| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <armnn/ArmNN.hpp> |
| |
| #include "armnn/src/armnnUtils/Permute.hpp" |
| #include "Utils.hpp" |
| |
| #include <ActivationFunctor.h> |
| #include <CpuExecutor.h> |
| #include <OperationsUtils.h> |
| |
| #include <boost/assert.hpp> |
| #include <boost/core/ignore_unused.hpp> |
| #include <boost/test/tools/floating_point_comparison.hpp> |
| |
| #include <log/log.h> |
| |
| namespace armnn_driver |
| { |
| |
| /// |
| /// Helper classes |
| /// |
| |
| struct ConversionData |
| { |
| ConversionData(armnn::Compute compute) |
| : m_Compute(compute) |
| , m_Network(nullptr, nullptr) |
| {} |
| |
| const armnn::Compute m_Compute; |
| armnn::INetworkPtr m_Network; |
| std::vector<armnn::IOutputSlot*> m_OutputSlotForOperand; |
| std::vector<android::nn::RunTimePoolInfo> m_MemPools; |
| }; |
| |
| class LayerInputHandle |
| { |
| public: |
| LayerInputHandle(); |
| LayerInputHandle(bool valid, armnn::IOutputSlot* outputSlot, armnn::TensorInfo tensorInfo); |
| |
| bool IsValid() const; |
| |
| void Connect(armnn::IInputSlot& inputSlot); |
| |
| const armnn::TensorInfo& GetTensorInfo() const; |
| |
| private: |
| armnn::IOutputSlot* m_OutputSlot; |
| bool m_Valid; |
| armnn::TensorInfo m_TensorInfo; |
| }; |
| |
| class ConstTensorPin |
| { |
| public: |
| // Creates an invalid tensor pin (can be used to signal errors) |
| // The optional flag can be set to indicate the tensor values were missing, but it was otherwise valid |
| ConstTensorPin(bool optional = false); |
| |
| // @param tensorInfo TensorInfo associated with the tensor. |
| // @param valueStart Start address of tensor data. Belongs to one of the memory pools associated with |
| // the model being converted. |
| // @param numBytes Number of bytes for the tensor data. |
| ConstTensorPin(const armnn::TensorInfo& tensorInfo, const void* valueStart, uint32_t numBytes, |
| const armnn::PermutationVector& mappings); |
| |
| ConstTensorPin(const ConstTensorPin& other) = delete; |
| ConstTensorPin(ConstTensorPin&& other) = default; |
| |
| bool IsValid() const; |
| bool IsOptional() const; |
| |
| const armnn::ConstTensor& GetConstTensor() const; |
| const armnn::ConstTensor* GetConstTensorPtr() const; |
| |
| private: |
| armnn::ConstTensor m_ConstTensor; |
| |
| // Owned memory for swizzled tensor data, only required if the tensor needed |
| // swizzling. Otherwise, @ref m_ConstTensor will reference memory from one of |
| // the pools associated with the model being converted. |
| std::vector<uint8_t> m_SwizzledTensorData; |
| |
| // optional flag to indicate that an invalid tensor pin is not an error, but the optional values were not given |
| bool m_Optional; |
| }; |
| |
| } // namespace armnn_driver |
| |
| /// |
| /// Utility functions |
| /// |
| |
| namespace |
| { |
| |
| using namespace armnn_driver; |
| using namespace android::nn; |
| |
| // Convenience function to log the reason for failing to convert a model. |
| // @return Always returns false (so that it can be used by callers as a quick way to signal an error and return) |
| template<class... Args> |
| static bool Fail(const char* formatStr, Args&&... args) |
| { |
| ALOGD(formatStr, std::forward<Args>(args)...); |
| return false; |
| } |
| |
| // Convenience function to call an Is*Supported function and log caller name together with reason for lack of support. |
| // Called as: IsLayerSupported(__func__, Is*Supported, a, b, c, d, e) |
| template<typename IsLayerSupportedFunc, typename ... Args> |
| bool IsLayerSupported(const char* funcName, IsLayerSupportedFunc f, Args&&... args) |
| { |
| std::vector<char> unsupportedReason(1024+1); |
| bool isSupported = f(std::forward<Args>(args)..., unsupportedReason.data(), unsupportedReason.size()-1); |
| if(isSupported) |
| { |
| return true; |
| } |
| else |
| { |
| std::string sUnsupportedReason(unsupportedReason.data()); |
| if (sUnsupportedReason.size() > 0) |
| { |
| ALOGD("%s: not supported by armnn: %s", funcName, sUnsupportedReason.c_str()); |
| } else |
| { |
| ALOGD("%s: not supported by armnn", funcName); |
