IVGCVSW-4447 Add Hal 1_3 Support
* Add new 1.3 files HalPolicy, ArmnnDriver, ArmnnDriverImpl
* Add new .rc file for 1.3 service
* Add ArmnnPreparedModel_1_3 and implement new functions
* Update Android.mk with 1.3 driver and service
* Refactor ifdef to include ARMNN_ANDROID_NN_V1_3
* Create Utils getMainModel for new 1.3 Model Main Subgraph
* Use android Utils to convertToV1_X in ArmnnPrepapredModel_1_3
* Refactor HAL 1.2 convert functions into ConversionUtils_1_2.hpp
* Replace ArmnnBurstExecutorWithCache with call to ExecutionBurstServer
Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I514069e9e1b16bcd1c4abfb5d563d25ac22d02e3
diff --git a/1.0/HalPolicy.hpp b/1.0/HalPolicy.hpp
index 9eb13b4..25bc47c 100644
--- a/1.0/HalPolicy.hpp
+++ b/1.0/HalPolicy.hpp
@@ -26,6 +26,7 @@
using Operation = V1_0::Operation;
using OperationType = V1_0::OperationType;
using getSupportedOperations_cb = V1_0::IDevice::getSupportedOperations_cb;
+ using ErrorStatus = V1_0::ErrorStatus;
static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
diff --git a/1.1/HalPolicy.hpp b/1.1/HalPolicy.hpp
index 806686b..18bb705 100644
--- a/1.1/HalPolicy.hpp
+++ b/1.1/HalPolicy.hpp
@@ -26,6 +26,7 @@
using Operation = V1_1::Operation;
using OperationType = V1_1::OperationType;
using getSupportedOperations_cb = V1_1::IDevice::getSupportedOperations_1_1_cb;
+ using ErrorStatus = V1_0::ErrorStatus;
static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index ca92318..9e547fa 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -4,17 +4,6 @@
//
#include "HalPolicy.hpp"
-#include "Utils.hpp"
-
-#include <armnn/TypesUtils.hpp>
-
-#include <armnnUtils/DataLayoutIndexed.hpp>
-#include <armnnUtils/TensorUtils.hpp>
-
-#include <Half.hpp>
-
-#include <cmath>
-#include <string>
namespace armnn_driver
{
@@ -26,58 +15,6 @@
namespace
{
-bool IsQSymmDequantizeForWeights(const HalPolicy::Operation& operation, const HalPolicy::Model& model)
-{
- const HalPolicy::Operand* operand = GetInputOperand<hal_1_2::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<hal_1_2::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 < model.operations.size(); ++operationIdx)
- {
- const auto& operationIt = model.operations[operationIdx];
- switch (operationIt.type)
- {
- case HalPolicy::OperationType::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 HalPolicy::OperationType::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;
-}
-
} // anonymous namespace
bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data)
@@ -237,57 +174,7 @@
ComparisonOperation comparisonOperation)
{
ALOGV("hal_1_2::HalPolicy::ConvertComparison()");
- ALOGV("comparisonOperation = %s", GetComparisonOperationAsCString(comparisonOperation));
-
- LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- if (!(input0.IsValid() && input1.IsValid()))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- ComparisonDescriptor descriptor(comparisonOperation);
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsComparisonSupported,
- data.m_Backends,
- isSupported,
- inputInfo0,
- inputInfo1,
- outputInfo,
- descriptor);
-
- 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;
- }
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertComparison_1_2<hal_1_2::HalPolicy>(operation, model, data, comparisonOperation);
}
bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
@@ -299,153 +186,7 @@
bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertConv2d()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- 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<hal_1_2::HalPolicy>(operation, 7, model)->type == OperandType::BOOL);
-
- if (implicitPadding)
- {
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 7, model, data);
- }
- else if (operation.inputs.size() >= 10)
- {
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::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<hal_1_2::HalPolicy>(operation, 1, model, data, OHWIToOIHW) :
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
- const ConstTensorPin biasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 6, activation, model, data) ||
- !GetOptionalConvolutionDilationParams<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 9, activation, model, data) ||
- !GetOptionalConvolutionDilationParams<hal_1_2::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;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsConvolution2dSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- desc,
- weights.GetInfo(),
- biases);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* startLayer =
- data.m_Network->AddConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
-
- if (!startLayer)
- {
- return Fail("%s: AddConvolution2dLayer failed", __func__);
- }
-
- IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
-
- if (!endLayer)
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-
- input.Connect(startLayer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data);
+ return ::ConvertConv2d_1_2<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data)
@@ -457,187 +198,13 @@
bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertDepthwiseConv2d()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
-
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // 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 Operand* weightsOperand = GetInputOperand<hal_1_2::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<hal_1_2::HalPolicy>(operation, 8, model)->type == OperandType::BOOL);
-
- // Look ahead to find the optional DataLayout, if present
- const uint32_t dataLayoutFlagIndex = implicitPadding ? 8 : 11;
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::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<hal_1_2::HalPolicy>(operation,
- 1,
- model,
- data,
- HWIMToMIHW,
- &weightsShape);
-
- // Bias is a 1D tensor
- const ConstTensorPin biasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data) ||
- !GetOptionalConvolutionDilationParams<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 10, activation, model, data) ||
- !GetOptionalConvolutionDilationParams<hal_1_2::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;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsDepthwiseConvolutionSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- desc,
- weights.GetInfo(),
- biases);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* startLayer =
- data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
-
- if (!startLayer)
- {
- return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
- }
-
- IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
- if (!endLayer)
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-
- input.Connect(startLayer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data);
+ return ::ConvertDepthwiseConv2d_1_2<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertDequantize()");
-
- if (IsQSymmDequantizeForWeights(operation, model))
- {
- // NOTE: QSymm8 weights are dequantized internally by the driver,
- // therefore this type of Dequantize is implicitly supported
- return true;
- }
-
- return ::ConvertDequantize<hal_1_2::HalPolicy>(operation, model, data);
+ return ::ConvertDequantize_1_2<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data)
@@ -652,120 +219,13 @@
UnaryOperation unaryOperation)
{
ALOGV("hal_1_2::HalPolicy::ConvertElementwiseUnary()");
- ALOGV("unaryOperation = %s", GetUnaryOperationAsCString(unaryOperation));
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
-
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid input", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- ElementwiseUnaryDescriptor descriptor(unaryOperation);
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsElementwiseUnarySupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- descriptor);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddElementwiseUnaryLayer(descriptor);
- assert(layer != nullptr);
-
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertElementwiseUnary<hal_1_2::HalPolicy>(operation, model, data, unaryOperation);
}
bool HalPolicy::ConvertExpandDims(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertExpandDims()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
-
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid input", __func__);
- }
-
- const Operand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Operation has invalid output", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- int32_t axis;
- if (!GetInputScalar<HalPolicy>(operation, 1, OperandType::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;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsReshapeSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outputInfo,
- reshapeDescriptor);
-
- 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);
+ return ::ConvertExpandDims<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
@@ -783,416 +243,13 @@
bool HalPolicy::ConvertGroupedConv2d(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertGroupedConv2d()");
-
- //
- // Parse data
- //
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
- const TensorInfo& inputInfo = input.GetTensorInfo();
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // Look ahead to determine data layout
- DataLayout dataLayout = DataLayout::NHWC;
- if (operation.inputs.size() == 12)
- {
- dataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 11, model, data);
- }
- else
- {
- dataLayout = OptionalDataLayout<hal_1_2::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<hal_1_2::HalPolicy>(operation, 1, model, data, ohwiToOihw) :
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
- const ConstTensorPin biasesPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 9, OperandType::INT32, numGroups, model, data) ||
- !GetInputActivationFunction<hal_1_2::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<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, numGroups, model, data) ||
- !GetInputActivationFunction<hal_1_2::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__);
- }
-
- const unsigned int outputChannels = outputShape[channelsIndex];
-
- 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 groupOutputShape(outputShape);
- groupOutputShape[channelsIndex] = 1;
-
- 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);
- groupOutputInfo.SetShape(groupOutputShape);
-
- const unsigned int weightsDataTypeSize = GetDataTypeSize(groupWeightsInfo.GetDataType());
- const unsigned int biasesDataTypeSize = GetDataTypeSize(groupBiasesInfo.GetDataType());
-
- std::vector<IConnectableLayer*> convLayers(numGroups * channelMultiplier, nullptr);
- for (unsigned int group = 0u; group < numGroups; ++group)
- {
- for (unsigned int m = 0u; m < channelMultiplier; ++m)
- {
- auto index = group * channelMultiplier + m;
-
- const unsigned int weightsDataOffset = groupWeightsShape.GetNumElements() * index * weightsDataTypeSize;
- const unsigned int biasesDataOffset = groupBiasesShape.GetNumElements() * index * biasesDataTypeSize;
-
- if (weightsInfo.HasPerAxisQuantization())
- {
- // Extract per-axis quantization scales for group weights
- const std::vector<float>& weightsQuantScales = weightsInfo.GetQuantizationScales();
- groupWeightsInfo.SetQuantizationScales(
- std::vector<float>(weightsQuantScales.begin() + index,
- weightsQuantScales.begin() + index + groupWeightsShape[0]));
-
- // Extract per-axis quantization scales for group biases
- const std::vector<float>& biasesQuantScales = biasesInfo.GetQuantizationScales();
- groupBiasesInfo.SetQuantizationScales(
- std::vector<float>(biasesQuantScales.begin() + index,
- biasesQuantScales.begin() + index + groupWeightsShape[0]));
- }
-
- // Extract weights and biases data for current group convolution
- ConstTensor groupWeights(groupWeightsInfo,
- static_cast<const void *>(reinterpret_cast<const char *>(weights.GetMemoryArea()) +
- weightsDataOffset));
- ConstTensor groupBiases(groupBiasesInfo,
- static_cast<const void *>(reinterpret_cast<const char *>(biases.GetMemoryArea()) +
- biasesDataOffset));
-
- isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsConvolution2dSupported,
- data.m_Backends,
- isSupported,
- groupInputInfo,
- groupOutputInfo,
- desc,
- groupWeightsInfo,
- Optional<TensorInfo>(groupBiasesInfo));
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer *convLayer =
- data.m_Network->AddConvolution2dLayer(desc, groupWeights, Optional<ConstTensor>(groupBiases));
- if (!convLayer)
- {
- return Fail("%s: AddConvolution2dLayer failed", __func__);
- }
-
- splitterLayer->GetOutputSlot(group).Connect(convLayer->GetInputSlot(0));
- convLayer->GetOutputSlot(0).SetTensorInfo(groupOutputInfo);
-
- convLayers[index] = convLayer;
- }
- }
-
- //
- // Set up Concat layer
- //
- ConcatDescriptor concatDescriptor(outputInfo.GetShape()[channelsIndex]);
- for (unsigned int group = 0u; group < numGroups; ++group)
- {
- for (unsigned int m = 0u; m < channelMultiplier; ++m)
- {
- auto index = group * channelMultiplier + m;
- concatDescriptor.SetViewOriginCoord(index, channelsIndex, index);
- concatDescriptor.SetConcatAxis(channelsIndex);
- }
- }
-
- isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsConcatSupported,
- data.m_Backends,
- isSupported,
- std::vector<const TensorInfo*>(numGroups * channelMultiplier, &groupOutputInfo),
- outputInfo,
- concatDescriptor);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* concatLayer = data.m_Network->AddConcatLayer(concatDescriptor);
- if (!concatLayer)
- {
- return Fail("%s: AddConcatLayer failed", __func__);
- }
-
- for (unsigned int group = 0u; group < numGroups; ++group)
- {
- for (unsigned int m = 0u; m < channelMultiplier; ++m)
- {
- auto index = group * channelMultiplier + m;
- convLayers[index]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(index));
- }
- }
- concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
- //
- // Set up Activation layer (if it is set)
- //
- IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, concatLayer, data);
- if (!endLayer)
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-
- return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+ return ::ConvertGroupedConv2d<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertInstanceNormalization(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertInstanceNormalization()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has an invalid input 0", __func__);
- }
-
- const Operand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Operation has an invalid output", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // Determine data type of input tensor
- OperandType inputType;
- if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, inputType))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- InstanceNormalizationDescriptor desc;
-
- // Read gamma, beta & epsilon
- if (inputType == OperandType::TENSOR_FLOAT16)
- {
- Half fp16Gamma;
- Half fp16Beta;
- Half fp16Epsilon;
-
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT16, fp16Gamma, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 2, OperandType::FLOAT16, fp16Beta, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::FLOAT16, fp16Epsilon, model, data))
- {
- return Fail("%s: Operation has invalid inputs (FLOAT16)", __func__);
- }
-
- desc.m_Gamma = static_cast<float>(fp16Gamma);
- desc.m_Beta = static_cast<float>(fp16Beta);
- desc.m_Eps = static_cast<float>(fp16Epsilon);
- }
- else if (inputType == OperandType::TENSOR_FLOAT32)
- {
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT32, desc.m_Gamma, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 2, OperandType::FLOAT32, desc.m_Beta, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::FLOAT32, desc.m_Eps, model, data))
- {
- return Fail("%s: Operation has invalid inputs (FLOAT32)", __func__);
- }
- }
- else
- {
- return Fail("%s: Unsupported input tensor type: %d", __func__, inputType);
- }
-
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 4, model, data);
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsInstanceNormalizationSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outputInfo,
- desc);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddInstanceNormalizationLayer(desc);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertInstanceNormalization<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data)
@@ -1224,85 +281,7 @@
bool HalPolicy::ConvertLogSoftmax(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertLogSoftmax()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Failed to read input 0", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Failed to read output", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // Determine data type of input tensor
- OperandType inputType;
- if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, inputType))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- LogSoftmaxDescriptor descriptor;
-
- // Read beta
- if (inputType == OperandType::TENSOR_FLOAT16)
- {
- Half fp16Beta;
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT16, fp16Beta, model, data))
- {
- return Fail("%s: Failed to read input 1 (FLOAT16)", __func__);
- }
-
- descriptor.m_Beta = static_cast<float>(fp16Beta);
- }
- else if (inputType == OperandType::TENSOR_FLOAT32)
- {
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT32, descriptor.m_Beta, model, data))
- {
- return Fail("%s: Failed to read input 1 (FLOAT32)", __func__);
- }
- }
- else
- {
- return Fail("%s: Unsupported input tensor type: %d", __func__, inputType);
- }
-
- // Read axis
- if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 2, descriptor.m_Axis, model, data))
- {
- return Fail("%s: Failed to read input 2", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsLogSoftmaxSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outputInfo,
- descriptor);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddLogSoftmaxLayer(descriptor);
- if (!layer)
- {
- return Fail("%s: AddLogSoftmaxLayer() returned nullptr", __func__);
- }
-
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertLogSoftmax<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data)
@@ -1314,50 +293,7 @@
bool HalPolicy::ConvertMaximum(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertMaximum()");
-
- LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- if (!input0.IsValid() || !input1.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!outputOperand)
- {
- return Fail("%s: Could not read output", __func__);
- }
-
- const TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
- if (IsDynamicTensor(outInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsMaximumSupported,
- data.m_Backends,
- isSupported,
- input0.GetTensorInfo(),
- input1.GetTensorInfo(),
- outInfo);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddMaximumLayer();
- assert(layer != nullptr);
- bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data);
- if (!isReshapeSupported)
- {
- return false;
- }
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertMaximum<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data)
@@ -1369,50 +305,7 @@
bool HalPolicy::ConvertMinimum(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertMinimum()");
-
- LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- if (!input0.IsValid() || !input1.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsMinimumSupported,
- data.m_Backends,
- isSupported,
- input0.GetTensorInfo(),
- input1.GetTensorInfo(),
- outputInfo);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddMinimumLayer();
- assert(layer != nullptr);
- bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data);
- if (!isReshapeSupported)
- {
- return false;
- }
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertMinimum<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data)
@@ -1430,401 +323,25 @@
bool HalPolicy::ConvertPadV2(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertPadV2()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Could not read output", __func__);
- }
-
- const TensorInfo& inputInfo = input.GetTensorInfo();
- unsigned int rank = inputInfo.GetNumDimensions();
-
- PadDescriptor descriptor;
- if (!ConvertPaddings<hal_1_2::HalPolicy>(operation, model, data, rank, descriptor))
- {
- return Fail("%s: Could not convert paddings", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // Determine type of padding value
- OperandType operandType0;
- OperandType operandType2;
-
- if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, operandType0) ||
- !GetOperandType<hal_1_2::HalPolicy>(operation, 2, model, operandType2))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- // Read value to use for padding
- if (operandType0 == OperandType::TENSOR_FLOAT16 && operandType2 == OperandType::FLOAT16)
- {
- Half f16PadValue;
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 2, operandType2, f16PadValue, model, data))
- {
- return Fail("%s: Could not read input 2 (FLOAT16)", __func__);
- }
-
- descriptor.m_PadValue = f16PadValue;
- }
- else if (operandType0 == OperandType::TENSOR_FLOAT32 && operandType2 == OperandType::FLOAT32)
- {
- if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 2, descriptor.m_PadValue, model, data))
- {
- return Fail("%s: Could not read input 2 (FLOAT32)", __func__);
- }
- }
- else if (operandType0 == OperandType::TENSOR_QUANT8_ASYMM && operandType2 == OperandType::INT32)
- {
- int32_t intPadValue = 0;
- if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 2, intPadValue, model, data))
- {
- return Fail("%s: Could not read input 2 (INT32)", __func__);
- }
- descriptor.m_PadValue = intPadValue;
- }
- else
- {
- return Fail("%s: Operation has invalid inputs: type mismatch", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsPadSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- descriptor);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertPadV2<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertPrelu(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertPrelu()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- LayerInputHandle alpha = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- if (!input.IsValid() || !alpha.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
-
- if (!output)
- {
- return Fail("%s: Could not read output", __func__);
- }
-
- const TensorInfo& inputInfo = input.GetTensorInfo();
- const TensorInfo& alphaInfo = alpha.GetTensorInfo();
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsPreluSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- alphaInfo,
- outputInfo);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddPreluLayer();
-
- if (!layer)
- {
- return Fail("%s: AddPreluLayer failed", __func__);
- }
-
- bool isReshapeSupported = BroadcastTensor(input, alpha, layer, data);
- if (!isReshapeSupported)
- {
- return false;
- }
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertPrelu<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertQuantize()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid input", __func__);
- }
-
- const Operand* const outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!outputOperand)
- {
- return Fail("%s: Operation has invalid outputs", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsQuantizeSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outputInfo);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddQuantizeLayer();
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertQuantize<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertQuantizedLstm()");
-
- //Inputs:
- // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize]
- // specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128.
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0: input", __func__);
- }
-
- //13: The previous cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape
- // [numBatches, outputSize] specifying the cell state from the previous time step of the LSTM cell.
- // It is quantized using a quantization range of -2^4, 2^4 * 32767/32768.
- LayerInputHandle previousCellStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 13, model, data);
- if (!previousCellStateIn.IsValid())
- {
- return Fail("%s: Could not read input 13: previousCellStateIn", __func__);
- }
-
- // 14: The previous output state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [numBathes, outputSize] specifying the output of the LSTM cell from previous time-step. Tensor
- // is quantized with a fixed quantization range of -1, 127/128.
- LayerInputHandle previousOutputIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 14, model, data);
- if (!previousOutputIn.IsValid())
- {
- return Fail("%s: Could not read input 14: previousOutputIn", __func__);
- }
-
- // Get the input tensors:
- // 1: The input-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, inputSize] specifying input-to-input part of weights for fully-connected layer inside the
- // LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin inputToInputWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- // 2: The input-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, inputSize] specifying input-to-forget part of weights for fully-connected layer inside the
- // LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin inputToForgetWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data);
-
- // 3: The input-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, inputSize] specifying input-to-cell part of weights for fully-connected layer inside the
- // LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin inputToCellWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 3, model, data);
-
- // 4: The input-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, inputSize] specifying input-to-output part of weights for fully-connected layer inside the
- // LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin inputToOutputWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 4, model, data);
-
- // 5: The recurrent-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, outputSize] specifying recurrent-to-input part of weights for fully-connected layer inside
- // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin recurrentToInputWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 5, model, data);
-
- // 6: The recurrent-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, outputSize] specifying recurrent-to-forget part of weights for fully-connected layer inside
- // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin recurrentToForgetWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 6, model, data);
-
- // 7: The recurrent-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, outputSize] specifying recurrent-to-cell part of weights for fully-connected layer inside
- // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin recurrentToCellWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 7, model, data);
-
- // 8: The recurrent-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
- // [outputSize, outputSize] specifying recurrent-to-output part of weights for fully-connected layer inside
- // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
- const ConstTensorPin recurrentToOutputWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 8, model, data);
-
- // 9: The input gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the
- // bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
- // of input and weights scales and zeroPoint equal to 0.
- const ConstTensorPin inputGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 9, model, data);
-
- // 10: The forget gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying
- // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
- // of input and weights scales and zeroPoint equal to 0.
- const ConstTensorPin forgetGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 10, model, data);
-
- // 11:The cell bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias
- // for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input
- // and weights scales and zeroPoint equal to 0.
- const ConstTensorPin cellBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 11, model, data);
-
- // 12:The output gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying
- // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
- // of input and weights scales and zeroPoint equal to 0.
- const ConstTensorPin outputGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 12, model, data);
-
- if (!inputToInputWeightsPin.IsValid() ||
- !inputToForgetWeightsPin.IsValid() ||
- !inputToCellWeightsPin.IsValid() ||
- !inputToOutputWeightsPin.IsValid() ||
- !recurrentToInputWeightsPin.IsValid() ||
- !recurrentToForgetWeightsPin.IsValid() ||
- !recurrentToCellWeightsPin.IsValid() ||
- !recurrentToOutputWeightsPin.IsValid() ||
- !inputGateBiasPin.IsValid() ||
- !forgetGateBiasPin.IsValid() ||
- !cellBiasPin.IsValid() ||
- !outputGateBiasPin.IsValid())
- {
- return Fail("%s: Operation has invalid tensor inputs", __func__);
- }
-
- // Outputs:
- // 0: The cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize]
- // which contains a cell state from the current time step. Tensor is quantized using a quantization range
- // of -2^4, 2^4 * 32767/32768.
- const Operand* cellStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!cellStateOut)
- {
- return Fail("%s: Could not read output 0: cellStateOut", __func__);
- }
-
- // 1: The output: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] which
- // contains the output value. Tensor is quantized with a fixed quantization range of -1, 127/128.
