IVGCVSW-1806: Refactor Android-NN-Driver ModelToINetworkConverter
* Moved conversion logic into new V1_0 and V1_1 HalPolicy classes
* Extracted common helper functions into ConversionUtils class
Change-Id: I1ab50edc266dd528c0cb22a5cd1aa65e103674d9
diff --git a/ModelToINetworkConverter.cpp b/ModelToINetworkConverter.cpp
index 70873b8..1a63280 100644
--- a/ModelToINetworkConverter.cpp
+++ b/ModelToINetworkConverter.cpp
@@ -6,466 +6,19 @@
#define LOG_TAG "ArmnnDriver"
#include "ModelToINetworkConverter.hpp"
-#include <OperationsUtils.h>
-
-#include <armnn/LayerSupport.hpp>
-#include <Permute.hpp>
#include <log/log.h>
-#include <cassert>
-
-#include <boost/format.hpp>
-#include <boost/core/ignore_unused.hpp>
-#include <boost/test/tools/floating_point_comparison.hpp>
-#include <boost/cast.hpp>
-#include <boost/optional.hpp>
-
-using namespace android::hardware;
namespace armnn_driver
{
-class LayerInputHandle
-{
-public:
- LayerInputHandle()
- : m_OutputSlot(nullptr)
- , m_Valid(false)
- {}
-
- LayerInputHandle(bool valid, armnn::IOutputSlot* outputSlot, armnn::TensorInfo tensorInfo)
- : m_OutputSlot(outputSlot)
- , m_Valid(valid)
- , m_TensorInfo(tensorInfo)
- {}
-
- bool IsValid() const { return m_Valid; }
- void Connect(armnn::IInputSlot& inputSlot)
- {
- assert(IsValid());
-
- if (m_OutputSlot)
- {
- m_OutputSlot->Connect(inputSlot);
- }
- }
- const armnn::TensorInfo& GetTensorInfo() const { return m_TensorInfo; }
-
-private:
- armnn::IOutputSlot* m_OutputSlot;
- bool m_Valid;
- armnn::TensorInfo m_TensorInfo;
-};
-
-} // namespace armnn_driver
-
-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);
-
- 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);
- 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();
- }
- }
-}
-
-void CreatePermutationParameters(const unsigned int numberOfDimensions,
- int32_t & concatDimension,
- std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutationPair)
-{
- assert(numberOfDimensions >= 3);
-
- // ArmNN uses Compute Library subtensors to perform concatenation
- // This only works when concatenating along dimension 0 or 1 for a 4-D tensor,
- // or along dimension 0 for a 3-D tensor.
- if (numberOfDimensions == 4)
- {
- if (concatDimension == 3)
- {
- concatDimension = 1;
- permutationPair = std::make_pair(NHWCToArmNN, ArmNNToNHWC);
- }
- else if (concatDimension == 2)
- {
- concatDimension = 1;
- permutationPair = std::make_pair(SwapDim1And2, SwapDim1And2);
- }
- else
- {
- permutationPair = std::make_pair(IdentityPermutation4D, IdentityPermutation4D);
- }
-
- }
- else if (numberOfDimensions == 3)
- {
- if (concatDimension == 2)
- {
- concatDimension = 0;
- permutationPair = std::make_pair(RotateTensorRight, RotateTensorLeft);
- }
- else if (concatDimension == 1)
- {
- concatDimension = 0;
- permutationPair = std::make_pair(RotateTensorLeft, RotateTensorRight);
- }
- else
- {
- permutationPair = std::make_pair(IdentityPermutation3D, IdentityPermutation3D);
- }
- }
-}
-
-} // anonymous namespace
-
-namespace armnn_driver
-{
-
-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) : m_Optional(optional) {}
-
- // @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)
- {
- boost::ignore_unused(numBytes);
- assert(tensorInfo.GetNumBytes() == numBytes);
-
- const bool needsSwizzling = (mappings.GetSize() > 0);
- if (needsSwizzling)
- {
- m_SwizzledTensorData.resize(tensorInfo.GetNumBytes());
- SwizzleAndroidNn4dTensorToArmNn(tensorInfo, valueStart, m_SwizzledTensorData.data(), mappings);
-
- m_ConstTensor = armnn::ConstTensor(armnnUtils::Permuted(tensorInfo, mappings), m_SwizzledTensorData.data());
- }
- else
- {
- m_ConstTensor = armnn::ConstTensor(tensorInfo, valueStart);
- }
- }
-
- ConstTensorPin(const ConstTensorPin& other) = delete;
- ConstTensorPin(ConstTensorPin&& other) = default;
-
- bool IsValid() const { return m_ConstTensor.GetMemoryArea() != nullptr; }
- bool IsOptional() const { return m_Optional; }
- const armnn::ConstTensor& GetConstTensor() const { return m_ConstTensor; }
- const armnn::ConstTensor* GetConstTensorPtr() const
- {
- if (IsValid() && m_ConstTensor.GetNumElements() > 0)
- {
- return &m_ConstTensor;
- }
- // tensor is either invalid, or has no elements (indicating an optional tensor that was not provided)
- return nullptr;
- }
-
-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;
-};
-
-template<typename HalVersion>
-ModelToINetworkConverter<HalVersion>::ModelToINetworkConverter(armnn::Compute compute,
+template<typename HalPolicy>
+ModelToINetworkConverter<HalPolicy>::ModelToINetworkConverter(armnn::Compute compute,
const HalModel& model,
const std::set<unsigned int>& forcedUnsupportedOperations)
- : m_Compute(compute)
+ : m_Data(compute)
, m_Model(model)
, m_ForcedUnsupportedOperations(forcedUnsupportedOperations)
- , m_Network(nullptr, nullptr)
, m_ConversionResult(ConversionResult::Success)
{
try
@@ -480,16 +33,16 @@
}
}
-template<typename HalVersion>
-void ModelToINetworkConverter<HalVersion>::Convert()
+template<typename HalPolicy>
+void ModelToINetworkConverter<HalPolicy>::Convert()
{
- using HalModel = typename HalVersion::Model;
+ using HalModel = typename HalPolicy::Model;
ALOGV("ModelToINetworkConverter::Convert(): %s", GetModelSummary<HalModel>(m_Model).c_str());
// map the memory pool into shared pointers
- m_MemPools.clear();
- if (!setRunTimePoolInfosFromHidlMemories(&m_MemPools, m_Model.pools))
+ m_Data.m_MemPools.clear();
+ if (!setRunTimePoolInfosFromHidlMemories(&m_Data.m_MemPools, m_Model.pools))
{
Fail("%s: Setting of run time pool infos from Hidl Memories has failed.", __func__);
m_ConversionResult = ConversionResult::ErrorMappingPools;
@@ -503,11 +56,11 @@
}
// Create armnn::INetwork
- m_Network = armnn::INetwork::Create();
+ m_Data.m_Network = armnn::INetwork::Create();
// add operations to it
// track which layer outputs each operand
- m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(m_Model.operands.size(), nullptr);
+ m_Data.m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(m_Model.operands.size(), nullptr);
try
{
@@ -517,13 +70,13 @@
uint32_t inputIndex = m_Model.inputIndexes[i];
const Operand& operand = m_Model.operands[inputIndex];
const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
- armnn::IConnectableLayer* layer = m_Network->AddInputLayer(i);
+ armnn::IConnectableLayer* layer = m_Data.m_Network->AddInputLayer(i);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand));
// store for later layers
- m_OutputSlotForOperand[inputIndex] = &outputSlot;
