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gerrit-public.fairphone.software / platform / external / android-nn-driver / d38a1ba9670190d53fe7c746054c20ac48d8195a / . / ConversionUtils.hpp
blob: 1747f61f8c504eb7b01ce413ed70849efd41cbf0 [file] [log] [blame]
//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "Utils.hpp"
#include <armnn/ArmNN.hpp>
#include <armnn/ILayerSupport.hpp>
#include <armnn/BackendHelper.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnnUtils/Transpose.hpp>
#include "1.0/FullyConnected.hpp"
#include <ActivationFunctor.h>
#include <CpuExecutor.h>
#include <OperationsUtils.h>
#include <armnnUtils/FloatingPointComparison.hpp>
#include <log/log.h>
#include <vector>
#ifdef __clang__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wunneeded-internal-declaration"
#pragma clang diagnostic ignored "-Wunused-function"
#pragma clang diagnostic ignored "-Wunused-variable"
#endif
namespace armnn_driver
{
///
/// Helper classes
///
#ifdef ARMNN_ANDROID_R
using OperandType = android::nn::OperandType;
#endif
struct ConversionData
{
ConversionData(const std::vector<armnn::BackendId>& backends)
: m_Backends(backends)
, m_Network(nullptr, nullptr)
, m_DynamicInputsEncountered(false)
{}
const std::vector<armnn::BackendId> m_Backends;
armnn::INetworkPtr m_Network;
std::vector<armnn::IOutputSlot*> m_OutputSlotForOperand;
std::vector<android::nn::RunTimePoolInfo> m_MemPools;
bool m_DynamicInputsEncountered;
};
class LayerInputHandle
{
public:
LayerInputHandle();
LayerInputHandle(bool valid, armnn::IOutputSlot* outputSlot, armnn::TensorInfo tensorInfo);
bool IsValid() const;
void Connect(armnn::IInputSlot& inputSlot);
void Disconnect(armnn::IInputSlot& inputSlot);
const armnn::TensorInfo& GetTensorInfo() const;
private:
armnn::IOutputSlot* m_OutputSlot;
bool m_Valid;
armnn::TensorInfo m_TensorInfo;
};
class ConstTensorPin
{
public:
// Creates an invalid tensor pin (can be used to signal errors)
// The optional flag can be set to indicate the tensor values were missing, but it was otherwise valid
ConstTensorPin(bool optional = false);
// @param tensorInfo TensorInfo associated with the tensor.
// @param valueStart Start address of tensor data. Belongs to one of the memory pools associated with
// the model being converted.
// @param numBytes Number of bytes for the tensor data.
ConstTensorPin(const armnn::TensorInfo& tensorInfo, const void* valueStart, uint32_t numBytes,
const armnn::PermutationVector& mappings);
ConstTensorPin(const ConstTensorPin& other) = delete;
ConstTensorPin(ConstTensorPin&& other) = default;
bool IsValid() const;
bool IsOptional() const;
const armnn::ConstTensor& GetConstTensor() const;
const armnn::ConstTensor* GetConstTensorPtr() const;
private:
armnn::ConstTensor m_ConstTensor;
// Owned memory for swizzled tensor data, only required if the tensor needed
// swizzling. Otherwise, @ref m_ConstTensor will reference memory from one of
// the pools associated with the model being converted.
std::vector<uint8_t> m_SwizzledTensorData;
// optional flag to indicate that an invalid tensor pin is not an error, but the optional values were not given
bool m_Optional;
};
} // namespace armnn_driver
///
/// Utility functions
///
namespace
{
using namespace armnn_driver;
using namespace android::nn;
// Convenience function to log the reason for failing to convert a model.
// @return Always returns false (so that it can be used by callers as a quick way to signal an error and return)
template<class... Args>
static bool Fail(const char* formatStr, Args&&... args)
{
ALOGD(formatStr, std::forward<Args>(args)...);
return false;
}
// Convenience macro to call an Is*Supported function and log caller name together with reason for lack of support.
// Called as: FORWARD_LAYER_SUPPORT_FUNC(__func__, Is*Supported, backends, a, b, c, d, e)
#define FORWARD_LAYER_SUPPORT_FUNC(funcName, func, backends, supported, ...) \
try \
{ \
for (auto&& backendId : backends) \
{ \
auto layerSupportObject = armnn::GetILayerSupportByBackendId(backendId); \
if (layerSupportObject) \
{ \
std::string reasonIfUnsupported; \
supported = \
layerSupportObject->func(__VA_ARGS__, armnn::Optional<std::string&>(reasonIfUnsupported)); \
if (supported) \
{ \
break; \
} \
else \
{ \
if (reasonIfUnsupported.size() > 0) \
{ \
ALOGD("%s: not supported by armnn: %s", funcName, reasonIfUnsupported.c_str()); \
} \
else \
{ \
ALOGD("%s: not supported by armnn", funcName); \
} \
} \
} \
else \
{ \
ALOGD("%s: backend not registered: %s", funcName, backendId.Get().c_str()); \
} \
} \
if (!supported) \
{ \
ALOGD("%s: not supported by any specified backend", funcName); \
} \
} \
catch (const armnn::InvalidArgumentException &e) \
{ \
throw armnn::InvalidArgumentException(e, "Failed to check layer support", CHECK_LOCATION()); \
}
template<typename HalOperand>
armnn::TensorShape GetTensorShapeForOperand(const HalOperand& operand)
{
return armnn::TensorShape(operand.dimensions.size(), operand.dimensions.data());
}
inline bool IsOperandTypeSupportedForTensors(V1_0::OperandType type)
{
return type == V1_0::OperandType::TENSOR_FLOAT32 ||
type == V1_0::OperandType::TENSOR_QUANT8_ASYMM ||
type == V1_0::OperandType::TENSOR_INT32;
}
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
// Support within the 1.2 driver for specific tensor data types
inline bool IsOperandTypeSupportedForTensors(V1_2::OperandType type)
{
return type == V1_2::OperandType::BOOL ||
type == V1_2::OperandType::TENSOR_BOOL8 ||
type == V1_2::OperandType::TENSOR_FLOAT16 ||
type == V1_2::OperandType::TENSOR_FLOAT32 ||
type == V1_2::OperandType::TENSOR_QUANT8_ASYMM ||
type == V1_2::OperandType::TENSOR_QUANT8_SYMM ||
type == V1_2::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
type == V1_2::OperandType::TENSOR_QUANT16_SYMM ||
type == V1_2::OperandType::TENSOR_INT32;
}
#endif
#ifdef ARMNN_ANDROID_NN_V1_3
// Support within the 1.3 driver for specific tensor data types
inline bool IsOperandTypeSupportedForTensors(V1_3::OperandType type)
{
return type == V1_3::OperandType::BOOL ||
type == V1_3::OperandType::TENSOR_BOOL8 ||
type == V1_3::OperandType::TENSOR_FLOAT16 ||
type == V1_3::OperandType::TENSOR_FLOAT32 ||
type == V1_3::OperandType::TENSOR_QUANT8_ASYMM ||
type == V1_3::OperandType::TENSOR_QUANT8_ASYMM_SIGNED ||
type == V1_3::OperandType::TENSOR_QUANT8_SYMM ||
type == V1_3::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
type == V1_3::OperandType::TENSOR_QUANT16_SYMM ||
type == V1_3::OperandType::TENSOR_INT32;
}
#endif
inline bool IsBool(V1_0::Operand)
{
return false;
}
inline bool Is12OrLaterOperand(V1_0::Operand)
{
return false;
}
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
inline bool IsBool(V1_2::Operand operand)
{
return operand.type == V1_2::OperandType::BOOL;
}
/// Checks if a operand is 1_2 Operand
inline bool Is12OrLaterOperand(V1_2::Operand)
{
return true;
}
#endif
#ifdef ARMNN_ANDROID_NN_V1_3
inline bool IsBool(V1_3::Operand operand)
{
return operand.type == V1_3::OperandType::BOOL;
}
/// Checks if a operand is 1_2 Operand
inline bool Is12OrLaterOperand(V1_3::Operand)
{
return true;
}
#endif
template<typename LayerHandleType>
armnn::IConnectableLayer& AddReshapeLayer(armnn::INetwork& network,
LayerHandleType& inputLayer,
armnn::TensorInfo reshapeInfo)
{
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape();
armnn::IConnectableLayer* reshapeLayer = network.AddReshapeLayer(reshapeDescriptor);
ARMNN_ASSERT(reshapeLayer != nullptr);
// Attach the input layer to the reshape layer
inputLayer.Connect(reshapeLayer->GetInputSlot(0));
reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapeInfo);
return *reshapeLayer;
}
bool BroadcastTensor(LayerInputHandle& input0,
LayerInputHandle& input1,
armnn::IConnectableLayer* startLayer,
ConversionData& data)
{
ARMNN_ASSERT(startLayer != nullptr);
const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& inputInfo1 = input1.GetTensorInfo();
unsigned int inputDimensions0 = inputInfo0.GetNumDimensions();
unsigned int inputDimensions1 = inputInfo1.GetNumDimensions();
if (inputDimensions0 == inputDimensions1)
{
// The inputs have the same number of dimensions, simply connect them to the given layer as they are
input0.Connect(startLayer->GetInputSlot(0));
input1.Connect(startLayer->GetInputSlot(1));
return true;
}
// Since the number of dimensions do not match then we need to add degenerate dimensions
// to the "smaller" tensor using a reshape, while keeping the order of the inputs.