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 1, model);
- if (!output)
- {
- return Fail("%s: Could not read output 1: output", __func__);
- }
-
- // Inputs
- const TensorInfo& inputInfo = input.GetTensorInfo();
- const TensorInfo& previousCellStateInInfo = previousCellStateIn.GetTensorInfo();
- const TensorInfo& previousOutputInInfo = previousOutputIn.GetTensorInfo();
-
- // Outputs
- const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- // Dynamic tensors currently not supported
- if (IsDynamicTensor(cellStateOutInfo) || IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- QuantizedLstmInputParams params;
-
- params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
- params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
- params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
- params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
- params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
- params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
- params.m_CellBias = cellBiasPin.GetConstTensorPtr();
- params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
-
- QuantizedLstmInputParamsInfo paramsInfo;
- paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
- paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo());
- paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo());
- paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo());
- paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
- paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
- paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo());
- paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
- paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
- paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo());
- paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo());
- paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo());
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsQuantizedLstmSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- previousCellStateInInfo,
- previousOutputInInfo,
- cellStateOutInfo,
- outputInfo,
- paramsInfo);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddQuantizedLstmLayer(params, "QuantizedLstm");
- input.Connect(layer->GetInputSlot(0));
- previousCellStateIn.Connect(layer->GetInputSlot(1));
- previousOutputIn.Connect(layer->GetInputSlot(2));
-
- return (SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, 0, model, data) &&
- SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 1, *layer, 1, model, data));
+ return ::ConvertQuantizedLstm<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
@@ -1857,134 +374,7 @@
ResizeMethod resizeMethod)
{
ALOGV("hal_1_2::HalPolicy::ConvertResize()");
- ALOGV("resizeMethod = %s", GetResizeMethodAsCString(resizeMethod));
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
-
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- ResizeDescriptor descriptor;
- descriptor.m_Method = resizeMethod;
- descriptor.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 3, model, data);
-
- OperandType operandType1;
- OperandType operandType2;
-
- if (!GetOperandType<hal_1_2::HalPolicy>(operation, 1, model, operandType1) ||
- !GetOperandType<hal_1_2::HalPolicy>(operation, 2, model, operandType2))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- if (operandType1 != operandType2)
- {
- return Fail("%s: Operation has invalid inputs. Type of input 1 and 2 should be the same", __func__);
- }
-
- if (operandType1 == OperandType::INT32)
- {
- // Case 1: resizing by shape
- int32_t targetWidth = 0;
- int32_t targetHeight = 0;
-
- if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 1, targetWidth, model, data) ||
- !GetInputInt32<hal_1_2::HalPolicy>(operation, 2, targetHeight, model, data))
- {
- return Fail("%s: Operation has invalid inputs for resizing by shape", __func__);
- }
-
- if (targetWidth < 0 || targetHeight < 0)
- {
- return Fail("%s: Operation has invalid inputs for resizing by shape. "
- "Target width/height cannot be < 0", __func__);
- }
-
- descriptor.m_TargetWidth = static_cast<uint32_t>(targetWidth);
- descriptor.m_TargetHeight = static_cast<uint32_t>(targetHeight);
- }
- else if (operandType1 == OperandType::FLOAT32)
- {
- // Case 2: resizing by scale
- float widthScale = 1.0f;
- float heightScale = 1.0f;
-
- if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 1, widthScale, model, data) ||
- !GetInputFloat32<hal_1_2::HalPolicy>(operation, 2, heightScale, model, data))
- {
- return Fail("%s: Operation has invalid inputs for resizing by scale", __func__);
- }
-
- const TensorShape& inputShape = inputInfo.GetShape();
- armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout);
-
- float width = inputShape[dataLayoutIndexed.GetWidthIndex()];
- float height = inputShape[dataLayoutIndexed.GetHeightIndex()];
-
- descriptor.m_TargetWidth = std::floor(width * widthScale);
- descriptor.m_TargetHeight = std::floor(height * heightScale);
- }
- else if (operandType1 == OperandType::FLOAT16)
- {
- Half widthScale;
- Half heightScale;
-
- if (!GetInputScalar<HalPolicy>(operation, 1, HalPolicy::OperandType::FLOAT16, widthScale, model, data) ||
- !GetInputScalar<HalPolicy>(operation, 2, HalPolicy::OperandType::FLOAT16, heightScale, model, data))
- {
- return Fail("%s: Operation has invalid inputs for resizing by scale", __func__);
- }
-
- const TensorShape& inputShape = inputInfo.GetShape();
- armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout);
-
- Half width = static_cast<Half>(inputShape[dataLayoutIndexed.GetWidthIndex()]);
- Half height = static_cast<Half>(inputShape[dataLayoutIndexed.GetHeightIndex()]);
-
- descriptor.m_TargetWidth = std::floor(width * widthScale);
- descriptor.m_TargetHeight = std::floor(height * heightScale);
- }
- else
- {
- return Fail("%s: Operand has invalid data type for resizing by scale", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsResizeSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- descriptor);
-
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddResizeLayer(descriptor);
-
- assert(layer != nullptr);
-
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertResize<hal_1_2::HalPolicy>(operation, model, data, resizeMethod);
}
bool HalPolicy::ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data)
@@ -1996,126 +386,13 @@
bool HalPolicy::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertSpaceToDepth()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid() )
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const TensorInfo& inputInfo = input.GetTensorInfo();
- unsigned int rank = inputInfo.GetNumDimensions();
- if (rank != 4)
- {
- return Fail("%s: Only inputs with rank 4 are supported", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- SpaceToDepthDescriptor desc;
-
- GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::INT32, desc.m_BlockSize, model, data);
-
- if (desc.m_BlockSize <= 1)
- {
- return Fail("%s: Block size must be at least 1 in all dimensions");
- }
-
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 2, model, data);
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsSpaceToDepthSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- desc);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertSpaceToDepth<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertSoftmax()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!outputOperand)
- {
- return Fail("%s: Operation has no outputs", __func__);
- }
-
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- SoftmaxDescriptor desc;
- if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 1, desc.m_Beta, model, data))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- if (operation.inputs.size() > 2 && !GetInputScalar<hal_1_2::HalPolicy>(operation,
- 2,
- HalPolicy::OperandType::INT32,
- desc.m_Axis,
- model,
- data))
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- if (input.GetTensorInfo().GetNumDimensions() > 2 ||
- !(desc.m_Axis == 1 ||
- (desc.m_Axis < 0 && static_cast<int>(input.GetTensorInfo().GetNumDimensions()) + desc.m_Axis == 1)))
- {
- return Fail("%s: Unsupported input greater than 2D or axis != 1", __func__);
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsSoftmaxSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outputInfo,
- desc);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertSoftmax<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data)
@@ -2130,450 +407,10 @@
return ::ConvertTanH<hal_1_2::HalPolicy>(operation, model, data);
}
-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;
-}
-
-
bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertLstm()");
-
- // Inputs:
- // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
- // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Could not read input 0: input", __func__);
- }
- // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
- LayerInputHandle outputStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 18, model, data);
- if (!outputStateIn.IsValid())
- {
- return Fail("%s: Could not read input 18: outputStateIn", __func__);
- }
- // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
- LayerInputHandle cellStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 19, model, data);
- if (!cellStateIn.IsValid())
- {
- return Fail("%s: Could not read input 19: cellStateIn", __func__);
- }
-
- // Get the mandatory input tensors:
- // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size].
- const ConstTensorPin inputToForgetWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 2));
- // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size].
- const ConstTensorPin inputToCellWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 3));
- // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size].
- const ConstTensorPin inputToOutputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 4));
- // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToForgetWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 6));
- // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToCellWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 7));
- // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size].
- const ConstTensorPin recurrentToOutputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 8));
- // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin forgetGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 13, model, data);
- // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 14, model, data);
- // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin outputGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 15, model, data);
-
- if (!inputToForgetWeightsPin.IsValid() ||
- !inputToCellWeightsPin.IsValid() ||
- !inputToOutputWeightsPin.IsValid() ||
- !recurrentToForgetWeightsPin.IsValid() ||
- !recurrentToCellWeightsPin.IsValid() ||
- !recurrentToOutputWeightsPin.IsValid() ||
- !forgetGateBiasPin.IsValid() ||
- !cellBiasPin.IsValid() ||
- !outputGateBiasPin.IsValid())
- {
- return Fail("%s: Operation has invalid tensor inputs", __func__);
- }
-
- // Get the optional input tensors:
- // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, input_size], where “num_units” corresponds to the number of cell units.
- const ConstTensorPin inputToInputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 1, true));
- // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
- // “num_units”), or the second dimension of the “projection_weights”, if defined.
- const ConstTensorPin recurrentToInputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 5, true));
- // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToInputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 9, true));
- // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToForgetWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 10, true));
- // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin cellToOutputWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 11, true));
- // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
- const ConstTensorPin inputGateBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
- 12,
- model,
- data,
- g_DontPermute,
- nullptr,
- true);
-
- // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
- // [output_size, num_units].
- const ConstTensorPin projectionWeightsPin =
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 16, true));
- // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
- const ConstTensorPin projectionBiasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
- 17,
- model,
- data,
- g_DontPermute,
- nullptr,
- true);
-
- if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
- (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) ||
- (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) ||
- (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) ||
- (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) ||
- (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) ||
- (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) ||
- (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
- {
- return Fail("%s: Operation has invalid tensor inputs", __func__);
- }
-
- // Get the mandatory input scalars (actually 1-D tensors of size 1):
- // 20: The activation function: A value indicating the activation function:
- // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid.
- // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip].
- // If set to 0.0 then clipping is disabled.
- // 22: The clipping threshold: for the output from the projection layer, such that values are bound within
- // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
- ActivationFn activation;
- float cellClip;
- float projClip;
- if (!GetInputActivationFunctionFromTensor<hal_1_2::HalPolicy>(operation, 20, activation, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 21, OperandType::FLOAT32, cellClip, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 22, OperandType::FLOAT32, projClip, model, data))
- {
- return Fail("%s: Operation has invalid scalar inputs", __func__);
- }
-
- // Get the normalization tensors
- // 23: The input layer normalization weights. A 1-D tensor of shape [num_units].
- // Used to rescale normalized inputs to activation at input gate.
- const ConstTensorPin inputLayerNormWeightsPin
- (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 23, true));
-
- // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units].
- // Used to rescale normalized inputs to activation at forget gate.
- const ConstTensorPin forgetLayerNormWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
- 24,
- model,
- data,
- g_DontPermute,
- nullptr,
- true);
-
- // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units].
- // Used to rescale normalized inputs to activation at cell gate.
- const ConstTensorPin cellLayerNormWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
- 25,
- model,
- data,
- g_DontPermute,
- nullptr,
- true);
-
- // 26: The output layer normalization weights. A 1-D tensor of shape [num_units].
- // Used to rescale normalized inputs to activation at output gate.
- const ConstTensorPin outputLayerNormWeightsPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
- 26,
- model,
- data,
- g_DontPermute,
- nullptr,
- true);
-
- // Outputs:
- // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4]
- // with CIFG, or [batch_size, num_units * 3] without CIFG.
- const Operand* scratchBuffer = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
- if (!scratchBuffer)
- {
- return Fail("%s: Could not read output 0: scratchBuffer", __func__);
- }
- // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
- const Operand* outputStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 1, model);
- if (!outputStateOut)
- {
- return Fail("%s: Could not read output 1: outputStateOut", __func__);
- }
- // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
- const Operand* cellStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 2, model);
- if (!cellStateOut)
- {
- return Fail("%s: Could not read output 2: cellStateOut", __func__);
- }
- // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is
- // effectively the same as the current “output state (out)” value.
- const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 3, model);
- if (!output)
- {
- return Fail("%s: Could not read output 3: output", __func__);
- }
-
- // set the params structure for the AddLstmLayer call
- LstmInputParams params;
- params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
- params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
- params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
- params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
- params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
- params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
- params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
- params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
- params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
- params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
- params.m_CellBias = cellBiasPin.GetConstTensorPtr();
- params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
- params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
- params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
- params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr();
- params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr();
- params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr();
- params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr();
-
- // set the layer descriptor
- LstmDescriptor desc;
- desc.m_ActivationFunc = activation;
- desc.m_ClippingThresCell = cellClip;
- desc.m_ClippingThresProj = projClip;
- desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
- params.m_RecurrentToInputWeights == nullptr ||
- params.m_InputGateBias == nullptr);
- desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
- params.m_CellToOutputWeights != nullptr);
- desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
- desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr ||
- params.m_ForgetLayerNormWeights != nullptr ||
- params.m_CellLayerNormWeights != nullptr ||
- params.m_OutputLayerNormWeights != nullptr);
-
- // validate the optional input groups
- if (desc.m_CifgEnabled &&
- (params.m_InputToInputWeights != nullptr ||
- params.m_RecurrentToInputWeights != nullptr ||
- params.m_InputGateBias != nullptr))
- {
- return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
- " and input gate bias must be provided", __func__);
- }
-
- if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
- {
- return Fail("%s: projection bias should not be provided without projection weights", __func__);
- }
-
- if (desc.m_PeepholeEnabled &&
- (params.m_CellToForgetWeights == nullptr ||
- params.m_CellToOutputWeights == nullptr ||
- (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
- {
- return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
- " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
- }
-
- if (desc.m_LayerNormEnabled &&
- (params.m_ForgetLayerNormWeights == nullptr ||
- params.m_CellLayerNormWeights == nullptr ||
- params.m_OutputLayerNormWeights == nullptr ||
- (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr)))
- {
- return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be"
- " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__);
- }
-
- // Check if the layer is supported
- // Inputs
- const TensorInfo& inputInfo = input.GetTensorInfo();
- const TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo();
- const TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo();
-
- // Outputs
- const TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer);
- const TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
- const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
- const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
-
- // Check if the scratch buffer shape was initialized,
- // In some cases the shape could be (0,0) which requires the driver
- // to infer the shape and set it up accordingly.
- // The code below does that.
- TensorInfo fixSbInfo = scratchBufferInfo;
- if (IsDynamicTensor(scratchBufferInfo))
- {
- auto & s = fixSbInfo.GetShape();
- s[0] = outputStateInInfo.GetShape()[0];
- if (desc.m_CifgEnabled)
- {
- // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG
- s[1] = cellStateOutInfo.GetShape()[1]*3;
- }
- else
- {
- // scratch_buffer [num_units * 4, batch_size] without CIFG
- s[1] = cellStateOutInfo.GetShape()[1]*4;
- }
- }
-
- if (IsDynamicTensor(outputStateOutInfo) ||
- IsDynamicTensor(cellStateOutInfo) ||
- IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__,
- IsDynamicTensor(scratchBufferInfo), IsDynamicTensor(outputStateOutInfo),
- IsDynamicTensor(cellStateOutInfo), IsDynamicTensor(outputInfo));
- }
-
- // Basic parameters
- LstmInputParamsInfo paramsInfo;
- paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo());
- paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo());
- paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo());
- paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
- paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo());
- paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
- paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo());
- paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo());
- paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo());
-
- // Optional parameters
- if(!desc.m_CifgEnabled)
- {
- paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
- paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
- if (params.m_CellToInputWeights != nullptr)
- {
- paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
- }
- paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
- }
-
- if(desc.m_ProjectionEnabled)
- {
- paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo());
- if (params.m_ProjectionBias != nullptr)
- {
- paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo());
- }
- }
-
- if(desc.m_PeepholeEnabled)
- {
- paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
- paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
- }
-
- if (desc.m_LayerNormEnabled)
- {
- if(!desc.m_CifgEnabled)
- {
- paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo());
- }
- paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo());
- paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo());
- paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo());
- }
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsLstmSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputStateInInfo,
- cellStateInInfo,
- fixSbInfo,
- outputStateOutInfo,
- cellStateOutInfo,
- outputInfo,
- desc,
- paramsInfo);
- if (!isSupported)
- {
- return false;
- }
-
- // Add the layer
- IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm");
-
- input.Connect(layer->GetInputSlot(0));
- outputStateIn.Connect(layer->GetInputSlot(1));
- cellStateIn.Connect(layer->GetInputSlot(2));
-
-
- return (
- (IsDynamicTensor(scratchBufferInfo)?
- SetupAndTrackLayerOutputSlotAndOverrideTensorInfo<hal_1_2::HalPolicy>(
- operation, 0, *layer, 0, model, data,fixSbInfo):
- SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(
- operation, 0, *layer, 0, model, data)) &&
- SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 1, *layer, 1, model, data) &&
- SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 2, *layer, 2, model, data) &&
- SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 3, *layer, 3, model, data));
+ return ::ConvertLstm<hal_1_2::HalPolicy>(operation, model, data);
}
bool HalPolicy::ConvertSqrt(const Operation& operation, const Model& model, ConversionData& data)
@@ -2605,175 +442,8 @@
bool HalPolicy::ConvertTransposeConv2d(const Operation& operation, const Model& model, ConversionData& data)
{
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
-
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_2::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);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- // 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 Operand* weightsOperand = GetInputOperand<hal_1_2::HalPolicy>(operation, 1, model);
-
- if (weightsOperand == nullptr)
- {
- return Fail("%s: Operand is invalid", __func__);
- }
- TransposeConvolution2dDescriptor desc;
- desc.m_DataLayout = DataLayout::NHWC;
-
- // Determine whether padding is implicit or explicit
- bool implicitPadding = operation.inputs.size() == 9;
-
- if (implicitPadding )
- {
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 8, model, data);
- }
- else
- {
- desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data);
- }
-
- armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
- unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
- unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
-
- const PermutationVector OHWIToOIHW = {0, 2, 3, 1};
-
- // The shape of the weight is [depth_out, filter_height, filter_width, depth_in].
- // We have to permute it to OIHW if the data layout is NCHW.
- const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ?
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data, OHWIToOIHW) :
- ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
-
- // Bias is a 1D tensor
- const ConstTensorPin biasPin =
- ConvertOperationInputToConstTensorPin<hal_1_2::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)
- {
- int32_t strideX{0};
- int32_t strideY{0};
- int32_t padLeft{0};
- int32_t padRight{0};
- int32_t padTop{0};
- int32_t padBottom{0};
-
- android::nn::PaddingScheme paddingScheme;
- if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 4, paddingScheme, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, strideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, strideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data))
- {
- return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
- }
-
- const uint32_t kernelX = weights.GetShape()[widthIndex];
- const uint32_t kernelY = weights.GetShape()[heightIndex];
- const uint32_t outputX = outputInfo.GetShape()[widthIndex];
- const uint32_t outputY = outputInfo.GetShape()[heightIndex];
-
- CalcPaddingTransposeConv(outputX, kernelX, strideX, padLeft, padRight, paddingScheme);
- CalcPaddingTransposeConv(outputY, kernelY, strideY, padTop, padBottom, paddingScheme);
-
- // NOTE: The Android NN API allows for negative padding values in TransposeConv2d,
- // but Arm NN only supports values >= 0
- if (padLeft < 0 || padRight < 0 || padTop < 0 || padBottom < 0)
- {
- return Fail("%s: Negative padding values are not supported", __func__);
- }
-
- desc.m_StrideX = boost::numeric_cast<uint32_t>(strideX);
- desc.m_StrideY = boost::numeric_cast<uint32_t>(strideY);
- desc.m_PadLeft = boost::numeric_cast<uint32_t>(padLeft);
- desc.m_PadRight = boost::numeric_cast<uint32_t>(padRight);
- desc.m_PadTop = boost::numeric_cast<uint32_t>(padTop);
- desc.m_PadBottom = boost::numeric_cast<uint32_t>(padBottom);
- }
- else if (operation.inputs.size() == 11)
- {
- // explicit padding
- if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
- !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 9, activation, 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;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsTransposeConvolution2dSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- desc,
- weights.GetInfo(),
- biases);
- if (!isSupported)
- {
- return false;
- }
-
- IConnectableLayer* startLayer =
- data.m_Network->AddTransposeConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
- if (!startLayer)
- {
- return Fail("%s: AddTransposeConvolution2dLayer failed", __func__);
- }
-
- IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
- if (!endLayer)
- {
- return Fail("%s: ProcessActivation failed", __func__);
- }
-
- input.Connect(startLayer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data);
+ ALOGV("hal_1_2::HalPolicy::ConvertTransposeConv2d()");
+ return ::ConvertTransposeConv2d<hal_1_2::HalPolicy>(operation, model, data);
}
} // namespace hal_1_2
diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp
index cd4f2da..b127a63 100644
--- a/1.2/HalPolicy.hpp
+++ b/1.2/HalPolicy.hpp
@@ -6,6 +6,7 @@
#pragma once
#include "../ConversionUtils.hpp"
+#include "../ConversionUtils_1_2.hpp"
#include <HalInterfaces.h>
@@ -29,6 +30,7 @@
using OperationType = V1_2::OperationType;
using ExecutionCallback = V1_2::IExecutionCallback;
using getSupportedOperations_cb = V1_2::IDevice::getSupportedOperations_1_2_cb;
+ using ErrorStatus = V1_0::ErrorStatus;
static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
diff --git a/1.3/ArmnnDriver.hpp b/1.3/ArmnnDriver.hpp
new file mode 100644
index 0000000..be35593
--- /dev/null
+++ b/1.3/ArmnnDriver.hpp
@@ -0,0 +1,294 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <HalInterfaces.h>
+
+#include "../ArmnnDevice.hpp"
+#include "ArmnnDriverImpl.hpp"
+#include "HalPolicy.hpp"
+
+#include "../ArmnnDriverImpl.hpp"
+#include "../1.3/ArmnnDriverImpl.hpp"
+#include "../1.3/HalPolicy.hpp"
+#include "../1.2/ArmnnDriverImpl.hpp"
+#include "../1.2/HalPolicy.hpp"
+#include "../1.1/ArmnnDriverImpl.hpp"
+#include "../1.1/HalPolicy.hpp"
+#include "../1.0/ArmnnDriverImpl.hpp"
+#include "../1.0/HalPolicy.hpp"
+
+#include <log/log.h>
+
+namespace armnn_driver
+{
+namespace hal_1_3
+{
+
+class ArmnnDriver : public ArmnnDevice, public V1_3::IDevice
+{
+public:
+
+ ArmnnDriver(DriverOptions options)
+ : ArmnnDevice(std::move(options))
+ {
+ ALOGV("hal_1_3::ArmnnDriver::ArmnnDriver()");
+ }
+ ~ArmnnDriver() {}
+
+ using HidlToken = android::hardware::hidl_array<uint8_t, ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN>;
+
+public:
+ Return<void> getCapabilities(V1_0::IDevice::getCapabilities_cb cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getCapabilities()");
+
+ return hal_1_0::ArmnnDriverImpl::getCapabilities(m_Runtime, cb);
+ }
+
+ Return<void> getSupportedOperations(const V1_0::Model& model,
+ V1_0::IDevice::getSupportedOperations_cb cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedOperations()");
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_0::HalPolicy>::getSupportedOperations(m_Runtime,
+ m_Options,
+ model,
+ cb);
+ }
+
+ Return<V1_0::ErrorStatus> prepareModel(const V1_0::Model& model,
+ const android::sp<V1_0::IPreparedModelCallback>& cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel()");
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_0::HalPolicy>::prepareModel(m_Runtime,
+ m_ClTunedParameters,
+ m_Options,
+ model,
+ cb);
+ }
+
+ Return<void> getCapabilities_1_1(V1_1::IDevice::getCapabilities_1_1_cb cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getCapabilities_1_1()");
+
+ return hal_1_1::ArmnnDriverImpl::getCapabilities_1_1(m_Runtime, cb);
+ }
+
+ Return<void> getSupportedOperations_1_1(const V1_1::Model& model,
+ V1_1::IDevice::getSupportedOperations_1_1_cb cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedOperations_1_1()");
+ return armnn_driver::ArmnnDriverImpl<hal_1_1::HalPolicy>::getSupportedOperations(m_Runtime,
+ m_Options,
+ model,
+ cb);
+ }
+
+ Return<V1_0::ErrorStatus> prepareModel_1_1(const V1_1::Model& model,
+ V1_1::ExecutionPreference preference,
+ const android::sp<V1_0::IPreparedModelCallback>& cb) override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_1()");
+
+ if (!(preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED))
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_1: Invalid execution preference");
+ cb->notify(V1_0::ErrorStatus::INVALID_ARGUMENT, nullptr);
+ return V1_0::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_1::HalPolicy>::prepareModel(m_Runtime,
+ m_ClTunedParameters,
+ m_Options,
+ model,
+ cb,
+ model.relaxComputationFloat32toFloat16
+ && m_Options.GetFp16Enabled());
+ }
+
+ Return<void> getCapabilities_1_2(getCapabilities_1_2_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getCapabilities()");
+
+ return hal_1_2::ArmnnDriverImpl::getCapabilities_1_2(m_Runtime, cb);
+ }
+
+ Return<void> getSupportedOperations_1_2(const V1_2::Model& model,
+ getSupportedOperations_1_2_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedOperations()");
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_2::HalPolicy>::getSupportedOperations(m_Runtime,
+ m_Options,
+ model,
+ cb);
+ }
+
+ Return<V1_0::ErrorStatus> prepareModel_1_2(const V1_2::Model& model, V1_1::ExecutionPreference preference,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&, const HidlToken&,
+ const android::sp<V1_2::IPreparedModelCallback>& cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_2()");
+
+ if (!(preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED))
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_2: Invalid execution preference");
+ cb->notify(V1_0::ErrorStatus::INVALID_ARGUMENT, nullptr);
+ return V1_0::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ return hal_1_2::ArmnnDriverImpl::prepareArmnnModel_1_2(m_Runtime,
+ m_ClTunedParameters,
+ m_Options,
+ model,
+ cb,
+ model.relaxComputationFloat32toFloat16
+ && m_Options.GetFp16Enabled());
+ }
+
+ Return<void> getCapabilities_1_3(getCapabilities_1_3_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getCapabilities()");
+
+ return hal_1_3::ArmnnDriverImpl::getCapabilities_1_3(m_Runtime, cb);
+ }
+
+ Return<void> getSupportedOperations_1_3(const V1_3::Model& model,
+ getSupportedOperations_1_3_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedOperations()");
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_3::HalPolicy>::getSupportedOperations(m_Runtime,
+ m_Options,
+ model,
+ cb);
+ }
+
+ Return<V1_3::ErrorStatus> prepareModel_1_3(const V1_3::Model& model,
+ V1_1::ExecutionPreference preference,
+ V1_3::Priority priority,
+ const V1_3::OptionalTimePoint&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const HidlToken&,
+ const android::sp<V1_3::IPreparedModelCallback>& cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_3()");
+
+ if (!(preference == ExecutionPreference::LOW_POWER ||
+ preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+ preference == ExecutionPreference::SUSTAINED_SPEED))
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModel_1_3: Invalid execution preference");
+ cb->notify_1_3(V1_3::ErrorStatus::INVALID_ARGUMENT, nullptr);
+ return V1_3::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ if (!android::nn::validatePriority(priority)) {
+ cb->notify_1_3(V1_3::ErrorStatus::INVALID_ARGUMENT, nullptr);
+ return V1_3::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+
+ return ArmnnDriverImpl::prepareArmnnModel_1_3(m_Runtime,
+ m_ClTunedParameters,
+ m_Options,
+ model,
+ cb,
+ model.relaxComputationFloat32toFloat16
+ && m_Options.GetFp16Enabled());
+ }
+
+ Return<void> getSupportedExtensions(getSupportedExtensions_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedExtensions()");
+ cb(V1_0::ErrorStatus::NONE, {/* No extensions. */});
+ return Void();
+ }
+
+ Return<void> getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getSupportedExtensions()");
+
+ // Set both numbers to be 0 for cache not supported.