+ m_Data.m_OutputSlotForOperand[inputIndex] = &outputSlot;
}
}
catch (UnsupportedOperand& e)
@@ -552,7 +105,7 @@
{
try
{
- ok = ConvertOperation(operation);
+ ok = HalPolicy::ConvertOperation(operation, m_Model, m_Data);
}
catch (UnsupportedOperand& e)
{
@@ -586,10 +139,10 @@
uint32_t outputIndex = m_Model.outputIndexes[i];
const Operand& operand = m_Model.operands[outputIndex];
const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
- armnn::IConnectableLayer* layer = m_Network->AddOutputLayer(i);
+ armnn::IConnectableLayer* layer = m_Data.m_Network->AddOutputLayer(i);
- assert(m_OutputSlotForOperand[outputIndex]);
- m_OutputSlotForOperand[outputIndex]->Connect(layer->GetInputSlot(0));
+ assert(m_Data.m_OutputSlotForOperand[outputIndex]);
+ m_Data.m_OutputSlotForOperand[outputIndex]->Connect(layer->GetInputSlot(0));
}
}
}
@@ -600,2067 +153,22 @@
}
}
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertOperation(const neuralnetworks::V1_0::Operation& operation)
-{
- switch (operation.type)
- {
- case neuralnetworks::V1_0::OperationType::ADD:
- return ConvertAdd(operation);
- case neuralnetworks::V1_0::OperationType::AVERAGE_POOL_2D:
- return ConvertAveragePool2d(operation);
- case neuralnetworks::V1_0::OperationType::CONCATENATION:
- return ConvertConcatenation(operation);
- case neuralnetworks::V1_0::OperationType::CONV_2D:
- return ConvertConv2d(operation);
- case neuralnetworks::V1_0::OperationType::DEPTHWISE_CONV_2D:
- return ConvertDepthwiseConv2d(operation);
- case neuralnetworks::V1_0::OperationType::FLOOR:
- return ConvertFloor(operation);
- case neuralnetworks::V1_0::OperationType::FULLY_CONNECTED:
- return ConvertFullyConnected(operation);
- case neuralnetworks::V1_0::OperationType::LOCAL_RESPONSE_NORMALIZATION:
- return ConvertLocalResponseNormalization(operation);
- case neuralnetworks::V1_0::OperationType::LOGISTIC:
- return ConvertLogistic(operation);
- case neuralnetworks::V1_0::OperationType::LSTM:
- return ConvertLstm(operation);
- case neuralnetworks::V1_0::OperationType::L2_NORMALIZATION:
- return ConvertL2Normalization(operation);
- case neuralnetworks::V1_0::OperationType::L2_POOL_2D:
- return ConvertL2Pool2d(operation);
- case neuralnetworks::V1_0::OperationType::MAX_POOL_2D:
- return ConvertMaxPool2d(operation);
- case neuralnetworks::V1_0::OperationType::MUL:
- return ConvertMul(operation);
- case neuralnetworks::V1_0::OperationType::RELU:
- return ConvertReLu(operation);
- case neuralnetworks::V1_0::OperationType::RELU1:
- return ConvertReLu1(operation);
- case neuralnetworks::V1_0::OperationType::RELU6:
- return ConvertReLu6(operation);
- case neuralnetworks::V1_0::OperationType::SOFTMAX:
- return ConvertSoftmax(operation);
- case neuralnetworks::V1_0::OperationType::TANH:
- return ConvertTanH(operation);
- case neuralnetworks::V1_0::OperationType::RESHAPE:
- return ConvertReshape(operation);
- case neuralnetworks::V1_0::OperationType::RESIZE_BILINEAR:
- return ConvertResizeBilinear(operation);
- default:
- return Fail("%s: Operation type %s not supported in ArmnnDriver",
- __func__, toString(operation.type).c_str());
- }
-}
-
-#if defined(ARMNN_ANDROID_NN_V1_1) // Using ::android::hardware::neuralnetworks::V1_1.
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertOperation(const neuralnetworks::V1_1::Operation& operation)
-{
- if (compliantWithV1_0(operation))
- {
- neuralnetworks::V1_0::Operation v1Operation = convertToV1_0(operation);
- return ConvertOperation(v1Operation);
- }
- else
- {
- switch (operation.type)
- {
- case neuralnetworks::V1_1::OperationType::DIV:
- return ConvertDiv(operation);
- default:
- return Fail("%s: Operation type %s not supported in ArmnnDriver",
- __func__, toString(operation.type).c_str());
- }
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertDiv(const neuralnetworks::V1_1::Operation& operation)
-{
- LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
- LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
-
- if (!input0.IsValid() || !input1.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- // The FuseActivation parameter is always the input index 2
- // and it should be optional
- ActivationFn activationFunction;
- if (!GetOptionalInputActivation(operation, 2, activationFunction))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return false;
- }
-
- const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
-
- if (!IsLayerSupported(__func__,
- armnn::IsDivisionSupported,
- m_Compute,
- input0.GetTensorInfo(),
- input1.GetTensorInfo(),
- outInfo))
- {
- return false;
- }
-
- armnn::IConnectableLayer* const startLayer = m_Network->AddDivisionLayer();
- armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer);
-
- const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
- const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
-
- if (endLayer)
- {
- BroadcastTensor(input0, input1, startLayer, *m_Network);
- return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
- }
-
- return Fail("%s: ProcessActivation failed", __func__);
-}
-#endif
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertAdd(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
- LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
-
- if (!input0.IsValid() || !input1.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- // The FuseActivation parameter is always the input index 2
- // and it should be optional
- ActivationFn activationFunction;
- if (!GetOptionalInputActivation(operation, 2, activationFunction))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return false;
- }
-
- const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
-
- if (!IsLayerSupported(__func__,
- armnn::IsAdditionSupported,
- m_Compute,
- input0.GetTensorInfo(),
- input1.GetTensorInfo(),
- outInfo))
- {
- return false;
- }
-
- armnn::IConnectableLayer* const startLayer = m_Network->AddAdditionLayer();
- armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer);
-
- const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
- const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
-
- if (endLayer != nullptr)
- {
- BroadcastTensor(input0, input1, startLayer, *m_Network);
- return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertAveragePool2d(const neuralnetworks::V1_0::Operation& operation)
-{
- return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertConcatenation(const neuralnetworks::V1_0::Operation& operation)
-{
- // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis.
