diff options
| -rw-r--r-- | 1.2/HalPolicy.cpp | 282 | ||||
| -rw-r--r-- | 1.2/HalPolicy.hpp | 4 |
2 files changed, 284 insertions, 2 deletions
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp index 11a1cefa..b3b1d69f 100644 --- a/1.2/HalPolicy.cpp +++ b/1.2/HalPolicy.cpp @@ -131,14 +131,292 @@ bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, switch (operation.type) { case V1_2::OperationType::CONV_2D: - return ConvertConv2d<hal_1_2::HalPolicy>(operation, model, data); + return ConvertConv2d(operation, model, data); case V1_2::OperationType::DEPTHWISE_CONV_2D: - return ConvertDepthwiseConv2d<hal_1_2::HalPolicy>(operation, model, data); + return ConvertDepthwiseConv2d(operation, model, data); default: return Fail("%s: Operation type %s not supported in ArmnnDriver", __func__, toString(operation.type).c_str()); } } +bool HalPolicy::ConvertConv2d(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 armnn::TensorInfo& inputInfo = input.GetTensorInfo(); + const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); + + // ArmNN does not currently support non-fixed weights or bias + const ConstTensorPin weightsPin = + ConvertOperationInputToConstTensorPin<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__); + } + + armnn::ConstTensor weights = weightsPin.GetConstTensor(); + armnn::ConstTensor bias = biasPin.GetConstTensor(); + SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); + + armnn::Convolution2dDescriptor desc; + desc.m_DataLayout = armnn::DataLayout::NHWC; + ActivationFn activation; + + // 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) + { + 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__); + } + + const uint32_t kernelX = weights.GetShape()[2]; + const uint32_t kernelY = weights.GetShape()[1]; + const uint32_t inputX = inputInfo.GetShape()[2]; + const uint32_t inputY = inputInfo.GetShape()[1]; + + CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); + CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); + + desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 7, model, data); + } + 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__); + } + desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data); + } + else + { + return Fail("%s: Unsupported number of operation inputs", __func__); + } + + desc.m_BiasEnabled = true; + armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo()); + + if (!IsLayerSupportedForAnyBackend(__func__, + armnn::IsConvolution2dSupported, + data.m_Backends, + inputInfo, + outputInfo, + desc, + weights.GetInfo(), + biases)) + { + return false; + } + + armnn::IConnectableLayer* startLayer = + data.m_Network->AddConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias)); + + if (!startLayer) + { + return Fail("%s: AddConvolution2dLayer failed", __func__); + } + + armnn::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); +} + +bool HalPolicy::ConvertDepthwiseConv2d(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 armnn::TensorInfo& inputInfo = input.GetTensorInfo(); + const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); + + // ArmNN does not currently support non-fixed weights or bias + // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ] + const Operand* weightsOperand = GetInputOperand<hal_1_2::HalPolicy>(operation, 1, model); + + if (weightsOperand == nullptr) + { + return Fail("%s: Operand is invalid", __func__); + } + armnn::DepthwiseConvolution2dDescriptor desc; + desc.m_DataLayout = armnn::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 ] + armnn::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 armnn::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__); + } + + armnn::ConstTensor weights = weightsPin.GetConstTensor(); + armnn::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_PadLeft, desc.m_PadRight, paddingScheme); + CalcPadding(inputY, kernelY, desc.m_StrideY, 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; + armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo()); + + if (!IsLayerSupportedForAnyBackend(__func__, + armnn::IsDepthwiseConvolutionSupported, + data.m_Backends, + inputInfo, + outputInfo, + desc, + weights.GetInfo(), + biases)) + { + return false; + } + + armnn::IConnectableLayer* startLayer = + data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias)); + if (!startLayer) + { + return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__); + } + + armnn::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); +} + } // namespace hal_1_2 } // namespace armnn_driver
\ No newline at end of file diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp index e6001c48..516e1ebd 100644 --- a/1.2/HalPolicy.hpp +++ b/1.2/HalPolicy.hpp @@ -27,6 +27,10 @@ public: using getSupportedOperations_cb = V1_2::IDevice::getSupportedOperations_1_2_cb; static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data); + +private: + static bool ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data); + static bool ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data); }; } // namespace hal_1_2 |