+ cb(V1_0::ErrorStatus::NONE, 0, 0);
+ return Void();
+ }
+
+ Return<DeviceStatus> getStatus() override
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getStatus()");
+
+ return armnn_driver::ArmnnDriverImpl<hal_1_3::HalPolicy>::getStatus();
+ }
+
+ Return<void> getVersionString(getVersionString_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getVersionString()");
+
+ cb(V1_0::ErrorStatus::NONE, "ArmNN");
+ return Void();
+ }
+
+ Return<void> getType(getType_cb cb)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::getType()");
+
+ cb(V1_0::ErrorStatus::NONE, V1_2::DeviceType::CPU);
+ return Void();
+ }
+
+ Return<V1_0::ErrorStatus> prepareModelFromCache(
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const HidlToken&,
+ const sp<V1_2::IPreparedModelCallback>& callback)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModelFromCache()");
+ callback->notify_1_2(V1_0::ErrorStatus::GENERAL_FAILURE, nullptr);
+ return V1_0::ErrorStatus::GENERAL_FAILURE;
+ }
+
+ Return<ErrorStatus> prepareModelFromCache_1_3(
+ V1_3::Priority,
+ const V1_3::OptionalTimePoint&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>&,
+ const HidlToken&,
+ const sp<V1_3::IPreparedModelCallback>& callback)
+ {
+ ALOGV("hal_1_3::ArmnnDriver::prepareModelFromCache()");
+ callback->notify_1_3(ErrorStatus::GENERAL_FAILURE, nullptr);
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+
+ Return<void> supportsDeadlines(supportsDeadlines_cb cb) {
+ // Set both numbers to be false for deadlines not supported.
+ cb(/*prepareModelDeadline=*/false, /*executionDeadline=*/false);
+ return Void();
+ }
+
+ Return<void> allocate(const V1_3::BufferDesc& /*desc*/,
+ const hidl_vec<sp<V1_3::IPreparedModel>>& /*preparedModels*/,
+ const hidl_vec<V1_3::BufferRole>& /*inputRoles*/,
+ const hidl_vec<V1_3::BufferRole>& /*outputRoles*/,
+ allocate_cb cb) {
+ ALOGV("hal_1_3::ArmnnDriver::allocate()");
+ cb(ErrorStatus::GENERAL_FAILURE, nullptr, 0);
+ return Void();
+ }
+
+};
+
+} // namespace hal_1_3
+} // namespace armnn_driver
diff --git a/1.3/ArmnnDriverImpl.cpp b/1.3/ArmnnDriverImpl.cpp
new file mode 100644
index 0000000..98d038c
--- /dev/null
+++ b/1.3/ArmnnDriverImpl.cpp
@@ -0,0 +1,338 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ArmnnDriverImpl.hpp"
+#include "../ArmnnPreparedModel_1_3.hpp"
+#include "../ModelToINetworkConverter.hpp"
+#include "../SystemPropertiesUtils.hpp"
+
+#include <log/log.h>
+
+namespace
+{
+
+const char *g_RelaxedFloat32toFloat16PerformanceExecTime = "ArmNN.relaxedFloat32toFloat16Performance.execTime";
+const char *g_RelaxedFloat32toFloat16PerformancePowerUsage = "ArmNN.relaxedFloat32toFloat16Performance.powerUsage";
+
+const char *g_OperandTypeTensorFloat32PerformanceExecTime = "Armnn.operandTypeTensorFloat32Performance.execTime";
+const char *g_OperandTypeTensorFloat32PerformancePowerUsage = "Armnn.operandTypeTensorFloat32Performance.powerUsage";
+
+const char *g_OperandTypeFloat32PerformanceExecTime = "Armnn.operandTypeFloat32Performance.execTime";
+const char *g_OperandTypeFloat32PerformancePowerUsage = "Armnn.operandTypeFloat32Performance.powerUsage";
+
+const char *g_OperandTypeTensorFloat16PerformanceExecTime = "Armnn.operandTypeTensorFloat16Performance.execTime";
+const char *g_OperandTypeTensorFloat16PerformancePowerUsage = "Armnn.operandTypeTensorFloat16Performance.powerUsage";
+
+const char *g_OperandTypeFloat16PerformanceExecTime = "Armnn.operandTypeFloat16Performance.execTime";
+const char *g_OperandTypeFloat16PerformancePowerUsage = "Armnn.operandTypeFloat16Performance.powerUsage";
+
+const char *g_OperandTypeTensorQuant8AsymmPerformanceExecTime =
+ "Armnn.operandTypeTensorQuant8AsymmPerformance.execTime";
+const char *g_OperandTypeTensorQuant8AsymmPerformancePowerUsage =
+ "Armnn.operandTypeTensorQuant8AsymmPerformance.powerUsage";
+
+const char *g_OperandTypeTensorQuant8AsymmSignedPerformanceExecTime =
+ "Armnn.operandTypeTensorQuant8AsymmSignedPerformance.execTime";
+const char *g_OperandTypeTensorQuant8AsymmSignedPerformancePowerUsage =
+ "Armnn.operandTypeTensorQuant8AsymmSignedPerformance.powerUsage";
+
+const char *g_OperandTypeTensorQuant16SymmPerformanceExecTime =
+ "Armnn.operandTypeTensorQuant16SymmPerformance.execTime";
+const char *g_OperandTypeTensorQuant16SymmPerformancePowerUsage =
+ "Armnn.operandTypeTensorQuant16SymmPerformance.powerUsage";
+
+const char *g_OperandTypeTensorQuant8SymmPerformanceExecTime =
+ "Armnn.operandTypeTensorQuant8SymmPerformance.execTime";
+const char *g_OperandTypeTensorQuant8SymmPerformancePowerUsage =
+ "Armnn.operandTypeTensorQuant8SymmPerformance.powerUsage";
+
+const char *g_OperandTypeTensorQuant8SymmPerChannelPerformanceExecTime =
+ "Armnn.operandTypeTensorQuant8SymmPerChannelPerformance.execTime";
+const char *g_OperandTypeTensorQuant8SymmPerChannelPerformancePowerUsage =
+ "Armnn.operandTypeTensorQuant8SymmPerChannelPerformance.powerUsage";
+
+
+const char *g_OperandTypeTensorInt32PerformanceExecTime = "Armnn.operandTypeTensorInt32Performance.execTime";
+const char *g_OperandTypeTensorInt32PerformancePowerUsage = "Armnn.operandTypeTensorInt32Performance.powerUsage";
+
+const char *g_OperandTypeInt32PerformanceExecTime = "Armnn.operandTypeInt32Performance.execTime";
+const char *g_OperandTypeInt32PerformancePowerUsage = "Armnn.operandTypeInt32Performance.powerUsage";
+
+
+void NotifyCallbackAndCheck(const sp<V1_3::IPreparedModelCallback>& callback,
+ V1_3::ErrorStatus errorStatus,
+ const sp<V1_3::IPreparedModel>& preparedModelPtr)
+{
+ Return<void> returned = callback->notify_1_3(errorStatus, preparedModelPtr);
+ // This check is required, if the callback fails and it isn't checked it will bring down the service
+ if (!returned.isOk())
+ {
+ ALOGE("ArmnnDriverImpl::prepareModel: hidl callback failed to return properly: %s ",
+ returned.description().c_str());
+ }
+}
+
+Return<V1_3::ErrorStatus> FailPrepareModel(V1_3::ErrorStatus error,
+ const std::string& message,
+ const sp<V1_3::IPreparedModelCallback>& callback)
+{
+ ALOGW("ArmnnDriverImpl::prepareModel: %s", message.c_str());
+ NotifyCallbackAndCheck(callback, error, nullptr);
+ return error;
+}
+
+} // anonymous namespace
+
+namespace armnn_driver
+{
+namespace hal_1_3
+{
+
+Return<V1_3::ErrorStatus> ArmnnDriverImpl::prepareArmnnModel_1_3(
+ const armnn::IRuntimePtr& runtime,
+ const armnn::IGpuAccTunedParametersPtr& clTunedParameters,
+ const DriverOptions& options,
+ const V1_3::Model& model,
+ const sp<V1_3::IPreparedModelCallback>& cb,
+ bool float32ToFloat16)
+{
+ ALOGV("ArmnnDriverImpl::prepareArmnnModel_1_3()");
+
+ if (cb.get() == nullptr)
+ {
+ ALOGW("ArmnnDriverImpl::prepareModel: Invalid callback passed to prepareModel");
+ return V1_3::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ if (!runtime)
+ {
+ return FailPrepareModel(V1_3::ErrorStatus::DEVICE_UNAVAILABLE, "Device unavailable", cb);
+ }
+
+ if (!android::nn::validateModel(model))
+ {
+ return FailPrepareModel(V1_3::ErrorStatus::INVALID_ARGUMENT, "Invalid model passed as input", cb);
+ }
+
+ // Deliberately ignore any unsupported operations requested by the options -
+ // at this point we're being asked to prepare a model that we've already declared support for
+ // and the operation indices may be different to those in getSupportedOperations anyway.
+ std::set<unsigned int> unsupportedOperations;
+ ModelToINetworkConverter<HalPolicy> modelConverter(options.GetBackends(),
+ model,
+ unsupportedOperations);
+
+ if (modelConverter.GetConversionResult() != ConversionResult::Success)
+ {
+ FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, "ModelToINetworkConverter failed", cb);
+ return V1_3::ErrorStatus::NONE;
+ }
+
+ // Optimize the network
+ armnn::IOptimizedNetworkPtr optNet(nullptr, nullptr);
+ armnn::OptimizerOptions OptOptions;
+ OptOptions.m_ReduceFp32ToFp16 = float32ToFloat16;
+
+ std::vector<std::string> errMessages;
+ try
+ {
+ optNet = armnn::Optimize(*modelConverter.GetINetwork(),
+ options.GetBackends(),
+ runtime->GetDeviceSpec(),
+ OptOptions,
+ errMessages);
+ }
+ catch (std::exception& e)
+ {
+ std::stringstream message;
+ message << "Exception (" << e.what() << ") caught from optimize.";
+ FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, message.str(), cb);
+ return V1_3::ErrorStatus::NONE;
+ }
+
+ // Check that the optimized network is valid.
+ if (!optNet)
+ {
+ std::stringstream message;
+ message << "Invalid optimized network";
+ for (const std::string& msg : errMessages)
+ {
+ message << "\n" << msg;
+ }
+ FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, message.str(), cb);
+ return V1_3::ErrorStatus::NONE;
+ }
+
+ // Export the optimized network graph to a dot file if an output dump directory
+ // has been specified in the drivers' arguments.
+ std::string dotGraphFileName = ExportNetworkGraphToDotFile(*optNet,
+ options.GetRequestInputsAndOutputsDumpDir());
+
+ // Load it into the runtime.
+ armnn::NetworkId netId = 0;
+ try
+ {
+ if (runtime->LoadNetwork(netId, move(optNet)) != armnn::Status::Success)
+ {
+ return FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, "Network could not be loaded", cb);
+ }
+ }
+ catch (std::exception& e)
+ {
+ std::stringstream message;
+ message << "Exception (" << e.what()<< ") caught from LoadNetwork.";
+ FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, message.str(), cb);
+ return V1_3::ErrorStatus::NONE;
+ }
+
+ // Now that we have a networkId for the graph rename the dump file to use it
+ // so that we can associate the graph file and the input/output tensor dump files
+ RenameGraphDotFile(dotGraphFileName,
+ options.GetRequestInputsAndOutputsDumpDir(),
+ netId);
+
+ std::unique_ptr<ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>> preparedModel(
+ new ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>(
+ netId,
+ runtime.get(),
+ model,
+ options.GetRequestInputsAndOutputsDumpDir(),
+ options.IsGpuProfilingEnabled()));
+
+ // Run a single 'dummy' inference of the model. This means that CL kernels will get compiled (and tuned if
+ // this is enabled) before the first 'real' inference which removes the overhead of the first inference.
+ if (!preparedModel->ExecuteWithDummyInputs())
+ {
+ return FailPrepareModel(V1_3::ErrorStatus::GENERAL_FAILURE, "Network could not be executed", cb);
+ }
+
+ if (clTunedParameters &&
+ options.GetClTunedParametersMode() == armnn::IGpuAccTunedParameters::Mode::UpdateTunedParameters)
+ {
+ // Now that we've done one inference the CL kernel parameters will have been tuned, so save the updated file.
+ try
+ {
+ clTunedParameters->Save(options.GetClTunedParametersFile().c_str());
+ }
+ catch (std::exception& error)
+ {
+ ALOGE("ArmnnDriverImpl::prepareModel: Failed to save CL tuned parameters file '%s': %s",
+ options.GetClTunedParametersFile().c_str(), error.what());
+ }
+ }
+
+ NotifyCallbackAndCheck(cb, V1_3::ErrorStatus::NONE, preparedModel.release());
+
+ return V1_3::ErrorStatus::NONE;
+}
+
+Return<void> ArmnnDriverImpl::getCapabilities_1_3(const armnn::IRuntimePtr& runtime,
+ V1_3::IDevice::getCapabilities_1_3_cb cb)
+{
+ ALOGV("hal_1_3::ArmnnDriverImpl::getCapabilities()");
+
+ V1_3::Capabilities capabilities;
+
+ float defaultValue = .1f;
+
+ if (runtime)
+ {
+ capabilities.relaxedFloat32toFloat16PerformanceScalar.execTime =
+ ParseSystemProperty(g_RelaxedFloat32toFloat16PerformanceExecTime, defaultValue);
+
+ capabilities.relaxedFloat32toFloat16PerformanceTensor.powerUsage =
+ ParseSystemProperty(g_RelaxedFloat32toFloat16PerformancePowerUsage, defaultValue);
+
+ // Set the base value for all operand types
+ capabilities.operandPerformance = nonExtensionOperandPerformance<HalVersion::V1_3>({FLT_MAX, FLT_MAX});
+
+ // Load supported operand types
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_FLOAT32,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorFloat32PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorFloat32PerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::FLOAT32,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeFloat32PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeFloat32PerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_FLOAT16,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorFloat16PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorFloat16PerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::FLOAT16,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeFloat16PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeFloat16PerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_QUANT8_ASYMM,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorQuant8AsymmPerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorQuant8AsymmPerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_QUANT8_SYMM,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorQuant8SymmPerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorQuant8SymmPerformancePowerUsage, defaultValue)
+ });
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorQuant8AsymmSignedPerformanceExecTime,
+ defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorQuant8AsymmSignedPerformancePowerUsage,
+ defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_QUANT16_SYMM,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorQuant16SymmPerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorQuant16SymmPerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL,
+ {
+ .execTime =
+ ParseSystemProperty(g_OperandTypeTensorQuant8SymmPerChannelPerformanceExecTime, defaultValue),
+ .powerUsage =
+ ParseSystemProperty(g_OperandTypeTensorQuant8SymmPerChannelPerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::TENSOR_INT32,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeTensorInt32PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeTensorInt32PerformancePowerUsage, defaultValue)
+ });
+
+ update(&capabilities.operandPerformance, V1_3::OperandType::INT32,
+ {
+ .execTime = ParseSystemProperty(g_OperandTypeInt32PerformanceExecTime, defaultValue),
+ .powerUsage = ParseSystemProperty(g_OperandTypeInt32PerformancePowerUsage, defaultValue)
+ });
+
+ cb(V1_3::ErrorStatus::NONE, capabilities);
+ }
+ else
+ {
+ capabilities.relaxedFloat32toFloat16PerformanceScalar.execTime = 0;
+ capabilities.relaxedFloat32toFloat16PerformanceTensor.execTime = 0;
+
+ // Set the base value for all operand types
+ capabilities.operandPerformance = nonExtensionOperandPerformance<HalVersion::V1_3>({0.f, 0.0f});
+
+ cb(V1_3::ErrorStatus::DEVICE_UNAVAILABLE, capabilities);
+ }
+
+ return Void();
+}
+
+} // namespace hal_1_3
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.3/ArmnnDriverImpl.hpp b/1.3/ArmnnDriverImpl.hpp
new file mode 100644
index 0000000..8a665ea
--- /dev/null
+++ b/1.3/ArmnnDriverImpl.hpp
@@ -0,0 +1,40 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <HalInterfaces.h>
+
+#include "../DriverOptions.hpp"
+
+#include <armnn/ArmNN.hpp>
+
+using namespace android::nn::hal;
+
+namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+namespace V1_2 = ::android::hardware::neuralnetworks::V1_2;
+namespace V1_3 = ::android::hardware::neuralnetworks::V1_3;
+
+namespace armnn_driver
+{
+namespace hal_1_3
+{
+
+class ArmnnDriverImpl
+{
+public:
+ static Return<V1_3::ErrorStatus> prepareArmnnModel_1_3(const armnn::IRuntimePtr& runtime,
+ const armnn::IGpuAccTunedParametersPtr& clTunedParameters,
+ const DriverOptions& options,
+ const V1_3::Model& model,
+ const android::sp<V1_3::IPreparedModelCallback>& cb,
+ bool float32ToFloat16 = false);
+
+ static Return<void> getCapabilities_1_3(const armnn::IRuntimePtr& runtime,
+ V1_3::IDevice::getCapabilities_1_3_cb cb);
+};
+
+} // namespace hal_1_3
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.3/HalPolicy.cpp b/1.3/HalPolicy.cpp
new file mode 100644
index 0000000..0de7573
--- /dev/null
+++ b/1.3/HalPolicy.cpp
@@ -0,0 +1,451 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "HalPolicy.hpp"
+
+namespace armnn_driver
+{
+namespace hal_1_3
+{
+
+using namespace armnn;
+
+namespace
+{
+
+} // anonymouse namespace
+
+bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data)
+{
+ switch (operation.type)
+ {
+ case V1_3::OperationType::ABS:
+ return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Abs);
+ case V1_3::OperationType::ADD:
+ return ConvertAdd(operation, model, data);
+ case V1_3::OperationType::ARGMAX:
+ return ConvertArgMinMax(operation, model, data, ArgMinMaxFunction::Max);
+ case V1_3::OperationType::ARGMIN:
+ return ConvertArgMinMax(operation, model, data, ArgMinMaxFunction::Min);
+ case V1_3::OperationType::AVERAGE_POOL_2D:
+ return ConvertAveragePool2d(operation, model, data);
+ case V1_3::OperationType::BATCH_TO_SPACE_ND:
+ return ConvertBatchToSpaceNd(operation, model, data);
+ case V1_3::OperationType::CONCATENATION:
+ return ConvertConcatenation(operation, model, data);
+ case V1_3::OperationType::CONV_2D:
+ return ConvertConv2d(operation, model, data);
+ case V1_3::OperationType::DEPTH_TO_SPACE:
+ return ConvertDepthToSpace(operation, model, data);
+ case V1_3::OperationType::DEPTHWISE_CONV_2D:
+ return ConvertDepthwiseConv2d(operation, model, data);
+ case V1_3::OperationType::DEQUANTIZE:
+ return ConvertDequantize(operation, model, data);
+ case V1_3::OperationType::DIV:
+ return ConvertDiv(operation, model, data);
+ case V1_3::OperationType::EQUAL:
+ return ConvertComparison(operation, model, data, ComparisonOperation::Equal);
+ case V1_3::OperationType::EXPAND_DIMS:
+ return ConvertExpandDims(operation, model, data);
+ case V1_3::OperationType::FLOOR:
+ return ConvertFloor(operation, model, data);
+ case V1_3::OperationType::FULLY_CONNECTED:
+ return ConvertFullyConnected(operation, model, data);
+ case V1_3::OperationType::GREATER:
+ return ConvertComparison(operation, model, data, ComparisonOperation::Greater);
+ case V1_3::OperationType::GREATER_EQUAL:
+ return ConvertComparison(operation, model, data, ComparisonOperation::GreaterOrEqual);
+ case V1_3::OperationType::GROUPED_CONV_2D:
+ return ConvertGroupedConv2d(operation, model, data);
+ case V1_3::OperationType::INSTANCE_NORMALIZATION:
+ return ConvertInstanceNormalization(operation, model, data);
+ case V1_3::OperationType::L2_NORMALIZATION:
+ return ConvertL2Normalization(operation, model, data);
+ case V1_3::OperationType::L2_POOL_2D:
+ return ConvertL2Pool2d(operation, model, data);
+ case V1_3::OperationType::LESS:
+ return ConvertComparison(operation, model, data, ComparisonOperation::Less);
+ case V1_3::OperationType::LESS_EQUAL:
+ return ConvertComparison(operation, model, data, ComparisonOperation::LessOrEqual);
+ case V1_3::OperationType::LOCAL_RESPONSE_NORMALIZATION:
+ return ConvertLocalResponseNormalization(operation, model, data);
+ case V1_3::OperationType::LOGISTIC:
+ return ConvertLogistic(operation, model, data);
+ case V1_3::OperationType::LOG_SOFTMAX:
+ return ConvertLogSoftmax(operation, model, data);
+ case V1_3::OperationType::LSTM:
+ return ConvertLstm(operation, model, data);
+ case V1_3::OperationType::MAX_POOL_2D:
+ return ConvertMaxPool2d(operation, model, data);
+ case V1_3::OperationType::MAXIMUM:
+ return ConvertMaximum(operation, model, data);
+ case V1_3::OperationType::MEAN:
+ return ConvertMean(operation, model, data);
+ case V1_3::OperationType::MINIMUM:
+ return ConvertMinimum(operation, model, data);
+ case V1_3::OperationType::MUL:
+ return ConvertMul(operation, model, data);
+ case V1_3::OperationType::NEG:
+ return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Neg);
+ case V1_3::OperationType::NOT_EQUAL:
+ return ConvertComparison(operation, model, data, ComparisonOperation::NotEqual);
+ case V1_3::OperationType::PAD:
+ return ConvertPad(operation, model, data);
+ case V1_3::OperationType::PAD_V2:
+ return ConvertPadV2(operation, model, data);
+ case V1_3::OperationType::PRELU:
+ return ConvertPrelu(operation, model, data);
+ case V1_3::OperationType::QUANTIZE:
+ return ConvertQuantize(operation, model, data);
+ case V1_3::OperationType::QUANTIZED_16BIT_LSTM:
+ return ConvertQuantizedLstm(operation, model, data);
+ case V1_3::OperationType::RELU:
+ return ConvertReLu(operation, model, data);
+ case V1_3::OperationType::RELU1:
+ return ConvertReLu1(operation, model, data);
+ case V1_3::OperationType::RELU6:
+ return ConvertReLu6(operation, model, data);
+ case V1_3::OperationType::RESHAPE:
+ return ConvertReshape(operation, model, data);
+ case V1_3::OperationType::RESIZE_BILINEAR:
+ return ConvertResize(operation, model, data, ResizeMethod::Bilinear);
+ case V1_3::OperationType::RESIZE_NEAREST_NEIGHBOR:
+ return ConvertResize(operation, model, data, ResizeMethod::NearestNeighbor);
+ case V1_3::OperationType::RSQRT:
+ return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Rsqrt);
+ case V1_3::OperationType::SQRT:
+ return ConvertSqrt(operation, model, data);
+ case V1_3::OperationType::SQUEEZE:
+ return ConvertSqueeze(operation, model, data);
+ case V1_3::OperationType::STRIDED_SLICE:
+ return ConvertStridedSlice(operation, model, data);
+ case V1_3::OperationType::TRANSPOSE:
+ return ConvertTranspose(operation, model, data);