- if (operation.inputs.size() <= 1)
- {
- return Fail("%s: Operation has insufficient arguments", __func__);
- }
-
- // Get inputs and outputs
- const std::size_t numInputTensors = operation.inputs.size() - 1;
-
- int32_t concatDim;
- if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* const outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return Fail("%s: Operation has no outputs", __func__);
- }
-
-
- armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*outputOperand);
- armnn::TensorShape outputShape = outputInfo.GetShape();
-
- //
- // handle negative concat dims along the lines of tensorflow as described here:
- // https://www.tensorflow.org/api_docs/python/tf/concat
- // "negative axis refers to axis + rank(values)-th dimension"
- //
- if (concatDim < 0)
- {
- concatDim += outputShape.GetNumDimensions();
- }
-
- if (concatDim >= static_cast<int32_t>(outputShape.GetNumDimensions()) || concatDim < 0)
- {
- return Fail("%s: Operation has invalid concat axis: %d", __func__, concatDim);
- }
-
- std::vector<LayerInputHandle> inputHandles;
- std::vector<armnn::TensorShape> inputShapes;
-
- inputHandles.reserve(numInputTensors);
- inputShapes.reserve(numInputTensors);
-
- bool inputsHaveBeenReshaped = false;
- unsigned int tensorDimensionsAdded = 0;
-
- for (uint32_t i = 0; i < numInputTensors; ++i)
- {
- const Operand* const operand = GetInputOperand(operation, i);
- if (!operand)
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::TensorShape operandShape = GetTensorShapeForOperand(*operand);
- LayerInputHandle operandInputHandle = ConvertToLayerInputHandle(operation, i);
-
- if (operandShape.GetNumDimensions() == 0)
- {
- return Fail("%s: Operands with rank 0 are not supported", __func__);
- }
-
- if (RequiresReshape(operandShape))
- {
- inputsHaveBeenReshaped = true;
-
- armnn::TensorInfo reshapeInfo = operandInputHandle.GetTensorInfo();
-
- // Expand the tensor to three dimensions
- if (operandShape.GetNumDimensions() == 2)
- {
- reshapeInfo.SetShape(armnn::TensorShape({1, operandShape[0], operandShape[1]}));
- tensorDimensionsAdded = 1;
- }
- else
- {
- reshapeInfo.SetShape(armnn::TensorShape({1, 1, operandShape[0]}));
- tensorDimensionsAdded = 2;
- }
-
- armnn::IConnectableLayer& newReshape = AddReshapeLayer(
- *m_Network,
- operandInputHandle,
- reshapeInfo
- );
-
- // Point to the reshape operation rather then the input operation
- operandShape = reshapeInfo.GetShape();
- operandInputHandle = LayerInputHandle(true, &newReshape.GetOutputSlot(0), reshapeInfo);
- }
-
- inputShapes.emplace_back(operandShape);
- inputHandles.emplace_back(operandInputHandle);
-
- if (!inputHandles.back().IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
- }
-
- assert(inputShapes.size() == inputHandles.size());
-
- if (inputsHaveBeenReshaped)
- {
- // Adjust the concatenation dimension by the amount of dimensions added (if any)
- concatDim += tensorDimensionsAdded;
-
- // Add extra dimensions to the output shape to reflect the addition of the reshape layers
- if (tensorDimensionsAdded == 1)
- {
- outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]});
- }
- else if (tensorDimensionsAdded == 2)
- {
- outputShape = armnn::TensorShape({1, 1, outputShape[0], outputShape[1]});
- }
- }
-
- // Get the pair of permutations required for the concatenation
- std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair =
- std::make_pair(IdentityPermutation4D, IdentityPermutation4D);
-
- CreatePermutationParameters(inputShapes[0].GetNumDimensions(), concatDim, permutationPair);
-
- outputShape = armnnUtils::Permuted(outputShape, permutationPair.first);
- outputInfo.SetShape(outputShape);
-
- // this is no-op for identity swizzles, otherwise it replaces both
- // the handles and shapes with the swizzled layer output handles and shapes
- SwizzleInputs(*m_Network, inputHandles, inputShapes, permutationPair.first);
-
- // Create an armnn merger layer descriptor - this will also perform validation on the input shapes
- armnn::OriginsDescriptor mergerDescriptor;
- try
- {
- // The merger descriptor is always created across the only supported concat
- // dimension, which is 0 or 1
- mergerDescriptor =
- armnn::CreateMergerDescriptorForConcatenation(
- inputShapes.begin(), inputShapes.end(), concatDim);
- }
- catch (const armnn::Exception& error)
- {
- return Fail("%s: Error preparing merger descriptor. %s", __func__, error.what());
- }
-
- // Validate the output shape is correct given the input shapes based on the
- // only valid concat dimension which is 0 or 1
- if (!ValidateConcatOutputShape(inputShapes, outputShape, concatDim))
- {
- return Fail("%s: Error validating the output shape for concat", __func__);
- }
-
- std::vector<const armnn::TensorInfo*> inputTensorInfos;
- std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos),
- [](const LayerInputHandle& h) -> const armnn::TensorInfo*{ return &h.GetTensorInfo(); });
- if (!IsLayerSupported(__func__,
- armnn::IsMergerSupported,
- m_Compute,
- inputTensorInfos,
- mergerDescriptor))
- {
- return false;
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddMergerLayer(mergerDescriptor);
- assert(layer != nullptr);
- layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
- // Connect inputs to the layer
- const int numInputSlots = layer->GetNumInputSlots();
- assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size());
- for (int i = 0; i < numInputSlots; ++i)
- {
- // connect the input directly to the merge (concat) layer
- inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i));
- }
-
- // Add permutation layer and connect the output to it, the permutation becomes the output layer
- armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(*m_Network,
- layer->GetOutputSlot(0),
- permutationPair.second);
- layer = &deswizzleLayer;
-
- if (inputsHaveBeenReshaped)
- {
- armnn::TensorInfo afterConcatInfo = layer->GetOutputSlot(0).GetTensorInfo();
-
- // Undo the reshape knowing the amount of dimensions added
- if (tensorDimensionsAdded == 1)
- {
- afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[1],
- afterConcatInfo.GetShape()[2] }));
- }
- else if (tensorDimensionsAdded == 2)
- {
- afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[2],
- afterConcatInfo.GetShape()[3] }));
- }
-
- layer = &AddReshapeLayer(
- *m_Network,
- layer->GetOutputSlot(0),
- afterConcatInfo
- );
- }
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertConv2d(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- // ArmNN does not currently support non-fixed weights or bias
- const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, NHWCToArmNN);
- const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2);
-
- if (!weightsPin.IsValid() || !biasPin.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::ConstTensor weights = weightsPin.GetConstTensor();
- armnn::ConstTensor bias = biasPin.GetConstTensor();
- SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
-
- armnn::Convolution2dDescriptor desc;
- ActivationFn activation;
-
- if (operation.inputs.size() == 10)
- {
- if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop) ||
- !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom) ||
- !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX) ||
- !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY) ||
- !GetInputActivationFunction(operation, 9, activation))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
- }
- else if (operation.inputs.size() == 7)
- {
- android::nn::PaddingScheme paddingScheme;
-
- if (!GetInputPaddingScheme(operation, 3, paddingScheme) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY) ||