| } |
| return false; |
| } |
| } |
| |
| armnn::TensorShape GetTensorShapeForOperand(const Operand& operand) |
| { |
| return armnn::TensorShape(operand.dimensions.size(), operand.dimensions.data()); |
| } |
| |
| inline bool IsOperandTypeSupportedForTensors(OperandType type) |
| { |
| return type == OperandType::TENSOR_FLOAT32 || |
| type == OperandType::TENSOR_QUANT8_ASYMM || |
| type == OperandType::TENSOR_INT32; |
| } |
| |
| void BroadcastTensor(LayerInputHandle& input0, LayerInputHandle& input1, armnn::IConnectableLayer* startLayer, |
| armnn::INetwork& network) |
| { |
| BOOST_ASSERT(startLayer != nullptr); |
| const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| |
| if (inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions()) |
| { |
| // If the number of dimensions do not match then we need to add degenerate dimensions |
| // to the "smaller" tensor using a reshape: |
| // Small Big |
| // | | |
| // Reshape | |
| // \ / |
| // Add |
| bool input0IsBigger = inputTensorInfo0.GetNumDimensions() > inputTensorInfo1.GetNumDimensions(); |
| |
| LayerInputHandle& smallTensorHandle = input0IsBigger ? input1 : input0; |
| const armnn::TensorInfo& smallTensorDims = smallTensorHandle.GetTensorInfo(); |
| |
| LayerInputHandle& bigTensorHandle = input0IsBigger ? input0 : input1; |
| const armnn::TensorInfo& bigTensorDims = bigTensorHandle.GetTensorInfo(); |
| |
| const unsigned int bigTensorDimsNumber = bigTensorDims.GetNumDimensions(); |
| std::vector<unsigned int> reshapedDims(bigTensorDimsNumber, 1); |
| unsigned int sizeDifference = bigTensorDimsNumber - smallTensorDims.GetNumDimensions(); |
| for (unsigned i = sizeDifference; i < bigTensorDimsNumber; ++i) |
| { |
| reshapedDims[i] = smallTensorDims.GetShape()[i-sizeDifference]; |
| } |
| armnn::TensorInfo reshapedInfo = smallTensorDims; |
| reshapedInfo.SetShape(armnn::TensorShape{ static_cast<unsigned int>(reshapedDims.size()), |
| reshapedDims.data() }); |
| |
| armnn::ReshapeDescriptor reshapeDesc; |
| reshapeDesc.m_TargetShape = reshapedInfo.GetShape(); |
| armnn::IConnectableLayer* const reshapeLayer = network.AddReshapeLayer(reshapeDesc); |
| smallTensorHandle.Connect(reshapeLayer->GetInputSlot(0)); |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo); |
| |
| // Connect the outputs from new reshape and original input layer |
| reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0)); |
| bigTensorHandle.Connect(startLayer->GetInputSlot(1)); |
| } |
| else |
| { |
| input0.Connect(startLayer->GetInputSlot(0)); |
| input1.Connect(startLayer->GetInputSlot(1)); |
| } |
| } |
| |
| void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t& outPadHead, uint32_t& outPadTail, |
| android::nn::PaddingScheme scheme) |
| { |
| int32_t padHead; |
| int32_t padTail; |
| calculateExplicitPadding(input, stride, kernel, scheme, &padHead, &padTail); |
| outPadHead = boost::numeric_cast<uint32_t>(padHead); |
| outPadTail = boost::numeric_cast<uint32_t>(padTail); |
| } |
| |
| Shape GetOperandShape(const Operand& operand) |
| { |
| Shape shape; |
| shape.type = operand.type; |
| shape.dimensions = operand.dimensions; |
| shape.scale = operand.scale; |
| shape.offset = operand.zeroPoint; |
| return shape; |
| } |
| |
| // ArmNN requires the bias scale to be equal to the product of the weight and input scales, which is also |
| // what AndroidNN requires. However for some of the AndroidNN tests the values don't exactly match so |
| // we accept some tolerance. We don't want to ArmNN itself to accept these inconsistencies as it is up to the user |
| // (us, in this case) to ensure they match. |
| void SanitizeBiasQuantizationScale(armnn::TensorInfo& biasInfo, |
| const armnn::TensorInfo& weightInfo, const armnn::TensorInfo& inputInfo) |
| { |
| const float expectedBiasScale = weightInfo.GetQuantizationScale() * inputInfo.GetQuantizationScale(); |
| if (biasInfo.GetQuantizationScale() != expectedBiasScale) |
| { |
| boost::math::fpc::close_at_tolerance<float> comparer(boost::math::fpc::percent_tolerance(1.0f)); |
