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/1.2/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