+ case V1_3::OperationType::TRANSPOSE_CONV_2D:
+ return ConvertTransposeConv2d(operation, model, data);
+ case V1_3::OperationType::SOFTMAX:
+ return ConvertSoftmax(operation, model, data);
+ case V1_3::OperationType::SPACE_TO_BATCH_ND :
+ return ConvertSpaceToBatchNd(operation, model, data);
+ case V1_3::OperationType::SPACE_TO_DEPTH:
+ return ConvertSpaceToDepth(operation, model, data);
+ case V1_3::OperationType::SUB:
+ return ConvertSub(operation, model, data);
+ case V1_3::OperationType::TANH:
+ return ConvertTanH(operation, model, data);
+ default:
+ return Fail("%s: Operation type %s not supported in ArmnnDriver",
+ __func__, toString(operation.type).c_str());
+ }
+}
+
+bool HalPolicy::ConvertAdd(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertAdd()");
+ return ::ConvertAdd<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertArgMinMax(const V1_3::Operation& operation,
+ const V1_3::Model& model,
+ ConversionData& data,
+ armnn::ArgMinMaxFunction argMinMaxFunction)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertArgMinMax()");
+ return ::ConvertArgMinMax<hal_1_3::HalPolicy>(operation, model, data, argMinMaxFunction);
+}
+
+bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertAveragePool2d()");
+ return ConvertPooling2d<hal_1_3::HalPolicy>(operation, __func__, PoolingAlgorithm::Average, model, data);
+}
+
+bool HalPolicy::ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertBatchToSpaceNd()");
+ return ::ConvertBatchToSpaceNd<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertComparison(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ ComparisonOperation comparisonOperation)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertComparison()");
+ return ::ConvertComparison_1_2<hal_1_3::HalPolicy>(operation, model, data, comparisonOperation);
+}
+
+
+bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertConcatenation()");
+ return ::ConvertConcatenation<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertConv2d()");
+ return ::ConvertConv2d_1_2<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertDepthToSpace()");
+ return ::ConvertDepthToSpace<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertDepthwiseConv2d()");
+ return ::ConvertDepthwiseConv2d_1_2<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertDequantize()");
+ return ::ConvertDequantize_1_2<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertDiv()");
+ return ::ConvertDiv<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertElementwiseUnary(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ UnaryOperation unaryOperation)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertElementwiseUnary()");
+ return ::ConvertElementwiseUnary<hal_1_3::HalPolicy>(operation, model, data, unaryOperation);
+}
+
+bool HalPolicy::ConvertExpandDims(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertExpandDims()");
+ return ::ConvertExpandDims<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertFloor()");
+ return ::ConvertFloor<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertFullyConnected()");
+ return ::ConvertFullyConnected<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertGroupedConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertGroupedConv2d()");
+ return ::ConvertGroupedConv2d<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertInstanceNormalization(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertInstanceNormalization()");
+ return ::ConvertInstanceNormalization<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertL2Normalization()");
+ return ::ConvertL2Normalization<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertL2Pool2d()");
+ return ConvertPooling2d<hal_1_3::HalPolicy>(operation, __func__, PoolingAlgorithm::L2, model, data);
+}
+
+bool HalPolicy::ConvertLocalResponseNormalization(const Operation& operation,
+ const Model& model,
+ ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertLocalResponseNormalization()");
+ return ::ConvertLocalResponseNormalization<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertLogistic()");
+ return ::ConvertLogistic<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertLogSoftmax(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertLogSoftmax()");
+ return ::ConvertLogSoftmax<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertLstm()");
+ return ::ConvertLstm<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertMaxPool2d()");
+ return ConvertPooling2d<hal_1_3::HalPolicy>(operation, __func__, PoolingAlgorithm::Max, model, data);
+}
+
+bool HalPolicy::ConvertMaximum(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertConvertMaximumMaximum()");
+ return ::ConvertMaximum<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertMean()");
+ return ::ConvertMean<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertMinimum(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertMinimum()");
+ return ::ConvertMinimum<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertMul()");
+ return ::ConvertMul<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertPad(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertPad()");
+ return ::ConvertPad<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertPadV2(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertPadV2()");
+ return ::ConvertPadV2<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertPrelu(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertPrelu()");
+ return ::ConvertPrelu<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertQuantize()");
+ return ::ConvertQuantize<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertQuantizedLstm()");
+ return ::ConvertQuantizedLstm<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertReLu()");
+ return ::ConvertReLu<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertReLu1()");
+ return ::ConvertReLu1<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertReLu6()");
+ return ::ConvertReLu6<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertReshape()");
+ return ::ConvertReshape<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertResize(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ ResizeMethod resizeMethod)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertResize()");
+ return ::ConvertResize<hal_1_3::HalPolicy>(operation, model, data, resizeMethod);
+}
+
+bool HalPolicy::ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSpaceToBatchNd()");
+ return ::ConvertSpaceToBatchNd<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSpaceToDepth()");
+ return ::ConvertSpaceToDepth<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSoftmax()");
+ return ::ConvertSoftmax<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSub()");
+ return ::ConvertSub<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertTanH()");
+ return ::ConvertTanH<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertTransposeConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertTransposeConv2d()");
+ return ::ConvertTransposeConv2d<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertSqrt(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSqrt()");
+ ActivationDescriptor desc;
+ desc.m_Function = ActivationFunction::Sqrt;
+
+ return ::ConvertToActivation<hal_1_3::HalPolicy>(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertSqueeze()");
+ return ::ConvertSqueeze<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertStridedSlice(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertStridedSlice()");
+ return ::ConvertStridedSlice<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+bool HalPolicy::ConvertTranspose(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_3::HalPolicy::ConvertTranspose()");
+ return ::ConvertTranspose<hal_1_3::HalPolicy>(operation, model, data);
+}
+
+} // namespace hal_1_3
+} // namespace armnn_driver
diff --git a/1.3/HalPolicy.hpp b/1.3/HalPolicy.hpp
new file mode 100644
index 0000000..f7771a6
--- /dev/null
+++ b/1.3/HalPolicy.hpp
@@ -0,0 +1,150 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "../ConversionUtils.hpp"
+#include "../ConversionUtils_1_2.hpp"
+
+#include <HalInterfaces.h>
+
+#include <armnn/Types.hpp>
+
+namespace V1_3 = ::android::hardware::neuralnetworks::V1_3;
+
+namespace armnn_driver
+{
+namespace hal_1_3
+{
+
+class HalPolicy
+{
+public:
+ using Model = V1_3::Model;
+ using Operand = V1_3::Operand;
+ using OperandLifeTime = V1_3::OperandLifeTime;
+ using OperandType = V1_3::OperandType;
+ using Operation = V1_3::Operation;
+ using OperationType = V1_3::OperationType;
+ using ExecutionCallback = V1_3::IExecutionCallback;
+ using getSupportedOperations_cb = V1_3::IDevice::getSupportedOperations_1_3_cb;
+ using ErrorStatus = V1_3::ErrorStatus;
+
+ static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
+
+private:
+ static bool ConvertAdd(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertArgMinMax(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ armnn::ArgMinMaxFunction argMinMaxFunction);
+
+ static bool ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertComparison(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ armnn::ComparisonOperation comparisonOperation);
+
+ static bool ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertDiv(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertElementwiseUnary(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ armnn::UnaryOperation unaryOperation);
+
+ static bool ConvertExpandDims(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertFloor(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertGroupedConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertInstanceNormalization(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertLocalResponseNormalization(const Operation& operation,
+ const Model& model,
+ ConversionData& data);
+
+ static bool ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertLogSoftmax(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertLstm(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMaximum(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMean(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMinimum(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMul(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertPad(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertPadV2(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertPrelu(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReshape(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertResize(const Operation& operation,
+ const Model& model,
+ ConversionData& data,
+ armnn::ResizeMethod resizeMethod);
+
+ static bool ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSqrt(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertStridedSlice(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSub(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertTanH(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertTranspose(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertTransposeConv2d(const Operation& operation, const Model& model, ConversionData& data);
+};
+
+} // namespace hal_1_3
+} // namespace armnn_driver
diff --git a/Android.mk b/Android.mk
index 6cc85ee..bac6db1 100644
--- a/Android.mk
+++ b/Android.mk
@@ -427,6 +427,114 @@
endif # PLATFORM_VERSION == Q
+ifeq ($(R_OR_LATER),1)
+# The following target is available starting from Android R
+
+#######################
+# libarmnn-driver@1.3 #
+#######################
+include $(CLEAR_VARS)
+
+LOCAL_MODULE := libarmnn-driver@1.3
+LOCAL_MODULE_TAGS := optional
+LOCAL_ARM_MODE := arm
+LOCAL_PROPRIETARY_MODULE := true
+# Mark source files as dependent on Android.mk
+LOCAL_ADDITIONAL_DEPENDENCIES := $(LOCAL_PATH)/Android.mk
+
+LOCAL_C_INCLUDES := \
+ $(ARMNN_HEADER_PATH) \
+ $(ARMNN_UTILS_HEADER_PATH) \
+ $(OPENCL_HEADER_PATH) \
+ $(NN_HEADER_PATH)
+
+LOCAL_CFLAGS := \
+ -std=$(CPP_VERSION) \
+ -fexceptions \
+ -Werror \
+ -Wno-format-security \
+ -DBOOST_NO_AUTO_PTR \
+ -DARMNN_ANDROID_NN_V1_3 \
+ -DARMNN_ANDROID_R
+
+ifeq ($(ARMNN_DRIVER_DEBUG),1)
+LOCAL_CFLAGS+= \
+ -UNDEBUG
+endif # ARMNN_DRIVER_DEBUG == 1
+
+ifeq ($(ARMNN_COMPUTE_CL_ENABLED),1)
+LOCAL_CFLAGS += \
+ -DARMCOMPUTECL_ENABLED
+endif # ARMNN_COMPUTE_CL_ENABLED == 1
+
+ifeq ($(ARMNN_COMPUTE_NEON_ENABLED),1)
+LOCAL_CFLAGS += \
+ -DARMCOMPUTENEON_ENABLED
+endif # ARMNN_COMPUTE_NEON_ENABLED == 1
+
+ifeq ($(ARMNN_REF_ENABLED),1)
+LOCAL_CFLAGS += \
+ -DARMNNREF_ENABLED
+endif # ARMNN_REF_ENABLED == 1
+
+LOCAL_SRC_FILES := \
+ 1.0/ArmnnDriverImpl.cpp \
+ 1.0/HalPolicy.cpp \
+ 1.1/ArmnnDriverImpl.cpp \
+ 1.1/HalPolicy.cpp \
+ 1.2/ArmnnDriverImpl.cpp \
+ 1.2/HalPolicy.cpp \
+ 1.3/ArmnnDriverImpl.cpp \
+ 1.3/HalPolicy.cpp \
+ ArmnnDevice.cpp \
+ ArmnnDriverImpl.cpp \
+ ArmnnPreparedModel.cpp \
+ ArmnnPreparedModel_1_2.cpp \
+ ArmnnPreparedModel_1_3.cpp \
+ ConversionUtils.cpp \
+ DriverOptions.cpp \
+ ModelToINetworkConverter.cpp \
+ RequestThread.cpp \
+ Utils.cpp
+
+LOCAL_STATIC_LIBRARIES := \
+ libneuralnetworks_common \
+ libboost_log \
+ libboost_program_options \
+ libboost_system \
+ libboost_thread \
+ libboost_filesystem \
+ arm_compute_library
+
+LOCAL_WHOLE_STATIC_LIBRARIES := libarmnn
+
+LOCAL_SHARED_LIBRARIES := \
+ libbase \
+ libhidlbase \
+ libhidltransport \
+ libhidlmemory \
+ liblog \
+ libutils \
+ libnativewindow \
+ libui \
+ libfmq \
+ libcutils \
+ android.hidl.allocator@1.0 \
+ android.hidl.memory@1.0 \
+ android.hardware.neuralnetworks@1.0 \
+ android.hardware.neuralnetworks@1.1 \
+ android.hardware.neuralnetworks@1.2 \
+ android.hardware.neuralnetworks@1.3
+
+ifeq ($(ARMNN_COMPUTE_CL_ENABLED),1)
+LOCAL_SHARED_LIBRARIES+= \
+ libOpenCL
+endif
+
+include $(BUILD_STATIC_LIBRARY)
+
+endif # PLATFORM_VERSION == R
+
#####################################################
# android.hardware.neuralnetworks@1.0-service-armnn #
#####################################################
@@ -714,6 +822,84 @@
endif # PLATFORM_VERSION == Q
+ifeq ($(R_OR_LATER),1)
+# The following target is available starting from Android R
+
+#####################################################
+# android.hardware.neuralnetworks@1.3-service-armnn #
+#####################################################
+include $(CLEAR_VARS)
+
+LOCAL_MODULE := android.hardware.neuralnetworks@1.3-service-armnn
+LOCAL_INIT_RC := android.hardware.neuralnetworks@1.3-service-armnn.rc
+LOCAL_MODULE_TAGS := optional
+LOCAL_ARM_MODE := arm
+LOCAL_MODULE_RELATIVE_PATH := hw
+LOCAL_PROPRIETARY_MODULE := true
+# Mark source files as dependent on Android.mk
+LOCAL_ADDITIONAL_DEPENDENCIES := $(LOCAL_PATH)/Android.mk
+
+LOCAL_C_INCLUDES := \
+ $(ARMNN_HEADER_PATH) \
+ $(NN_HEADER_PATH)
+
+LOCAL_CFLAGS := \
+ -std=$(CPP_VERSION) \
+ -fexceptions \
+ -DARMNN_ANDROID_NN_V1_3 \
+ -DBOOST_NO_AUTO_PTR \
+ -DARMNN_ANDROID_R
+
+ifeq ($(ARMNN_DRIVER_DEBUG),1)
+LOCAL_CFLAGS += \
+ -UNDEBUG
+endif # ARMNN_DRIVER_DEBUG == 1
+
+LOCAL_SRC_FILES := \
+ service.cpp
+
+LOCAL_STATIC_LIBRARIES := \
+ libneuralnetworks_common \
+ libboost_log \
+ libboost_program_options \
+ libboost_system \
+ libboost_thread \
+ libboost_filesystem \
+ arm_compute_library
+
+LOCAL_WHOLE_STATIC_LIBRARIES := \
+ libarmnn-driver@1.3
+
+LOCAL_SHARED_LIBRARIES := \
+ libbase \
+ libhidlbase \
+ libhidltransport \
+ libhidlmemory \
+ libdl \
+ libhardware \
+ liblog \
+ libtextclassifier_hash \
+ libutils \
+ libnativewindow \
+ libui \
+ libfmq \
+ libcutils \
+ android.hidl.allocator@1.0 \
+ android.hidl.memory@1.0 \
+ android.hardware.neuralnetworks@1.0 \
+ android.hardware.neuralnetworks@1.1 \
+ android.hardware.neuralnetworks@1.2 \
+ android.hardware.neuralnetworks@1.3
+
+ifeq ($(ARMNN_COMPUTE_CL_ENABLED),1)
+LOCAL_SHARED_LIBRARIES+= \
+ libOpenCL
+endif
+
+include $(BUILD_EXECUTABLE)
+
+endif # PLATFORM_VERSION == R
+
##########################
# armnn module and tests #
##########################
diff --git a/ArmnnDriver.hpp b/ArmnnDriver.hpp
index d961f86..a6fd9b2 100644
--- a/ArmnnDriver.hpp
+++ b/ArmnnDriver.hpp
@@ -9,7 +9,28 @@
#include <log/log.h>
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+
+#include "1.1/ArmnnDriver.hpp"
+#include "1.2/ArmnnDriver.hpp"
+#include "1.3/ArmnnDriver.hpp"
+
+namespace armnn_driver
+{
+
+class ArmnnDriver : public hal_1_3::ArmnnDriver
+{
+public:
+ ArmnnDriver(DriverOptions options)
+ : hal_1_3::ArmnnDriver(std::move(options))
+ {
+ ALOGV("ArmnnDriver::ArmnnDriver()");
+ }
+ ~ArmnnDriver() {}
+};
+
+} // namespace armnn_driver
+#elif ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
#include "1.1/ArmnnDriver.hpp"
#include "1.2/ArmnnDriver.hpp"
diff --git a/ArmnnDriverImpl.cpp b/ArmnnDriverImpl.cpp
index eab9598..9c6d51f 100644
--- a/ArmnnDriverImpl.cpp
+++ b/ArmnnDriverImpl.cpp
@@ -8,10 +8,16 @@
#include "ArmnnDriverImpl.hpp"
#include "ArmnnPreparedModel.hpp"
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) // Using ::android::hardware::neuralnetworks::V1_2
#include "ArmnnPreparedModel_1_2.hpp"
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_2
+#include "ArmnnPreparedModel_1_3.hpp"
+#endif
+
+#include "Utils.hpp"
+
#include "ModelToINetworkConverter.hpp"
#include "SystemPropertiesUtils.hpp"
#include <ValidateHal.h>
@@ -227,14 +233,14 @@
if (!runtime)
{
- cb(V1_0::ErrorStatus::DEVICE_UNAVAILABLE, result);
+ cb(HalErrorStatus::DEVICE_UNAVAILABLE, result);
return Void();
}
// Run general model validation, if this doesn't pass we shouldn't analyse the model anyway.
if (!android::nn::validateModel(model))
{
- cb(V1_0::ErrorStatus::INVALID_ARGUMENT, result);
+ cb(HalErrorStatus::INVALID_ARGUMENT, result);
return Void();
}
@@ -246,20 +252,22 @@
if (modelConverter.GetConversionResult() != ConversionResult::Success
&& modelConverter.GetConversionResult() != ConversionResult::UnsupportedFeature)
{
- cb(V1_0::ErrorStatus::GENERAL_FAILURE, result);
+ cb(HalErrorStatus::GENERAL_FAILURE, result);
return Void();
}
// Check each operation if it was converted successfully and copy the flags
// into the result (vector<bool>) that we need to return to Android.
- result.reserve(model.operations.size());
- for (uint32_t operationIdx = 0; operationIdx < model.operations.size(); operationIdx++)
+ result.reserve(getMainModel(model).operations.size());
+ for (uint32_t operationIdx = 0;
+ operationIdx < getMainModel(model).operations.size();
+ ++operationIdx)
{
bool operationSupported = modelConverter.IsOperationSupported(operationIdx);
result.push_back(operationSupported);
}
- cb(V1_0::ErrorStatus::NONE, result);
+ cb(HalErrorStatus::NONE, result);
return Void();
}
@@ -286,4 +294,10 @@
template class ArmnnDriverImpl<hal_1_2::HalPolicy>;
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3
+template class ArmnnDriverImpl<hal_1_1::HalPolicy>;
+template class ArmnnDriverImpl<hal_1_2::HalPolicy>;
+template class ArmnnDriverImpl<hal_1_3::HalPolicy>;
+#endif
+
} // namespace armnn_driver
diff --git a/ArmnnDriverImpl.hpp b/ArmnnDriverImpl.hpp
index dfaafb3..cdff905 100644
--- a/ArmnnDriverImpl.hpp
+++ b/ArmnnDriverImpl.hpp
@@ -20,6 +20,11 @@
namespace V1_2 = ::android::hardware::neuralnetworks::V1_2;
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+namespace V1_2 = ::android::hardware::neuralnetworks::V1_2;
+namespace V1_3 = ::android::hardware::neuralnetworks::V1_3;
+#endif
+
namespace armnn_driver
{
@@ -36,6 +41,7 @@
public:
using HalModel = typename HalPolicy::Model;
using HalGetSupportedOperations_cb = typename HalPolicy::getSupportedOperations_cb;
+ using HalErrorStatus = typename HalPolicy::ErrorStatus;
static Return<void> getSupportedOperations(
const armnn::IRuntimePtr& runtime,
diff --git a/ArmnnPreparedModel.cpp b/ArmnnPreparedModel.cpp
index d095e41..f990d3b 100644
--- a/ArmnnPreparedModel.cpp
+++ b/ArmnnPreparedModel.cpp
@@ -294,7 +294,7 @@
{
std::vector<std::vector<char>> storage;
armnn::InputTensors inputTensors;
- for (unsigned int i = 0; i < m_Model.inputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
{
const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
storage.emplace_back(inputTensorInfo.GetNumBytes());
@@ -304,7 +304,7 @@
}
armnn::OutputTensors outputTensors;
- for (unsigned int i = 0; i < m_Model.outputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
{
const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
storage.emplace_back(outputTensorInfo.GetNumBytes());
@@ -349,4 +349,10 @@
template class ArmnnPreparedModel<hal_1_1::HalPolicy>;
template class ArmnnPreparedModel<hal_1_2::HalPolicy>;
#endif
+
+#ifdef ARMNN_ANDROID_NN_V1_3
+template class ArmnnPreparedModel<hal_1_1::HalPolicy>;
+template class ArmnnPreparedModel<hal_1_2::HalPolicy>;
+template class ArmnnPreparedModel<hal_1_3::HalPolicy>;
+#endif
} // namespace armnn_driver
diff --git a/ArmnnPreparedModel_1_2.cpp b/ArmnnPreparedModel_1_2.cpp
index 5031c5f..76ef426 100644
--- a/ArmnnPreparedModel_1_2.cpp
+++ b/ArmnnPreparedModel_1_2.cpp
@@ -2,9 +2,6 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
-// Note: the ArmnnBurstExecutorWithCache in this file is based on Android code
-// under the Apache 2.0 license. See comment below for details.