| if (comparer(biasInfo.GetQuantizationScale(), expectedBiasScale)) |
| { |
| ALOGW("Bias quantization scale has been modified to match input*weights"); |
| biasInfo.SetQuantizationScale(expectedBiasScale); |
| } |
| } |
| } |
| |
| // 4D Tensor Permutations |
| const armnn::PermutationVector IdentityPermutation4D({ 0U, 1U, 2U, 3U }); |
| const armnn::PermutationVector NHWCToArmNN({ 0U, 2U, 3U, 1U }); |
| const armnn::PermutationVector ArmNNToNHWC({ 0U, 3U, 1U, 2U }); |
| const armnn::PermutationVector SwapDim1And2({ 0U, 2U, 1U, 3U }); |
| |
| // 3D Permutation Vectors |
| const armnn::PermutationVector IdentityPermutation3D({ 0U, 1U, 2U }); |
| const armnn::PermutationVector RotateTensorLeft({ 2U, 0U, 1U }); |
| const armnn::PermutationVector RotateTensorRight({ 1U, 2U, 0U }); |
| |
| template<typename OSlot> |
| armnn::IConnectableLayer& AddPermuteLayer(armnn::INetwork& network, OSlot& input, |
| const armnn::PermutationVector& mappings) |
| { |
| // Add swizzle layer |
| armnn::IConnectableLayer* const layer = network.AddPermuteLayer(mappings); |
| |
| BOOST_ASSERT(layer != nullptr); |
| |
| // Connect input to swizzle layer |
| input.Connect(layer->GetInputSlot(0)); |
| |
| // Setup swizzled output |
| const armnn::TensorInfo outInfo = armnnUtils::Permuted(input.GetTensorInfo(), mappings); |
| layer->GetOutputSlot(0).SetTensorInfo(outInfo); |
| |
| return *layer; |
| } |
| |
| void SwizzleIn(armnn::INetwork& network, LayerInputHandle& input, armnn::IConnectableLayer& layer, unsigned int index) |
| { |
| // Add swizzle layer |
| armnn::IConnectableLayer& swizzleLayer = AddPermuteLayer(network, input, NHWCToArmNN); |
| // Connect swizzled input to layer |
| swizzleLayer.GetOutputSlot(0).Connect(layer.GetInputSlot(index)); |
| } |
| |
| armnn::IConnectableLayer& DeswizzleOut(armnn::INetwork& network, armnn::IConnectableLayer& layer, unsigned int index) |
| { |
| // Add deswizzle layer |
| armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(network, layer.GetOutputSlot(index), ArmNNToNHWC); |
| return deswizzleLayer; |
| } |
| |
| // only suitable for input/output slot index 0, for other slots, use SwizzleIn and DeswizzleOut directly |
| armnn::IConnectableLayer& SwizzleInDeswizzleOut(armnn::INetwork& network, |
| LayerInputHandle& input, |
| armnn::IConnectableLayer& firstLayer, |
| armnn::IConnectableLayer& lastLayer) |
| { |
| SwizzleIn(network, input, firstLayer, 0); |
| return DeswizzleOut(network, lastLayer, 0); |
| } |
| |
| // only suitable for input/output slot index 0, for other slots, use SwizzleIn and DeswizzleOut directly |
| armnn::IConnectableLayer& SwizzleInDeswizzleOut(armnn::INetwork& network, LayerInputHandle& input, |
| armnn::IConnectableLayer& layer) |
| { |
| return SwizzleInDeswizzleOut(network, input, layer, layer); |
| } |
| |
| bool ValidateConcatOutputShape(const std::vector<armnn::TensorShape> & inputShapes, |
| const armnn::TensorShape & outputShape, |
| uint32_t concatDim) |
| { |
| // Validate the output shape is correct given the input shapes (which have just been validated) |
| unsigned int numDimensions = inputShapes[0].GetNumDimensions(); |
| if (outputShape.GetNumDimensions() != numDimensions) |
| { |
| return Fail("%s: Output shape has wrong number of dimensions", __func__); |
| } |
| |
| unsigned int outputSizeAlongConcatenatedDimension = 0; |
| for (unsigned int i = 0; i < inputShapes.size(); i++) |
| { |
| outputSizeAlongConcatenatedDimension += inputShapes[i][concatDim]; |
| } |
| |
| for (unsigned int i = 0; i < numDimensions; ++i) |
| { |
| if (i == concatDim) |
| { |
| if (outputShape[i] != outputSizeAlongConcatenatedDimension) |
| { |
| return Fail( |
| "%s: Invalid output shape for dimension %d (%d != %d)", |
| __func__, |
| i, |
| outputShape[i], |
| outputSizeAlongConcatenatedDimension); |
| } |
| } |
| else |
| { |
| if (outputShape[i] != inputShapes[0][i]) |
| { |
| return Fail("%s: Invalid output shape", __func__); |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| bool RequiresReshape(armnn::TensorShape & inputShape) |
| { |
| return inputShape.GetNumDimensions() < 3; |
