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ConversionUtils_1_2.hpp
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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "Utils.hpp"
#include "ConversionUtils.hpp"
#include <armnn/utility/NumericCast.hpp>
#include <armnnUtils/TensorUtils.hpp>
#include <half/half.hpp>
using Half = half_float::half;
namespace armnn_driver
{
using namespace armnn;
using namespace android::nn;
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool IsQSymmDequantizeForWeights(const HalOperation& operation, const HalModel& model)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperationType = typename HalPolicy::OperationType;
const HalOperand* operand = GetInputOperand<HalPolicy>(operation, 0, model);
if (!operand)
{
return false;
}
if(!IsQSymm8(*operand))
{
// Only QSymm8 weights are dequantized on the fly by the driver
return false;
}
if (!IsOperandConstant<HalPolicy>(*operand))
{
// Non-const input is not accepted for weights
return false;
}
// Iterate through all the operations and find the operation feeding from the Dequantize output
const size_t outputIndex = operation.outputs[0];
for (uint32_t operationIdx = 0; operationIdx < getMainModel(model).operations.size(); ++operationIdx)
{
const auto& operationIt = getMainModel(model).operations[operationIdx];
switch (operationIt.type)
{
case HalOperationType::FULLY_CONNECTED:
if (outputIndex == operationIt.inputs[1]) // Weights are bound to slot 1
{
// If the output is going into the FC weights return true
return true;
}
break;
case HalOperationType::LSTM:
for (size_t k = 0; k < operationIt.inputs.size(); ++k)
{
if (outputIndex == operationIt.inputs[k])
{
// If the output is going into the LSTM weights return true
return true;
}
}
break;
default:
break;
}
}
return false;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool SetupAndTrackLayerOutputSlotAndOverrideTensorInfo(const HalOperation& operation,
uint32_t operationOutputIndex,
armnn::IConnectableLayer& layer,
uint32_t layerOutputIndex,
const HalModel& model,
ConversionData& data,
const armnn::TensorInfo tensor_info)
{
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);
const uint32_t operandIndex = operation.outputs[operationOutputIndex];
data.m_OutputSlotForOperand[operandIndex] = &outputSlot;
outputSlot.SetTensorInfo(tensor_info);
return true;
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertComparison_1_2(const HalOperation& operation,
const HalModel& model,
ConversionData& data,
ComparisonOperation comparisonOperation)
{
using HalOperand = typename HalPolicy::Operand;
ALOGV("HalPolicy::ConvertComparison()");
ALOGV("comparisonOperation = %s", GetComparisonOperationAsCString(comparisonOperation));
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__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const TensorInfo& inputInfo0 = input0.GetTensorInfo();
const TensorInfo& inputInfo1 = input1.GetTensorInfo();
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
ComparisonDescriptor descriptor(comparisonOperation);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsComparisonSupported,
data.m_Backends,
isSupported,
inputInfo0,
inputInfo1,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
IConnectableLayer* layer = data.m_Network->AddComparisonLayer(descriptor);
assert(layer != nullptr);
bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data);
if (!isReshapeSupported)
{
return false;
}
if(IsDynamicTensor(outputInfo))
{
input0.Connect(layer->GetInputSlot(0));
input1.Connect(layer->GetInputSlot(1));
}
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_1_2(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
ALOGV("HalPolicy::ConvertConv2d_1_2()");
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 TensorInfo& inputInfo = input.GetTensorInfo();
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
Convolution2dDescriptor desc;
desc.m_DataLayout = DataLayout::NHWC;
// Determine whether padding is implicit or explicit
bool implicitPadding = operation.inputs.size() == 7 ||
(operation.inputs.size() >= 8 &&
GetInputOperand<HalPolicy>(operation, 7, model)->type == HalOperandType::BOOL);
if (implicitPadding)
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 7, model, data);
}
else if (operation.inputs.size() >= 10)
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 10, model, data);
}
const PermutationVector OHWIToOIHW = {0, 2, 3, 1};
// ArmNN does not currently support non-fixed weights or bias
// The NNAPI filter is always OHWI [depth_out, filter_height, filter_width, depth_in] but ArmNN expects the
// filter's height and width indices to match the input's height and width indices so we permute it to OIHW if
// the DataLayout is NCHW
const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ?