unsigned int maxInputDimensions = std::max(inputDimensions0, inputDimensions1);
unsigned int sizeDifference = std::abs(armnn::numeric_cast<int>(inputDimensions0) -
armnn::numeric_cast<int>(inputDimensions1));
bool input0IsSmaller = inputDimensions0 < inputDimensions1;
LayerInputHandle& smallInputHandle = input0IsSmaller ? input0 : input1;
const armnn::TensorInfo& smallInfo = smallInputHandle.GetTensorInfo();
const armnn::TensorShape& smallShape = smallInfo.GetShape();
std::vector<unsigned int> reshapedDimensions(maxInputDimensions, 1);
for (unsigned int i = sizeDifference; i < maxInputDimensions; i++)
{
reshapedDimensions[i] = smallShape[i - sizeDifference];
}
armnn::TensorInfo reshapedInfo = smallInfo;
reshapedInfo.SetShape(armnn::TensorShape{ armnn::numeric_cast<unsigned int>(reshapedDimensions.size()),
reshapedDimensions.data() });
// RehsapeDescriptor that is ignored in the IsReshapeSupported function
armnn::ReshapeDescriptor reshapeDescriptor;
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsReshapeSupported,
data.m_Backends,
isSupported,
smallInfo,
reshapedInfo,
reshapeDescriptor);
if (!isSupported)
{
return false;
}
ARMNN_ASSERT(data.m_Network != nullptr);
armnn::IConnectableLayer& reshapeLayer = AddReshapeLayer(*data.m_Network, smallInputHandle, reshapedInfo);
if (input0IsSmaller)
{
// Input0 is the "smaller" tensor, connect the reshape layer as follows:
//
// Input0 Input1
// | |
// Reshape |
// \ /
// StartLayer
reshapeLayer.GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
input1.Connect(startLayer->GetInputSlot(1));
}
else
{
// Input1 is the "smaller" tensor, connect the reshape layer as follows:
//
// Input0 Input1
// | |
// | Reshape
// \ /
// StartLayer
input0.Connect(startLayer->GetInputSlot(0));
reshapeLayer.GetOutputSlot(0).Connect(startLayer->GetInputSlot(1));
}
return true;
}
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 = armnn::numeric_cast<uint32_t>(padHead);
outPadTail = armnn::numeric_cast<uint32_t>(padTail);
}
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t dilation, uint32_t& outPadHead,
uint32_t& outPadTail, android::nn::PaddingScheme scheme)
{
int32_t padHead;
int32_t padTail;
calculateExplicitPadding(input, stride, dilation, kernel, scheme, &padHead, &padTail);
outPadHead = armnn::numeric_cast<uint32_t>(padHead);
outPadTail = armnn::numeric_cast<uint32_t>(padTail);
}
void CalcPaddingTransposeConv(uint32_t output, uint32_t kernel, int32_t stride, int32_t& outPadHead,
int32_t& outPadTail, android::nn::PaddingScheme scheme)
{
calculateExplicitPaddingTransposeConv(output, stride, kernel, scheme, &outPadHead, &outPadTail);
}
#endif
Shape GetOperandShape(const V1_0::Operand& operand)
{
Shape shape;
shape.type = android::nn::OperandType(operand.type);
shape.dimensions = operand.dimensions;
shape.scale = operand.scale;
shape.offset = operand.zeroPoint;
return shape;
}
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
Shape GetOperandShape(const V1_2::Operand& operand)
{
Shape shape;
shape.type = android::nn::OperandType(operand.type);
shape.dimensions = operand.dimensions;
shape.scale = operand.scale;
shape.offset = operand.zeroPoint;
return shape;
}
#endif
#ifdef ARMNN_ANDROID_NN_V1_3
Shape GetOperandShape(const V1_3::Operand& operand)
{
Shape shape;
shape.type = OperandType(operand.type);
shape.dimensions = operand.dimensions;
shape.scale = operand.scale;
shape.offset = operand.zeroPoint;
return shape;
}
#endif
// 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 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)
{
if (weightInfo.HasPerAxisQuantization())
{
// NOTE: Bias scale is always set to 0 for per-axis quantization and
// it needs to be calculated: scale[i] = input_scale * weight_scale[i]
auto UpdateBiasScaleValue = [&inputInfo](float biasScale) -> float
{
return biasScale * inputInfo.GetQuantizationScale();
};
std::vector<float> biasScales(weightInfo.GetQuantizationScales());
std::transform(biasScales.begin(), biasScales.end(), biasScales.begin(), UpdateBiasScaleValue);
biasInfo.SetQuantizationScales(biasScales);
biasInfo.SetQuantizationDim(weightInfo.GetQuantizationDim());
ALOGV("Bias quantization params have been updated for per-axis quantization");
}
else
{
const float expectedBiasScale = weightInfo.GetQuantizationScale() * inputInfo.GetQuantizationScale();
if (biasInfo.GetQuantizationScale() != expectedBiasScale)
{
if (armnnUtils::within_percentage_tolerance(biasInfo.GetQuantizationScale(), expectedBiasScale, 1.0f))
{
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 IdentityPermutation3D({ 0U, 1U, 2U });
const armnn::PermutationVector SwapDim1And2({ 0U, 2U, 1U, 3U });
// 3D Permutation Vectors
const armnn::PermutationVector RotateTensorLeft({ 1U, 2U, 0U });
const armnn::PermutationVector RotateTensorRight({ 2U, 0U, 1U });
template<typename OSlot>
armnn::IConnectableLayer& AddTransposeLayer(armnn::INetwork& network, OSlot& input,
const armnn::PermutationVector& mappings)
{
// Add swizzle layer
armnn::IConnectableLayer* const layer = network.AddTransposeLayer(mappings);
ARMNN_ASSERT(layer != nullptr);
// Connect input to swizzle layer
input.Connect(layer->GetInputSlot(0));
// Setup swizzled output
const armnn::TensorInfo outInfo = armnnUtils::TransposeTensorShape(input.GetTensorInfo(), mappings);
layer->GetOutputSlot(0).SetTensorInfo(outInfo);
return *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;
}
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 = AddTransposeLayer(network, inputs[i], mapping);
auto& outputSlot = swizzleLayer.GetOutputSlot(0);
auto& outputInfo = outputSlot.GetTensorInfo();
// replace inputs with the swizzled ones
inputs[i] = LayerInputHandle(true, &outputSlot, outputInfo);
inputShapes[i] = inputs[i].GetTensorInfo().GetShape();
}
}
}
bool TransposeInputTensors(ConversionData& data,
std::vector<LayerInputHandle>& inputs,
std::vector<armnn::TensorShape>& inputShapes,
const armnn::PermutationVector& mapping)
{
// If we have a IdentityPermutation4D or IdentityPermutation3D then we are not permuting
if (!mapping.IsEqual(IdentityPermutation4D) && !mapping.IsEqual(IdentityPermutation3D))
{
armnn::TensorInfo outputTransposeInfo;
size_t nInputs = inputs.size();
for (size_t i=0; i<nInputs; ++i)
{
// check permute layer
armnn::TransposeDescriptor transposeDesc;
transposeDesc.m_DimMappings = mapping;
outputTransposeInfo = armnnUtils::TransposeTensorShape(inputs[i].GetTensorInfo(), mapping);
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsTransposeSupported,
data.m_Backends,
isSupported,
inputs[i].GetTensorInfo(),
outputTransposeInfo,
transposeDesc);
if (!isSupported)
{
return false;
}
}
SwizzleInputs(*data.m_Network, inputs, inputShapes, mapping);
}
return true;
}
bool CreateConcatPermutationParameters(const unsigned int numberOfDimensions,
int32_t & concatDimension,
std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutationPair)
{
bool needPermute = false;
ARMNN_ASSERT(numberOfDimensions >= 3);
// ArmNN uses Compute Library subtensors to perform concatenation
// This only works when concatenating along dimension 0, 1 or 3 for a 4-D tensor,
// or along dimension 0 or 2 for a 3-D tensor.
if (numberOfDimensions == 4 && concatDimension == 2)
{
concatDimension = 1;
permutationPair = std::make_pair(SwapDim1And2, SwapDim1And2);
needPermute = true;
}
else if (numberOfDimensions == 3 && concatDimension == 1)
{
concatDimension = 0;
permutationPair = std::make_pair(RotateTensorLeft, RotateTensorRight);
needPermute = true;
}
// If the tensor is 3-D and the concat dimension is 2 then we don't need to permute but we do need to change the
// permutation identity to only have 3 dimensions
else if (numberOfDimensions == 3 && concatDimension == 2)
{
permutationPair = std::make_pair(IdentityPermutation3D, IdentityPermutation3D);
}
return needPermute;
}
} // anonymous namespace
namespace armnn_driver
{
//// Creates an ArmNN activation layer and connects it to the given layer, if the
//// passed in AndroidNN activation function requires so.
//// @return The end layer of the sequence of layers built for the given AndroidNN
//// activation function or nullptr if an error occurred (e.g. unsupported activation).