| } |
| |
| template<typename OSlot> |
| armnn::IConnectableLayer& AddReshapeLayer(armnn::INetwork& network, OSlot& inputLayer, |
| armnn::TensorInfo reshapeInfo) |
| { |
| armnn::ReshapeDescriptor reshapeDescriptor; |
| reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape(); |
| |
| armnn::IConnectableLayer* reshapeLayer = network.AddReshapeLayer(reshapeDescriptor); |
| BOOST_ASSERT(reshapeLayer != nullptr); |
| |
| // Attach the input layer to the reshape layer |
| inputLayer.Connect(reshapeLayer->GetInputSlot(0)); |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapeInfo); |
| |
| return *reshapeLayer; |
| } |
| |
| void SwizzleInputs(armnn::INetwork& network, |
| std::vector<LayerInputHandle>& inputs, |
| std::vector<armnn::TensorShape>& inputShapes, |
| const armnn::PermutationVector& mapping) |
| { |
| if (!mapping.IsEqual(IdentityPermutation4D)) |
| { |
| size_t nInputs = inputs.size(); |
| for (size_t i=0; i<nInputs; ++i) |
| { |
| // add swizzle layer |
| armnn::IConnectableLayer& swizzleLayer = AddPermuteLayer(network, inputs[i], mapping); |
| auto& outputSlot = swizzleLayer.GetOutputSlot(0); |
| auto& outputInfo = outputSlot.GetTensorInfo(); |
| // replace inputs with the swizzled ones |
| inputs[i] = LayerInputHandle(true, &outputSlot, outputInfo); |
| inputShapes[i] = inputs[i].GetTensorInfo().GetShape(); |
| } |
| } |
| } |
| |
| bool CreateConcatPermutationParameters(const unsigned int numberOfDimensions, |
| int32_t & concatDimension, |
| std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutationPair) |
| { |
| bool needPermute = false; |
| BOOST_ASSERT(numberOfDimensions >= 3); |
| |
| // ArmNN uses Compute Library subtensors to perform concatenation |
| // This only works when concatenating along dimension 0, 1 or 3 for a 4-D tensor, |
| // or along dimension 0 or 2 for a 3-D tensor. |
| if (numberOfDimensions == 4 && concatDimension == 2) |
| { |
| concatDimension = 1; |
| permutationPair = std::make_pair(SwapDim1And2, SwapDim1And2); |
| needPermute = true; |
| } |
| else if (numberOfDimensions == 3 && concatDimension == 1) |
| { |
| concatDimension = 0; |
| permutationPair = std::make_pair(RotateTensorLeft, RotateTensorRight); |
| needPermute = true; |
| } |
| return needPermute; |
| } |
| |
| } // anonymous namespace |
| |
| namespace armnn_driver |
| { |
| |
| //// Creates an ArmNN activation layer and connects it to the given layer, if the |
| //// passed in AndroidNN activation function requires so. |
| //// @return The end layer of the sequence of layers built for the given AndroidNN |
| //// activation function or nullptr if an error occurred (e.g. unsupported activation). |
| //// Note that the end layer matches the input layer if no activation is required |
| //// (the sequence of layers has length 1). |
| armnn::IConnectableLayer* ProcessActivation(const armnn::TensorInfo& tensorInfo, |
| ActivationFn activation, |
| armnn::IConnectableLayer* prevLayer, |
| ConversionData& data); |
| |
| } // namespace armnn_driver |
| |
| /// |
| /// Utility templates |
| /// |
| |
| namespace armnn_driver |
| { |
| |
| using namespace android::nn; |
| |
| template<typename HalOperation, typename HalModel> |
| const Operand* GetInputOperand(const HalOperation& operation, uint32_t inputIndex, const HalModel& model, |
| bool failOnIndexOutOfBounds = true) |
| { |
| if (inputIndex >= operation.inputs.size()) |
| { |
| if (failOnIndexOutOfBounds) |
| { |
| Fail("%s: invalid input index: %i out of %i", __func__, inputIndex, operation.inputs.size()); |
| } |
| return nullptr; |
| } |
| |
| BOOST_ASSERT(operation.inputs[inputIndex] < model.operands.size()); // Model should have been validated beforehand |
| return &model.operands[operation.inputs[inputIndex]]; |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| const Operand* GetOutputOperand(const HalOperation& operation, uint32_t outputIndex, const HalModel& model) |
| { |
| if (outputIndex >= operation.outputs.size()) |
| { |
| Fail("%s: invalid output index: %i out of %i", __func__, outputIndex, operation.outputs.size()); |
