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review.mlplatform.org / ml / android-nn-driver / 93e48980920ddcc8c6390fa6cbfdfc9740786617 / . / ModelToINetworkConverter.cpp
blob: 70873b8fd2c5e207cd11a562139f1ab0d73e4150 [file] [log] [blame]
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#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,
const HalModel& model,
const std::set<unsigned int>& forcedUnsupportedOperations)
: m_Compute(compute)
, m_Model(model)
, m_ForcedUnsupportedOperations(forcedUnsupportedOperations)
, m_Network(nullptr, nullptr)
, m_ConversionResult(ConversionResult::Success)
{
try
{
Convert();
}
catch (armnn::Exception& e)
{
m_ConversionResult = ConversionResult::UnsupportedFeature;
ALOGE("%s: Unexpected exception: %s", __func__, e.what());
assert(false);
}
}
template<typename HalVersion>
void ModelToINetworkConverter<HalVersion>::Convert()
{
using HalModel = typename HalVersion::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))
{
Fail("%s: Setting of run time pool infos from Hidl Memories has failed.", __func__);
m_ConversionResult = ConversionResult::ErrorMappingPools;
return;
}
uint32_t totalPoolSize = 0;
for (auto&& pool : m_Model.pools)
{
totalPoolSize += pool.size();
}
// Create armnn::INetwork
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);
try
{
for (uint32_t i = 0; i < m_Model.inputIndexes.size(); i++)
{
// inputs in android nn are represented by operands
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::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand));
// store for later layers
m_OutputSlotForOperand[inputIndex] = &outputSlot;
}
}
catch (UnsupportedOperand& e)
{
Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
m_ConversionResult = ConversionResult::UnsupportedFeature;
}
catch (const armnn::InvalidArgumentException& e)
{
Fail("%s: Failed to convert input operand to TensorShape: %s", __func__, e.what());
m_ConversionResult = ConversionResult::UnsupportedFeature;
}
for (uint32_t operationIdx = 0; operationIdx < m_Model.operations.size(); operationIdx++)
{
const auto& operation = m_Model.operations[operationIdx];
bool ok = true;
if (m_ForcedUnsupportedOperations.find(operationIdx) != m_ForcedUnsupportedOperations.end())
{
Fail("%s: Operation at index %i has been forced to be unsupported.", __func__, operationIdx);
ok = false;
}
if (ok)
{
try
{
ok = ConvertOperation(operation);
}
catch (UnsupportedOperand& e)
{
Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
ok = false;
}
catch (const armnn::InvalidArgumentException& e)
{
Fail("%s: Failed to convert operation in %s", __func__, e.what());
ok = false;
}
}
// Store whether this operation was successfully converted.
m_OperationSupported.emplace(operationIdx, ok);
// Any single operation failing will fail the entire conversion.
// We still need to continue and check the other ones.
if (!ok)
{
m_ConversionResult = ConversionResult::UnsupportedFeature;
}
}
try
{
if (m_ConversionResult == ConversionResult::Success)
{
for (uint32_t i = 0; i < m_Model.outputIndexes.size(); i++)
{
// outputs in android nn are represented by operands
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);
assert(m_OutputSlotForOperand[outputIndex]);
m_OutputSlotForOperand[outputIndex]->Connect(layer->GetInputSlot(0));
}
}
}
catch (const armnn::InvalidArgumentException& e)
{
Fail("%s: Failed to convert output operand to TensorShape: %s", __func__, e.what());
m_ConversionResult = ConversionResult::UnsupportedFeature;
}
}
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
{
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>;
#if defined(ARMNN_ANDROID_NN_V1_1) // Using ::android::hardware::neuralnetworks::V1_1.
template class ModelToINetworkConverter<HalVersion_1_1>;
#endif
} // armnn_driver