-//
#define LOG_TAG "ArmnnDriver"
@@ -215,27 +212,6 @@
return Execute(request, measureTiming, cb);
}
-OutputShape ComputeShape(const armnn::TensorInfo& info)
-{
- OutputShape shape;
-
- hidl_vec<uint32_t> dimensions;
-
- armnn::TensorShape tensorShape = info.GetShape();
- const unsigned int numDims = tensorShape.GetNumDimensions();
- dimensions.resize(numDims);
-
- for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx)
- {
- dimensions[outputIdx] = tensorShape[outputIdx];
- }
-
- shape.dimensions = dimensions;
- shape.isSufficient = true;
-
- return shape;
-}
-
template<typename HalVersion>
Return<V1_0::ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::PrepareMemoryForInputs(
armnn::InputTensors& inputs,
@@ -348,27 +324,6 @@
return V1_0::ErrorStatus::NONE;
}
-void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools)
-{
- if (memPools.empty())
- {
- return;
- }
- // Commit output buffers.
- // Note that we update *all* pools, even if they aren't actually used as outputs -
- // this is simpler and is what the CpuExecutor does.
- for (auto& pool : memPools)
- {
- // Type android::nn::RunTimePoolInfo has changed between Android P & Q and Android R, where
- // update() has been removed and flush() added.
-#if defined(ARMNN_ANDROID_R) // Use the new Android implementation.
- pool.flush();
-#else
- pool.update();
-#endif
- }
-}
-
template<typename HalVersion>
Return<void> ArmnnPreparedModel_1_2<HalVersion>::executeSynchronously(const V1_0::Request& request,
MeasureTiming measureTiming,
@@ -514,7 +469,7 @@
{
std::vector<std::vector<char>> storage;
armnn::InputTensors inputTensors;
- for (unsigned int i = 0; i < m_Model.inputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
{
const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
storage.emplace_back(inputTensorInfo.GetNumBytes());
@@ -524,7 +479,7 @@
}
armnn::OutputTensors outputTensors;
- for (unsigned int i = 0; i < m_Model.outputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
{
const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
storage.emplace_back(outputTensorInfo.GetNumBytes());
@@ -600,77 +555,6 @@
return V1_0::ErrorStatus::NONE;
}
-
-/// This class is strongly inspired by the default implementation in Android named DefaultBurstExecutorWithCache.
-/// The original code is licensed under Apache-2.0 and can be found at the following link:
-/// https://android.googlesource.com/platform/frameworks/
-/// ml/+/refs/tags/android-10.0.0_r20/nn/common/ExecutionBurstServer.cpp
-class ArmnnBurstExecutorWithCache : public ExecutionBurstServer::IBurstExecutorWithCache {
-public:
- ArmnnBurstExecutorWithCache(V1_2::IPreparedModel* preparedModel)
- : m_PreparedModel(preparedModel)
- {}
-
- bool isCacheEntryPresent(int32_t slot) const override
- {
- const auto it = m_MemoryCache.find(slot);
- return (it != m_MemoryCache.end()) && it->second.valid();
- }
-
- void addCacheEntry(const hidl_memory& memory, int32_t slot) override
- {
- m_MemoryCache[slot] = memory;
- }
-
- void removeCacheEntry(int32_t slot) override
- {
- m_MemoryCache.erase(slot);
- }
-
- std::tuple<V1_0::ErrorStatus, hidl_vec<OutputShape>, Timing> execute(
- const V1_0::Request& request, const std::vector<int32_t>& slots,
- MeasureTiming measure) override
- {
- ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache::execute");
- hidl_vec<hidl_memory> pools(slots.size());
-
- std::transform(slots.begin(), slots.end(), pools.begin(), [this](int32_t slot)
- {
- return m_MemoryCache[slot];
- });
-
- V1_0::Request fullRequest = request;
- fullRequest.pools = std::move(pools);
-
- // Setup Callback
- V1_0::ErrorStatus returnedStatus = V1_0::ErrorStatus::GENERAL_FAILURE;
- hidl_vec<OutputShape> returnedOutputShapes;
- Timing returnedTiming;
- auto cb = [&returnedStatus, &returnedOutputShapes, &returnedTiming](V1_0::ErrorStatus status,
- const hidl_vec<OutputShape>& outputShapes,
- const Timing& timing)
- {
- returnedStatus = status;
- returnedOutputShapes = outputShapes;
- returnedTiming = timing;
- };
-
- // Execute
- ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache executing");
- const Return<void> ret = m_PreparedModel->executeSynchronously(fullRequest, measure, cb);
-
- if (!ret.isOk() || returnedStatus != V1_0::ErrorStatus::NONE)
- {
- ALOGE("ArmnnPreparedModel_1_2::BurstExecutorWithCache::error executing");
- }
- return std::make_tuple(returnedStatus, std::move(returnedOutputShapes), returnedTiming);
- }
-
-private:
- V1_2::IPreparedModel* const m_PreparedModel;
- std::map<int, hidl_memory> m_MemoryCache;
-};
-
template<typename HalVersion>
Return<void> ArmnnPreparedModel_1_2<HalVersion>::configureExecutionBurst(
const sp<V1_2::IBurstCallback>& callback,
@@ -679,12 +563,10 @@
V1_2::IPreparedModel::configureExecutionBurst_cb cb)
{
ALOGV("ArmnnPreparedModel_1_2::configureExecutionBurst");
- const std::shared_ptr<ArmnnBurstExecutorWithCache> executorWithCache =
- std::make_shared<ArmnnBurstExecutorWithCache>(this);
const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback,
requestChannel,
resultChannel,
- executorWithCache);
+ this);
if (burst == nullptr)
{
@@ -697,9 +579,7 @@
return Void();
}
-
-
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
template class ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>;
template bool ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>::ExecuteGraph<CallbackContext_1_2>(
std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
diff --git a/ArmnnPreparedModel_1_3.cpp b/ArmnnPreparedModel_1_3.cpp
new file mode 100644
index 0000000..155f8b2
--- /dev/null
+++ b/ArmnnPreparedModel_1_3.cpp
@@ -0,0 +1,698 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#define LOG_TAG "ArmnnDriver"
+
+#include "ArmnnPreparedModel_1_3.hpp"
+#include "Utils.hpp"
+
+#include <Utils.h>
+#include <boost/format.hpp>
+#include <log/log.h>
+#include <OperationsUtils.h>
+#include <ExecutionBurstServer.h>
+#include <ValidateHal.h>
+
+#include <cassert>
+#include <cinttypes>
+
+using namespace android;
+using namespace android::hardware;
+
+namespace {
+
+static const Timing g_NoTiming = {.timeOnDevice = UINT64_MAX, .timeInDriver = UINT64_MAX};
+using namespace armnn_driver;
+using TimePoint = std::chrono::steady_clock::time_point;
+
+TimePoint Now()
+{
+ return std::chrono::steady_clock::now();
+}
+
+unsigned long MicrosecondsDuration(TimePoint endPoint, TimePoint startPoint)
+{
+ return static_cast<unsigned long>(std::chrono::duration_cast<std::chrono::microseconds>(
+ endPoint - startPoint).count());
+}
+
+void NotifyCallbackAndCheck(const ::android::sp<V1_0::IExecutionCallback>& callback,
+ V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape>,
+ const Timing,
+ std::string callingFunction)
+{
+ Return<void> returned = callback->notify(convertToV1_0(errorStatus));
+ // This check is required, if the callback fails and it isn't checked it will bring down the service
+ if (!returned.isOk())
+ {
+ ALOGE("ArmnnDriver::%s: hidl callback failed to return properly: %s",
+ callingFunction.c_str(), returned.description().c_str());
+ }
+}
+
+void NotifyCallbackAndCheck(const ::android::sp<V1_2::IExecutionCallback>& callback,
+ V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing timing,
+ std::string callingFunction)
+{
+ Return<void> returned = callback->notify_1_2(convertToV1_0(errorStatus), outputShapes, timing);
+ // This check is required, if the callback fails and it isn't checked it will bring down the service
+ if (!returned.isOk())
+ {
+ ALOGE("ArmnnDriver::%s: hidl callback failed to return properly: %s",
+ callingFunction.c_str(), returned.description().c_str());
+ }
+}
+
+void NotifyCallbackAndCheck(const ::android::sp<V1_3::IExecutionCallback>& callback,
+ V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing timing,
+ std::string callingFunction)
+{
+ Return<void> returned = callback->notify_1_3(errorStatus, outputShapes, timing);
+ // This check is required, if the callback fails and it isn't checked it will bring down the service
+ if (!returned.isOk())
+ {
+ ALOGE("ArmnnDriver::%s: hidl callback failed to return properly: %s",
+ callingFunction.c_str(), returned.description().c_str());
+ }
+}
+
+bool ValidateRequestArgument(const RequestArgument& requestArg, const armnn::TensorInfo& tensorInfo)
+{
+ if (requestArg.dimensions.size() != 0)
+ {
+ if (requestArg.dimensions.size() != tensorInfo.GetNumDimensions())
+ {
+ ALOGE("Mismatched dimensions (request argument: %zu, expected: %u)",
+ requestArg.dimensions.size(), tensorInfo.GetNumDimensions());
+ return false;
+ }
+
+ for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d)
+ {
+ if (requestArg.dimensions[d] != tensorInfo.GetShape()[d])
+ {
+ ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)",
+ d, requestArg.dimensions[d], tensorInfo.GetShape()[d]);
+ return false;
+ }
+ }
+ }
+
+ return true;
+}
+
+armnn::Tensor GetTensorForRequestArgument(const RequestArgument& requestArg,
+ const armnn::TensorInfo& tensorInfo,
+ const std::vector<::android::nn::RunTimePoolInfo>& requestPools)
+{
+ if (!ValidateRequestArgument(requestArg, tensorInfo))
+ {
+ return armnn::Tensor();
+ }
+
+ return armnn::Tensor(tensorInfo, GetMemoryFromPool(requestArg.location, requestPools));
+}
+
+inline std::string BuildTensorName(const char* tensorNamePrefix, std::size_t index)
+{
+ return tensorNamePrefix + std::to_string(index);
+}
+
+} // anonymous namespace
+
+using namespace android::hardware;
+
+namespace armnn_driver
+{
+
+template<typename HalVersion>
+RequestThread<ArmnnPreparedModel_1_3, HalVersion, CallbackContext_1_3>
+ ArmnnPreparedModel_1_3<HalVersion>::m_RequestThread;
+
+template<typename HalVersion>
+template<typename TensorBindingCollection>
+void ArmnnPreparedModel_1_3<HalVersion>::DumpTensorsIfRequired(char const* tensorNamePrefix,
+ const TensorBindingCollection& tensorBindings)
+{
+ if (!m_RequestInputsAndOutputsDumpDir.empty())
+ {
+ const std::string requestName = boost::str(boost::format("%1%_%2%.dump") % m_NetworkId % m_RequestCount);
+ for (std::size_t i = 0u; i < tensorBindings.size(); ++i)
+ {
+ DumpTensor(m_RequestInputsAndOutputsDumpDir,
+ requestName,
+ BuildTensorName(tensorNamePrefix, i),
+ tensorBindings[i].second);
+ }
+ }
+}
+
+template<typename HalVersion>
+ArmnnPreparedModel_1_3<HalVersion>::ArmnnPreparedModel_1_3(armnn::NetworkId networkId,
+ armnn::IRuntime* runtime,
+ const V1_3::Model& model,
+ const std::string& requestInputsAndOutputsDumpDir,
+ const bool gpuProfilingEnabled)
+ : m_NetworkId(networkId)
+ , m_Runtime(runtime)
+ , m_Model(model)
+ , m_RequestCount(0)
+ , m_RequestInputsAndOutputsDumpDir(requestInputsAndOutputsDumpDir)
+ , m_GpuProfilingEnabled(gpuProfilingEnabled)
+{
+ // Enable profiling if required.
+ m_Runtime->GetProfiler(m_NetworkId)->EnableProfiling(m_GpuProfilingEnabled);
+}
+
+template<typename HalVersion>
+ArmnnPreparedModel_1_3<HalVersion>::~ArmnnPreparedModel_1_3()
+{
+ // Get a hold of the profiler used by this model.
+ std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId);
+
+ // Unload the network associated with this model.
+ m_Runtime->UnloadNetwork(m_NetworkId);
+
+ // Dump the profiling info to a file if required.
+ DumpJsonProfilingIfRequired(m_GpuProfilingEnabled, m_RequestInputsAndOutputsDumpDir, m_NetworkId, profiler.get());
+}
+
+template<typename HalVersion>
+Return <V1_0::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute(const V1_0::Request& request,
+ const ::android::sp<V1_0::IExecutionCallback>& callback)
+{
+ if (callback.get() == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::execute invalid callback passed");
+ return V1_0::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ auto cb = [callback](V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing& timing,
+ std::string callingFunction)
+ {
+ NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction);
+ };
+
+
+ return convertToV1_0(Execute(convertToV1_3(request), MeasureTiming::NO, cb));
+}
+
+template<typename HalVersion>
+Return <V1_0::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute_1_2(
+ const V1_0::Request& request,
+ MeasureTiming measureTiming,
+ const sp<V1_2::IExecutionCallback>& callback)
+{
+ if (callback.get() == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::execute_1_2 invalid callback passed");
+ return V1_0::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ auto cb = [callback](V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing& timing,
+ std::string callingFunction)
+ {
+ NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction);
+ };
+
+ return convertToV1_0(Execute(convertToV1_3(request), measureTiming, cb));
+}
+
+template<typename HalVersion>
+Return <V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::execute_1_3(
+ const V1_3::Request& request,
+ MeasureTiming measureTiming,
+ const V1_3::OptionalTimePoint&,
+ const sp<V1_3::IExecutionCallback>& callback)
+{
+ if (callback.get() == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::execute_1_3 invalid callback passed");
+ return V1_3::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ auto cb = [callback](V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing& timing,
+ std::string callingFunction)
+ {
+ NotifyCallbackAndCheck(callback, errorStatus, outputShapes, timing, callingFunction);
+ };
+
+ return Execute(request, measureTiming, cb);
+}
+
+template<typename HalVersion>
+Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeFenced(const V1_3::Request&,
+ const hidl_vec<hidl_handle>&,
+ MeasureTiming,
+ const OptionalTimePoint&,
+ const OptionalTimeoutDuration&,
+ executeFenced_cb cb)
+{
+ cb(ErrorStatus::DEVICE_UNAVAILABLE, hidl_handle(nullptr), nullptr);
+ return Void();
+}
+
+template<typename HalVersion>
+Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForInputs(
+ armnn::InputTensors& inputs,
+ const V1_3::Request& request,
+ const std::vector<android::nn::RunTimePoolInfo>& memPools)
+{
+ inputs.reserve(request.inputs.size());
+ for (unsigned int i = 0; i < request.inputs.size(); i++)
+ {
+ const auto& inputArg = request.inputs[i];
+
+ const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
+ const armnn::Tensor inputTensor = GetTensorForRequestArgument(inputArg, inputTensorInfo, memPools);
+
+ if (inputTensor.GetMemoryArea() == nullptr)
+ {
+ ALOGE("Cannot execute request. Error converting request input %u to tensor", i);
+ return V1_3::ErrorStatus::GENERAL_FAILURE;
+ }
+
+ inputs.emplace_back(i, inputTensor);
+ }
+
+ return V1_3::ErrorStatus::NONE;
+}
+
+template<typename HalVersion>
+Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForOutputs(
+ armnn::OutputTensors& outputs,
+ std::vector<OutputShape> &outputShapes,
+ const V1_3::Request& request,
+ const std::vector<android::nn::RunTimePoolInfo>& memPools)
+{
+ outputs.reserve(request.outputs.size());
+ for (unsigned int i = 0; i < request.outputs.size(); i++)
+ {
+ const auto& outputArg = request.outputs[i];
+
+ const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
+ const armnn::Tensor outputTensor = GetTensorForRequestArgument(outputArg, outputTensorInfo, memPools);
+ if (outputTensor.GetMemoryArea() == nullptr)
+ {
+ ALOGE("Cannot execute request. Error converting request output %u to tensor", i);
+ return V1_3::ErrorStatus::GENERAL_FAILURE;
+ }
+
+ const size_t outputSize = outputTensorInfo.GetNumBytes();
+ const size_t bufferSize = memPools.at(outputArg.location.poolIndex).getHidlMemory().size();
+ if (bufferSize < outputSize)
+ {
+ ALOGW("ArmnnPreparedModel_1_3::Execute failed");
+ return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
+ }
+
+ outputs.emplace_back(i, outputTensor);
+ outputShapes[i] = ComputeShape(outputTensorInfo);
+ }
+
+ return V1_3::ErrorStatus::NONE;
+}
+
+template<typename HalVersion>
+std::tuple<V1_3::ErrorStatus, hidl_vec<OutputShape>, Timing, std::string>
+ ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForIO(armnn::InputTensors& inputs,
+ armnn::OutputTensors& outputs,
+ std::vector<android::nn::RunTimePoolInfo>& memPools,
+ const V1_3::Request& request)
+{
+ if (!setRunTimePoolInfosFromMemoryPools(&memPools, request.pools))
+ {
+ return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+ }
+
+ // add the inputs and outputs with their data
+ try
+ {
+ if (PrepareMemoryForInputs(inputs, request, memPools) != V1_3::ErrorStatus::NONE)
+ {
+ return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+ }
+
+ std::vector<OutputShape> outputShapes(request.outputs.size());
+
+ auto errorStatus = PrepareMemoryForOutputs(outputs, outputShapes, request, memPools);
+ if (errorStatus != V1_3::ErrorStatus::NONE)
+ {
+ return {errorStatus, outputShapes, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+ }
+ }
+ catch (armnn::Exception& e)
+ {
+ ALOGW("armnn::Exception caught while preparing for EnqueueWorkload: %s", e.what());
+ return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+ }
+ catch (std::exception& e)
+ {
+ ALOGE("std::exception caught while preparing for EnqueueWorkload: %s", e.what());
+ return {ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+ }
+
+ return {V1_3::ErrorStatus::NONE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute"};
+}
+
+template<typename HalVersion>
+template<typename CallbackContext>
+Return<void> ArmnnPreparedModel_1_3<HalVersion>::ExecuteSynchronously(const V1_3::Request& request,
+ CallbackContext cbCtx)
+{
+ if (cbCtx.ctx.measureTimings == MeasureTiming::YES)
+ {
+ cbCtx.ctx.driverStart = Now();
+ }
+
+ if (!android::nn::validateRequest(convertToV1_3(request), m_Model))
+ {
+ ALOGE("ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model");
+ cbCtx.callback(V1_3::ErrorStatus::INVALID_ARGUMENT,
+ {},
+ g_NoTiming,
+ "ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model");
+ return Void();
+ }
+
+ if (!android::nn::validateRequest(request, m_Model))
+ {
+ ALOGE("ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model");
+ cbCtx.callback(V1_3::ErrorStatus::INVALID_ARGUMENT,
+ {},
+ g_NoTiming,
+ "ArmnnPreparedModel_1_3::ExecuteSynchronously invalid request model");
+ }
+
+
+ // map the memory pool into shared pointers
+ // use a shared memory pools vector on the heap, as it is passed to the request thread
+ auto memPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>();
+
+ // allocate the tensors on the heap, as they are passed to the request thread
+ auto inputs = std::make_shared<armnn::InputTensors>();
+ auto outputs = std::make_shared<armnn::OutputTensors>();
+
+ auto [status, outputShapes, timing, message] = PrepareMemoryForIO(*inputs, *outputs, *memPools, request);
+ if (status != V1_3::ErrorStatus::NONE)
+ {
+ cbCtx.callback(status, outputShapes, timing, message);
+ }
+
+ ALOGV("ArmnnPreparedModel_1_3::ExecuteSynchronously() before Execution");
+
+ ExecuteGraph(memPools, *inputs, *outputs, cbCtx);
+ return Void();
+}
+
+template<typename HalVersion>
+Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeSynchronously(const V1_0::Request& request,
+ MeasureTiming measureTiming,
+ executeSynchronously_cb cb)
+{
+ ALOGV("ArmnnPreparedModel_1_3::executeSynchronously(): %s", GetModelSummary(m_Model).c_str());
+ m_RequestCount++;
+
+ if (cb == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::executeSynchronously invalid callback passed");
+ return Void();
+ }
+
+ auto cbWrapper = [cb](V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing& timing,
+ std::string)
+ {
+ cb(convertToV1_0(errorStatus), outputShapes, timing);
+ };
+
+ CallbackContext_1_3 cbCtx;
+ cbCtx.callback = cbWrapper;
+ cbCtx.ctx.measureTimings = measureTiming;
+
+ ExecuteSynchronously(convertToV1_3(request), cbCtx);
+ return Void();
+}
+
+template<typename HalVersion>
+Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeSynchronously_1_3(const V1_3::Request& request,
+ MeasureTiming measureTiming,
+ const V1_3::OptionalTimePoint& deadline,
+ executeSynchronously_1_3_cb cb)
+{
+ ALOGV("ArmnnPreparedModel_1_3::executeSynchronously_1_3(): %s", GetModelSummary(m_Model).c_str());
+ m_RequestCount++;
+
+ if (cb == nullptr)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::executeSynchronously_1_3 invalid callback passed");
+ return Void();
+ }
+
+ if (deadline.getDiscriminator() != OptionalTimePoint::hidl_discriminator::none)
+ {
+ ALOGE("ArmnnPreparedModel_1_3::executeSynchronously_1_3 invalid request model");
+ cb(V1_3::ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming);
+ return Void();
+ }
+
+ auto cbWrapper = [cb](V1_3::ErrorStatus errorStatus,
+ std::vector<OutputShape> outputShapes,
+ const Timing& timing,
+ std::string)
+ {
+ cb(errorStatus, outputShapes, timing);
+ };
+
+ CallbackContext_1_3 cbCtx;
+ cbCtx.callback = cbWrapper;
+ cbCtx.ctx.measureTimings = measureTiming;
+
+ ExecuteSynchronously(request, cbCtx);
+ return Void();
+}
+
+template<typename HalVersion>
+Return<void> ArmnnPreparedModel_1_3<HalVersion>::configureExecutionBurst(
+ const sp<V1_2::IBurstCallback>& callback,
+ const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+ const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+ V1_3::IPreparedModel::configureExecutionBurst_cb cb)
+{
+ ALOGV("ArmnnPreparedModel_1_3::configureExecutionBurst");
+ const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback,
+ requestChannel,
+ resultChannel,
+ this);
+
+ if (burst == nullptr)
+ {
+ cb(V1_0::ErrorStatus::GENERAL_FAILURE, {});
+ }
+ else
+ {
+ cb(V1_0::ErrorStatus::NONE, burst);
+ }
+ return Void();
+}
+
+template<typename HalVersion>
+template<typename CallbackContext>
+bool ArmnnPreparedModel_1_3<HalVersion>::ExecuteGraph(
+ std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
+ armnn::InputTensors& inputTensors,
+ armnn::OutputTensors& outputTensors,
+ CallbackContext cb)
+{
+ ALOGV("ArmnnPreparedModel_1_3::ExecuteGraph(...)");
+
+ TimePoint driverEnd, deviceStart, deviceEnd;
+
+ DumpTensorsIfRequired("Input", inputTensors);
+
+ std::vector<OutputShape> outputShapes(outputTensors.size());
+ for (unsigned int i = 0; i < outputTensors.size(); i++)
+ {
+ std::pair<int, armnn::Tensor> outputTensorPair = outputTensors[i];
+ const armnn::Tensor outputTensor = outputTensorPair.second;
+ const armnn::TensorInfo outputTensorInfo = outputTensor.GetInfo();
+
+ outputShapes[i] = ComputeShape(outputTensorInfo);
+ }
+
+ // run it
+ try
+ {
+ if (cb.ctx.measureTimings == MeasureTiming::YES)
+ {
+ deviceStart = Now();
+ }
+
+ armnn::Status status = m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors);
+
+ if (cb.ctx.measureTimings == MeasureTiming::YES)
+ {
+ deviceEnd = Now();
+ }
+ if (status != armnn::Status::Success)
+ {
+ ALOGW("EnqueueWorkload failed");
+ cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming,
+ "ArmnnPreparedModel_1_3::ExecuteGraph");
+ return false;
+ }
+ }
+ catch (armnn::Exception& e)
+ {
+ ALOGW("armnn:Exception caught from EnqueueWorkload: %s", e.what());
+ cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph");
+ return false;
+ }
+ catch (std::exception& e)
+ {
+ ALOGE("std::exception caught from EnqueueWorkload: %s", e.what());
+ cb.callback(V1_3::ErrorStatus::GENERAL_FAILURE, {}, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph");
+ return false;
+ }
+
+ CommitPools(*pMemPools);
+
+ DumpTensorsIfRequired("Output", outputTensors);
+
+ if (cb.ctx.measureTimings == MeasureTiming::YES)
+ {
+ driverEnd = Now();
+ Timing timing;
+ timing.timeOnDevice = MicrosecondsDuration(deviceEnd, deviceStart);
+ timing.timeInDriver = MicrosecondsDuration(driverEnd, cb.ctx.driverStart);
+ ALOGV("ArmnnPreparedModel_1_2::execute timing - Device = %lu Driver = %lu", timing.timeOnDevice,
+ timing.timeInDriver);
+ cb.callback(V1_3::ErrorStatus::NONE, outputShapes, timing, "ArmnnPreparedModel_1_3::ExecuteGraph");
+ } else {
+ cb.callback(V1_3::ErrorStatus::NONE, outputShapes, g_NoTiming, "ArmnnPreparedModel_1_3::ExecuteGraph");
+ }
+
+ return true;
+}
+
+template<typename HalVersion>
+bool ArmnnPreparedModel_1_3<HalVersion>::ExecuteWithDummyInputs()
+{
+ std::vector<std::vector<char>> storage;
+ armnn::InputTensors inputTensors;
+ for (unsigned int i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
+ {
+ const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
+ storage.emplace_back(inputTensorInfo.GetNumBytes());
+ const armnn::ConstTensor inputTensor(inputTensorInfo, storage.back().data());
+
+ inputTensors.emplace_back(i, inputTensor);
+ }
+
+ armnn::OutputTensors outputTensors;
+ for (unsigned int i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
+ {
+ const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
+ storage.emplace_back(outputTensorInfo.GetNumBytes());
+ const armnn::Tensor outputTensor(outputTensorInfo, storage.back().data());
+
+ outputTensors.emplace_back(i, outputTensor);
+ }
+
+ auto nullCallback = [](V1_3::ErrorStatus, std::vector<OutputShape>, const Timing&, std::string) {};
+ CallbackContext_1_3 callbackContext;
+ callbackContext.callback = nullCallback;