| return nullptr; |
| } |
| |
| // Model should have been validated beforehand |
| BOOST_ASSERT(operation.outputs[outputIndex] < model.operands.size()); |
| |
| return &model.operands[operation.outputs[outputIndex]]; |
| } |
| |
| template<typename HalModel> |
| ConstTensorPin ConvertOperandToConstTensorPin(const Operand& operand, |
| const HalModel& model, |
| const ConversionData& data, |
| const armnn::PermutationVector& dimensionMappings = g_DontPermute, |
| const armnn::TensorShape* overrideTensorShape = nullptr, |
| bool optional = false) |
| { |
| if (!IsOperandTypeSupportedForTensors(operand.type)) |
| { |
| Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand.type).c_str()); |
| return ConstTensorPin(); |
| } |
| |
| if (operand.lifetime != OperandLifeTime::CONSTANT_COPY && operand.lifetime != OperandLifeTime::CONSTANT_REFERENCE) |
| { |
| Fail("%s: invalid operand lifetime: %s", __func__, toString(operand.lifetime).c_str()); |
| return ConstTensorPin(); |
| } |
| |
| const void* const valueStart = GetOperandValueReadOnlyAddress(operand, model, data); |
| if (!valueStart) |
| { |
| if (optional) |
| { |
| // optional tensor with no values is not really an error; return it as invalid, but marked as optional |
| return ConstTensorPin(true); |
| } |
| // mandatory tensor with no values |
| Fail("%s: failed to get operand address", __func__); |
| return ConstTensorPin(); |
| } |
| |
| armnn::TensorInfo tensorInfo = GetTensorInfoForOperand(operand); |
| if (overrideTensorShape != nullptr) |
| { |
| tensorInfo.SetShape(*overrideTensorShape); |
| } |
| return ConstTensorPin(tensorInfo, valueStart, operand.location.length, dimensionMappings); |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| ConstTensorPin ConvertOperationInputToConstTensorPin(const HalOperation& operation, |
| uint32_t inputIndex, |
| const HalModel& model, |
| const ConversionData& data, |
| const armnn::PermutationVector& dimensionMappings = g_DontPermute, |
| const armnn::TensorShape* overrideTensorShape = nullptr, |
| bool optional = false) |
| { |
| const Operand* operand = GetInputOperand(operation, inputIndex, model); |
| if (!operand) |
| { |
| Fail("%s: failed to get input operand: index=%u", __func__, inputIndex); |
| return ConstTensorPin(); |
| } |
| return ConvertOperandToConstTensorPin(*operand, |
| model, |
| data, |
| dimensionMappings, |
| overrideTensorShape, |
| optional); |
| } |
| |
| template<typename HalModel> |
| const void* GetOperandValueReadOnlyAddress(const Operand& operand, const HalModel& model, const ConversionData& data) |
| { |
| const void* valueStart = nullptr; |
| |
| switch (operand.lifetime) |
| { |
| case OperandLifeTime::CONSTANT_COPY: |
| { |
| // Constant found in model.operandValues |
| valueStart = &model.operandValues[operand.location.offset]; |
| break; |
| } |
| case OperandLifeTime::CONSTANT_REFERENCE: |
| { |
| // Constant specified via a Memory object |
| valueStart = GetMemoryFromPool(operand.location, data.m_MemPools); |
| break; |
| } |
| default: |
| { |
| // Unsupported/invalid (e.g. can't get value of an input to the model) |
| Fail("%s: unsupported/invalid operand lifetime: %s", |
| __func__, toString(operand.lifetime).c_str()); |
| valueStart = nullptr; |
| } |
| } |
| |
| return valueStart; |
| } |
| |
| template<typename HalOperation, typename HalModel, typename OutputType> |
| bool GetInputScalar(const HalOperation& operation, |
| uint32_t inputIndex, |
| OperandType type, |
| OutputType& outValue, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| const Operand* operand = GetInputOperand(operation, inputIndex, model); |
| if (!operand) |
| { |
| return Fail("%s: invalid input operand at index %i", __func__, inputIndex); |
| } |
| |
| if (operand->type != type) |
| { |
| return Fail("%s: unexpected operand type: %s (should be %s)", |
| __func__, toString(operand->type).c_str(), toString(type).c_str()); |
| } |
| |
| if (operand->location.length != sizeof(OutputType)) |
| { |
| return Fail("%s: incorrect operand location length: %i (should be %i)", |