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1,
model, data, OHWIToOIHW) :
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1, model, data);
const ConstTensorPin biasPin =
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
if (!weightsPin.IsValid())
{
return Fail("%s: Operation has invalid weights", __func__);
}
if (!biasPin.IsValid())
{
return Fail("%s: Operation has invalid biases", __func__);
}
ConstTensor weights = weightsPin.GetConstTensor();
ConstTensor bias = biasPin.GetConstTensor();
SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
ActivationFn activation;
if (implicitPadding)
{
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) ||
!GetOptionalConvolutionDilationParams<HalPolicy>(operation, 8, desc, model, data))
{
return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
}
armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
const uint32_t kernelX = weights.GetShape()[widthIndex];
const uint32_t kernelY = weights.GetShape()[heightIndex];
const uint32_t inputX = inputInfo.GetShape()[widthIndex];
const uint32_t inputY = inputInfo.GetShape()[heightIndex];
CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
}
else if (operation.inputs.size() >= 10)
{
// explicit padding
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) ||
!GetOptionalConvolutionDilationParams<HalPolicy>(operation, 11, desc, model, data))
{
return Fail("%s: Operation has invalid inputs (explicit padding)", __func__);
}
}
else
{
return Fail("%s: Unsupported number of operation inputs", __func__);
}
desc.m_BiasEnabled = true;
Optional<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;
}
IConnectableLayer* startLayer =
data.m_Network->AddConvolution2dLayer(desc, weights, Optional<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 ConvertDepthwiseConv2d_1_2(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
ALOGV("HalPolicy::ConvertDepthwiseConv2d_1_2()");
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 TensorInfo& inputInfo = input.GetTensorInfo();
const 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__);
}
if ( weightsOperand->dimensions[0] != 1)
{
return Fail("%s: Invalid weights; for depthwise convolution, dimension 0 must be 1 but it is %i",
__func__, weightsOperand->dimensions[0] );
}
DepthwiseConvolution2dDescriptor desc;
desc.m_DataLayout = DataLayout::NHWC;
// Determine whether padding is implicit or explicit
bool implicitPadding = operation.inputs.size() == 8 ||
(operation.inputs.size() >= 9 &&
GetInputOperand<HalPolicy>(operation, 8, model)->type == HalOperandType::BOOL);
// Look ahead to find the optional DataLayout, if present
const uint32_t dataLayoutFlagIndex = implicitPadding ? 8 : 11;
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, dataLayoutFlagIndex, model, data);
armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
// Reinterpret weight data as [ H, W, I, M ]
TensorShape weightsShape({ weightsOperand->dimensions[1],
weightsOperand->dimensions[2],
inputInfo.GetShape()[channelsIndex],
weightsOperand->dimensions[3] / inputInfo.GetShape()[channelsIndex] });
// Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
const 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())
{
return Fail("%s: Operation has invalid weights", __func__);
}
if (!biasPin.IsValid())
{
return Fail("%s: Operation has invalid biases", __func__);
}
ConstTensor weights = weightsPin.GetConstTensor();
ConstTensor bias = biasPin.GetConstTensor();
SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
ActivationFn activation;
if (implicitPadding)
{
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) ||
!GetOptionalConvolutionDilationParams<HalPolicy>(operation, 9, desc, model, data))
{
return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
}
const uint32_t kernelX = weights.GetShape()[3];
const uint32_t kernelY = weights.GetShape()[2];
const uint32_t inputX = inputInfo.GetShape()[widthIndex];
const uint32_t inputY = inputInfo.GetShape()[heightIndex];
CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
}
else if (operation.inputs.size() >= 11)
{
// explicit padding
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) ||
!GetOptionalConvolutionDilationParams<HalPolicy>(operation, 12, desc, model, data))
{
return Fail("%s: Operation has invalid inputs (explicit padding)", __func__);
}
}
else
{
return Fail("%s: Unsupported number of operation inputs", __func__);
}
desc.m_BiasEnabled = true;
Optional<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;
}
IConnectableLayer* startLayer =
data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, Optional<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_1_2(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
ALOGV("HalPolicy::ConvertDequantize()");
if (IsQSymmDequantizeForWeights<HalPolicy>(operation, model))
{
// NOTE: QSymm8 weights are dequantized internally by the driver,
// therefore this type of Dequantize is implicitly supported
return true;
}
return ::ConvertDequantize<HalPolicy>(operation, model, data);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertElementwiseUnary(const HalOperation& operation,
const HalModel& model,
ConversionData& data,