//// Note that the end layer matches the input layer if no activation is required
//// (the sequence of layers has length 1).
armnn::IConnectableLayer* ProcessActivation(const armnn::TensorInfo& tensorInfo,
ActivationFn activation,
armnn::IConnectableLayer* prevLayer,
ConversionData& data);
} // namespace armnn_driver
///
/// Utility templates
///
namespace armnn_driver
{
using namespace android::nn;
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
const HalOperand* GetInputOperand(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
bool failOnIndexOutOfBounds = true)
{
if (inputIndex >= operation.inputs.size())
{
if (failOnIndexOutOfBounds)
{
Fail("%s: invalid input index: %i out of %i", __func__, inputIndex, operation.inputs.size());
}
return nullptr;
}
// Model should have been validated beforehand
ARMNN_ASSERT(operation.inputs[inputIndex] < getMainModel(model).operands.size());
return &getMainModel(model).operands[operation.inputs[inputIndex]];
}
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
const HalOperand* GetOutputOperand(const HalOperation& operation,
uint32_t outputIndex,
const HalModel& model)
{
if (outputIndex >= operation.outputs.size())
{
Fail("%s: invalid output index: %i out of %i", __func__, outputIndex, operation.outputs.size());
return nullptr;
}
// Model should have been validated beforehand
ARMNN_ASSERT(operation.outputs[outputIndex] < getMainModel(model).operands.size());
return &getMainModel(model).operands[operation.outputs[outputIndex]];
}
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand,
typename HalModel = typename HalPolicy::Model>
const void* GetOperandValueReadOnlyAddress(const HalOperand& operand,
const HalModel& model,
const ConversionData& data,
bool optional = false)
{
using HalOperandLifeTime = typename HalPolicy::OperandLifeTime;
const void* valueStart = nullptr;
switch (operand.lifetime)
{
case HalOperandLifeTime::CONSTANT_COPY:
{
// Constant found in model.operandValues
valueStart = &model.operandValues[operand.location.offset];
break;
}
case HalOperandLifeTime::CONSTANT_REFERENCE:
{
// Constant specified via a Memory object
valueStart = GetMemoryFromPool(operand.location, data.m_MemPools);
break;
}
case HalOperandLifeTime::NO_VALUE:
{
// An optional input tensor with no values is not an error so should not register as a fail
if (optional)
{
valueStart = nullptr;
break;
}
[[fallthrough]];
}
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 HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model,
typename HalOperandType = typename HalPolicy::OperandType>
bool GetOperandType(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
HalOperandType& type)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
return Fail("%s: invalid input operand at index %i", __func__, inputIndex);
}
type = operand->type;
return true;
}
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand>
bool IsOperandConstant(const HalOperand& operand)
{
using HalOperandLifeTime = typename HalPolicy::OperandLifeTime;
HalOperandLifeTime lifetime = operand.lifetime;
return lifetime == HalOperandLifeTime::CONSTANT_COPY ||
lifetime == HalOperandLifeTime::CONSTANT_REFERENCE ||
lifetime == HalOperandLifeTime::NO_VALUE;
}
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand,
typename HalModel = typename HalPolicy::Model>
ConstTensorPin ConvertOperandToConstTensorPin(const HalOperand& operand,
const HalModel& model,
const ConversionData& data,
const armnn::PermutationVector& dimensionMappings = g_DontPermute,
const armnn::TensorShape* overrideTensorShape = nullptr,
bool optional = false)
{
if (!IsOperandTypeSupportedForTensors(operand.type))
{
Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand.type).c_str());
return ConstTensorPin();
}
if (!optional && !IsOperandConstant<HalPolicy>(operand))
{
Fail("%s: invalid operand lifetime: %s", __func__, toString(operand.lifetime).c_str());
return ConstTensorPin();
}
const void* const valueStart = GetOperandValueReadOnlyAddress<HalPolicy>(operand, model, data, optional);
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);
// Android datalayout might be different than armnn datalayout, e.g. the kernel for the depthwise convolution.
if (tensorInfo.HasPerAxisQuantization())
{
tensorInfo.SetQuantizationDim(dimensionMappings[tensorInfo.GetQuantizationDim().value()]);
}
if (overrideTensorShape != nullptr)
{
tensorInfo.SetShape(*overrideTensorShape);
}
return ConstTensorPin(tensorInfo, valueStart, operand.location.length, dimensionMappings);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
ConstTensorPin ConvertOperationInputToConstTensorPin(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
const ConversionData& data,
const armnn::PermutationVector& dimensionMappings = g_DontPermute,
const armnn::TensorShape* overrideTensorShape = nullptr,
bool optional = false)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
Fail("%s: failed to get input operand: index=%u", __func__, inputIndex);
return ConstTensorPin();
}
return ConvertOperandToConstTensorPin<HalPolicy>(*operand,
model,
data,
dimensionMappings,
overrideTensorShape,
optional);
}
template<typename HalPolicy,
typename OutputType,
typename HalOperandType = typename HalPolicy::OperandType,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputScalar(const HalOperation& operation,
uint32_t inputIndex,
HalOperandType type,
OutputType& outValue,
const HalModel& model,
const ConversionData& data,
bool optional = false)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!optional && !operand)
{
return Fail("%s: invalid input operand at index %i", __func__, inputIndex);
}
if (!optional && operand->type != type)
{
return Fail("%s: unexpected operand type: %s (should be %s)",
__func__, toString(operand->type).c_str(), toString(type).c_str());
}
if (!optional && operand->location.length != sizeof(OutputType))
{
return Fail("%s: incorrect operand location length: %i (should be %i)",
__func__, operand->location.length, sizeof(OutputType));
}
const void* valueAddress = GetOperandValueReadOnlyAddress<HalPolicy>(*operand, model, data);
if (!optional && !valueAddress)
{
return Fail("%s: failed to get address for operand", __func__);
}
if(!optional)
{
outValue = *(static_cast<const OutputType*>(valueAddress));
}
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputInt32(const HalOperation& operation,
uint32_t inputIndex,
int32_t& outValue,
const HalModel& model,
const ConversionData& data)
{
return GetInputScalar<HalPolicy>(operation, inputIndex, HalPolicy::OperandType::INT32, outValue, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputFloat32(const HalOperation& operation,
uint32_t inputIndex,
float& outValue,
const HalModel& model,
const ConversionData& data)
{
return GetInputScalar<HalPolicy>(operation, inputIndex, HalPolicy::OperandType::FLOAT32, outValue, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalOperandType = typename HalPolicy::OperandType,
typename HalModel = typename HalPolicy::Model>
bool GetInputActivationFunctionImpl(const HalOperation& operation,
uint32_t inputIndex,
HalOperandType type,
ActivationFn& outActivationFunction,
const HalModel& model,
const ConversionData& data)
{
if (type != HalOperandType::INT32 && type != HalOperandType::TENSOR_INT32)
{
return Fail("%s: unexpected operand type: %s (should be %s or %s)",
__func__,
toString(type).c_str(),
toString(HalOperandType::INT32).c_str(),
toString(HalOperandType::TENSOR_INT32).c_str());
}
int32_t activationFunctionAsInt;
if (!GetInputScalar<HalPolicy>(operation, inputIndex, type, activationFunctionAsInt, model, data))
{
return Fail("%s: failed to get activation input value", __func__);
}
outActivationFunction = static_cast<ActivationFn>(activationFunctionAsInt);
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputActivationFunction(const HalOperation& operation,
uint32_t inputIndex,
ActivationFn& outActivationFunction,
const HalModel& model,
const ConversionData& data)
{
return GetInputActivationFunctionImpl<HalPolicy>(operation,
inputIndex,
HalPolicy::OperandType::INT32,
outActivationFunction,
model,
data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputActivationFunctionFromTensor(const HalOperation& operation,
uint32_t inputIndex,
ActivationFn& outActivationFunction,
const HalModel& model,
const ConversionData& data)
{
// This only accepts a 1-D tensor of size 1
return GetInputActivationFunctionImpl<HalPolicy>(operation,
inputIndex,
HalPolicy::OperandType::INT32,
outActivationFunction,
model,
data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetOptionalInputActivation(const HalOperation& operation,
uint32_t inputIndex,
ActivationFn& activationFunction,
const HalModel& model,
const ConversionData& data)
{
if (operation.inputs.size() <= inputIndex)
{
activationFunction = ActivationFn::kActivationNone;
}
else
{
if (!GetInputActivationFunction<HalPolicy>(operation, inputIndex, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
}
return true;
}
template<typename HalPolicy,
typename ConvolutionDescriptor,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetOptionalConvolutionDilationParams(const HalOperation& operation,
uint32_t dilationXIndex,
ConvolutionDescriptor& descriptor,
const HalModel& model,
const ConversionData& data)
{
bool success = true;
if (operation.inputs.size() >= dilationXIndex + 2)
{
success &= GetInputScalar<HalPolicy>(operation,
dilationXIndex,
HalPolicy::OperandType::INT32,
descriptor.m_DilationX,
model,
data);
success &= GetInputScalar<HalPolicy>(operation,
dilationXIndex + 1,
HalPolicy::OperandType::INT32,
descriptor.m_DilationY,
model,
data);
}
return success;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetOptionalBool(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
const ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
return false;
}
if (!IsBool(*operand))
{
return false;
}
const void* valueAddress = GetOperandValueReadOnlyAddress<HalPolicy>(*operand, model, data);
if (!valueAddress)
{
return false;
}
if (*(static_cast<const bool*>(valueAddress)))
{
return true;
}
else
{
return false;
}
}
template<typename HalPolicy,
typename HalOperand = typename HalPolicy::Operand,
typename HalModel = typename HalPolicy::Model>
bool GetTensorInt32Values(const HalOperand& operand,
std::vector<int32_t>& outValues,
const HalModel& model,
const ConversionData& data)
{
if (operand.type != HalPolicy::OperandType::TENSOR_INT32)
{
return Fail("%s: invalid operand type: %s", __func__, toString(operand.type).c_str());
}
const void* startAddress = GetOperandValueReadOnlyAddress<HalPolicy>(operand, model, data);
if (!startAddress)
{
return Fail("%s: failed to get operand address", __func__, operand.type);
}
// Check number of bytes is sensible
const uint32_t numBytes = operand.location.length;
if (numBytes % sizeof(int32_t) != 0)
{
return Fail("%s: invalid number of bytes: %i, expected to be a multiple of %i",
__func__, numBytes, sizeof(int32_t));
}
outValues.resize(numBytes / sizeof(int32_t));
memcpy(outValues.data(), startAddress, numBytes);
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool GetInputPaddingScheme(const HalOperation& operation,
uint32_t inputIndex,
PaddingScheme& outPaddingScheme,
const HalModel& model,
const ConversionData& data)
{
int32_t paddingSchemeAsInt;
if (!GetInputInt32<HalPolicy>(operation, inputIndex, paddingSchemeAsInt, model, data))
{
return Fail("%s: failed to get padding scheme input value", __func__);
}
outPaddingScheme = static_cast<android::nn::PaddingScheme>(paddingSchemeAsInt);
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
LayerInputHandle ConvertToLayerInputHandle(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
using HalOperandLifeTime = typename HalPolicy::OperandLifeTime;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
Fail("%s: failed to get input operand %i", __func__, inputIndex);
return LayerInputHandle();
}
if (!IsOperandTypeSupportedForTensors(operand->type))
{
Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand->type).c_str());
return LayerInputHandle();
}
try
{
armnn::TensorInfo operandTensorInfo = GetTensorInfoForOperand(*operand);
if (IsDynamicTensor(operandTensorInfo))
{
Fail("%s: dynamic input tensors are not supported", __func__);
return LayerInputHandle();
}
switch (operand->lifetime)
{
case HalOperandLifeTime::MODEL_INPUT:
{
// NOTE: We must check whether we can support the input tensor on at least one
// of the provided backends; otherwise we cannot convert the operation
bool isInputSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsInputSupported,
data.m_Backends,
isInputSupported,
operandTensorInfo);
if (!isInputSupported)
{
Fail("%s: unsupported input tensor", __func__);
return LayerInputHandle();
}
[[clang::fallthrough]]; // intentional fallthrough
}
case HalOperandLifeTime::TEMPORARY_VARIABLE: // intentional fallthrough
case HalOperandLifeTime::MODEL_OUTPUT:
{
// The tensor is either an operand internal to the model, or a model input.