| __func__, operand->location.length, sizeof(OutputType)); |
| } |
| |
| const void* valueAddress = GetOperandValueReadOnlyAddress(*operand, model, data); |
| if (!valueAddress) |
| { |
| return Fail("%s: failed to get address for operand", __func__); |
| } |
| |
| outValue = *(static_cast<const OutputType*>(valueAddress)); |
| return true; |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputInt32(const HalOperation& operation, |
| uint32_t inputIndex, |
| int32_t& outValue, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| return GetInputScalar(operation, inputIndex, OperandType::INT32, outValue, model, data); |
| } |
| |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputFloat32(const HalOperation& operation, |
| uint32_t inputIndex, |
| float& outValue, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| return GetInputScalar(operation, inputIndex, OperandType::FLOAT32, outValue, model, data); |
| } |
| |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputActivationFunctionImpl(const HalOperation& operation, |
| uint32_t inputIndex, |
| OperandType type, |
| ActivationFn& outActivationFunction, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| if (type != OperandType::INT32 && type != OperandType::TENSOR_INT32) |
| { |
| return Fail("%s: unexpected operand type: %s (should be %s or %s)", |
| __func__, |
| toString(type).c_str(), |
| toString(OperandType::INT32).c_str(), |
| toString(OperandType::TENSOR_INT32).c_str()); |
| } |
| |
| int32_t activationFunctionAsInt; |
| if (!GetInputScalar(operation, inputIndex, type, activationFunctionAsInt, model, data)) |
| { |
| return Fail("%s: failed to get activation input value", __func__); |
| } |
| outActivationFunction = static_cast<ActivationFn>(activationFunctionAsInt); |
| return true; |
| } |
| |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputActivationFunction(const HalOperation& operation, |
| uint32_t inputIndex, |
| ActivationFn& outActivationFunction, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| return GetInputActivationFunctionImpl(operation, |
| inputIndex, |
| OperandType::INT32, |
| outActivationFunction, |
| model, |
| data); |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputActivationFunctionFromTensor(const HalOperation& operation, |
| uint32_t inputIndex, |
| ActivationFn& outActivationFunction, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| // This only accepts a 1-D tensor of size 1 |
| return GetInputActivationFunctionImpl(operation, |
| inputIndex, |
| OperandType::INT32, |
| outActivationFunction, |
| model, |
| data); |
| } |
| |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetOptionalInputActivation(const HalOperation& operation, |
| uint32_t inputIndex, |
| ActivationFn& activationFunction, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| if (operation.inputs.size() <= inputIndex) |
| { |
| activationFunction = ActivationFn::kActivationNone; |
| } |
| else |
| { |
| if (!GetInputActivationFunction(operation, inputIndex, activationFunction, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| } |
| return true; |
| } |
| |
| template<typename HalModel> |
| bool GetTensorInt32Values(const Operand& operand, |
| std::vector<int32_t>& outValues, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| if (operand.type != OperandType::TENSOR_INT32) |
| { |
| return Fail("%s: invalid operand type: %s", __func__, toString(operand.type).c_str()); |
| } |
| |
| const void* startAddress = GetOperandValueReadOnlyAddress(operand, model, data); |
| if (!startAddress) |
| { |
| return Fail("%s: failed to get operand address", __func__, operand.type); |
| } |
| |
| // Check number of bytes is sensible |
| const uint32_t numBytes = operand.location.length; |
| if (numBytes % sizeof(int32_t) != 0) |
| { |
| return Fail("%s: invalid number of bytes: %i, expected to be a multiple of %i", |
| __func__, numBytes, sizeof(int32_t)); |
| } |
| |
| outValues.resize(numBytes / sizeof(int32_t)); |
| memcpy(outValues.data(), startAddress, numBytes); |