+ callbackContext.ctx.measureTimings = MeasureTiming::NO;
+ auto memPools = std::make_shared<std::vector<::android::nn::RunTimePoolInfo>>();
+ return ExecuteGraph(memPools,
+ inputTensors,
+ outputTensors,
+ callbackContext);
+}
+
+template<typename HalVersion>
+Return <V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::Execute(const V1_3::Request& request,
+ MeasureTiming measureTiming,
+ CallbackAsync_1_3 callback)
+{
+ ExecutionContext_1_3 ctx;
+ if (measureTiming == MeasureTiming::YES)
+ {
+ ctx.measureTimings = measureTiming;
+ ctx.driverStart = Now();
+ }
+
+ ALOGV("ArmnnPreparedModel_1_3::execute(): %s", GetModelSummary(m_Model).c_str());
+ m_RequestCount++;
+
+ if (!android::nn::validateRequest(request, m_Model))
+ {
+ callback(V1_3::ErrorStatus::INVALID_ARGUMENT, {}, g_NoTiming, "ArmnnPreparedModel_1_3::execute");
+ return V1_3::ErrorStatus::INVALID_ARGUMENT;
+ }
+
+ if (!m_RequestInputsAndOutputsDumpDir.empty())
+ {
+ ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(&callback));
+ }
+
+ // map the memory pool into shared pointers
+ // use a shared memory pools vector on the heap, as it is passed to the request thread
+ auto memPools = std::make_shared<std::vector<android::nn::RunTimePoolInfo>>();
+
+ // allocate the tensors on the heap, as they are passed to the request thread
+ auto inputTensors = std::make_shared<armnn::InputTensors>();
+ auto outputTensors = std::make_shared<armnn::OutputTensors>();
+
+ auto [status, outShapes, timing, message] = PrepareMemoryForIO(*inputTensors, *outputTensors,
+ *memPools, request);
+ if (status != V1_3::ErrorStatus::NONE)
+ {
+ callback(status, outShapes, timing, message);
+ }
+
+ switch(status)
+ {
+ case V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ return V1_3::ErrorStatus::NONE;
+ case V1_3::ErrorStatus::GENERAL_FAILURE:
+ return V1_3::ErrorStatus::GENERAL_FAILURE;
+ default:
+ {}
+ }
+
+ ALOGV("ArmnnPreparedModel_1_3::execute(...) before PostMsg");
+
+ // post the request for asynchronous execution
+ CallbackContext_1_3 cb;
+ cb.callback = callback;
+ cb.ctx = ctx;
+ m_RequestThread.PostMsg(this, memPools, inputTensors, outputTensors, cb);
+ ALOGV("ArmnnPreparedModel_1_3::execute(...) after PostMsg");
+ return V1_3::ErrorStatus::NONE;
+}
+
+#ifdef ARMNN_ANDROID_NN_V1_3
+template class ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>;
+template bool ArmnnPreparedModel_1_3<hal_1_3::HalPolicy>::ExecuteGraph<CallbackContext_1_3>(
+ std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
+ armnn::InputTensors& pInputTensors,
+ armnn::OutputTensors& pOutputTensors,
+ CallbackContext_1_3 cb);
+#endif
+
+} // namespace armnn_driver
diff --git a/ArmnnPreparedModel_1_3.hpp b/ArmnnPreparedModel_1_3.hpp
new file mode 100644
index 0000000..247149c
--- /dev/null
+++ b/ArmnnPreparedModel_1_3.hpp
@@ -0,0 +1,137 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ArmnnDriver.hpp"
+#include "ArmnnDriverImpl.hpp"
+#include "RequestThread.hpp"
+#include "ModelToINetworkConverter.hpp"
+
+#include <NeuralNetworks.h>
+#include <armnn/ArmNN.hpp>
+
+#include <string>
+#include <vector>
+
+namespace armnn_driver
+{
+using CallbackAsync_1_3 = std::function<
+ void(V1_3::ErrorStatus errorStatus,
+ std::vector<::android::hardware::neuralnetworks::V1_2::OutputShape> outputShapes,
+ const ::android::hardware::neuralnetworks::V1_2::Timing& timing,
+ std::string callingFunction)>;
+
+struct ExecutionContext_1_3
+{
+ ::android::hardware::neuralnetworks::V1_2::MeasureTiming measureTimings =
+ ::android::hardware::neuralnetworks::V1_2::MeasureTiming::NO;
+ TimePoint driverStart;
+};
+
+using CallbackContext_1_3 = CallbackContext<CallbackAsync_1_3, ExecutionContext_1_3>;
+
+using executeFenced_cb = std::function<void(::android::hardware::neuralnetworks::V1_3::ErrorStatus status,
+ const ::android::hardware::hidl_handle& syncFence,
+ const ::android::sp<::android::hardware::neuralnetworks::V1_3::IFencedExecutionCallback>& callback)>;
+
+template <typename HalVersion>
+class ArmnnPreparedModel_1_3 : public V1_3::IPreparedModel
+{
+public:
+ using HalModel = typename V1_3::Model;
+
+ ArmnnPreparedModel_1_3(armnn::NetworkId networkId,
+ armnn::IRuntime* runtime,
+ const HalModel& model,
+ const std::string& requestInputsAndOutputsDumpDir,
+ const bool gpuProfilingEnabled);
+
+ virtual ~ArmnnPreparedModel_1_3();
+
+ Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
+ const sp<V1_0::IExecutionCallback>& callback) override;
+
+ Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, MeasureTiming measure,
+ const sp<V1_2::IExecutionCallback>& callback) override;
+
+ Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request,
+ V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint&,
+ const sp<V1_3::IExecutionCallback>& callback) override;
+
+ Return<void> executeSynchronously(const V1_0::Request &request,
+ MeasureTiming measure,
+ V1_3::IPreparedModel::executeSynchronously_cb cb) override;
+
+ Return<void> executeSynchronously_1_3(const V1_3::Request &request,
+ MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ V1_3::IPreparedModel::executeSynchronously_1_3_cb cb) override;
+
+ Return<void> executeFenced(const V1_3::Request& request,
+ const android::hardware::hidl_vec<android::hardware::hidl_handle>& wait_for,
+ MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& duration,
+ executeFenced_cb callback) override;
+
+ Return<void> configureExecutionBurst(
+ const sp<V1_2::IBurstCallback>& callback,
+ const android::hardware::MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+ const android::hardware::MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+ configureExecutionBurst_cb cb) override;
+
+ template<typename CallbackContext>
+ Return<void> ExecuteSynchronously(const V1_3::Request& request, CallbackContext cbCtx);
+
+ /// execute the graph prepared from the request
+ template<typename CallbackContext>
+ bool ExecuteGraph(std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
+ armnn::InputTensors& inputTensors,
+ armnn::OutputTensors& outputTensors,
+ CallbackContext callback);
+
+ /// Executes this model with dummy inputs (e.g. all zeroes).
+ /// \return false on failure, otherwise true
+ bool ExecuteWithDummyInputs();
+
+private:
+ Return <V1_3::ErrorStatus> Execute(const V1_3::Request& request,
+ MeasureTiming measureTiming,
+ CallbackAsync_1_3 callback);
+
+ Return<V1_3::ErrorStatus> PrepareMemoryForInputs(
+ armnn::InputTensors& inputs,
+ const V1_3::Request& request,
+ const std::vector<android::nn::RunTimePoolInfo>& memPools);
+
+ Return<V1_3::ErrorStatus> PrepareMemoryForOutputs(
+ armnn::OutputTensors& outputs,
+ std::vector<OutputShape> &outputShapes,
+ const V1_3::Request& request,
+ const std::vector<android::nn::RunTimePoolInfo>& memPools);
+
+ std::tuple<V1_3::ErrorStatus, hidl_vec<OutputShape>, Timing, std::string> PrepareMemoryForIO(
+ armnn::InputTensors& inputs,
+ armnn::OutputTensors& outputs,
+ std::vector<android::nn::RunTimePoolInfo>& memPools,
+ const V1_3::Request& request);
+
+ template <typename TensorBindingCollection>
+ void DumpTensorsIfRequired(char const* tensorNamePrefix, const TensorBindingCollection& tensorBindings);
+
+ armnn::NetworkId m_NetworkId;
+ armnn::IRuntime* m_Runtime;
+ V1_3::Model m_Model;
+ // There must be a single RequestThread for all ArmnnPreparedModel objects to ensure serial execution of workloads
+ // It is specific to this class, so it is declared as static here
+ static RequestThread<ArmnnPreparedModel_1_3, HalVersion, CallbackContext_1_3> m_RequestThread;
+ uint32_t m_RequestCount;
+ const std::string& m_RequestInputsAndOutputsDumpDir;
+ const bool m_GpuProfilingEnabled;
+};
+
+}
diff --git a/ConversionUtils.hpp b/ConversionUtils.hpp
index 90b1c7d..315089c 100644
--- a/ConversionUtils.hpp
+++ b/ConversionUtils.hpp
@@ -183,7 +183,7 @@
type == V1_0::OperandType::TENSOR_INT32;
}
-#ifdef ARMNN_ANDROID_NN_V1_2
+#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)
@@ -201,17 +201,34 @@
#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_FLOAT16 ||
+ type == V1_3::OperandType::TENSOR_FLOAT32 ||
+ type == V1_3::OperandType::TENSOR_QUANT8_ASYMM ||
+ 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 Is12Operand(V1_0::Operand)
+inline bool Is12OrLaterOperand(V1_0::Operand)
{
return false;
}
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
inline bool IsBool(V1_2::Operand operand)
{
@@ -219,7 +236,22 @@
}
/// Checks if a operand is 1_2 Operand
-inline bool Is12Operand(V1_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;
}
@@ -351,7 +383,7 @@
outPadTail = boost::numeric_cast<uint32_t>(padTail);
}
-#ifdef ARMNN_ANDROID_NN_V1_2
+#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)
@@ -381,7 +413,7 @@
return shape;
}
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
Shape GetOperandShape(const V1_2::Operand& operand)
{
@@ -395,6 +427,20 @@
#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
@@ -636,8 +682,9 @@
return nullptr;
}
- BOOST_ASSERT(operation.inputs[inputIndex] < model.operands.size()); // Model should have been validated beforehand
- return &model.operands[operation.inputs[inputIndex]];
+ // Model should have been validated beforehand
+ BOOST_ASSERT(operation.inputs[inputIndex] < getMainModel(model).operands.size());
+ return &getMainModel(model).operands[operation.inputs[inputIndex]];
}
template<typename HalPolicy,
@@ -655,9 +702,9 @@
}
// Model should have been validated beforehand
- BOOST_ASSERT(operation.outputs[outputIndex] < model.operands.size());
+ BOOST_ASSERT(operation.outputs[outputIndex] < getMainModel(model).operands.size());
- return &model.operands[operation.outputs[outputIndex]];
+ return &getMainModel(model).operands[operation.outputs[outputIndex]];
}
template<typename HalPolicy,
@@ -1165,6 +1212,120 @@
}
}
+
+#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))
+ {
+ Fail("%s: 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();
+ }
+
+ BOOST_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>
@@ -1448,7 +1609,7 @@
return Fail("%s: Operation has invalid inputs", operationName);
}
- if (Is12Operand(*output))
+ if (Is12OrLaterOperand(*output))
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 10, model, data);
}
@@ -1467,7 +1628,7 @@
return Fail("%s: Operation has invalid inputs", operationName);
}
- if (Is12Operand(*output))
+ if (Is12OrLaterOperand(*output))
{
desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 7, model, data);
}
@@ -2106,7 +2267,7 @@
}
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
- if (Is12Operand(*output))
+ if (Is12OrLaterOperand(*output))
{
descriptor.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 2, model, data);
}
@@ -2440,7 +2601,7 @@
return false;
}
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
inline bool IsQSymm8(const V1_2::Operand& operand)
{
@@ -2449,6 +2610,15 @@
#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,
@@ -2484,10 +2654,10 @@
// 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 < model.operations.size(); ++operationIdx)
+ 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 = model.operations[operationIdx];
+ const auto& operationIt = getMainModel(model).operations[operationIdx];
if (operationIt.type != HalPolicy::OperationType::DEQUANTIZE)
{
continue;
@@ -3525,7 +3695,7 @@
batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend());
batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC;
- if (Is12Operand(*output))
+ if (Is12OrLaterOperand(*output))
{
batchToSpaceNdDesc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 2, model, data);
}
@@ -3633,7 +3803,7 @@
descriptor.m_BlockShape.assign(blockShape.cbegin(), blockShape.cend());
descriptor.m_PadList.assign(paddingList.cbegin(), paddingList.cend());
- if (Is12Operand(*output))
+ if (Is12OrLaterOperand(*output))
{
descriptor.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 3, model, data);
}
diff --git a/ConversionUtils_1_2.hpp b/ConversionUtils_1_2.hpp
new file mode 100644
index 0000000..460c88b
--- /dev/null
+++ b/ConversionUtils_1_2.hpp
@@ -0,0 +1,2590 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "Utils.hpp"
+
+#include "ConversionUtils.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);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ ComparisonDescriptor descriptor(comparisonOperation);
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsComparisonSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo0,
+ inputInfo1,
+ outputInfo,
+ descriptor);
+
+ 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;
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
+
+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);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ 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;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsConvolution2dSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* startLayer =
+ data.m_Network->AddConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
+
+ if (!startLayer)
+ {
+ return Fail("%s: AddConvolution2dLayer failed", __func__);
+ }
+
+ IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
+
+ if (!endLayer)
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+
+ input.Connect(startLayer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+}
+
+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);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // 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;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsDepthwiseConvolutionSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* startLayer =
+ data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
+
+ if (!startLayer)
+ {
+ return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
+ }
+
+ IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
+ if (!endLayer)
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+
+ input.Connect(startLayer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+}
+
+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);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ ElementwiseUnaryDescriptor descriptor(unaryOperation);
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsElementwiseUnarySupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ descriptor);
+
+ 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);
+}
+
+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);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ 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;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsReshapeSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outputInfo,
+ reshapeDescriptor);
+
+ 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);
+}
+
+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__);
+ }
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // 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__);
+ }
+
+ const unsigned int outputChannels = outputShape[channelsIndex];
+
+ 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 groupOutputShape(outputShape);
+ groupOutputShape[channelsIndex] = 1;
+
+ 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);
+ groupOutputInfo.SetShape(groupOutputShape);
+
+ const unsigned int weightsDataTypeSize = GetDataTypeSize(groupWeightsInfo.GetDataType());
+ const unsigned int biasesDataTypeSize = GetDataTypeSize(groupBiasesInfo.GetDataType());
+
+ std::vector<IConnectableLayer*> convLayers(numGroups * channelMultiplier, nullptr);
+ for (unsigned int group = 0u; group < numGroups; ++group)
+ {
+ for (unsigned int m = 0u; m < channelMultiplier; ++m)
+ {
+ auto index = group * channelMultiplier + m;
+
+ const unsigned int weightsDataOffset = groupWeightsShape.GetNumElements() * index * weightsDataTypeSize;
+ const unsigned int biasesDataOffset = groupBiasesShape.GetNumElements() * index * biasesDataTypeSize;
+
+ if (weightsInfo.HasPerAxisQuantization())
+ {
+ // Extract per-axis quantization scales for group weights
+ const std::vector<float>& weightsQuantScales = weightsInfo.GetQuantizationScales();
+ groupWeightsInfo.SetQuantizationScales(
+ std::vector<float>(weightsQuantScales.begin() + index,
+ weightsQuantScales.begin() + index + groupWeightsShape[0]));
+
+ // Extract per-axis quantization scales for group biases
+ const std::vector<float>& biasesQuantScales = biasesInfo.GetQuantizationScales();
+ groupBiasesInfo.SetQuantizationScales(
+ std::vector<float>(biasesQuantScales.begin() + index,
+ biasesQuantScales.begin() + index + groupWeightsShape[0]));
+ }
+
+ // Extract weights and biases data for current group convolution
+ ConstTensor groupWeights(groupWeightsInfo,
+ static_cast<const void *>(reinterpret_cast<const char *>(weights.GetMemoryArea()) +
+ weightsDataOffset));
+ ConstTensor groupBiases(groupBiasesInfo,
+ static_cast<const void *>(reinterpret_cast<const char *>(biases.GetMemoryArea()) +
+ biasesDataOffset));
+
+ isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsConvolution2dSupported,
+ data.m_Backends,
+ isSupported,
+ groupInputInfo,
+ groupOutputInfo,
+ desc,
+ groupWeightsInfo,
+ Optional<TensorInfo>(groupBiasesInfo));
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* convLayer =
+ data.m_Network->AddConvolution2dLayer(desc, groupWeights, Optional<ConstTensor>(groupBiases));
+ if (!convLayer)
+ {
+ return Fail("%s: AddConvolution2dLayer failed", __func__);
+ }
+
+ splitterLayer->GetOutputSlot(group).Connect(convLayer->GetInputSlot(0));
+ convLayer->GetOutputSlot(0).SetTensorInfo(groupOutputInfo);
+
+ convLayers[index] = convLayer;
+ }
+ }
+
+ //
+ // Set up Concat layer
+ //
+ ConcatDescriptor concatDescriptor(outputInfo.GetShape()[channelsIndex]);
+ for (unsigned int group = 0u; group < numGroups; ++group)
+ {
+ for (unsigned int m = 0u; m < channelMultiplier; ++m)
+ {
+ auto index = group * channelMultiplier + m;
+ concatDescriptor.SetViewOriginCoord(index, channelsIndex, index);
+ concatDescriptor.SetConcatAxis(channelsIndex);
+ }
+ }
+
+ isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsConcatSupported,
+ data.m_Backends,
+ isSupported,
+ std::vector<const TensorInfo*>(numGroups * channelMultiplier, &groupOutputInfo),
+ outputInfo,
+ concatDescriptor);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* concatLayer = data.m_Network->AddConcatLayer(concatDescriptor);
+ if (!concatLayer)
+ {
+ return Fail("%s: AddConcatLayer failed", __func__);
+ }
+
+ for (unsigned int group = 0u; group < numGroups; ++group)
+ {
+ for (unsigned int m = 0u; m < channelMultiplier; ++m)
+ {
+ auto index = group * channelMultiplier + m;
+ convLayers[index]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(index));
+ }
+ }
+ concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ //
+ // Set up Activation layer (if it is set)
+ //
+ IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, concatLayer, data);
+ if (!endLayer)
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertInstanceNormalization(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertInstanceNormalization()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has an invalid input 0", __func__);
+ }
+
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Operation has an invalid output", __func__);
+ }
+
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // Determine data type of input tensor
+ HalOperandType inputType;
+ if (!GetOperandType<HalPolicy>(operation, 0, model, inputType))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ InstanceNormalizationDescriptor desc;
+
+ // Read gamma, beta & epsilon
+ if (inputType == HalOperandType::TENSOR_FLOAT16)
+ {
+ Half fp16Gamma;
+ Half fp16Beta;
+ Half fp16Epsilon;
+
+ if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::FLOAT16, fp16Gamma, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 2, HalOperandType::FLOAT16, fp16Beta, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 3, HalOperandType::FLOAT16, fp16Epsilon, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (FLOAT16)", __func__);
+ }
+
+ desc.m_Gamma = static_cast<float>(fp16Gamma);
+ desc.m_Beta = static_cast<float>(fp16Beta);
+ desc.m_Eps = static_cast<float>(fp16Epsilon);
+ }
+ else if (inputType == HalOperandType::TENSOR_FLOAT32)
+ {
+ if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::FLOAT32, desc.m_Gamma, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 2, HalOperandType::FLOAT32, desc.m_Beta, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 3, HalOperandType::FLOAT32, desc.m_Eps, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (FLOAT32)", __func__);
+ }
+ }
+ else
+ {
+ return Fail("%s: Unsupported input tensor type: %d", __func__, inputType);
+ }
+
+ desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 4, model, data);
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsInstanceNormalizationSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outputInfo,
+ desc);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* layer = data.m_Network->AddInstanceNormalizationLayer(desc);
+ 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 ConvertLogSoftmax(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertLogSoftmax()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Failed to read input 0", __func__);
+ }
+
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Failed to read output", __func__);
+ }
+
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // Determine data type of input tensor
+ HalOperandType inputType;
+ if (!GetOperandType<HalPolicy>(operation, 0, model, inputType))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ LogSoftmaxDescriptor descriptor;
+
+ // Read beta
+ if (inputType == HalOperandType::TENSOR_FLOAT16)
+ {
+ Half fp16Beta;
+ if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::FLOAT16, fp16Beta, model, data))
+ {
+ return Fail("%s: Failed to read input 1 (FLOAT16)", __func__);
+ }
+
+ descriptor.m_Beta = static_cast<float>(fp16Beta);
+ }
+ else if (inputType == HalOperandType::TENSOR_FLOAT32)
+ {
+ if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::FLOAT32, descriptor.m_Beta, model, data))
+ {
+ return Fail("%s: Failed to read input 1 (FLOAT32)", __func__);
+ }
+ }
+ else
+ {
+ return Fail("%s: Unsupported input tensor type: %d", __func__, inputType);
+ }
+
+ // Read axis
+ if (!GetInputInt32<HalPolicy>(operation, 2, descriptor.m_Axis, model, data))
+ {
+ return Fail("%s: Failed to read input 2", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsLogSoftmaxSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outputInfo,
+ descriptor);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* layer = data.m_Network->AddLogSoftmaxLayer(descriptor);
+ if (!layer)
+ {
+ return Fail("%s: AddLogSoftmaxLayer() returned nullptr", __func__);
+ }
+
+ 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 ConvertMaximum(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+
+ ALOGV("HalPolicy::ConvertMaximum()");
+
+ 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* outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!outputOperand)
+ {
+ return Fail("%s: Could not read output", __func__);
+ }
+
+ const TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
+ if (IsDynamicTensor(outInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsMaximumSupported,
+ data.m_Backends,
+ isSupported,
+ input0.GetTensorInfo(),
+ input1.GetTensorInfo(),
+ outInfo);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* layer = data.m_Network->AddMaximumLayer();
+ assert(layer != nullptr);
+ bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data);
+ if (!isReshapeSupported)
+ {
+ return false;
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertMinimum(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+
+ ALOGV("HalPolicy::ConvertMinimum()");
+
+ 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& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsMinimumSupported,
+ data.m_Backends,
+ isSupported,
+ input0.GetTensorInfo(),
+ input1.GetTensorInfo(),
+ outputInfo);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddMinimumLayer();
+ assert(layer != nullptr);
+ bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data);
+ if (!isReshapeSupported)
+ {
+ return false;
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertPadV2(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertPadV2()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0", __func__);
+ }
+
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output", __func__);
+ }
+
+ const TensorInfo& inputInfo = input.GetTensorInfo();
+ unsigned int rank = inputInfo.GetNumDimensions();
+
+ PadDescriptor descriptor;
+ if (!ConvertPaddings<HalPolicy>(operation, model, data, rank, descriptor))
+ {
+ return Fail("%s: Could not convert paddings", __func__);
+ }
+
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // Determine type of padding value
+ HalOperandType operandType0;
+ HalOperandType operandType2;
+
+ if (!GetOperandType<HalPolicy>(operation, 0, model, operandType0) ||