// It can be associated with an ArmNN output slot for an existing layer.
// m_OutputSlotForOperand[...] can be nullptr if the previous layer could not be converted
const uint32_t operandIndex = operation.inputs[inputIndex];
return LayerInputHandle(true, data.m_OutputSlotForOperand[operandIndex], operandTensorInfo);
}
case HalOperandLifeTime::CONSTANT_COPY: // intentional fallthrough
case HalOperandLifeTime::CONSTANT_REFERENCE:
{
// The tensor has an already known constant value, and can be converted into an ArmNN Constant layer.
ConstTensorPin tensorPin = ConvertOperandToConstTensorPin<HalPolicy>(*operand, model, data);
if (tensorPin.IsValid())
{
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsConstantSupported,
data.m_Backends,
isSupported,
tensorPin.GetConstTensor().GetInfo());
if (!isSupported)
{
return LayerInputHandle();
}
armnn::IConnectableLayer* constantLayer =
data.m_Network->AddConstantLayer(tensorPin.GetConstTensor());
armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(tensorPin.GetConstTensor().GetInfo());
return LayerInputHandle(true, &outputSlot, operandTensorInfo);
}
else
{
Fail("%s: invalid operand tensor", __func__);
return LayerInputHandle();
}
break;
}
default:
{
// Unsupported lifetime for an input tensor
Fail("%s: unsupported lifetime for input tensor: %s",
__func__, toString(operand->lifetime).c_str());
return LayerInputHandle();
}
}
}
catch (UnsupportedOperand<HalOperandType>& e)
{
Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
return LayerInputHandle();
}
}
#ifdef ARMNN_ANDROID_NN_V1_3
template<typename HalPolicy>
LayerInputHandle ConvertToLayerInputHandle(const ::android::hardware::neuralnetworks::V1_3::Operation& operation,
uint32_t inputIndex,
const::android::hardware::neuralnetworks::V1_3::Model& model,
ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
using HalOperandLifeTime = typename HalPolicy::OperandLifeTime;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
Fail("%s: failed to get input operand %i", __func__, inputIndex);
return LayerInputHandle();
}
if (!IsOperandTypeSupportedForTensors(operand->type))
{
Fail("%s: unsupported operand type for tensor %s", __func__, toString(operand->type).c_str());
return LayerInputHandle();
}
try
{
armnn::TensorInfo operandTensorInfo = GetTensorInfoForOperand(*operand);
if (IsDynamicTensor(operandTensorInfo))
{
data.m_DynamicInputsEncountered = true;
const uint32_t operandIndex = operation.inputs[inputIndex];
// Check if the dynamic input tensors have been inferred by one of the previous layers
// If not we can't support them
if (data.m_OutputSlotForOperand.size() >= operandIndex && data.m_OutputSlotForOperand[operandIndex])
{
operandTensorInfo = data.m_OutputSlotForOperand[operandIndex]->GetTensorInfo();
}
else
{
Fail("%s: Type 2 dynamic input tensors are not supported", __func__);
return LayerInputHandle();
}
}
switch (operand->lifetime)
{
case HalOperandLifeTime::SUBGRAPH_INPUT:
{
// NOTE: We must check whether we can support the input tensor on at least one
// of the provided backends; otherwise we cannot convert the operation
bool isInputSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsInputSupported,
data.m_Backends,
isInputSupported,
operandTensorInfo);
if (!isInputSupported)
{
Fail("%s: unsupported input tensor", __func__);
return LayerInputHandle();
}
[[clang::fallthrough]]; // intentional fallthrough
}
case HalOperandLifeTime::TEMPORARY_VARIABLE: // intentional fallthrough
case HalOperandLifeTime::SUBGRAPH_OUTPUT:
{
// The tensor is either an operand internal to the model, or a model input.
// It can be associated with an ArmNN output slot for an existing layer.
// m_OutputSlotForOperand[...] can be nullptr if the previous layer could not be converted
const uint32_t operandIndex = operation.inputs[inputIndex];
return LayerInputHandle(true, data.m_OutputSlotForOperand[operandIndex], operandTensorInfo);
}
case HalOperandLifeTime::CONSTANT_COPY: // intentional fallthrough
case HalOperandLifeTime::CONSTANT_REFERENCE:
{
// The tensor has an already known constant value, and can be converted into an ArmNN Constant layer.
ConstTensorPin tensorPin = ConvertOperandToConstTensorPin<HalPolicy>(*operand, model, data);
if (tensorPin.IsValid())
{
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsConstantSupported,
data.m_Backends,
isSupported,
tensorPin.GetConstTensor().GetInfo());
if (!isSupported)
{
return LayerInputHandle();
}
armnn::IConnectableLayer* constantLayer =
data.m_Network->AddConstantLayer(tensorPin.GetConstTensor());
armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(tensorPin.GetConstTensor().GetInfo());
return LayerInputHandle(true, &outputSlot, operandTensorInfo);
}
else
{
Fail("%s: invalid operand tensor", __func__);
return LayerInputHandle();
}
break;
}
default:
{
// Unsupported lifetime for an input tensor
Fail("%s: unsupported lifetime for input tensor: %s",
__func__, toString(operand->lifetime).c_str());
return LayerInputHandle();
}
}
}
catch (UnsupportedOperand<HalOperandType>& e)
{
Fail("%s: Operand type %s not supported in ArmnnDriver", __func__, toString(e.m_type).c_str());
return LayerInputHandle();
}
}
#endif
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool SetupAndTrackLayerOutputSlot(const HalOperation& operation,
uint32_t operationOutputIndex,
armnn::IConnectableLayer& layer,
uint32_t layerOutputIndex,
const HalModel& model,
ConversionData& data,
const armnn::TensorInfo* overrideOutputInfo = nullptr,
const std::function <void (const armnn::TensorInfo&, bool&)>& validateFunc = nullptr,
const ActivationFn& activationFunction = ActivationFn::kActivationNone,
bool inferOutputShapes = false)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, operationOutputIndex, model);
if ((outputOperand == nullptr) || (operationOutputIndex >= layer.GetNumOutputSlots()))
{
return false;
}
armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(layerOutputIndex);
if (overrideOutputInfo == nullptr)
{
outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
}
else
{
outputSlot.SetTensorInfo(*overrideOutputInfo);
}
bool isSupported = false;
if (validateFunc && (IsDynamicTensor(outputSlot.GetTensorInfo()) || inferOutputShapes))
{
// Type one dynamic tensors require the previous layer's output shape for inference
for (unsigned int inputSlotIndex = 0; inputSlotIndex < layer.GetNumInputSlots(); ++inputSlotIndex)
{
if(!layer.GetInputSlot(inputSlotIndex).GetConnection())
{
return false;
}
}
// IsTensorInfoSet will infer the dynamic output shape
outputSlot.IsTensorInfoSet();
// Once the shape is inferred we can validate it
validateFunc(outputSlot.GetTensorInfo(), isSupported);
if(!isSupported)
{
for (unsigned int inputSlotIndex = 0; inputSlotIndex < layer.GetNumInputSlots(); ++inputSlotIndex)
{
layer.GetInputSlot(inputSlotIndex).GetConnection()->Disconnect(layer.GetInputSlot(inputSlotIndex));
}
return false;
}
}
const uint32_t operandIndex = operation.outputs[operationOutputIndex];
if (activationFunction != ActivationFn::kActivationNone)
{
const armnn::TensorInfo& activationOutputInfo = outputSlot.GetTensorInfo();
armnn::IConnectableLayer* const endLayer = ProcessActivation(activationOutputInfo, activationFunction,
&layer, data);
if (!endLayer)
{
return Fail("%s: ProcessActivation failed", __func__);
}
armnn::IOutputSlot& activationOutputSlot = endLayer->GetOutputSlot(layerOutputIndex);
data.m_OutputSlotForOperand[operandIndex] = &activationOutputSlot;
}
else
{
data.m_OutputSlotForOperand[operandIndex] = &outputSlot;
}
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
armnn::DataLayout OptionalDataLayout(const HalOperation& operation,
uint32_t inputIndex,
const HalModel& model,
ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
if (!operand)
{
return armnn::DataLayout::NHWC;
}
if (!IsBool(*operand))
{
return armnn::DataLayout::NHWC;
}
const void* valueAddress = GetOperandValueReadOnlyAddress<HalPolicy>(*operand, model, data);
if (!valueAddress)
{
return armnn::DataLayout::NHWC;
}
if (*(static_cast<const bool*>(valueAddress)))
{
return armnn::DataLayout::NCHW;
}
else
{
return armnn::DataLayout::NHWC;
}
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool SetupAndTrackLayerOutputSlot(const HalOperation& operation,
uint32_t outputIndex,
armnn::IConnectableLayer& layer,
const HalModel& model,
ConversionData& data,
const armnn::TensorInfo* overrideOutputInfo = nullptr,
const std::function <void (const armnn::TensorInfo&, bool&)>& validateFunc = nullptr,
const ActivationFn& activationFunction = ActivationFn::kActivationNone)
{
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation,
outputIndex,
layer,
outputIndex,
model,
data,
overrideOutputInfo,
validateFunc,
activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertToActivation(const HalOperation& operation,
const char* operationName,
const armnn::ActivationDescriptor& activationDesc,
const HalModel& model,
ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Input 0 is invalid", operationName);
}
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!outputOperand)
{
return false;
}
const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsActivationSupported,
data.m_Backends,
isSupported,
input.GetTensorInfo(),
outInfo,
activationDesc);
};
if(IsDynamicTensor(outInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddActivationLayer(activationDesc);
ARMNN_ASSERT(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertReLu(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::ReLu;
return ConvertToActivation<HalPolicy>(operation, __func__, desc, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertReLu1(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::BoundedReLu;
desc.m_A = 1.0f;
desc.m_B = -1.0f;
return ConvertToActivation<HalPolicy>(operation, __func__, desc, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertReLu6(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::BoundedReLu;
desc.m_A = 6.0f;
return ConvertToActivation<HalPolicy>(operation, __func__, desc, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertTanH(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
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<HalPolicy>(operation, __func__, desc, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertPaddings(const HalOperation& operation,
const HalModel& model,
ConversionData& data,
unsigned int rank,
armnn::PadDescriptor& padDescriptor)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* paddingsOperand = GetInputOperand<HalPolicy>(operation, 1, model);
if (!paddingsOperand)
{
return Fail("%s: Could not read paddings operand", __func__);
}
armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand);
if (paddingsOperandShape.GetNumDimensions() != 2 || paddingsOperandShape.GetNumElements() != rank * 2)
{
return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, rank);
}
std::vector<int32_t> paddings;
if (!GetTensorInt32Values<HalPolicy>(*paddingsOperand, paddings, model, data))
{
return Fail("%s: Operation has invalid or unsupported paddings operand", __func__);
}
// add padding for each dimension of input tensor.