UnaryOperation unaryOperation)
{
using HalOperand = typename HalPolicy::Operand;
ALOGV("HalPolicy::ConvertElementwiseUnary()");
ALOGV("unaryOperation = %s", GetUnaryOperationAsCString(unaryOperation));
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid input", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
const TensorInfo& inputInfo = input.GetTensorInfo();
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
ElementwiseUnaryDescriptor descriptor(unaryOperation);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsElementwiseUnarySupported,
data.m_Backends,
isSupported,
inputInfo,
outputInfo,
descriptor);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
IConnectableLayer* layer = data.m_Network->AddElementwiseUnaryLayer(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 ConvertExpandDims(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
ALOGV("HalPolicy::ConvertExpandDims()");
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid input", __func__);
}
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Operation has invalid output", __func__);
}
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
int32_t axis;
if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, axis, model, data))
{
return Fail("%s: failed to get axis input value", __func__);
}
TensorShape targetShape;
try
{
targetShape = armnnUtils::ExpandDims(input.GetTensorInfo().GetShape(), axis);
}
catch (const std::exception& e)
{
return Fail("%s: %s", __func__, e.what());
}
if (targetShape != outputInfo.GetShape())
{
return Fail("%s: Shape of the output operand does not match the resolved expanded shape", __func__);
}
ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = targetShape;
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;
}
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 ConvertGather(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
ALOGV("HalPolicy::ConvertGather()");
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid input", __func__);
}
auto inputDimensions = input.GetTensorInfo().GetNumDimensions();
LayerInputHandle indices = ConvertToLayerInputHandle<HalPolicy>(operation, 2, model, data);
if (!indices.IsValid())
{
return Fail("%s: Operation has invalid indices", __func__);
}
auto indicesDimensions = indices.GetTensorInfo().GetNumDimensions();
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Operation has invalid output", __func__);
}
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
auto outputDimensions = outputInfo.GetNumDimensions();
if (outputDimensions != inputDimensions + indicesDimensions - 1)
{
return Fail("%s: Operation has invalid output dimensions: %d. Output must be an (%d + %d - 1)-D tensor",
__func__, outputDimensions, inputDimensions, indicesDimensions);
}
int32_t axis;
if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, axis, model, data))
{
return Fail("%s: Operation has invalid or unsupported axis operand", __func__);
}
if (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0)))
{
return Fail("%s: Operation has invalid axis: %d. It is out of bounds [-%d, %d))", __func__, axis,
inputDimensions, inputDimensions);
}
GatherDescriptor desc;
desc.m_Axis = axis;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsGatherSupported,
data.m_Backends,
isSupported,
input.GetTensorInfo(),
indices.GetTensorInfo(),
outputInfo,
desc);
};
if(!IsDynamicTensor(outputInfo))
{
validateFunc(outputInfo, isSupported);
}
else
{
isSupported = AreDynamicTensorsSupported();
}
if (!isSupported)
{
return false;
}
IConnectableLayer* layer = data.m_Network->AddGatherLayer(desc);
assert(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
indices.Connect(layer->GetInputSlot(1));
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
typename HalOperation = typename HalPolicy::Operation,
typename HalModel = typename HalPolicy::Model>
bool ConvertGroupedConv2d(const HalOperation& operation, const HalModel& model, ConversionData& data)
{
using HalOperand = typename HalPolicy::Operand;
using HalOperandType = typename HalPolicy::OperandType;
ALOGV("HalPolicy::ConvertGroupedConv2d()");
//
// Parse data
//
LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
if (!input.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
const TensorInfo& inputInfo = input.GetTensorInfo();
const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
if (!output)
{
return Fail("%s: Could not read output 0", __func__);
}
TensorInfo outputInfo = GetTensorInfoForOperand(*output);
// Look ahead to determine data layout
DataLayout dataLayout = DataLayout::NHWC;
if (operation.inputs.size() == 12)
{
dataLayout = OptionalDataLayout<HalPolicy>(operation, 11, model, data);
}
else
{
dataLayout = OptionalDataLayout<HalPolicy>(operation, 8, model, data);
}
// NOTE:
// NNAPI weights are always OHWI, i.e. [depth_out, filter_height, filter_width, depth_group],
// but Arm NN expects the filter's height and width indices to match the input's height and
// width indices so when the DataLayout is NCHW, we need to permute the weights to OIHW
const PermutationVector ohwiToOihw = { 0u, 2u, 3u, 1u };
const ConstTensorPin weightsPin = (dataLayout == DataLayout::NCHW) ?