| return true; |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool GetInputPaddingScheme(const HalOperation& operation, |
| uint32_t inputIndex, |
| PaddingScheme& outPaddingScheme, |
| const HalModel& model, |
| const ConversionData& data) |
| { |
| int32_t paddingSchemeAsInt; |
| if (!GetInputInt32(operation, inputIndex, paddingSchemeAsInt, model, data)) |
| { |
| return Fail("%s: failed to get padding scheme input value", __func__); |
| } |
| |
| outPaddingScheme = static_cast<android::nn::PaddingScheme>(paddingSchemeAsInt); |
| return true; |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| LayerInputHandle ConvertToLayerInputHandle(const HalOperation& operation, |
| uint32_t inputIndex, |
| const HalModel& model, |
| ConversionData& data) |
| { |
| const Operand* operand = GetInputOperand(operation, inputIndex, model); |
| if (!operand) |
| { |
| Fail("%s: failed to get input operand %i", __func__, inputIndex); |
| return LayerInputHandle(); |
| } |
| |
| if (!IsOperandTypeSupportedForTensors(operand->type)) |
| { |
| Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand->type).c_str()); |
| return LayerInputHandle(); |
| } |
| |
| armnn::TensorInfo operandTensorInfo = GetTensorInfoForOperand(*operand); |
| |
| switch (operand->lifetime) |
| { |
| case OperandLifeTime::TEMPORARY_VARIABLE: // intentional fallthrough |
| case OperandLifeTime::MODEL_INPUT: |
| case OperandLifeTime::MODEL_OUTPUT: |
| { |
| // The tensor is either an operand internal to the model, or a model input. |
| // It can be associated with an ArmNN output slot for an existing layer. |
| |
| // m_OutputSlotForOperand[...] can be nullptr if the previous layer could not be converted |
| const uint32_t operandIndex = operation.inputs[inputIndex]; |
| return LayerInputHandle(true, data.m_OutputSlotForOperand[operandIndex], operandTensorInfo); |
| break; |
| } |
| case OperandLifeTime::CONSTANT_COPY: |
| case OperandLifeTime::CONSTANT_REFERENCE: |
| { |
| // The tensor has an already known constant value, and can be converted into an ArmNN Constant layer. |
| ConstTensorPin tensorPin = ConvertOperandToConstTensorPin(*operand, model, data); |
| if (tensorPin.IsValid()) |
| { |
| if (!IsLayerSupported(__func__, |
| armnn::IsConstantSupported, |
| data.m_Compute, |
| tensorPin.GetConstTensor().GetInfo())) |
| { |
| return LayerInputHandle(); |
| } |
| |
| armnn::IConnectableLayer* constantLayer = data.m_Network->AddConstantLayer(tensorPin.GetConstTensor()); |
| armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(tensorPin.GetConstTensor().GetInfo()); |
| |
| return LayerInputHandle(true, &outputSlot, operandTensorInfo); |
| } |
| else |
| { |
| Fail("%s: invalid operand tensor", __func__); |
| return LayerInputHandle(); |
| } |
| break; |
| } |
| default: |
| { |
| // Unsupported lifetime for an input tensor |
| Fail("%s: unsupported lifetime for input tensor: %s", |
| __func__, toString(operand->lifetime).c_str()); |
| return LayerInputHandle(); |
| } |
| } |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool ConvertToActivation(const HalOperation& operation, |
| const char* operationName, |
| const armnn::ActivationDescriptor& activationDesc, |
| const HalModel& model, |
| ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Input 0 is invalid", operationName); |
| } |
| |
| const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| if (!outputOperand) |
| { |
| return false; |
| } |
| const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand); |
| if (!IsLayerSupported(__func__, |
| armnn::IsActivationSupported, |
| data.m_Compute, |
| input.GetTensorInfo(), |
| outInfo, |
| activationDesc)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddActivationLayer(activationDesc); |
| BOOST_ASSERT(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool SetupAndTrackLayerOutputSlot(const HalOperation& operation, |
| uint32_t operationOutputIndex, |
| armnn::IConnectableLayer& layer, |
| uint32_t layerOutputIndex, |
| const HalModel& model, |
| ConversionData& data) |
| { |
| const Operand* outputOperand = GetOutputOperand(operation, operationOutputIndex, model); |