for (unsigned int i = 0; i < paddings.size() - 1; i += 2)
{
int paddingBeforeInput = paddings[i];
int paddingAfterInput = paddings[i + 1];
if (paddingBeforeInput < 0 || paddingAfterInput < 0)
{
return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__);
}
padDescriptor.m_PadList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput);
}
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertPooling2d(const HalOperation& operation,
const char* operationName,
armnn::PoolingAlgorithm poolType,
const HalModel& model,
ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation Could not read input 0", operationName);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
armnn::Pooling2dDescriptor desc;
desc.m_PoolType = poolType;
desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
desc.m_DataLayout = armnn::DataLayout::NHWC;
ActivationFn activation;
auto inputSize = operation.inputs.size();
if (inputSize >= 10)
{
// one input, 9 parameters (padding l r t b, stridex, stridey, width, height, activation type)
if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, desc.m_PadLeft, model, data) ||
!GetInputScalar<HalPolicy>(operation, 2, HalOperandType::INT32, desc.m_PadRight, model, data) ||
!GetInputScalar<HalPolicy>(operation, 3, HalOperandType::INT32, desc.m_PadTop, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_PadBottom, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 6, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputScalar<HalPolicy>(operation, 7, HalOperandType::INT32, desc.m_PoolWidth, model, data) ||
!GetInputScalar<HalPolicy>(operation, 8, HalOperandType::INT32, desc.m_PoolHeight, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 9, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", operationName);
}
if (Is12OrLaterOperand(*output))
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 10, model, data);
}
}
else
{
// one input, 6 parameters (padding, stridex, stridey, width, height, activation type)
android::nn::PaddingScheme scheme;
if (!GetInputPaddingScheme<HalPolicy>(operation, 1, scheme, model, data) ||
!GetInputScalar<HalPolicy>(operation, 2, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 3, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_PoolWidth, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_PoolHeight, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 6, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", operationName);
}
if (Is12OrLaterOperand(*output))
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 7, model, data);
}
const armnnUtils::DataLayoutIndexed dataLayout(desc.m_DataLayout);
const unsigned int inputWidth = inputInfo.GetShape()[dataLayout.GetWidthIndex()];
const unsigned int inputHeight = inputInfo.GetShape()[dataLayout.GetHeightIndex()];
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);
}
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsPooling2dSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
desc);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* pooling2dLayer = data.m_Network->AddPooling2dLayer(desc);
if (!pooling2dLayer)
{
return Fail("%s: AddPooling2dLayer failed", __func__);
}
input.Connect(pooling2dLayer->GetInputSlot(0));
if (!isSupported)
{
return false;
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *pooling2dLayer, model,
data, nullptr, validateFunc, activation);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertAdd(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input0 = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
LayerInputHandle input1 = ConvertToLayerInputHandle<HalPolicy>(operation, 1, model, data);
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<HalPolicy>(operation, 2, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!outputOperand)
{
return false;
}
const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& inputInfo1 = input1.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsAdditionSupported,
data.m_Backends,
isSupported,
inputInfo0,
inputInfo1,
outputInfo);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const startLayer = data.m_Network->AddAdditionLayer();
bool isReshapeSupported = BroadcastTensor(input0, input1, startLayer, data);
if (!isReshapeSupported)
{
return false;
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertArgMinMax(const HalOperation& operation,
const HalModel& model,
ConversionData& data,
armnn::ArgMinMaxFunction argMinMaxFunction)
{
ALOGV("argMinMaxFunction = %s", GetArgMinMaxFunctionAsCString(argMinMaxFunction));
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input0 = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input0.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
int32_t axis;
if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, axis, model, data))
{
return Fail("%s: Operation has invalid inputs. Failed to read axis.", __func__);
}
const armnn::TensorInfo& inputInfo = input0.GetTensorInfo();
int rank = static_cast<int>(inputInfo.GetNumDimensions());
if (((axis < -rank) && (axis < 0)) || ((axis >= rank) && (axis > 0)))
{
// Square bracket denotes inclusive n while parenthesis denotes exclusive n
// E.g. Rank 4 tensor can have axis in range [-4, 3)
// -1 == 3, -2 == 2, -3 == 1, -4 == 0
return Fail("%s: Axis must be in range [-n, n)", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
armnn::ArgMinMaxDescriptor descriptor;
descriptor.m_Function = argMinMaxFunction;
descriptor.m_Axis = axis;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsArgMinMaxSupported,
data.m_Backends,
isSupported,
inputInfo0,
outputInfo,
descriptor);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddArgMinMaxLayer(descriptor);
assert(layer != nullptr);
input0.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertConcatenation(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
// 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<HalPolicy>(operation, numInputTensors, HalOperandType::INT32, concatDim, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!outputOperand)
{
return Fail("%s: Operation has no outputs", __func__);
}
armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*outputOperand);
armnn::TensorShape outputShape = outputInfo.GetShape();
const bool isDynamicTensor = IsDynamicTensor(outputInfo);
//
// 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 HalOperand* operand = GetInputOperand<HalPolicy>(operation, i, model);
if (!operand)
{
return Fail("%s: Operation has invalid inputs", __func__);
}
LayerInputHandle operandInputHandle = ConvertToLayerInputHandle<HalPolicy>(operation, i, model, data);
if (!operandInputHandle.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
armnn::TensorShape operandShape = GetTensorShapeForOperand(*operand);
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::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape();
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsReshapeSupported,
data.m_Backends,
isSupported,
operandInputHandle.GetTensorInfo(),
reshapeInfo,
reshapeDescriptor);
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer& newReshape = AddReshapeLayer(*data.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__);
}
}
ARMNN_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)
{
if (IsDynamicTensor(outputInfo))
{
outputShape = armnn::TensorShape({1, 0, 0}, {true, false, false});
}
else
{
outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]});
}
}
else if (tensorDimensionsAdded == 2)
{
if (IsDynamicTensor(outputInfo))
{
outputShape = armnn::TensorShape({1, 1, 0}, {true, true, false});
}
else
{
outputShape = armnn::TensorShape({1, 1, outputShape[0]});
}
}
}
// Check if permutations is required and get the pair of permutations required for the concatenation.