+ !GetOperandType<HalPolicy>(operation, 2, model, operandType2))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ // Read value to use for padding
+ if (operandType0 == HalOperandType::TENSOR_FLOAT16 && operandType2 == HalOperandType::FLOAT16)
+ {
+ Half f16PadValue;
+ if (!GetInputScalar<HalPolicy>(operation, 2, operandType2, f16PadValue, model, data))
+ {
+ return Fail("%s: Could not read input 2 (FLOAT16)", __func__);
+ }
+
+ descriptor.m_PadValue = f16PadValue;
+ }
+ else if (operandType0 == HalOperandType::TENSOR_FLOAT32 && operandType2 == HalOperandType::FLOAT32)
+ {
+ if (!GetInputFloat32<HalPolicy>(operation, 2, descriptor.m_PadValue, model, data))
+ {
+ return Fail("%s: Could not read input 2 (FLOAT32)", __func__);
+ }
+ }
+ else if (operandType0 == HalOperandType::TENSOR_QUANT8_ASYMM && operandType2 == HalOperandType::INT32)
+ {
+ int32_t intPadValue = 0;
+ if (!GetInputInt32<HalPolicy>(operation, 2, intPadValue, model, data))
+ {
+ return Fail("%s: Could not read input 2 (INT32)", __func__);
+ }
+ descriptor.m_PadValue = intPadValue;
+ }
+ else
+ {
+ return Fail("%s: Operation has invalid inputs: type mismatch", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsPadSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ descriptor);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor);
+ assert(layer != nullptr);
+ input.Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertPrelu(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+
+ ALOGV("HalPolicy::ConvertPrelu()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ LayerInputHandle alpha = ConvertToLayerInputHandle<HalPolicy>(operation, 1, model, data);
+
+ if (!input.IsValid() || !alpha.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", __func__);
+ }
+
+ const TensorInfo& inputInfo = input.GetTensorInfo();
+ const TensorInfo& alphaInfo = alpha.GetTensorInfo();
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsPreluSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ alphaInfo,
+ outputInfo);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddPreluLayer();
+
+ if (!layer)
+ {
+ return Fail("%s: AddPreluLayer failed", __func__);
+ }
+
+ bool isReshapeSupported = BroadcastTensor(input, alpha, layer, data);
+ if (!isReshapeSupported)
+ {
+ return false;
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertQuantize(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+
+ ALOGV("HalPolicy::ConvertQuantize()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid input", __func__);
+ }
+
+ const HalOperand* const outputOperand = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!outputOperand)
+ {
+ return Fail("%s: Operation has invalid outputs", __func__);
+ }
+
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsQuantizeSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outputInfo);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddQuantizeLayer();
+ 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 ConvertQuantizedLstm(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+
+ ALOGV("HalPolicy::ConvertQuantizedLstm()");
+
+ //Inputs:
+ // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize]
+ // specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128.
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0: input", __func__);
+ }
+
+ //13: The previous cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape
+ // [numBatches, outputSize] specifying the cell state from the previous time step of the LSTM cell.
+ // It is quantized using a quantization range of -2^4, 2^4 * 32767/32768.
+ LayerInputHandle previousCellStateIn = ConvertToLayerInputHandle<HalPolicy>(operation, 13, model, data);
+ if (!previousCellStateIn.IsValid())
+ {
+ return Fail("%s: Could not read input 13: previousCellStateIn", __func__);
+ }
+
+ // 14: The previous output state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [numBathes, outputSize] specifying the output of the LSTM cell from previous time-step. Tensor
+ // is quantized with a fixed quantization range of -1, 127/128.
+ LayerInputHandle previousOutputIn = ConvertToLayerInputHandle<HalPolicy>(operation, 14, model, data);
+ if (!previousOutputIn.IsValid())
+ {
+ return Fail("%s: Could not read input 14: previousOutputIn", __func__);
+ }
+
+ // Get the input tensors:
+ // 1: The input-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, inputSize] specifying input-to-input part of weights for fully-connected layer inside the
+ // LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin inputToInputWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1, model, data);
+
+ // 2: The input-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, inputSize] specifying input-to-forget part of weights for fully-connected layer inside the
+ // LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin inputToForgetWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
+
+ // 3: The input-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, inputSize] specifying input-to-cell part of weights for fully-connected layer inside the
+ // LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin inputToCellWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 3, model, data);
+
+ // 4: The input-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, inputSize] specifying input-to-output part of weights for fully-connected layer inside the
+ // LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin inputToOutputWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 4, model, data);
+
+ // 5: The recurrent-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, outputSize] specifying recurrent-to-input part of weights for fully-connected layer inside
+ // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin recurrentToInputWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 5, model, data);
+
+ // 6: The recurrent-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, outputSize] specifying recurrent-to-forget part of weights for fully-connected layer inside
+ // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin recurrentToForgetWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 6, model, data);
+
+ // 7: The recurrent-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, outputSize] specifying recurrent-to-cell part of weights for fully-connected layer inside
+ // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin recurrentToCellWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 7, model, data);
+
+ // 8: The recurrent-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape
+ // [outputSize, outputSize] specifying recurrent-to-output part of weights for fully-connected layer inside
+ // the LSTM cell. Quantization zero point and scale must be the same across all the weights.
+ const ConstTensorPin recurrentToOutputWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 8, model, data);
+
+ // 9: The input gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the
+ // bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
+ // of input and weights scales and zeroPoint equal to 0.
+ const ConstTensorPin inputGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 9, model, data);
+
+ // 10: The forget gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying
+ // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
+ // of input and weights scales and zeroPoint equal to 0.
+ const ConstTensorPin forgetGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 10, model, data);
+
+ // 11:The cell bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias
+ // for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input
+ // and weights scales and zeroPoint equal to 0.
+ const ConstTensorPin cellBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 11, model, data);
+
+ // 12:The output gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying
+ // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product
+ // of input and weights scales and zeroPoint equal to 0.
+ const ConstTensorPin outputGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 12, model, data);
+
+ if (!inputToInputWeightsPin.IsValid() ||
+ !inputToForgetWeightsPin.IsValid() ||
+ !inputToCellWeightsPin.IsValid() ||
+ !inputToOutputWeightsPin.IsValid() ||
+ !recurrentToInputWeightsPin.IsValid() ||
+ !recurrentToForgetWeightsPin.IsValid() ||
+ !recurrentToCellWeightsPin.IsValid() ||
+ !recurrentToOutputWeightsPin.IsValid() ||
+ !inputGateBiasPin.IsValid() ||
+ !forgetGateBiasPin.IsValid() ||
+ !cellBiasPin.IsValid() ||
+ !outputGateBiasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid tensor inputs", __func__);
+ }
+
+ // Outputs:
+ // 0: The cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize]
+ // which contains a cell state from the current time step. Tensor is quantized using a quantization range
+ // of -2^4, 2^4 * 32767/32768.
+ const HalOperand* cellStateOut = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!cellStateOut)
+ {
+ return Fail("%s: Could not read output 0: cellStateOut", __func__);
+ }
+
+ // 1: The output: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] which
+ // contains the output value. Tensor is quantized with a fixed quantization range of -1, 127/128.
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 1, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 1: output", __func__);
+ }
+
+ // Inputs
+ const TensorInfo& inputInfo = input.GetTensorInfo();
+ const TensorInfo& previousCellStateInInfo = previousCellStateIn.GetTensorInfo();
+ const TensorInfo& previousOutputInInfo = previousOutputIn.GetTensorInfo();
+
+ // Outputs
+ const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ // Dynamic tensors currently not supported
+ if (IsDynamicTensor(cellStateOutInfo) || IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ QuantizedLstmInputParams params;
+
+ params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
+ params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
+ params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
+ params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
+ params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
+ params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
+ params.m_CellBias = cellBiasPin.GetConstTensorPtr();
+ params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
+
+ QuantizedLstmInputParamsInfo paramsInfo;
+ paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+ paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo());
+ paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo());
+ paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo());
+ paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+ paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
+ paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo());
+ paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
+ paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
+ paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo());
+ paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo());
+ paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo());
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsQuantizedLstmSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ previousCellStateInInfo,
+ previousOutputInInfo,
+ cellStateOutInfo,
+ outputInfo,
+ paramsInfo);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddQuantizedLstmLayer(params, "QuantizedLstm");
+ input.Connect(layer->GetInputSlot(0));
+ previousCellStateIn.Connect(layer->GetInputSlot(1));
+ previousOutputIn.Connect(layer->GetInputSlot(2));
+
+ return (SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 0, model, data) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 1, *layer, 1, model, data));
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertResize(const HalOperation& operation,
+ const HalModel& model,
+ ConversionData& data,
+ ResizeMethod resizeMethod)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+ ALOGV("HalPolicy::ConvertResize()");
+ ALOGV("resizeMethod = %s", GetResizeMethodAsCString(resizeMethod));
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0", __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);
+
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ ResizeDescriptor descriptor;
+ descriptor.m_Method = resizeMethod;
+ descriptor.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 3, model, data);
+
+ HalOperandType operandType1;
+ HalOperandType operandType2;
+
+ if (!GetOperandType<HalPolicy>(operation, 1, model, operandType1) ||
+ !GetOperandType<HalPolicy>(operation, 2, model, operandType2))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ if (operandType1 != operandType2)
+ {
+ return Fail("%s: Operation has invalid inputs. Type of input 1 and 2 should be the same", __func__);
+ }
+
+ if (operandType1 == HalOperandType::INT32)
+ {
+ // Case 1: resizing by shape
+ int32_t targetWidth = 0;
+ int32_t targetHeight = 0;
+
+ if (!GetInputInt32<HalPolicy>(operation, 1, targetWidth, model, data) ||
+ !GetInputInt32<HalPolicy>(operation, 2, targetHeight, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs for resizing by shape", __func__);
+ }
+
+ if (targetWidth < 0 || targetHeight < 0)
+ {
+ return Fail("%s: Operation has invalid inputs for resizing by shape. "
+ "Target width/height cannot be < 0", __func__);
+ }
+
+ descriptor.m_TargetWidth = static_cast<uint32_t>(targetWidth);
+ descriptor.m_TargetHeight = static_cast<uint32_t>(targetHeight);
+ }
+ else if (operandType1 == HalOperandType::FLOAT32)
+ {
+ // Case 2: resizing by scale
+ float widthScale = 1.0f;
+ float heightScale = 1.0f;
+
+ if (!GetInputFloat32<HalPolicy>(operation, 1, widthScale, model, data) ||
+ !GetInputFloat32<HalPolicy>(operation, 2, heightScale, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs for resizing by scale", __func__);
+ }
+
+ const TensorShape& inputShape = inputInfo.GetShape();
+ armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout);
+
+ float width = inputShape[dataLayoutIndexed.GetWidthIndex()];
+ float height = inputShape[dataLayoutIndexed.GetHeightIndex()];
+
+ descriptor.m_TargetWidth = std::floor(width * widthScale);
+ descriptor.m_TargetHeight = std::floor(height * heightScale);
+ }
+ else if (operandType1 == HalOperandType::FLOAT16)
+ {
+ Half widthScale;
+ Half heightScale;
+
+ if (!GetInputScalar<HalPolicy>(operation, 1, HalOperandType::FLOAT16, widthScale, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 2, HalOperandType::FLOAT16, heightScale, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs for resizing by scale", __func__);
+ }
+
+ const TensorShape& inputShape = inputInfo.GetShape();
+ armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout);
+
+ Half width = static_cast<Half>(inputShape[dataLayoutIndexed.GetWidthIndex()]);
+ Half height = static_cast<Half>(inputShape[dataLayoutIndexed.GetHeightIndex()]);
+
+ descriptor.m_TargetWidth = std::floor(width * widthScale);
+ descriptor.m_TargetHeight = std::floor(height * heightScale);
+ }
+ else
+ {
+ return Fail("%s: Operand has invalid data type for resizing by scale", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsResizeSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ descriptor);
+
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* layer = data.m_Network->AddResizeLayer(descriptor);
+
+ 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 ConvertSpaceToDepth(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertSpaceToDepth()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid() )
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const 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 TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ SpaceToDepthDescriptor desc;
+
+ GetInputScalar<HalPolicy>(operation, 1, HalOperandType::INT32, desc.m_BlockSize, model, data);
+
+ if (desc.m_BlockSize <= 1)
+ {
+ return Fail("%s: Block size must be at least 1 in all dimensions");
+ }
+
+ desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 2, model, data);
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsSpaceToDepthSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc);
+ 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 ConvertSoftmax(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertSoftmax()");
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ 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__);
+ }
+
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ SoftmaxDescriptor desc;
+ if (!GetInputFloat32<HalPolicy>(operation, 1, desc.m_Beta, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ if (operation.inputs.size() > 2 && !GetInputScalar<HalPolicy>(operation,
+ 2,
+ HalOperandType::INT32,
+ desc.m_Axis,
+ model,
+ data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ if (input.GetTensorInfo().GetNumDimensions() > 2 ||
+ !(desc.m_Axis == 1 ||
+ (desc.m_Axis < 0 && static_cast<int>(input.GetTensorInfo().GetNumDimensions()) + desc.m_Axis == 1)))
+ {
+ return Fail("%s: Unsupported input greater than 2D or axis != 1", __func__);
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsSoftmaxSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outputInfo,
+ desc);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc);
+ 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 ConvertLstm(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ ALOGV("HalPolicy::ConvertLstm()");
+
+ // Inputs:
+ // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
+ // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0: input", __func__);
+ }
+ // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
+ LayerInputHandle outputStateIn = ConvertToLayerInputHandle<HalPolicy>(operation, 18, model, data);
+ if (!outputStateIn.IsValid())
+ {
+ return Fail("%s: Could not read input 18: outputStateIn", __func__);
+ }
+ // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
+ LayerInputHandle cellStateIn = ConvertToLayerInputHandle<HalPolicy>(operation, 19, model, data);
+ if (!cellStateIn.IsValid())
+ {
+ return Fail("%s: Could not read input 19: cellStateIn", __func__);
+ }
+
+ // Get the mandatory input tensors:
+ // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size].
+ const ConstTensorPin inputToForgetWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 2));
+ // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size].
+ const ConstTensorPin inputToCellWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 3));
+ // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size].
+ const ConstTensorPin inputToOutputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 4));
+ // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToForgetWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 6));
+ // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToCellWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 7));
+ // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToOutputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 8));
+ // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin forgetGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 13, model, data);
+ // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 14, model, data);
+ // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin outputGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 15, model, data);
+
+ if (!inputToForgetWeightsPin.IsValid() ||
+ !inputToCellWeightsPin.IsValid() ||
+ !inputToOutputWeightsPin.IsValid() ||
+ !recurrentToForgetWeightsPin.IsValid() ||
+ !recurrentToCellWeightsPin.IsValid() ||
+ !recurrentToOutputWeightsPin.IsValid() ||
+ !forgetGateBiasPin.IsValid() ||
+ !cellBiasPin.IsValid() ||
+ !outputGateBiasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid tensor inputs", __func__);
+ }
+
+ // Get the optional input tensors:
+ // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size], where “num_units” corresponds to the number of cell units.
+ const ConstTensorPin inputToInputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 1, true));
+ // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
+ // “num_units”), or the second dimension of the “projection_weights”, if defined.
+ const ConstTensorPin recurrentToInputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 5, true));
+ // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToInputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 9, true));
+ // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToForgetWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 10, true));
+ // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToOutputWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 11, true));
+ // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin inputGateBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+ 12,
+ model,
+ data,
+ g_DontPermute,
+ nullptr,
+ true);
+
+ // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [output_size, num_units].
+ const ConstTensorPin projectionWeightsPin =
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 16, true));
+ // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
+ const ConstTensorPin projectionBiasPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+ 17,
+ model,
+ data,
+ g_DontPermute,
+ nullptr,
+ true);
+
+ if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
+ (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) ||
+ (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) ||
+ (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) ||
+ (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) ||
+ (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) ||
+ (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) ||
+ (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
+ {
+ return Fail("%s: Operation has invalid tensor inputs", __func__);
+ }
+
+ // Get the mandatory input scalars (actually 1-D tensors of size 1):
+ // 20: The activation function: A value indicating the activation function:
+ // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid.
+ // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip].
+ // If set to 0.0 then clipping is disabled.
+ // 22: The clipping threshold: for the output from the projection layer, such that values are bound within
+ // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ ActivationFn activation;
+ float cellClip;
+ float projClip;
+ if (!GetInputActivationFunctionFromTensor<HalPolicy>(operation, 20, activation, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 21, HalOperandType::FLOAT32, cellClip, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 22, HalOperandType::FLOAT32, projClip, model, data))
+ {
+ return Fail("%s: Operation has invalid scalar inputs", __func__);
+ }
+
+ // Get the normalization tensors
+ // 23: The input layer normalization weights. A 1-D tensor of shape [num_units].
+ // Used to rescale normalized inputs to activation at input gate.
+ const ConstTensorPin inputLayerNormWeightsPin
+ (DequantizeAndMakeConstTensorPin<HalPolicy>(operation, model, data, 23, true));
+
+ // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units].
+ // Used to rescale normalized inputs to activation at forget gate.
+ const ConstTensorPin forgetLayerNormWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+ 24,
+ model,
+ data,
+ g_DontPermute,
+ nullptr,
+ true);
+
+ // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units].
+ // Used to rescale normalized inputs to activation at cell gate.
+ const ConstTensorPin cellLayerNormWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+ 25,
+ model,
+ data,
+ g_DontPermute,
+ nullptr,
+ true);
+
+ // 26: The output layer normalization weights. A 1-D tensor of shape [num_units].
+ // Used to rescale normalized inputs to activation at output gate.
+ const ConstTensorPin outputLayerNormWeightsPin =
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+ 26,
+ model,
+ data,
+ g_DontPermute,
+ nullptr,
+ true);
+
+ // Outputs:
+ // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4]
+ // with CIFG, or [batch_size, num_units * 3] without CIFG.
+ const HalOperand* scratchBuffer = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!scratchBuffer)
+ {
+ return Fail("%s: Could not read output 0: scratchBuffer", __func__);
+ }
+ // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
+ const HalOperand* outputStateOut = GetOutputOperand<HalPolicy>(operation, 1, model);
+ if (!outputStateOut)
+ {
+ return Fail("%s: Could not read output 1: outputStateOut", __func__);
+ }
+ // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
+ const HalOperand* cellStateOut = GetOutputOperand<HalPolicy>(operation, 2, model);
+ if (!cellStateOut)
+ {
+ return Fail("%s: Could not read output 2: cellStateOut", __func__);
+ }
+ // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is
+ // effectively the same as the current “output state (out)” value.
