_InputTensorInfos[2]); } // Report Profiling Details @@ -30,18 +30,25 @@ RefConvolution3dWorkload::RefConvolution3dWorkload( descriptor.m_Parameters, detailsInfo, this->GetGuid()); +} - m_Weight = std::make_unique(*( descriptor.m_Weight )); - const TensorInfo& rFilterInfo = m_Weight->GetTensorInfo(); +void RefConvolution3dWorkload::PostAllocationConfigure() +{ + PostAllocationConfigure(m_Data.m_Inputs, m_Data.m_Outputs); +} +void RefConvolution3dWorkload::PostAllocationConfigure(std::vector inputs, + std::vector outputs) +{ + IgnoreUnused(outputs); + const TensorInfo& rFilterInfo = GetTensorInfo(inputs[1]); m_FilterShape = rFilterInfo.GetShape(); - m_FilterDecoder = MakeDecoder(rFilterInfo, m_Weight.get()->Map(true)); + m_FilterDecoder = MakeDecoder(rFilterInfo); - if ( descriptor.m_Parameters.m_BiasEnabled ) + if (m_Data.m_Parameters.m_BiasEnabled) { - m_Bias = std::make_unique(*( descriptor.m_Bias )); - const TensorInfo& biasInfo = m_Bias->GetTensorInfo(); - m_BiasDecoder = MakeDecoder(biasInfo, m_Bias->Map(true)); + const TensorInfo& biasInfo = GetTensorInfo(inputs[2]); + m_BiasDecoder = MakeDecoder(biasInfo); } } @@ -52,6 +59,8 @@ void RefConvolution3dWorkload::Execute() const void RefConvolution3dWorkload::ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor) { + PostAllocationConfigure(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs); + Execute(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs); } @@ -65,6 +74,12 @@ void RefConvolution3dWorkload::Execute(std::vector inputs, std:: const TensorShape& inputShape = GetTensorInfo(inputs[0]).GetShape(); const TensorShape& outputShape = GetTensorInfo(outputs[0]).GetShape(); + m_FilterDecoder->Reset(inputs[1]->Map()); + if (m_Data.m_Parameters.m_BiasEnabled) + { + m_BiasDecoder->Reset(inputs[2]->Map()); + } + Convolve3d(inputShape, *inputDecoder, outputShape, *outputEncoder, m_FilterShape, *m_FilterDecoder, m_Data.m_Parameters.m_BiasEnabled, m_BiasDecoder.get(), m_Data.m_Parameters.m_DataLayout, diff --git a/src/backends/reference/workloads/RefConvolution3dWorkload.hpp b/src/backends/reference/workloads/RefConvolution3dWorkload.hpp index 0373a8b900..4d97512095 100644 --- a/src/backends/reference/workloads/RefConvolution3dWorkload.hpp +++ b/src/backends/reference/workloads/RefConvolution3dWorkload.hpp @@ -19,14 +19,14 @@ public: explicit RefConvolution3dWorkload(const Convolution3dQueueDescriptor& descriptor, const WorkloadInfo& info); + void PostAllocationConfigure() override; void Execute() const override; void ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor) override; private: + void PostAllocationConfigure(std::vector inputs, std::vector outputs); void Execute(std::vector inputs, std::vector outputs) const; - std::unique_ptr m_Weight; - std::unique_ptr m_Bias; std::unique_ptr> m_FilterDecoder; std::unique_ptr> m_BiasDecoder; -- cgit v1.2.1