- !GetInputActivationFunction(operation, 6, activation))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const uint32_t kernelX = weights.GetShape()[3];
- const uint32_t kernelY = weights.GetShape()[2];
- const uint32_t inputX = swizzledInputInfo.GetShape()[3];
- const uint32_t inputY = swizzledInputInfo.GetShape()[2];
-
- CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
- CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
- }
- else
- {
- return Fail("%s: Unsupported number of operation inputs", __func__);
- }
-
- desc.m_BiasEnabled = true;
- auto biases = boost::make_optional(bias.GetInfo());
-
- if (!IsLayerSupported(__func__,
- armnn::IsConvolution2dSupported,
- m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo,
- desc,
- weights.GetInfo(),
- biases))
- {
- return false;
- }
-
- armnn::IConnectableLayer* startLayer = m_Network->AddConvolution2dLayer(desc, weights, bias);
- armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
-
- if (endLayer != nullptr)
- {
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertDepthwiseConv2d(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- // ArmNN does not currently support non-fixed weights or bias
-
- // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
- // but in ArmNN it needs to be [ M, I, H, W ]
- const Operand* weightsOperand = GetInputOperand(operation, 1);
-
- if (weightsOperand == nullptr)
- {
- return Fail("%s: Operand is invalid", __func__);
- }
-
- // Reinterpret weight data as [ H, W, I, M ]
- armnn::TensorShape weightsShape({ weightsOperand->dimensions[1], weightsOperand->dimensions[2],
- inputInfo.GetShape()[3],
- weightsOperand->dimensions[3] / inputInfo.GetShape()[3] });
-
- // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
- const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
- ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, HWIMToMIHW, &weightsShape);
-
- // Bias is a 1D tensor
- ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2);
-
- if (!weightsPin.IsValid() || !biasPin.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::ConstTensor weights = weightsPin.GetConstTensor();
- armnn::ConstTensor bias = biasPin.GetConstTensor();
- SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
-
- armnn::DepthwiseConvolution2dDescriptor desc;
- ActivationFn activation;
-
- if (operation.inputs.size() == 11)
- {
- if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop) ||
- !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom) ||
- !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX) ||
- !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY) ||
- !GetInputActivationFunction(operation, 10, activation))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
- }
- else if (operation.inputs.size() == 8)
- {
- android::nn::PaddingScheme paddingScheme;
-
- if (!GetInputPaddingScheme(operation, 3, paddingScheme) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY) ||
- !GetInputActivationFunction(operation, 7, activation))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const uint32_t kernelX = weights.GetShape()[3];
- const uint32_t kernelY = weights.GetShape()[2];
- const uint32_t inputX = swizzledInputInfo.GetShape()[3];
- const uint32_t inputY = swizzledInputInfo.GetShape()[2];
-
- CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
- CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
- }
- else
- {
- return Fail("%s: Unsupported number of operation inputs", __func__);
- }
-
- desc.m_BiasEnabled = true;
- auto biases = boost::make_optional(bias.GetInfo());
-
- if (!IsLayerSupported(__func__,
- armnn::IsDepthwiseConvolutionSupported,
- m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo,
- desc,
- weights.GetInfo(),
- biases))
- {
- return false;
- }
-
- armnn::IConnectableLayer* startLayer = m_Network->AddDepthwiseConvolution2dLayer(desc, weights, bias);
- armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
-
- if (endLayer != nullptr)
- {
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertFloor(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* const outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return Fail("%s: Operation has invalid outputs", __func__);
- }
-
- if (!IsLayerSupported(__func__,
- armnn::IsFloorSupported,
- m_Compute,
- input.GetTensorInfo(),
- GetTensorInfoForOperand(*outputOperand)))
- {
- return false;
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddFloorLayer();
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertFullyConnected(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- // ArmNN does not currently support non-fixed weights or bias
- ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1); // 2D
- ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2); // 1D
-
- if (!weightsPin.IsValid() || !biasPin.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::ConstTensor weights = weightsPin.GetConstTensor();
- armnn::ConstTensor bias = biasPin.GetConstTensor();
-
- armnn::TensorInfo reshapedInfo = inputInfo;
- if (inputInfo.GetNumDimensions() > 2U)
- {
- unsigned int dim0 = inputInfo.GetShape()[0];
- unsigned int dim1 = inputInfo.GetShape()[1];
-
- for (unsigned int i = 2U; i < inputInfo.GetNumDimensions(); ++i)
- {
- dim1 *= inputInfo.GetShape()[i];
- }
-
- unsigned int divisor = weights.GetInfo().GetShape()[1] / dim1;
- if(dim0 % divisor != 0)
- {
- return Fail("%s: Failed to deduce tensor shape", __func__);
- }
-
- reshapedInfo.SetShape(armnn::TensorShape({dim0 / divisor, dim1 * divisor}));
- }
-
- // ensuring that the bias value is within 1% of the weights input (small float differences can exist)
- SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo);
-
- ActivationFn activationFunction;
- if (!GetInputActivationFunction(operation, 3, activationFunction))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::FullyConnectedDescriptor desc;
- desc.m_TransposeWeightMatrix = true;
- desc.m_BiasEnabled = true;
-
- if (!IsLayerSupported(__func__,
- armnn::IsFullyConnectedSupported,
- m_Compute,
- inputInfo,
- outputInfo,
- weights.GetInfo(),
- bias.GetInfo(),
- desc))
- {
- return false;
- }
-
- armnn::IConnectableLayer* startLayer = m_Network->AddFullyConnectedLayer(desc, weights, bias);
- armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activationFunction, startLayer);
-
- if (endLayer != nullptr)
- {
- if (inputInfo.GetNumDimensions() > 2U)
- {
- armnn::ReshapeDescriptor reshapeDescriptor;
- reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
-
- armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor);
- assert(reshapeLayer != nullptr);
- input.Connect(reshapeLayer->GetInputSlot(0));
- reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
- reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
- }
- else
- {
- input.Connect(startLayer->GetInputSlot(0));
- }
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertLocalResponseNormalization(
- const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- armnn::NormalizationDescriptor descriptor;
-
- descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
- descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-
- if (!input.IsValid() ||
- !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize) ||
- !GetInputFloat32(operation, 2, descriptor.m_K) ||
- !GetInputFloat32(operation, 3, descriptor.m_Alpha) ||
- !GetInputFloat32(operation, 4, descriptor.m_Beta))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- // ArmNN expects normSize to be the full size of the normalization
- // window rather than the radius as in AndroidNN.
- descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);
-
- if (!IsLayerSupported(__func__,
- armnn::IsNormalizationSupported,
- m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo,
- descriptor))
- {
- return false;
- }
-
-
- armnn::IConnectableLayer* layer = m_Network->AddNormalizationLayer(descriptor);
- assert(layer != nullptr);
- layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
-
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
-
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertLogistic(const neuralnetworks::V1_0::Operation& operation)
-{
- armnn::ActivationDescriptor desc;
- desc.m_Function = armnn::ActivationFunction::Sigmoid;
-
- return ConvertToActivation(operation, __func__, desc);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertL2Normalization(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- if (!IsLayerSupported(__func__,
- armnn::IsL2NormalizationSupported,
- m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo))
- {
- return false;
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddL2NormalizationLayer();
- assert(layer != nullptr);
- layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
-
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
-
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertL2Pool2d(const neuralnetworks::V1_0::Operation& operation)
-{
- return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertMaxPool2d(const neuralnetworks::V1_0::Operation& operation)
-{
- return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertMul(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
- LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
-
- if (!input0.IsValid() || !input1.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- // The FuseActivation parameter is always the input index 2
- // and it should be optional
- ActivationFn activationFunction;
- if (!GetOptionalInputActivation(operation, 2, activationFunction))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand(operation, 0);
-
- if (outputOperand == nullptr)
- {
- return false;
- }
-
- const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
-
- if (!IsLayerSupported(__func__,
- armnn::IsMultiplicationSupported,
- m_Compute,
- input0.GetTensorInfo(),
- input1.GetTensorInfo(),
- outInfo))
- {
- return false;
- }
-
- armnn::IConnectableLayer* const startLayer = m_Network->AddMultiplicationLayer();
- armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer);
-
- const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
- const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
-
- if (endLayer != nullptr)
- {
- BroadcastTensor(input0, input1, startLayer, *m_Network);
- return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertReLu(const neuralnetworks::V1_0::Operation& operation)
-{
- armnn::ActivationDescriptor desc;
- desc.m_Function = armnn::ActivationFunction::ReLu;
-
- return ConvertToActivation(operation, __func__, desc);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertReLu1(const neuralnetworks::V1_0::Operation& operation)
-{
- armnn::ActivationDescriptor desc;
- desc.m_Function = armnn::ActivationFunction::BoundedReLu;
- desc.m_A = 1.0f;
- desc.m_B = -1.0f;
-
- return ConvertToActivation(operation, __func__, desc);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertReLu6(const neuralnetworks::V1_0::Operation& operation)
-{
- armnn::ActivationDescriptor desc;
- desc.m_Function = armnn::ActivationFunction::BoundedReLu;
- desc.m_A = 6.0f;
-
- return ConvertToActivation(operation, __func__, desc);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertSoftmax(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return Fail("%s: Operation has no outputs", __func__);
- }
-
- const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
-
- armnn::SoftmaxDescriptor desc;
- if (!GetInputFloat32(operation, 1, desc.m_Beta))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- if (!IsLayerSupported(__func__,
- armnn::IsSoftmaxSupported,
- m_Compute,
- input.GetTensorInfo(),
- outInfo,
- desc))
- {
- return false;
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddSoftmaxLayer(desc);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertTanH(const neuralnetworks::V1_0::Operation& operation)
-{
- armnn::ActivationDescriptor desc;
- desc.m_Function = armnn::ActivationFunction::TanH;
- desc.m_A = 1.0f; // android nn does not support tanH parameters
- desc.m_B = 1.0f; // set to 1.0f for unity scaling
-
- return ConvertToActivation(operation, __func__, desc);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertReshape(const neuralnetworks::V1_0::Operation& operation)
-{
- const Operand* inputOperand = GetInputOperand(operation, 0);
- const Operand* requestedShapeOperand = GetInputOperand(operation, 1);
- const Operand* outputOperand = GetOutputOperand(operation, 0);
-
- if (inputOperand == nullptr
- || requestedShapeOperand == nullptr
- || outputOperand == nullptr)
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
-
- if (requestedShapeOperand->dimensions.size() != 1)
- {
- return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)",
- __func__, requestedShapeOperand->dimensions.size());
- }
-
- std::vector<int32_t> targetDimensions;
- if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions))
- {
- return Fail("%s: Could not read values of input 1", __func__);
- }
-
- const Shape inputOperandShape = GetOperandShape(*inputOperand);
-
- Shape requestedShape;
- // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility
- // function that resolves these values into a fully specified tensor shape.
- if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape))
- {
- return Fail("%s: Failed to resolve the requested shape", __func__);
- }
-
- const Shape outputOperandShape = GetOperandShape(*outputOperand);
- if (!SameShape(requestedShape, outputOperandShape))
- {
- return Fail("%s: Shape of output operand does not match resolved requested shape", __func__);
- }
-
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0", __func__);
- }
-
- if (!IsLayerSupported(__func__,
- armnn::IsReshapeSupported,
- m_Compute,
- input.GetTensorInfo()))
- {
- return false;
- }
-
-
- armnn::ReshapeDescriptor reshapeDescriptor;
- reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(),
- requestedShape.dimensions.data());
-
- armnn::IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDescriptor);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertResizeBilinear(const neuralnetworks::V1_0::Operation& operation)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0", __func__);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- if (!IsLayerSupported(__func__,
- armnn::IsResizeBilinearSupported,
- m_Compute,
- swizzledInputInfo))
- {
- return false;
- }
-
- armnn::ResizeBilinearDescriptor desc;
-
- if ( !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight)
- || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddResizeBilinearLayer(desc);
- assert(layer != nullptr);
- layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
-
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *layer);
-
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
-
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertLstm(const neuralnetworks::V1_0::Operation& operation)
-{
- // Inputs:
- // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
- // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0: input", __func__);
- }
- // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
- LayerInputHandle outputStateIn = ConvertToLayerInputHandle(operation, 18);
- if (!outputStateIn.IsValid())
- {
- return Fail("%s: Could not read input 18: outputStateIn", __func__);
- }
- // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
- LayerInputHandle cellStateIn = ConvertToLayerInputHandle(operation, 19);
- if (!cellStateIn.IsValid())
- {
- return Fail("%s: Could not read input 19: cellStateIn", __func__);
- }
-
- // Get the mandatory input tensors:
- // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size].
- const ConstTensorPin inputToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 2);
- // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size].
- const ConstTensorPin inputToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 3);
- // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size].
- const ConstTensorPin inputToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 4);
- // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 6);
- // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 7);
- // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 8);
- // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 13);
- // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin(operation, 14);
- // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin outputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 15);
-
- if (!inputToForgetWeightsPin.IsValid() ||
- !inputToCellWeightsPin.IsValid() ||
- !inputToOutputWeightsPin.IsValid() ||
- !recurrentToForgetWeightsPin.IsValid() ||
- !recurrentToCellWeightsPin.IsValid() ||
- !recurrentToOutputWeightsPin.IsValid() ||
- !forgetGateBiasPin.IsValid() ||
- !cellBiasPin.IsValid() ||
- !outputGateBiasPin.IsValid())
- {
- return Fail("%s: Operation has invalid tensor inputs", __func__);
- }
-
- // Get the optional input tensors:
- // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size], where “num_units” corresponds to the number of cell units.