| if ((outputOperand == nullptr) || (operationOutputIndex >= layer.GetNumOutputSlots())) |
| { |
| return false; |
| } |
| |
| armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(layerOutputIndex); |
| |
| const uint32_t operandIndex = operation.outputs[operationOutputIndex]; |
| data.m_OutputSlotForOperand[operandIndex] = &outputSlot; |
| |
| outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand)); |
| |
| return true; |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool SetupAndTrackLayerOutputSlot(const HalOperation& operation, |
| uint32_t outputIndex, |
| armnn::IConnectableLayer& layer, |
| const HalModel& model, |
| ConversionData& data) |
| { |
| return SetupAndTrackLayerOutputSlot(operation, outputIndex, layer, outputIndex, model, data); |
| } |
| |
| template<typename HalOperation, typename HalModel> |
| bool ConvertPooling2d(const HalOperation& operation, |
| const char* operationName, |
| armnn::PoolingAlgorithm poolType, |
| const HalModel& model, |
| ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Could not read input 0", operationName); |
| } |
| |
| const Operand* output = GetOutputOperand(operation, 0, model); |
| if (!output) |
| { |
| return Fail("%s: Could not read output 0", __func__); |
| } |
| |
| const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| |
| armnn::Pooling2dDescriptor desc; |
| desc.m_PoolType = poolType; |
| desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| ActivationFn activation; |
| |
| if (operation.inputs.size() == 7) |
| { |
| // one input, 6 parameters (padding, stridex, stridey, width, height, activation type) |
| android::nn::PaddingScheme scheme; |
| if (!GetInputPaddingScheme(operation, 1, scheme, model, data) |
| || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_StrideX, model, data) |
| || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_StrideY, model, data) |
| || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PoolWidth, model, data) |
| || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PoolHeight, model, data) |
| || !GetInputActivationFunction(operation, 6, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", operationName); |
| } |
| |
| const unsigned int inputWidth = inputInfo.GetShape()[2]; |
| const unsigned int inputHeight = inputInfo.GetShape()[1]; |
| |
| CalcPadding(inputWidth, desc.m_PoolWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, scheme); |
| CalcPadding(inputHeight, desc.m_PoolHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, scheme); |
| } |
| else |
| { |
| // one input, 9 parameters (padding l r t b, stridex, stridey, width, height, activation type) |
| if (!GetInputScalar(operation, 1, OperandType::INT32, desc.m_PadLeft, model, data) |
| || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_PadRight, model, data) |
| || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadTop, model, data) |
| || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadBottom, model, data) |
| || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideX, model, data) |
| || !GetInputScalar(operation, 6, OperandType::INT32, desc.m_StrideY, model, data) |
| || !GetInputScalar(operation, 7, OperandType::INT32, desc.m_PoolWidth, model, data) |
| || !GetInputScalar(operation, 8, OperandType::INT32, desc.m_PoolHeight, model, data) |
| || !GetInputActivationFunction(operation, 9, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", operationName); |
| } |
| } |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsPooling2dSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| desc)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* pooling2dLayer = data.m_Network->AddPooling2dLayer(desc); |
| if (!pooling2dLayer) |
| { |
| return Fail("%s: AddPooling2dLayer failed", __func__); |
| } |
| |
| armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, pooling2dLayer, data); |
| if (!endLayer) |
| { |
| return Fail("%s: ProcessActivation failed", __func__); |
| } |
| |
| input.Connect(pooling2dLayer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| } |
| |
| } // namespace armnn_driver |