// Permutation is required when the concat dimension is 2 for a 4D tensor or 1 for a 3D tensor.
std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair =
std::make_pair(IdentityPermutation4D, IdentityPermutation4D);
bool needPermute = CreateConcatPermutationParameters(inputShapes[0].GetNumDimensions(),
concatDim,
permutationPair);
// Only relevant to static tensors as dynamic output tensors will be transposed as a result of inferring from input
if (!isDynamicTensor)
{
if (needPermute)
{
outputShape = armnnUtils::TransposeTensorShape(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
if (!TransposeInputTensors(data, inputHandles, inputShapes, permutationPair.first))
{
return false;
}
// Create an armnn concat layer descriptor - this will also perform validation on the input shapes
armnn::OriginsDescriptor concatDescriptor;
try
{
// The concat descriptor is always created across the only supported concat dimension
// which is 0, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor.
concatDescriptor = armnn::CreateDescriptorForConcatenation(inputShapes.begin(),
inputShapes.end(),
concatDim);
} catch (std::exception& error)
{
return Fail("%s: Error preparing concat 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, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor.
if (!isDynamicTensor)
{
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(); });
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported){
FORWARD_LAYER_SUPPORT_FUNC(__func__, IsConcatSupported, data.m_Backends, isSupported, inputTensorInfos,
outputInfo, concatDescriptor);
};
if (!isDynamicTensor)
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddConcatLayer(concatDescriptor);
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));
}
// Transpose the output shape
auto transposeOutputShape = [&](){
armnn::TransposeDescriptor transposeDesc;
transposeDesc.m_DimMappings = permutationPair.second;
armnn::TensorInfo inputTransposeInfo = layer->GetOutputSlot(0).GetTensorInfo();
armnn::TensorInfo outputTransposeInfo = armnnUtils::TransposeTensorShape(inputTransposeInfo,
permutationPair.second);
isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsTransposeSupported,
data.m_Backends,
isSupported,
inputTransposeInfo,
outputTransposeInfo,
transposeDesc);
if (!isSupported)
{
return false;
}
// Add permutation layer and connect the output to it, the permutation becomes the output layer
armnn::IConnectableLayer& deswizzleLayer = AddTransposeLayer(*data.m_Network, layer->GetOutputSlot(0),
permutationPair.second);
layer = &deswizzleLayer;
return true;
};
if (needPermute && !isDynamicTensor)
{
transposeOutputShape();
}
if (inputsHaveBeenReshaped)
{
if (isDynamicTensor)
{
// Infer the output shapes of concat if outputs are type 1 dynamic
ARMNN_ASSERT(layer->GetOutputSlot(0).IsTensorInfoSet());
if (!ValidateConcatOutputShape(inputShapes,
layer->GetOutputSlot(0).GetTensorInfo().GetShape(),
concatDim))
{
return Fail("%s: Error validating the output shape for concat", __func__);
}
transposeOutputShape();
}
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]}));
}
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = afterConcatInfo.GetShape();
armnn::TensorInfo concatInfo = layer->GetOutputSlot(0).GetTensorInfo();
isSupported = false;
auto validateReshapeFunc = [&](const armnn::TensorInfo& afterConcatInfo, bool& isSupported){
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsReshapeSupported,
data.m_Backends,
isSupported,
concatInfo,
afterConcatInfo,
reshapeDescriptor);
};
if (!IsDynamicTensor(afterConcatInfo))
{
validateReshapeFunc(afterConcatInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
layer = &AddReshapeLayer(*data.m_Network, layer->GetOutputSlot(0), afterConcatInfo);
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation,
0,
*layer,
model,
data,
nullptr,
validateReshapeFunc);
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertConv2d(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
// ArmNN does not currently support non-fixed weights or bias
const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1, model, data);
const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
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(), inputInfo);
armnn::Convolution2dDescriptor desc;
desc.m_DataLayout = armnn::DataLayout::NHWC;
ActivationFn activation;
if (operation.inputs.size() == 10)
{
if (!GetInputScalar<HalPolicy>(operation, 3, HalOperandType::INT32, desc.m_PadLeft, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_PadRight, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_PadTop, model, data) ||
!GetInputScalar<HalPolicy>(operation, 6, HalOperandType::INT32, desc.m_PadBottom, model, data) ||
!GetInputScalar<HalPolicy>(operation, 7, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 8, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 9, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
}
else if (operation.inputs.size() == 7)
{
android::nn::PaddingScheme paddingScheme;
if (!GetInputPaddingScheme<HalPolicy>(operation, 3, paddingScheme, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 6, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const uint32_t kernelX = weights.GetShape()[2];
const uint32_t kernelY = weights.GetShape()[1];
const uint32_t inputX = inputInfo.GetShape()[2];
const uint32_t inputY = inputInfo.GetShape()[1];
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;
armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsConvolution2dSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
desc,
weights.GetInfo(),
biases);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* startLayer =
data.m_Network->AddConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
if (!startLayer)
{
return Fail("%s: AddConvolution2dLayer failed", __func__);
}
input.Connect(startLayer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activation);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertDepthToSpace(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid() )
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
if (rank != 4)
{
return Fail("%s: Only inputs with rank 4 are supported", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
armnn::DepthToSpaceDescriptor descriptor;
GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, descriptor.m_BlockSize, model, data);
if (descriptor.m_BlockSize <= 1)
{
return Fail("%s: Block size must be at least 1 in all dimensions");
}
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
if (Is12OrLaterOperand(*output))
{
descriptor.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 2, model, data);
}
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsDepthToSpaceSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddDepthToSpaceLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertDepthwiseConv2d(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
// ArmNN 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 ]
const HalOperand* weightsOperand = GetInputOperand<HalPolicy>(operation, 1, model);
if (weightsOperand == nullptr)
{
return Fail("%s: Operand is invalid", __func__);
}
armnn::DepthwiseConvolution2dDescriptor desc;
desc.m_DataLayout = armnn::DataLayout::NHWC;
// 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 };
const ConstTensorPin weightsPin =
ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
1,
model,
data,
HWIMToMIHW,
&weightsShape);
// Bias is a 1D tensor
const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
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(), inputInfo);
ActivationFn activation;
if (operation.inputs.size() == 11)
{
if (!GetInputScalar<HalPolicy>(operation, 3, HalOperandType::INT32, desc.m_PadLeft, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_PadRight, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_PadTop, model, data) ||
!GetInputScalar<HalPolicy>(operation, 6, HalOperandType::INT32, desc.m_PadBottom, model, data) ||
!GetInputScalar<HalPolicy>(operation, 7, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 8, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 10, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
}
else if (operation.inputs.size() == 8)
{
android::nn::PaddingScheme paddingScheme;
if (!GetInputPaddingScheme<HalPolicy>(operation, 3, paddingScheme, model, data) ||
!GetInputScalar<HalPolicy>(operation, 4, HalOperandType::INT32, desc.m_StrideX, model, data) ||
!GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 7, activation, model, data))
{
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 = inputInfo.GetShape()[2];
const uint32_t inputY = inputInfo.GetShape()[1];
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;
armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsDepthwiseConvolutionSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
desc,
weights.GetInfo(),
biases);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* startLayer =
data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
if (!startLayer)
{
return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
}
input.Connect(startLayer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activation);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertDequantize(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid input", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::Optional<unsigned int>& quantizationDim = inputInfo.GetQuantizationDim();
if (quantizationDim.has_value() && quantizationDim.value() != 0)
{
return Fail("%s: Operation has quantization dimension different than 0", __func__);
}
const HalOperand* const outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!outputOperand)
{
return Fail("%s: Operation has invalid outputs", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsDequantizeSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddDequantizeLayer();
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertDiv(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input0 = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
LayerInputHandle input1 = ConvertToLayerInputHandle<HalPolicy>(operation, 1, model, data);
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<HalPolicy>(operation, 2, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsDivisionSupported,
data.m_Backends,
isSupported,
input0.GetTensorInfo(),
input1.GetTensorInfo(),
outputInfo);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const startLayer = data.m_Network->AddDivisionLayer();
bool isReshapeSupported = BroadcastTensor(input0, input1, startLayer, data);
if (!isReshapeSupported)
{
return false;
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertFloor(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* const outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!outputOperand)
{
return Fail("%s: Operation has invalid outputs", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsFloorSupported,
data.m_Backends,
isSupported,
input.GetTensorInfo(),
outputInfo);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddFloorLayer();
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
inline bool IsQSymm8(const V1_0::Operand&)
{
return false;
}
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
inline bool IsQSymm8(const V1_2::Operand& operand)
{
return operand.type == V1_2::OperandType::TENSOR_QUANT8_SYMM;
}
#endif
#ifdef ARMNN_ANDROID_NN_V1_3
inline bool IsQSymm8(const V1_3::Operand& operand)
{
return operand.type == V1_3::OperandType::TENSOR_QUANT8_SYMM;
}
#endif
enum class DequantizeStatus
{
SUCCESS,
NOT_REQUIRED,
INVALID_OPERAND
};
using DequantizeResult = std::tuple<std::unique_ptr<float[]>, size_t, armnn::TensorInfo, DequantizeStatus>;
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
DequantizeResult DequantizeIfRequired(size_t operand_index,
const HalOperation& operation,
const HalModel& model,
const ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* weightsOperand = GetInputOperand<HalPolicy>(operation, operand_index, model);
if (!weightsOperand)
{
return { nullptr, 0, armnn::TensorInfo(), DequantizeStatus::INVALID_OPERAND };
}
if (IsOperandConstant<HalPolicy>(*weightsOperand))
{
// Weights are already constant
return { nullptr, 0, armnn::TensorInfo(), DequantizeStatus::NOT_REQUIRED };
}
const size_t weightsInputIndex = operation.inputs[operand_index];
// The weights are a non const tensor, this indicates they might be the output of a dequantize op.