- const ConstTensorPin inputToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 1);
- // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
- // “num_units”), or the second dimension of the “projection_weights”, if defined.
- const ConstTensorPin recurrentToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 5);
- // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 9);
- // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 10);
- // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 11);
- // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 12);
- // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [output_size, num_units].
- const ConstTensorPin projectionWeightsPin = ConvertOperationInputToConstTensorPin(operation, 16);
- // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
- const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin(operation, 17);
-
- if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
- (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) ||
- (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) ||
- (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) ||
- (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) ||
- (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) ||
- (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) ||
- (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
- {
- return Fail("%s: Operation has invalid tensor inputs", __func__);
- }
-
- // Get the mandatory input scalars (actually 1-D tensors of size 1):
- // 20: The activation function: A value indicating the activation function:
- // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid.
- // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip].
- // If set to 0.0 then clipping is disabled.
- // 22: The clipping threshold: for the output from the projection layer, such that values are bound within
- // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
- ActivationFn activation;
- float cellClip;
- float projClip;
- if (!GetInputActivationFunctionFromTensor(operation, 20, activation) ||
- !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip) ||
- !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip))
- {
- return Fail("%s: Operation has invalid scalar inputs", __func__);
- }
-
- // Outputs:
- // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with
- // CIFG, or [batch_size, num_units * 3] without CIFG.
- const Operand* scratchBuffer = GetOutputOperand(operation, 0);
- if (!scratchBuffer)
- {
- return Fail("%s: Could not read output 0: scratchBuffer", __func__);
- }
- // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
- const Operand* outputStateOut = GetOutputOperand(operation, 1);
- if (!outputStateOut)
- {
- return Fail("%s: Could not read output 1: outputStateOut", __func__);
- }
- // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
- const Operand* cellStateOut = GetOutputOperand(operation, 2);
- if (!cellStateOut)
- {
- return Fail("%s: Could not read output 2: cellStateOut", __func__);
- }
- // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is
- // effectively the same as the current “output state (out)” value.
- const Operand* output = GetOutputOperand(operation, 3);
- if (!output)
- {
- return Fail("%s: Could not read output 3: output", __func__);
- }
-
- // set the params structure for the AddLstmLayer call
- armnn::LstmInputParams params;
- params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
- params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
- params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
- params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
- params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
- params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
- params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
- params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
- params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
- params.m_CellBias = cellBiasPin.GetConstTensorPtr();
- params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
- params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
- params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
-
- // set the layer descriptor
- armnn::LstmDescriptor desc;
- desc.m_ActivationFunc = activation;
- desc.m_ClippingThresCell = cellClip;
- desc.m_ClippingThresProj = projClip;
- desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
- params.m_RecurrentToInputWeights == nullptr ||
- params.m_InputGateBias == nullptr);
- desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
- params.m_CellToOutputWeights != nullptr);
- desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
-
- // validate the optional input groups
- if (desc.m_CifgEnabled &&
- (params.m_InputToInputWeights != nullptr ||
- params.m_RecurrentToInputWeights != nullptr ||
- params.m_InputGateBias != nullptr))
- {
- return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
- " and input gate bias must be provided", __func__);
- }
-
- if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
- {
- return Fail("%s: projection bias should not be provided without projection weights", __func__);
- }
-
- if (desc.m_PeepholeEnabled &&
- (params.m_CellToForgetWeights == nullptr ||
- params.m_CellToOutputWeights == nullptr ||
- (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
- {
- return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
- " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
- }
-
- // Check if the layer is supported
- // Inputs
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo();
- const armnn::TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo();
-
- // Outputs
- const armnn::TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer);
- const armnn::TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
- const armnn::TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- // Basic parameters
- const armnn::TensorInfo& inputToForgetWeights = params.m_InputToForgetWeights->GetInfo();
- const armnn::TensorInfo& inputToCellWeights = params.m_InputToCellWeights->GetInfo();
- const armnn::TensorInfo& inputToOutputWeights = params.m_InputToOutputWeights->GetInfo();
- const armnn::TensorInfo& recurrentToForgetWeights = params.m_RecurrentToForgetWeights->GetInfo();
- const armnn::TensorInfo& recurrentToCellWeights = params.m_RecurrentToCellWeights->GetInfo();
- const armnn::TensorInfo& recurrentToOutputWeights = params.m_RecurrentToOutputWeights->GetInfo();
- const armnn::TensorInfo& forgetGateBias = params.m_ForgetGateBias->GetInfo();
- const armnn::TensorInfo& cellBias = params.m_CellBias->GetInfo();
- const armnn::TensorInfo& outputGateBias = params.m_OutputGateBias->GetInfo();
-
- //Optional parameters
- const armnn::TensorInfo* inputToInputWeights = nullptr;
- const armnn::TensorInfo* recurrentToInputWeights = nullptr;
- const armnn::TensorInfo* cellToInputWeights = nullptr;
- const armnn::TensorInfo* inputGateBias = nullptr;
- const armnn::TensorInfo* projectionWeights = nullptr;
- const armnn::TensorInfo* projectionBias = nullptr;
- const armnn::TensorInfo* cellToForgetWeights = nullptr;
- const armnn::TensorInfo* cellToOutputWeights = nullptr;
-
- if(!desc.m_CifgEnabled)
- {
- inputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
- recurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
- if (params.m_CellToInputWeights != nullptr)
- {
- cellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
- }
- inputGateBias = &(params.m_InputGateBias->GetInfo());
- }
-
- if(desc.m_ProjectionEnabled)
- {
- projectionWeights = &(params.m_ProjectionWeights->GetInfo());
- if (params.m_ProjectionBias != nullptr)
- {
- projectionBias = &(params.m_ProjectionBias->GetInfo());
- }
- }
-
- if(desc.m_PeepholeEnabled)
- {
- cellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
- cellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
- }
-
- if (!IsLayerSupported(__func__,
- armnn::IsLstmSupported,
- m_Compute,
- inputInfo,
- outputStateInInfo,
- cellStateInInfo,
- scratchBufferInfo,
- outputStateOutInfo,
- cellStateOutInfo,
- outputInfo,
- desc,
- inputToForgetWeights,
- inputToCellWeights,
- inputToOutputWeights,
- recurrentToForgetWeights,
- recurrentToCellWeights,
- recurrentToOutputWeights,
- forgetGateBias,
- cellBias,
- outputGateBias,
- inputToInputWeights,
- recurrentToInputWeights,
- cellToInputWeights,
- inputGateBias,
- projectionWeights,
- projectionBias,
- cellToForgetWeights,
- cellToOutputWeights))
- {
- return false;
- }
-
- // Add the layer
- armnn::IConnectableLayer* layer = m_Network->AddLstmLayer(desc, params, "Lstm");
-
- input.Connect(layer->GetInputSlot(0));
- outputStateIn.Connect(layer->GetInputSlot(1));
- cellStateIn.Connect(layer->GetInputSlot(2));
-
- return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0) &&
- SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1) &&
- SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2) &&
- SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3));
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertToActivation(const neuralnetworks::V1_0::Operation& operation,
- const char* operationName,
- const armnn::ActivationDescriptor& activationDesc)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Input 0 is invalid", operationName);
- }
-
- const Operand* outputOperand = GetOutputOperand(operation, 0);
- if (!outputOperand)
- {
- return false;
- }
- const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
- if (!IsLayerSupported(__func__,
- armnn::IsActivationSupported,
- m_Compute,
- input.GetTensorInfo(),
- outInfo,
- activationDesc))
- {
- return false;
- }
-
- armnn::IConnectableLayer* layer = m_Network->AddActivationLayer(activationDesc);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::ConvertPooling2d(const neuralnetworks::V1_0::Operation& operation,
- const char* operationName,
- armnn::PoolingAlgorithm poolType)
-{
- LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0", operationName);
- }
-
- const Operand* output = GetOutputOperand(operation, 0);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
- armnn::Pooling2dDescriptor desc;
- desc.m_PoolType = poolType;
- desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-
- 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)
- || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_StrideX)
- || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_StrideY)
- || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PoolWidth)
- || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PoolHeight)
- || !GetInputActivationFunction(operation, 6, activation))
- {
- return Fail("%s: Operation has invalid inputs", operationName);
- }
-
- const unsigned int inputWidth = swizzledInputInfo.GetShape()[3];
- const unsigned int inputHeight = swizzledInputInfo.GetShape()[2];
-
- 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)
- || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_PadRight)
- || !GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadTop)
- || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadBottom)
- || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideX)
- || !GetInputScalar(operation, 6, OperandType::INT32, desc.m_StrideY)
- || !GetInputScalar(operation, 7, OperandType::INT32, desc.m_PoolWidth)
- || !GetInputScalar(operation, 8, OperandType::INT32, desc.m_PoolHeight)
- || !GetInputActivationFunction(operation, 9, activation))
- {
- return Fail("%s: Operation has invalid inputs", operationName);
- }
- }
-
- // ArmNN does not accept a pool size of 1, but the ArmNN driver is expected to cope.