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1,
model, data, ohwiToOihw) :
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1, model, data);
const ConstTensorPin biasesPin =
ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
if (!weightsPin.IsValid() || !biasesPin.IsValid())
{
return Fail("%s: Operation has invalid inputs", __func__);
}
ConstTensor weights = weightsPin.GetConstTensor();
ConstTensor biases = biasesPin.GetConstTensor();
SanitizeBiasQuantizationScale(biases.GetInfo(), weights.GetInfo(), inputInfo);
const TensorShape& inputShape = inputInfo.GetShape();
const TensorShape& outputShape = outputInfo.GetShape();
const TensorShape& weightsShape = weights.GetShape();
const TensorShape& biasesShape = biases.GetShape();
armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
Convolution2dDescriptor desc;
desc.m_DataLayout = dataLayout;
desc.m_BiasEnabled = true;
int numGroups;
ActivationFn activation;
if (operation.inputs.size() == 12)
{
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) ||
!GetInputScalar<HalPolicy>(operation, 9, HalOperandType::INT32, numGroups, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 10, activation, model, data))
{
return Fail("%s: Operation has invalid inputs (explicit padding)", __func__);
}
}
else if (operation.inputs.size() == 9)
{
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) ||
!GetInputScalar<HalPolicy>(operation, 6, HalOperandType::INT32, numGroups, model, data) ||
!GetInputActivationFunction<HalPolicy>(operation, 7, activation, model, data))
{
return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
}
const uint32_t inputX = inputInfo.GetShape()[widthIndex];
const uint32_t inputY = inputInfo.GetShape()[heightIndex];
const uint32_t kernelX = weightsShape[widthIndex];
const uint32_t kernelY = weightsShape[heightIndex];
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__);
}
// Equivalent to outputShape[channelsIndex], but we can't know the outputShape in the case of dynamic tensors
const unsigned int outputChannels = weightsShape[0];
const unsigned int channelsPerGroup = weightsShape[channelsIndex];
const unsigned int channelMultiplier = outputChannels / numGroups;
//
// Validate all relevant inputs
//
if (numGroups <= 0)
{
return Fail("%s: Number of groups must be greater than 0. Got: %d", __func__, numGroups);
}
if (outputChannels % numGroups != 0u)
{
return Fail("%s: Output channels must be divisible by the number of groups", __func__);
}
//
// Set up Splitter layer
//
unsigned int splitterDimSizes[4] = { inputShape[0], inputShape[1], inputShape[2], inputShape[3] };
splitterDimSizes[channelsIndex] /= numGroups; // split in depth
TensorInfo splitterOutputInfo(4,
splitterDimSizes,
inputInfo.GetDataType(),
inputInfo.GetQuantizationScale(),
inputInfo.GetQuantizationOffset());
std::vector<std::reference_wrapper<TensorInfo>> splitterOutputInfos(numGroups, std::ref(splitterOutputInfo));
ViewsDescriptor splitterDesc(numGroups);
for (unsigned int group = 0u; group < numGroups; ++group)
{
splitterDesc.SetViewOriginCoord(group, channelsIndex, splitterDimSizes[channelsIndex] * group);
for (unsigned int dimIdx = 0u; dimIdx < 4u; dimIdx++)
{
splitterDesc.SetViewSize(group, dimIdx, splitterDimSizes[dimIdx]);
}
}
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC(__func__,
IsSplitterSupported,
data.m_Backends,
isSupported,
inputInfo,
splitterOutputInfos,
splitterDesc);
if (!isSupported)
{
return false;
}
IConnectableLayer* splitterLayer = data.m_Network->AddSplitterLayer(splitterDesc);
if (!splitterLayer)
{
return Fail("%s: Failed to add SplitterLayer", __func__);
}
input.Connect(splitterLayer->GetInputSlot(0));
for (unsigned int group = 0u; group < splitterLayer->GetNumOutputSlots(); ++group)
{
splitterLayer->GetOutputSlot(group).SetTensorInfo(splitterOutputInfo);
}
//
// Set up Convolution2d layers for each group
//
// Set up group tensor shapes
TensorShape groupInputShape(inputShape);
groupInputShape[channelsIndex] = channelsPerGroup;
TensorShape groupWeightsShape(weightsShape);
groupWeightsShape[0] /= channelMultiplier * numGroups;
TensorShape groupBiasesShape({ 1 });
// Set up group tensor infos
TensorInfo groupInputInfo(inputInfo);
groupInputInfo.SetShape(groupInputShape);
const TensorInfo& weightsInfo = weights.GetInfo();
TensorInfo groupWeightsInfo(weightsInfo);
groupWeightsInfo.SetShape(groupWeightsShape);
const TensorInfo& biasesInfo = biases.GetInfo();
TensorInfo groupBiasesInfo(biasesInfo);
groupBiasesInfo.SetShape(groupBiasesShape);
TensorInfo groupOutputInfo(outputInfo);
TensorShape groupOutputShape(outputShape);
const bool isDynamic = IsDynamicTensor(outputInfo);
if (!isDynamic)
{
groupOutputShape[channelsIndex] = 1;
}
groupOutputInfo.SetShape(groupOutputShape);