// Iterate over the nodes and find the previous operation which should be DEQUANTIZE
for (uint32_t operationIdx = 0; operationIdx < getMainModel(model).operations.size(); ++operationIdx)
{
// Search for the DEQUANTIZE op which has the operand with index equal to operandIndex
const auto& operationIt = getMainModel(model).operations[operationIdx];
if (operationIt.type != HalPolicy::OperationType::DEQUANTIZE)
{
continue;
}
size_t outOpIndex = weightsInputIndex + 1;
for (size_t i = 0; outOpIndex != weightsInputIndex && i < operationIt.outputs.size(); ++i)
{
outOpIndex = operationIt.outputs[i];
}
if (outOpIndex != weightsInputIndex)
{
continue;
}
const HalOperand* operand = GetInputOperand<HalPolicy>(operationIt, 0, model);
ARMNN_ASSERT(operand);
if (!IsQSymm8(*operand))
{
// Only supporting dequantize from QSYMM8 to FLOAT
break;
}
// Allocate a new buffer for the dequantized data and manually dequantize
const void* startValue = GetOperandValueReadOnlyAddress<HalPolicy>(*operand, model, data);
if (!startValue)
{
// Failed to get the operand address
break;
}
const uint8_t* quantizedBuffer = reinterpret_cast<const uint8_t*>(startValue);
size_t dequantizedBufferLength = operand->location.length;
const float quantizationScale = operand->scale;
auto dequantizedBuffer = std::make_unique<float[]>(dequantizedBufferLength + 1);
for (size_t i = 0; i < dequantizedBufferLength; ++i)
{
float* dstPtr = dequantizedBuffer.get();
ARMNN_ASSERT(dstPtr);
*dstPtr++ = quantizedBuffer[i] * quantizationScale;
}
// Construct tensor info for dequantized ConstTensor
armnn::TensorInfo tensorInfo(operand->dimensions.size(),
operand->dimensions.data(),
armnn::DataType::Float32);
return { std::move(dequantizedBuffer), dequantizedBufferLength * sizeof(float),
std::move(tensorInfo),
DequantizeStatus::SUCCESS };
}
return { nullptr, 0, armnn::TensorInfo() , DequantizeStatus::NOT_REQUIRED};
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
ConstTensorPin DequantizeAndMakeConstTensorPin(const HalOperation& operation,
const HalModel& model,
const ConversionData& data,
size_t operandIndex,
bool optional = false)
{
DequantizeResult dequantized = DequantizeIfRequired<HalPolicy>(operandIndex,operation, model, data);
DequantizeStatus status = std::get<3>(dequantized);
switch (status)
{
case DequantizeStatus::INVALID_OPERAND:
{
// return invalid const tensor pin
return ConstTensorPin();
}
case DequantizeStatus::NOT_REQUIRED:
{
return ConvertOperationInputToConstTensorPin<HalPolicy>(
operation, operandIndex, model, data, g_DontPermute, nullptr, optional);
}
case DequantizeStatus::SUCCESS:
default:
{
return ConstTensorPin(
std::get<2>(dequantized), std::get<0>(dequantized).get(), std::get<1>(dequantized), g_DontPermute);
}
}
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertFullyConnected(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
ConstTensorPin weightsPin = DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 1);
ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data); // 1D
if (!weightsPin.IsValid())
{
return Fail("%s: Operation has invalid weights", __func__);
}
if (!biasPin.IsValid())
{
return Fail("%s: Operation has invalid bias", __func__);
}
armnn::ConstTensor weights = weightsPin.GetConstTensor();
armnn::ConstTensor bias = biasPin.GetConstTensor();
armnn::TensorInfo reshapedInfo = inputInfo;
try
{
reshapedInfo.SetShape(FlattenFullyConnectedInput(inputInfo.GetShape(), weights.GetInfo().GetShape()));
}
catch (const std::exception& e)
{
return Fail("%s: %s", __func__, e.what());
}
// 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<HalPolicy>(operation, 3, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
armnn::FullyConnectedDescriptor desc;
desc.m_TransposeWeightMatrix = true;
desc.m_BiasEnabled = true;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
if (!VerifyFullyConnectedShapes(reshapedInfo.GetShape(),
weights.GetInfo().GetShape(),
outputInfo.GetShape(),
desc.m_TransposeWeightMatrix))
{
isSupported = false;
Fail("%s: Expected outputShape does not match actual outputShape", __func__);
return;
}
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsFullyConnectedSupported,
data.m_Backends,
isSupported,
reshapedInfo,
outputInfo,
weights.GetInfo(),
bias.GetInfo(),
desc);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* startLayer =
data.m_Network->AddFullyConnectedLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
if (inputInfo.GetNumDimensions() > 2U)
{
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
armnn::IConnectableLayer* reshapeLayer = data.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<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertL2Normalization(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
if (operation.inputs.size() != 1)
{
return Fail("%s: Optional inputs are not supported", __func__);
}
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
if (outputInfo.GetNumDimensions() != 4u)
{
return Fail("%s: Tensor Rank other than 4 is not supported", __func__);
}
armnn::L2NormalizationDescriptor desc;
desc.m_DataLayout = armnn::DataLayout::NHWC;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsL2NormalizationSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
desc);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddL2NormalizationLayer(desc);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertLocalResponseNormalization(const HalOperation& operation,
const HalModel& model,
ConversionData& data)
{
if (operation.inputs.size() != 5)
{
return Fail("%s: Optional inputs are not supported", __func__);
}
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
if (outputInfo.GetNumDimensions() != 4u)
{
return Fail("%s: Tensor Rank other than 4 is not supported", __func__);
}
armnn::NormalizationDescriptor descriptor;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
if (!input.IsValid() ||
!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, descriptor.m_NormSize, model, data) ||
!GetInputFloat32<HalPolicy>(operation, 2, descriptor.m_K, model, data) ||
!GetInputFloat32<HalPolicy>(operation, 3, descriptor.m_Alpha, model, data) ||
!GetInputFloat32<HalPolicy>(operation, 4, descriptor.m_Beta, model, data))
{
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);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsNormalizationSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddNormalizationLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertLogistic(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::Sigmoid;
return ConvertToActivation<HalPolicy>(operation, __func__, desc, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertMean(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
const HalOperand* axisOperand = GetInputOperand<HalPolicy>(operation, 1, model);
if (!axisOperand)
{
return Fail("%s: Could not read input 1", __func__);
}
std::vector<int32_t> axis;
if (!GetTensorInt32Values<HalPolicy>(*axisOperand, axis, model, data))
{
return Fail("%s: Input 1 has invalid values", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
// Convert the axis to unsigned int and remove duplicates.
unsigned int rank = inputInfo.GetNumDimensions();
std::set<unsigned int> uniqueAxis;
std::transform(axis.begin(), axis.end(),
std::inserter(uniqueAxis, uniqueAxis.begin()),
[rank](int i) -> unsigned int { return (i + rank) % rank; });
// Get the "keep dims" flag.
int32_t keepDims = 0;
if (!GetInputInt32<HalPolicy>(operation, 2, keepDims, model, data))
{
return Fail("%s: Could not read input 2", __func__);
}
armnn::MeanDescriptor descriptor;
descriptor.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end());
descriptor.m_KeepDims = keepDims > 0;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsMeanSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddMeanLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertMul(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input0 = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
LayerInputHandle input1 = ConvertToLayerInputHandle<HalPolicy>(operation, 1, model, data);
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<HalPolicy>(operation, 2, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
if (outputOperand == nullptr)
{
return false;
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsMultiplicationSupported,
data.m_Backends,
isSupported,
input0.GetTensorInfo(),
input1.GetTensorInfo(),
outputInfo);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const startLayer = data.m_Network->AddMultiplicationLayer();
const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
bool isReshapeSupported = BroadcastTensor(input0, input1, startLayer, data);
if (!isReshapeSupported)
{
return false;
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertPad(HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
armnn::PadDescriptor descriptor;
if (!ConvertPaddings<HalPolicy>(operation, model, data, rank, descriptor))
{
return Fail("%s: Could not convert paddings", __func__);
}
// For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor,
// the scale and zeroPoint must be the same as input0
// Before Android Q, the pad value for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM was undefined. Since Android Q the pad
// value must be "logical zero" we set it to be equal to the QuantizationOffset so effectively it ends up as
// (QuantizationOffset - QuantizationOffset) * scale = 0.
if (inputInfo.GetDataType() == armnn::DataType::QAsymmU8 || inputInfo.GetDataType() == armnn::DataType::QAsymmS8)
{
descriptor.m_PadValue = inputInfo.GetQuantizationOffset();
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsPadSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertReshape(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
const HalOperand* inputOperand = GetInputOperand<HalPolicy>(operation, 0, model);
const HalOperand* requestedShapeOperand = GetInputOperand<HalPolicy>(operation, 1, model);
const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
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<HalPolicy>(*requestedShapeOperand, targetDimensions, model, data))
{
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__);
}
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Could not read input 0", __func__);
}
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(),
requestedShape.dimensions.data());
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsReshapeSupported,
data.m_Backends,
isSupported,
input.GetTensorInfo(),
outputInfo,
reshapeDescriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertSub(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input0 = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
LayerInputHandle input1 = ConvertToLayerInputHandle<HalPolicy>(operation, 1, model, data);
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<HalPolicy>(operation, 2, activationFunction, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsSubtractionSupported,
data.m_Backends,
isSupported,
input0.GetTensorInfo(),
input1.GetTensorInfo(),
outputInfo);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const startLayer = data.m_Network->AddSubtractionLayer();
const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
bool isReshapeSupported = BroadcastTensor(input0, input1, startLayer, data);
if (!isReshapeSupported)
{
return false;
}
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *startLayer, model,
data, nullptr, validateFunc, activationFunction);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertSqueeze(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
if (rank > 4)
{
Fail("%s: Inputs with rank greater than 4 are not supported", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
if (IsDynamicTensor(GetTensorInfoForOperand(*output)) && !(AreDynamicTensorsSupported()))
{
return Fail("%s: Dynamic output tensors are not supported", __func__);
}
// NOTE: Axis is an optional parameter to SQUEEZE, therefore we do not want to generate a failure
// if the operand index is out of bounds.