- // This is mapped to a trivial splitter instead.
- armnn::IConnectableLayer* startLayer = nullptr;
- if (desc.m_PoolWidth != 1 || desc.m_PoolHeight != 1)
- {
- if (!IsLayerSupported(__func__,
- armnn::IsPooling2dSupported,
- m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo,
- desc))
- {
- return false;
- }
-
- startLayer = m_Network->AddPooling2dLayer(desc);
- }
- else
- {
- const unsigned int numDims = swizzledOutputInfo.GetNumDimensions();
-
- armnn::ViewsDescriptor viewsDesc(1, numDims);
-
- for (unsigned int i = 0; i < numDims; ++i)
- {
- viewsDesc.SetViewOriginCoord(0, i, 0);
- viewsDesc.SetViewSize(0, i, swizzledOutputInfo.GetShape()[i]);
- }
-
- if (!IsLayerSupported(__func__,
- armnn::IsSplitterSupported,
- m_Compute,
- swizzledInputInfo,
- viewsDesc))
- {
- return false;
- }
-
- startLayer = m_Network->AddSplitterLayer(viewsDesc);
- }
-
- armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer);
-
- if (endLayer != nullptr)
- {
- armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*m_Network, input, *startLayer, *endLayer);
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
- }
- else
- {
- return Fail("%s: ProcessActivation failed", operationName);
- }
-}
-
-template<typename HalVersion>
-const void* ModelToINetworkConverter<HalVersion>::GetOperandValueReadOnlyAddress(const Operand& operand) const
-{
- const void* valueStart = nullptr;
-
- switch (operand.lifetime)
- {
- case OperandLifeTime::CONSTANT_COPY:
- {
- // Constant found in model.operandValues
- valueStart = &m_Model.operandValues[operand.location.offset];
- break;
- }
- case OperandLifeTime::CONSTANT_REFERENCE:
- {
- // Constant specified via a Memory object
- valueStart = GetMemoryFromPool(operand.location, 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 HalVersion>
-template<typename HalOperation>
-const Operand* ModelToINetworkConverter<HalVersion>::GetInputOperand(const HalOperation& operation,
- uint32_t inputIndex) const
-{
- if (inputIndex >= operation.inputs.size())
- {
- Fail("%s: invalid input index: %i out of %i", __func__, inputIndex, operation.inputs.size());
- return nullptr;
- }
-
- assert(operation.inputs[inputIndex] < m_Model.operands.size()); // Model should have been validated beforehand
- return &m_Model.operands[operation.inputs[inputIndex]];
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-const Operand* ModelToINetworkConverter<HalVersion>::GetOutputOperand(const HalOperation& operation,
- uint32_t outputIndex) const
-{
- if (outputIndex >= operation.outputs.size())
- {
- Fail("%s: invalid output index: %i out of %i", __func__, outputIndex, operation.outputs.size());
- return nullptr;
- }
-
- assert(operation.outputs[outputIndex] < m_Model.operands.size()); // Model should have been validated beforehand
- return &m_Model.operands[operation.outputs[outputIndex]];
-}
-
-template<typename HalVersion>
-template<typename HalOperation, typename T>
-bool ModelToINetworkConverter<HalVersion>::GetInputScalar(const HalOperation& operation,
- uint32_t inputIndex,
- OperandType type,
- T& outValue) const
-{
- const Operand* operand = GetInputOperand(operation, inputIndex);
- 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(T))
- {
- return Fail("%s: incorrect operand location length: %i (should be %i)",
- __func__, operand->location.length, sizeof(T));
- }
-
- const void* valueAddress = GetOperandValueReadOnlyAddress(*operand);
- if (!valueAddress)
- {
- return Fail("%s: failed to get address for operand", __func__);
- }
-
- outValue = *(static_cast<const T*>(valueAddress));
- return true;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputInt32(const HalOperation& operation,
- uint32_t inputIndex,
- int32_t& outValue) const
-{
- return GetInputScalar(operation, inputIndex, OperandType::INT32, outValue);
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputFloat32(const HalOperation& operation,
- uint32_t inputIndex,
- float& outValue) const
-{
- return GetInputScalar(operation, inputIndex, OperandType::FLOAT32, outValue);
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunctionImpl(const HalOperation& operation,
- uint32_t inputIndex,
- OperandType type,
- ActivationFn& outActivationFunction) const
-{
- 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))
- {
- return Fail("%s: failed to get activation input value", __func__);
- }
- outActivationFunction = static_cast<ActivationFn>(activationFunctionAsInt);
- return true;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunction(const HalOperation& operation,
- uint32_t inputIndex,
- ActivationFn& outActivationFunction) const
-{
- return GetInputActivationFunctionImpl(operation, inputIndex, OperandType::INT32, outActivationFunction);
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunctionFromTensor(
- const HalOperation& operation,
- uint32_t inputIndex,
- ActivationFn& outActivationFunction) const
-{
- // This only accepts a 1-D tensor of size 1
- return GetInputActivationFunctionImpl(operation, inputIndex, OperandType::INT32, outActivationFunction);
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetOptionalInputActivation(const HalOperation& operation,
- uint32_t inputIndex,
- ActivationFn& activationFunction) const
-{
- if (operation.inputs.size() <= inputIndex)
- {
- activationFunction = ActivationFn::kActivationNone;
- }
- else
- {
- if (!GetInputActivationFunction(operation, inputIndex, activationFunction))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
- }
- return true;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::GetInputPaddingScheme(const HalOperation& operation,
- uint32_t inputIndex,
- PaddingScheme& outPaddingScheme) const
-{
- int32_t paddingSchemeAsInt;
- if (!GetInputInt32(operation, inputIndex, paddingSchemeAsInt))
- {
- return Fail("%s: failed to get padding scheme input value", __func__);
- }
-
- outPaddingScheme = static_cast<android::nn::PaddingScheme>(paddingSchemeAsInt);