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 3, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 3: output", __func__);
+ }
+
+ // set the params structure for the AddLstmLayer call
+ LstmInputParams params;
+ params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
+ params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
+ params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
+ params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
+ params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
+ params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
+ params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
+ params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
+ params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
+ params.m_CellBias = cellBiasPin.GetConstTensorPtr();
+ params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
+ params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
+ params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
+ params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr();
+ params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr();
+ params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr();
+ params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr();
+
+ // set the layer descriptor
+ LstmDescriptor desc;
+ desc.m_ActivationFunc = activation;
+ desc.m_ClippingThresCell = cellClip;
+ desc.m_ClippingThresProj = projClip;
+ desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
+ params.m_RecurrentToInputWeights == nullptr ||
+ params.m_InputGateBias == nullptr);
+ desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
+ params.m_CellToOutputWeights != nullptr);
+ desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
+ desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr ||
+ params.m_ForgetLayerNormWeights != nullptr ||
+ params.m_CellLayerNormWeights != nullptr ||
+ params.m_OutputLayerNormWeights != nullptr);
+
+ // validate the optional input groups
+ if (desc.m_CifgEnabled &&
+ (params.m_InputToInputWeights != nullptr ||
+ params.m_RecurrentToInputWeights != nullptr ||
+ params.m_InputGateBias != nullptr))
+ {
+ return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
+ " and input gate bias must be provided", __func__);
+ }
+
+ if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
+ {
+ return Fail("%s: projection bias should not be provided without projection weights", __func__);
+ }
+
+ if (desc.m_PeepholeEnabled &&
+ (params.m_CellToForgetWeights == nullptr ||
+ params.m_CellToOutputWeights == nullptr ||
+ (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
+ {
+ return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
+ " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
+ }
+
+ if (desc.m_LayerNormEnabled &&
+ (params.m_ForgetLayerNormWeights == nullptr ||
+ params.m_CellLayerNormWeights == nullptr ||
+ params.m_OutputLayerNormWeights == nullptr ||
+ (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr)))
+ {
+ return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be"
+ " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__);
+ }
+
+ // Check if the layer is supported
+ // Inputs
+ const TensorInfo& inputInfo = input.GetTensorInfo();
+ const TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo();
+ const TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo();
+
+ // Outputs
+ const TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer);
+ const TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
+ const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ // Check if the scratch buffer shape was initialized,
+ // In some cases the shape could be (0,0) which requires the driver
+ // to infer the shape and set it up accordingly.
+ // The code below does that.
+ TensorInfo fixSbInfo = scratchBufferInfo;
+ if (IsDynamicTensor(scratchBufferInfo))
+ {
+ auto & s = fixSbInfo.GetShape();
+ s[0] = outputStateInInfo.GetShape()[0];
+ if (desc.m_CifgEnabled)
+ {
+ // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG
+ s[1] = cellStateOutInfo.GetShape()[1]*3;
+ }
+ else
+ {
+ // scratch_buffer [num_units * 4, batch_size] without CIFG
+ s[1] = cellStateOutInfo.GetShape()[1]*4;
+ }
+ }
+
+ if (IsDynamicTensor(outputStateOutInfo) ||
+ IsDynamicTensor(cellStateOutInfo) ||
+ IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__,
+ IsDynamicTensor(scratchBufferInfo), IsDynamicTensor(outputStateOutInfo),
+ IsDynamicTensor(cellStateOutInfo), IsDynamicTensor(outputInfo));
+ }
+
+ // Basic parameters
+ LstmInputParamsInfo paramsInfo;
+ paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo());
+ paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo());
+ paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo());
+ paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
+ paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo());
+ paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
+ paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo());
+ paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo());
+ paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo());
+
+ // Optional parameters
+ if (!desc.m_CifgEnabled)
+ {
+ paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+ paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+ if (params.m_CellToInputWeights != nullptr)
+ {
+ paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
+ }
+ paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
+ }
+
+ if (desc.m_ProjectionEnabled)
+ {
+ paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo());
+ if (params.m_ProjectionBias != nullptr)
+ {
+ paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo());
+ }
+ }
+
+ if (desc.m_PeepholeEnabled)
+ {
+ paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
+ paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
+ }
+
+ if (desc.m_LayerNormEnabled)
+ {
+ if(!desc.m_CifgEnabled)
+ {
+ paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo());
+ }
+ paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo());
+ paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo());
+ paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo());
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsLstmSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputStateInInfo,
+ cellStateInInfo,
+ fixSbInfo,
+ outputStateOutInfo,
+ cellStateOutInfo,
+ outputInfo,
+ desc,
+ paramsInfo);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ // Add the layer
+ IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm");
+
+ input.Connect(layer->GetInputSlot(0));
+ outputStateIn.Connect(layer->GetInputSlot(1));
+ cellStateIn.Connect(layer->GetInputSlot(2));
+
+
+ return (
+ (IsDynamicTensor(scratchBufferInfo)?
+ SetupAndTrackLayerOutputSlotAndOverrideTensorInfo<HalPolicy>(
+ operation, 0, *layer, 0, model, data,fixSbInfo):
+ SetupAndTrackLayerOutputSlot<HalPolicy>(
+ operation, 0, *layer, 0, model, data)) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 1, *layer, 1, model, data) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 2, *layer, 2, model, data) &&
+ SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 3, *layer, 3, model, data));
+}
+
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertTransposeConv2d(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 TensorInfo& inputInfo = input.GetTensorInfo();
+ const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // 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__);
+ }
+ TransposeConvolution2dDescriptor desc;
+ desc.m_DataLayout = DataLayout::NHWC;
+
+ // Determine whether padding is implicit or explicit
+ bool implicitPadding = operation.inputs.size() == 9;
+
+ if (implicitPadding )
+ {
+ desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 8, model, data);
+ }
+ else
+ {
+ desc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 10, model, data);
+ }
+
+ armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
+ unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
+ unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
+
+ const PermutationVector OHWIToOIHW = {0, 2, 3, 1};
+
+ // The shape of the weight is [depth_out, filter_height, filter_width, depth_in].
+ // We have to permute it to OIHW if the data layout is NCHW.
+ const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ?
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1,
+ model, data, OHWIToOIHW) :
+ ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 1, model, data);
+
+ // 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)
+ {
+ int32_t strideX{0};
+ int32_t strideY{0};
+ int32_t padLeft{0};
+ int32_t padRight{0};
+ int32_t padTop{0};
+ int32_t padBottom{0};
+
+ android::nn::PaddingScheme paddingScheme;
+ if (!GetInputPaddingScheme<HalPolicy>(operation, 4, paddingScheme, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 5, HalOperandType::INT32, strideX, model, data) ||
+ !GetInputScalar<HalPolicy>(operation, 6, HalOperandType::INT32, strideY, model, data) ||
+ !GetInputActivationFunction<HalPolicy>(operation, 7, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[widthIndex];
+ const uint32_t kernelY = weights.GetShape()[heightIndex];
+ const uint32_t outputX = outputInfo.GetShape()[widthIndex];
+ const uint32_t outputY = outputInfo.GetShape()[heightIndex];
+
+ CalcPaddingTransposeConv(outputX, kernelX, strideX, padLeft, padRight, paddingScheme);
+ CalcPaddingTransposeConv(outputY, kernelY, strideY, padTop, padBottom, paddingScheme);
+
+ // NOTE: The Android NN API allows for negative padding values in TransposeConv2d,
+ // but Arm NN only supports values >= 0
+ if (padLeft < 0 || padRight < 0 || padTop < 0 || padBottom < 0)
+ {
+ return Fail("%s: Negative padding values are not supported", __func__);
+ }
+
+ desc.m_StrideX = boost::numeric_cast<uint32_t>(strideX);
+ desc.m_StrideY = boost::numeric_cast<uint32_t>(strideY);
+ desc.m_PadLeft = boost::numeric_cast<uint32_t>(padLeft);
+ desc.m_PadRight = boost::numeric_cast<uint32_t>(padRight);
+ desc.m_PadTop = boost::numeric_cast<uint32_t>(padTop);
+ desc.m_PadBottom = boost::numeric_cast<uint32_t>(padBottom);
+ }
+ 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, 9, activation, 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;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsTransposeConvolution2dSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ IConnectableLayer* startLayer =
+ data.m_Network->AddTransposeConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias));
+ if (!startLayer)
+ {
+ return Fail("%s: AddTransposeConvolution2dLayer failed", __func__);
+ }
+
+ IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
+ if (!endLayer)
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+
+ input.Connect(startLayer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+}
+
+} // armnn_driver namespace
\ No newline at end of file
diff --git a/ModelToINetworkConverter.cpp b/ModelToINetworkConverter.cpp
index 05e6046..24fb490 100644
--- a/ModelToINetworkConverter.cpp
+++ b/ModelToINetworkConverter.cpp
@@ -6,8 +6,10 @@
#define LOG_TAG "ArmnnDriver"
#include "ModelToINetworkConverter.hpp"
+#include "Utils.hpp"
#include <log/log.h>
+#include <type_traits>
namespace armnn_driver
{
@@ -62,21 +64,29 @@
// add operations to it
// track which layer outputs each operand
- m_Data.m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(m_Model.operands.size(), nullptr);
-
+ ALOGV("ModelToINetworkConverter::Convert(): m_OutputSlotForOperand");
+ m_Data.m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(getMainModel(m_Model).operands.size(), nullptr);
try
{
- for (uint32_t i = 0; i < m_Model.inputIndexes.size(); i++)
+ ALOGV("ModelToINetworkConverter::Convert(): for getMainModel(m_Model).inputIndexes.size()");
+ for (uint32_t i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
{
+ ALOGV("ModelToINetworkConverter::Convert(): getMainModel(m_Model).inputIndexes[i]");
// inputs in android nn are represented by operands
- uint32_t inputIndex = m_Model.inputIndexes[i];
- const HalOperand& operand = m_Model.operands[inputIndex];
+ uint32_t inputIndex = getMainModel(m_Model).inputIndexes[i];
+ ALOGV("ModelToINetworkConverter::Convert(): getMainModel(m_Model).operands[inputIndex];");
+ const HalOperand& operand = getMainModel(m_Model).operands[inputIndex];
+ ALOGV("ModelToINetworkConverter::Convert(): GetTensorInfoForOperand(operand)");
const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
+ ALOGV("ModelToINetworkConverter::Convert(): m_Data.m_Network->AddInputLayer(i)");
armnn::IConnectableLayer* layer = m_Data.m_Network->AddInputLayer(i);
+ ALOGV("ModelToINetworkConverter::Convert(): layer->GetOutputSlot(0)");
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+ ALOGV("ModelToINetworkConverter::Convert(): outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand))");
outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand));
+ ALOGV("ModelToINetworkConverter::Convert(): m_Data.m_OutputSlotForOperand[inputIndex] = &outputSlot");
// store for later layers
m_Data.m_OutputSlotForOperand[inputIndex] = &outputSlot;
}
@@ -92,9 +102,9 @@
m_ConversionResult = ConversionResult::UnsupportedFeature;
}
- for (uint32_t operationIdx = 0; operationIdx < m_Model.operations.size(); operationIdx++)
+ for (uint32_t operationIdx = 0; operationIdx < getMainModel(m_Model).operations.size(); operationIdx++)
{
- const auto& operation = m_Model.operations[operationIdx];
+ const auto& operation = getMainModel(m_Model).operations[operationIdx];
bool ok = true;
if (m_ForcedUnsupportedOperations.find(operationIdx) != m_ForcedUnsupportedOperations.end())
@@ -135,11 +145,11 @@
{
if (m_ConversionResult == ConversionResult::Success)
{
- for (uint32_t i = 0; i < m_Model.outputIndexes.size(); i++)
+ for (uint32_t i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
{
// outputs in android nn are represented by operands
- uint32_t outputIndex = m_Model.outputIndexes[i];
- const HalOperand& operand = m_Model.operands[outputIndex];
+ uint32_t outputIndex = getMainModel(m_Model).outputIndexes[i];
+ const HalOperand& operand = getMainModel(m_Model).operands[outputIndex];
const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand);
armnn::IConnectableLayer* layer = m_Data.m_Network->AddOutputLayer(i);
@@ -178,4 +188,10 @@
template class ModelToINetworkConverter<hal_1_2::HalPolicy>;
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3
+template class ModelToINetworkConverter<hal_1_1::HalPolicy>;
+template class ModelToINetworkConverter<hal_1_2::HalPolicy>;
+template class ModelToINetworkConverter<hal_1_3::HalPolicy>;
+#endif
+
} // armnn_driver
diff --git a/RequestThread.cpp b/RequestThread.cpp
index 22a3ac3..50c5161 100644
--- a/RequestThread.cpp
+++ b/RequestThread.cpp
@@ -12,6 +12,11 @@
#include "ArmnnPreparedModel_1_2.hpp"
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3
+#include "ArmnnPreparedModel_1_2.hpp"
+#include "ArmnnPreparedModel_1_3.hpp"
+#endif
+
#include <boost/assert.hpp>
#include <log/log.h>
@@ -151,4 +156,12 @@
template class RequestThread<ArmnnPreparedModel_1_2, hal_1_2::HalPolicy, CallbackContext_1_2>;
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3
+template class RequestThread<ArmnnPreparedModel, hal_1_1::HalPolicy, CallbackContext_1_0>;
+template class RequestThread<ArmnnPreparedModel, hal_1_2::HalPolicy, CallbackContext_1_0>;
+template class RequestThread<ArmnnPreparedModel, hal_1_3::HalPolicy, CallbackContext_1_0>;
+template class RequestThread<ArmnnPreparedModel_1_2, hal_1_2::HalPolicy, CallbackContext_1_2>;
+template class RequestThread<ArmnnPreparedModel_1_3, hal_1_3::HalPolicy, CallbackContext_1_3>;
+#endif
+
} // namespace armnn_driver
diff --git a/Utils.cpp b/Utils.cpp
index c548f84..8a17b53 100644
--- a/Utils.cpp
+++ b/Utils.cpp
@@ -103,7 +103,7 @@
return ret;
}
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)// Using ::android::hardware::neuralnetworks::V1_2
armnn::TensorInfo GetTensorInfoForOperand(const V1_2::Operand& operand)
{
@@ -164,13 +164,74 @@
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+
+armnn::TensorInfo GetTensorInfoForOperand(const V1_3::Operand& operand)
+{
+ using namespace armnn;
+ bool perChannel = false;
+
+ DataType type;
+ switch (operand.type)
+ {
+ case V1_3::OperandType::TENSOR_FLOAT32:
+ type = armnn::DataType::Float32;
+ break;
+ case V1_3::OperandType::TENSOR_FLOAT16:
+ type = armnn::DataType::Float16;
+ break;
+ case V1_3::OperandType::TENSOR_QUANT8_ASYMM:
+ type = armnn::DataType::QAsymmU8;
+ break;
+ case V1_3::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ perChannel=true;
+ ARMNN_FALLTHROUGH;
+ case V1_3::OperandType::TENSOR_QUANT8_SYMM:
+ type = armnn::DataType::QSymmS8;
+ break;
+ case V1_3::OperandType::TENSOR_QUANT16_SYMM:
+ type = armnn::DataType::QSymmS16;
+ break;
+ case V1_3::OperandType::TENSOR_INT32:
+ type = armnn::DataType::Signed32;
+ break;
+ case V1_3::OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+ type = armnn::DataType::QAsymmS8;
+ break;
+ default:
+ throw UnsupportedOperand<V1_3::OperandType>(operand.type);
+ }
+
+ TensorInfo ret(operand.dimensions.size(), operand.dimensions.data(), type);
+ if (perChannel)
+ {
+ // ExtraParams is expected to be of type channelQuant
+ BOOST_ASSERT(operand.extraParams.getDiscriminator() ==
+ V1_3::Operand::ExtraParams::hidl_discriminator::channelQuant);
+
+ auto perAxisQuantParams = operand.extraParams.channelQuant();
+
+ ret.SetQuantizationScales(perAxisQuantParams.scales);
+ ret.SetQuantizationDim(MakeOptional<unsigned int>(perAxisQuantParams.channelDim));
+ }
+ else
+ {
+ ret.SetQuantizationScale(operand.scale);
+ ret.SetQuantizationOffset(operand.zeroPoint);
+ }
+
+ return ret;
+}
+
+#endif
+
std::string GetOperandSummary(const V1_0::Operand& operand)
{
return android::hardware::details::arrayToString(operand.dimensions, operand.dimensions.size()) + " " +
toString(operand.type);
}
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) // Using ::android::hardware::neuralnetworks::V1_2
std::string GetOperandSummary(const V1_2::Operand& operand)
{
@@ -180,6 +241,16 @@
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+
+std::string GetOperandSummary(const V1_3::Operand& operand)
+{
+ return android::hardware::details::arrayToString(operand.dimensions, operand.dimensions.size()) + " " +
+ toString(operand.type);
+}
+
+#endif
+
using DumpElementFunction = void (*)(const armnn::ConstTensor& tensor,
unsigned int elementIndex,
std::ofstream& fileStream);
@@ -449,6 +520,27 @@
}
}
+void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools)
+{
+ if (memPools.empty())
+ {
+ return;
+ }
+ // Commit output buffers.
+ // Note that we update *all* pools, even if they aren't actually used as outputs -
+ // this is simpler and is what the CpuExecutor does.
+ for (auto& pool : memPools)
+ {
+ // Type android::nn::RunTimePoolInfo has changed between Android P & Q and Android R, where
+ // update() has been removed and flush() added.
+#if defined(ARMNN_ANDROID_R) // Use the new Android implementation.
+ pool.flush();
+#else
+ pool.update();
+#endif
+ }
+}
+
} // namespace armnn_driver
diff --git a/Utils.hpp b/Utils.hpp
index 6256655..b61ddb2 100644
--- a/Utils.hpp
+++ b/Utils.hpp
@@ -19,11 +19,16 @@
#include <iomanip>
namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+namespace V1_1 = ::android::hardware::neuralnetworks::V1_1;
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
namespace V1_2 = ::android::hardware::neuralnetworks::V1_2;
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3
+namespace V1_3 = ::android::hardware::neuralnetworks::V1_3;
+#endif
+
namespace armnn_driver
{
@@ -31,6 +36,17 @@
using DataLocation = ::android::nn::hal::DataLocation;
#endif
+inline const V1_0::Model& getMainModel(const V1_0::Model& model) { return model; }
+inline const V1_1::Model& getMainModel(const V1_1::Model& model) { return model; }
+
+#if defined (ARMNN_ANDROID_NN_V1_2) || defined (ARMNN_ANDROID_NN_V1_3)
+inline const V1_2::Model& getMainModel(const V1_2::Model& model) { return model; }
+#endif
+
+#ifdef ARMNN_ANDROID_NN_V1_3
+inline const V1_3::Subgraph& getMainModel(const V1_3::Model& model) { return model.main; }
+#endif
+
extern const armnn::PermutationVector g_DontPermute;
template <typename OperandType>
@@ -56,42 +72,53 @@
/// Can throw UnsupportedOperand
armnn::TensorInfo GetTensorInfoForOperand(const V1_0::Operand& operand);
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) // Using ::android::hardware::neuralnetworks::V1_2
armnn::TensorInfo GetTensorInfoForOperand(const V1_2::Operand& operand);
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+armnn::TensorInfo GetTensorInfoForOperand(const V1_3::Operand& operand);
+#endif
+
std::string GetOperandSummary(const V1_0::Operand& operand);
-#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) // Using ::android::hardware::neuralnetworks::V1_2
std::string GetOperandSummary(const V1_2::Operand& operand);
#endif
+#ifdef ARMNN_ANDROID_NN_V1_3 // Using ::android::hardware::neuralnetworks::V1_3
+std::string GetOperandSummary(const V1_3::Operand& operand);
+#endif
+
template <typename HalModel>
std::string GetModelSummary(const HalModel& model)
{
std::stringstream result;
- result << model.inputIndexes.size() << " input(s), " << model.operations.size() << " operation(s), " <<
- model.outputIndexes.size() << " output(s), " << model.operands.size() << " operand(s)" << std::endl;
+ result << getMainModel(model).inputIndexes.size() << " input(s), "
+ << getMainModel(model).operations.size() << " operation(s), "
+ << getMainModel(model).outputIndexes.size() << " output(s), "
+ << getMainModel(model).operands.size() << " operand(s) "
+ << std::endl;
result << "Inputs: ";
- for (uint32_t i = 0; i < model.inputIndexes.size(); i++)
+ for (uint32_t i = 0; i < getMainModel(model).inputIndexes.size(); i++)
{
- result << GetOperandSummary(model.operands[model.inputIndexes[i]]) << ", ";
+ result << GetOperandSummary(getMainModel(model).operands[getMainModel(model).inputIndexes[i]]) << ", ";
}
result << std::endl;
result << "Operations: ";
- for (uint32_t i = 0; i < model.operations.size(); i++)
+ for (uint32_t i = 0; i < getMainModel(model).operations.size(); i++)
{
- result << toString(model.operations[i].type).c_str() << ", ";
+ result << toString(getMainModel(model).operations[i].type).c_str() << ", ";
}
result << std::endl;
result << "Outputs: ";
- for (uint32_t i = 0; i < model.outputIndexes.size(); i++)
+ for (uint32_t i = 0; i < getMainModel(model).outputIndexes.size(); i++)
{
- result << GetOperandSummary(model.operands[model.outputIndexes[i]]) << ", ";
+ result << GetOperandSummary(getMainModel(model).operands[getMainModel(model).outputIndexes[i]]) << ", ";
}
result << std::endl;
@@ -118,4 +145,29 @@
std::string GetFileTimestamp();
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
+inline V1_2::OutputShape ComputeShape(const armnn::TensorInfo& info)
+{
+ V1_2::OutputShape shape;
+
+ android::hardware::hidl_vec<uint32_t> dimensions;
+
+ armnn::TensorShape tensorShape = info.GetShape();
+ const unsigned int numDims = tensorShape.GetNumDimensions();
+ dimensions.resize(numDims);
+
+ for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx)
+ {
+ dimensions[outputIdx] = tensorShape[outputIdx];
+ }
+
+ shape.dimensions = dimensions;
+ shape.isSufficient = true;
+
+ return shape;
+}
+#endif
+
+void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools);
+
} // namespace armnn_driver
diff --git a/android.hardware.neuralnetworks@1.3-service-armnn.rc b/android.hardware.neuralnetworks@1.3-service-armnn.rc
new file mode 100644
index 0000000..3f84d9c
--- /dev/null
+++ b/android.hardware.neuralnetworks@1.3-service-armnn.rc
@@ -0,0 +1,4 @@
+service neuralnetworks_hal_service_armnn /vendor/bin/hw/android.hardware.neuralnetworks@1.3-service-armnn
+ class hal
+ user system
+ group system
diff --git a/test/Convolution2D.hpp b/test/Convolution2D.hpp
index 002677f..38216f1 100644
--- a/test/Convolution2D.hpp
+++ b/test/Convolution2D.hpp
@@ -32,9 +32,7 @@
void SetModelFp16Flag(V1_0::Model& model, bool fp16Enabled);
-#if defined(ARMNN_ANDROID_NN_V1_1) || defined(ARMNN_ANDROID_NN_V1_2)
void SetModelFp16Flag(V1_1::Model& model, bool fp16Enabled);
-#endif
template<typename HalPolicy>
void PaddingTestImpl(android::nn::PaddingScheme paddingScheme, bool fp16Enabled = false)