const HalOperand* axisOperand = GetInputOperand<HalPolicy>(operation, 1, model, false);
const uint32_t dimensionSequence[] = { 0, 1, 2, 3 };
std::vector<int32_t> axis;
if (!axisOperand)
{
axis.assign(dimensionSequence,
dimensionSequence + rank);
}
else if (!GetTensorInt32Values<HalPolicy>(*axisOperand, axis, model, data))
{
return Fail("%s: Operation has an invalid or unsupported axis operand", __func__);
}
std::vector<uint32_t> outputDims;
for (unsigned int i = 0; i < rank; i++)
{
bool skipSqueeze = (std::find(axis.begin(), axis.end(), i) == axis.end());
auto currentDimension = inputInfo.GetShape()[i];
if (skipSqueeze || currentDimension != 1)
{
outputDims.push_back(currentDimension);
}
}
armnn::TensorShape outShape = armnn::TensorShape(outputDims.size(), outputDims.data());
armnn::TensorInfo outputInfo = inputInfo;
outputInfo.SetShape(outShape);
armnn::ReshapeDescriptor reshapeDesc;
reshapeDesc.m_TargetShape = outputInfo.GetShape();
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsReshapeSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
reshapeDesc);
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddReshapeLayer(reshapeDesc);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertStridedSlice(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
if (rank > 4)
{
Fail("%s: Inputs with rank greater than 4 are not supported", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
const HalOperand* beginOperand = GetInputOperand<HalPolicy>(operation, 1, model);
const HalOperand* endOperand = GetInputOperand<HalPolicy>(operation, 2, model);
const HalOperand* stridesOperand = GetInputOperand<HalPolicy>(operation, 3, model);
std::vector<int32_t> beginValues;
std::vector<int32_t> endValues;
std::vector<int32_t> stridesValues;
// The length of the beginOperand, endOperand and stridesOperand must be of a rank(input)
auto ValidateInputOperands = [&] (const HalOperand& operand, std::vector<int32_t>& operandValues)
{
if (!GetTensorInt32Values<HalPolicy>(operand, operandValues, model, data))
{
return false;
}
if (operandValues.size() != rank)
{
return false;
}
return true;
};
if (!ValidateInputOperands(*beginOperand, beginValues)
|| !ValidateInputOperands(*endOperand, endValues)
|| !ValidateInputOperands(*stridesOperand, stridesValues))
{
return Fail("%s: Operation has invalid input operand", __func__);
}
// Stride cannot have value '0'
if (std::any_of(stridesValues.cbegin(), stridesValues.cend(), [](int32_t i){ return i == 0; }))
{
return Fail("%s: Stride must be non-zero value.", __func__);
}
armnn::StridedSliceDescriptor descriptor;
descriptor.m_Begin.assign(beginValues.cbegin(), beginValues.cend());
descriptor.m_End.assign(endValues.cbegin(), endValues.cend());
descriptor.m_Stride.assign(stridesValues.cbegin(), stridesValues.cend());
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
// Get the "begin_mask", "end_mask", and "shrink_axis_mask" flags
if (!GetInputInt32<HalPolicy>(operation, 4, descriptor.m_BeginMask, model, data) ||
!GetInputInt32<HalPolicy>(operation, 5, descriptor.m_EndMask, model, data) ||
!GetInputInt32<HalPolicy>(operation, 6, descriptor.m_ShrinkAxisMask, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsStridedSliceSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
// Check if slice can fit in a inferred output
armnn::TensorShape inputShape = inputInfo.GetShape();
for (unsigned int i = 0; i < inputShape.GetNumDimensions(); i++)
{
int stride = descriptor.m_Stride[i];
int start = descriptor.GetStartForAxis(inputShape, i);
int stop = descriptor.GetStopForAxis(inputShape, i, start);
if (descriptor.m_ShrinkAxisMask & (1 << i))
{
// If the difference between the start point and the end point of the slice on an axis being shrunk
// is greater than 1 then throw an error as the output will not be large enough to hold the slice
if (((descriptor.m_Begin[i] - descriptor.m_End[i]) > 1)
|| ((descriptor.m_Begin[i] - descriptor.m_End[i]) < -1))
{
return Fail("%s: StridedSlice: Output will not be large enough to hold the slice", __func__);
}
if(stride < 0)
{
return Fail("%s: StridedSlice: Stride can not be negative while ShrinkAxisMask is set.", __func__);
}
}
}
armnn::IConnectableLayer* const layer = data.m_Network->AddStridedSliceLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertTranspose(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandLifeTime = typename HalPolicy::OperandLifeTime;
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
if (rank > 4)
{
Fail("%s: Inputs with rank greater than 4 are not supported", __func__);
}
// NOTE: Axis is an optional parameter to TRANSPOSE, therefore we do not want to generate a failure
// if the operand index is out of bounds.
const HalOperand* permOperand = GetInputOperand<HalPolicy>(operation, 1, model, false);
std::vector<int32_t> perm(rank);
if (!permOperand || (permOperand->lifetime == HalOperandLifeTime::NO_VALUE))
{
for (unsigned int i = rank; i > 0; i--)
{
perm[rank - i] = armnn::numeric_cast<int> (i - 1);
}
}
else if (!GetTensorInt32Values<HalPolicy>(*permOperand, perm, model, data))
{
return Fail("%s: Operation has an invalid or unsupported permutation operand", __func__);
}
std::vector<uint32_t> outputDims(perm.begin(), perm.begin() + rank);
armnn::TransposeDescriptor transposeDesc;
transposeDesc.m_DimMappings = armnn::PermutationVector(outputDims.data(), outputDims.size());
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsTransposeSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
transposeDesc);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddTransposeLayer(transposeDesc);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalOperand = typename HalPolicy::Operand,
typename HalModel = typename HalPolicy::Model>
bool ConvertBatchToSpaceNd(const HalOperation& operation,
const HalModel& model,
ConversionData& data)
{
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
const HalOperand* blockOperand = GetInputOperand<HalPolicy>(operation, 1, model);
if (!blockOperand)
{
return Fail("%s: Could not read input 1", __func__);
}
// Convert the block operand to int32
std::vector<int32_t> block;
if (!GetTensorInt32Values<HalPolicy>(*blockOperand, block, model, data))
{
return Fail("%s: Input 1 has invalid values", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
if (rank != 4)
{
Fail("%s: Only inputs with rank equal to 4 are supported", __func__);
}
if (std::any_of(block.cbegin(), block.cend(), [](int32_t i){ return i < 1; }))
{
return Fail("%s: Block sizes for each spatial dimension of the input tensor must be"
" greater than or equal to 1", __func__);
}
armnn::BatchToSpaceNdDescriptor batchToSpaceNdDesc;
batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend());
batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC;
if (Is12OrLaterOperand(*output))
{
batchToSpaceNdDesc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 2, model, data);
}
// Setting crops to 0,0 0,0 as it is not supported in Android NN API
batchToSpaceNdDesc.m_Crops = {{0, 0}, {0, 0}};
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsBatchToSpaceNdSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
batchToSpaceNdDesc);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddBatchToSpaceNdLayer(batchToSpaceNdDesc);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalOperand = typename HalPolicy::Operand,
typename HalModel = typename HalPolicy::Model>
bool ConvertSpaceToBatchNd(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
unsigned int rank = inputInfo.GetNumDimensions();
unsigned int spatialDim = rank - 2;
if (rank != 4)
{
Fail("%s: Only inputs with rank 4 are supported", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
const HalOperand* blockShapeOperand = GetInputOperand<HalPolicy>(operation, 1, model);
const HalOperand* paddingsOperand = GetInputOperand<HalPolicy>(operation, 2, model);
armnn::TensorShape blockShapeOperandShape = GetTensorShapeForOperand(*blockShapeOperand);
if (blockShapeOperandShape.GetNumDimensions() != 1 || blockShapeOperandShape.GetNumElements() != spatialDim)
{
return Fail("%s: Operation has invalid block shape operand: expected shape [%d]", __func__, spatialDim);
}
std::vector<int32_t> blockShape;
if (!GetTensorInt32Values<HalPolicy>(*blockShapeOperand, blockShape, model, data))
{
return Fail("%s: Operation has an invalid or unsupported block size operand", __func__);
}
if (std::any_of(blockShape.cbegin(), blockShape.cend(), [](int32_t i){ return i < 1; }))
{
return Fail("%s: Block shape must be at least 1 in all dimensions.", __func__);
}
armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand);
if (paddingsOperandShape.GetNumDimensions() != 2 || paddingsOperandShape.GetNumElements() != 2 * spatialDim)
{
return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, spatialDim);
}
std::vector<std::pair<unsigned int, unsigned int>> paddingList;
std::vector<int32_t> paddings;
if (!GetTensorInt32Values<HalPolicy>(*paddingsOperand, paddings, model, data))
{
return Fail("%s: Operation has an invalid or unsupported paddings operand", __func__);
}
for (unsigned int i = 0; i < paddings.size() - 1; i += 2)
{
int paddingBeforeInput = paddings[i];
int paddingAfterInput = paddings[i + 1];
if (paddingBeforeInput < 0 || paddingAfterInput < 0)
{
return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__);
}
paddingList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput);
}
armnn::SpaceToBatchNdDescriptor descriptor;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
descriptor.m_BlockShape.assign(blockShape.cbegin(), blockShape.cend());
descriptor.m_PadList.assign(paddingList.cbegin(), paddingList.cend());
if (Is12OrLaterOperand(*output))
{
descriptor.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 3, model, data);
}
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsSpaceToBatchNdSupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(IsDynamicTensor(outputInfo))
{
isSupported = AreDynamicTensorsSupported();
}
else
{
validateFunc(outputInfo, isSupported);
}
if (!isSupported)
{
return false;
}
armnn::IConnectableLayer* const layer = data.m_Network->AddSpaceToBatchNdLayer(descriptor);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
} // namespace armnn_driver
#ifdef __clang__
#pragma clang diagnostic pop
#endif