- return true;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-LayerInputHandle ModelToINetworkConverter<HalVersion>::ConvertToLayerInputHandle(const HalOperation& operation,
- uint32_t inputIndex)
-{
- const Operand* operand = GetInputOperand(operation, inputIndex);
- 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:
- {
- // 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, 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);
- if (tensorPin.IsValid())
- {
- if (!IsLayerSupported(__func__,
- armnn::IsConstantSupported,
- m_Compute,
- tensorPin.GetConstTensor().GetInfo()))
- {
- return LayerInputHandle();
- }
-
- armnn::IConnectableLayer* constantLayer = 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 HalVersion>
-template<typename HalOperation>
-ConstTensorPin ModelToINetworkConverter<HalVersion>::ConvertOperationInputToConstTensorPin(
- const HalOperation& operation,
- uint32_t inputIndex,
- const armnn::PermutationVector& dimensionMappings,
- const armnn::TensorShape* overrideTensorShape,
- bool optional)
-{
- const Operand* operand = GetInputOperand(operation, inputIndex);
- if (!operand)
- {
- Fail("%s: failed to get input operand: index=%u", __func__, inputIndex);
- return ConstTensorPin();
- }
- return ConvertOperandToConstTensorPin(*operand, dimensionMappings, overrideTensorShape, optional);
-}
-
-template<typename HalVersion>
-ConstTensorPin ModelToINetworkConverter<HalVersion>::ConvertOperandToConstTensorPin(const Operand& operand,
- const armnn::PermutationVector& dimensionMappings, const armnn::TensorShape* overrideTensorShape, bool optional)
-{
- 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);
- 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 HalVersion>
-bool ModelToINetworkConverter<HalVersion>::GetTensorInt32Values(const Operand& operand,
- std::vector<int32_t>& outValues) const
-{
- if (operand.type != OperandType::TENSOR_INT32)
- {
- return Fail("%s: invalid operand type: %s", __func__, toString(operand.type).c_str());
- }
-
- const void* startAddress = GetOperandValueReadOnlyAddress(operand);
- 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;
-}
-
-// 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).
-template<typename HalVersion>
-armnn::IConnectableLayer* ModelToINetworkConverter<HalVersion>::ProcessActivation(const armnn::TensorInfo& tensorInfo,
- ActivationFn activation, armnn::IConnectableLayer* prevLayer)
-{
- assert(prevLayer->GetNumOutputSlots() == 1);
-
- prevLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
-
- armnn::IConnectableLayer* activationLayer = prevLayer;
-
- if (activation != ActivationFn::kActivationNone)
- {
- armnn::ActivationDescriptor activationDesc;
- switch (activation)
- {
- case ActivationFn::kActivationRelu:
- {
- activationDesc.m_Function = armnn::ActivationFunction::ReLu;
- break;
- }
- case ActivationFn::kActivationRelu1:
- {
- activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
- activationDesc.m_A = 1.0f;
- activationDesc.m_B = -1.0f;
- break;
- }
- case ActivationFn::kActivationRelu6:
- {
- activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
- activationDesc.m_A = 6.0f;
- break;
- }
- case ActivationFn::kActivationSigmoid:
- {
- activationDesc.m_Function = armnn::ActivationFunction::Sigmoid;
- break;
- }
- case ActivationFn::kActivationTanh:
- {
- activationDesc.m_Function = armnn::ActivationFunction::TanH;
- activationDesc.m_A = 1.0f;
- activationDesc.m_B = 1.0f;
- break;
- }
- default:
- {
- Fail("%s: Invalid activation enum value %i", __func__, activation);
- return nullptr;
- }
- }
-
- if (!IsLayerSupported(__func__, armnn::IsActivationSupported, m_Compute,
- prevLayer->GetOutputSlot(0).GetTensorInfo(), tensorInfo, activationDesc))
- {
- return nullptr;
- }
-
- activationLayer = m_Network->AddActivationLayer(activationDesc);
-
- prevLayer->GetOutputSlot(0).Connect(activationLayer->GetInputSlot(0));
- activationLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- }
-
- return activationLayer;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::SetupAndTrackLayerOutputSlot(const HalOperation& operation,
- uint32_t operationOutputIndex,
- armnn::IConnectableLayer& layer,
- uint32_t layerOutputIndex)
-{
- const Operand* outputOperand = GetOutputOperand(operation, operationOutputIndex);
-
- if ((outputOperand == nullptr) || (operationOutputIndex >= layer.GetNumOutputSlots()))
- {
- return false;
- }
-
- armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(layerOutputIndex);
-
- const uint32_t operandIndex = operation.outputs[operationOutputIndex];
- m_OutputSlotForOperand[operandIndex] = &outputSlot;
-
- outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
-
- return true;
-}
-
-template<typename HalVersion>
-template<typename HalOperation>
-bool ModelToINetworkConverter<HalVersion>::SetupAndTrackLayerOutputSlot(const HalOperation& operation,
- uint32_t outputIndex,
- armnn::IConnectableLayer& layer)
-{
- return SetupAndTrackLayerOutputSlot(operation, outputIndex, layer, outputIndex);
-}
-
-template<typename HalVersion>
-bool ModelToINetworkConverter<HalVersion>::IsOperationSupported(uint32_t operationIndex) const
+template<typename HalPolicy>
+bool ModelToINetworkConverter<HalPolicy>::IsOperationSupported(uint32_t operationIndex) const
{
std::map<uint32_t, bool>::const_iterator it = m_OperationSupported.find(operationIndex);
assert(it != m_OperationSupported.end());
return it->second;
}
-template class ModelToINetworkConverter<HalVersion_1_0>;
+///
+/// Class template specializations
+///
-#if defined(ARMNN_ANDROID_NN_V1_1) // Using ::android::hardware::neuralnetworks::V1_1.
-template class ModelToINetworkConverter<HalVersion_1_1>;
+template class ModelToINetworkConverter<hal_1_0::HalPolicy>;
+
+#if defined(ARMNN_ANDROID_NN_V1_1)
+template class ModelToINetworkConverter<hal_1_1::HalPolicy>;
#endif
} // armnn_driver