| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "HalPolicy.hpp" |
| |
| #include "armnn/Optional.hpp" |
| |
| namespace armnn_driver |
| { |
| namespace hal_1_0 |
| { |
| |
| bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| switch (operation.type) |
| { |
| case V1_0::OperationType::ADD: |
| return ConvertAdd(operation, model, data); |
| case V1_0::OperationType::AVERAGE_POOL_2D: |
| return ConvertAveragePool2d(operation, model, data); |
| case V1_0::OperationType::CONCATENATION: |
| return ConvertConcatenation(operation, model, data); |
| case V1_0::OperationType::CONV_2D: |
| return ConvertConv2d(operation, model, data); |
| case V1_0::OperationType::DEPTHWISE_CONV_2D: |
| return ConvertDepthwiseConv2d(operation, model, data); |
| case V1_0::OperationType::FLOOR: |
| return ConvertFloor(operation, model, data); |
| case V1_0::OperationType::FULLY_CONNECTED: |
| return ConvertFullyConnected(operation, model, data); |
| case V1_0::OperationType::LOCAL_RESPONSE_NORMALIZATION: |
| return ConvertLocalResponseNormalization(operation, model, data); |
| case V1_0::OperationType::LOGISTIC: |
| return ConvertLogistic(operation, model, data); |
| case V1_0::OperationType::LSTM: |
| return ConvertLstm(operation, model, data); |
| case V1_0::OperationType::L2_NORMALIZATION: |
| return ConvertL2Normalization(operation, model, data); |
| case V1_0::OperationType::L2_POOL_2D: |
| return ConvertL2Pool2d(operation, model, data); |
| case V1_0::OperationType::MAX_POOL_2D: |
| return ConvertMaxPool2d(operation, model, data); |
| case V1_0::OperationType::MUL: |
| return ConvertMul(operation, model, data); |
| case V1_0::OperationType::RELU: |
| return ConvertReLu(operation, model, data); |
| case V1_0::OperationType::RELU1: |
| return ConvertReLu1(operation, model, data); |
| case V1_0::OperationType::RELU6: |
| return ConvertReLu6(operation, model, data); |
| case V1_0::OperationType::SOFTMAX: |
| return ConvertSoftmax(operation, model, data); |
| case V1_0::OperationType::TANH: |
| return ConvertTanH(operation, model, data); |
| case V1_0::OperationType::RESHAPE: |
| return ConvertReshape(operation, model, data); |
| case V1_0::OperationType::RESIZE_BILINEAR: |
| return ConvertResizeBilinear(operation, model, data); |
| default: |
| return Fail("%s: Operation type %s not supported in ArmnnDriver", |
| __func__, toString(operation.type).c_str()); |
| } |
| } |
| |
| bool HalPolicy::ConvertAdd(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| |
| if (!input0.IsValid() || !input1.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| // The FuseActivation parameter is always the input index 2 |
| // and it should be optional |
| ActivationFn activationFunction; |
| if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| if (!outputOperand) |
| { |
| return false; |
| } |
| |
| const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand); |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsAdditionSupported, |
| data.m_Compute, |
| input0.GetTensorInfo(), |
| input1.GetTensorInfo(), |
| outInfo)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* const startLayer = data.m_Network->AddAdditionLayer(); |
| armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| |
| const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| |
| if (endLayer != nullptr) |
| { |
| BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| } |
| else |
| { |
| return Fail("%s: ProcessActivation failed", __func__); |
| } |
| } |
| |
| bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average, model, data); |
| } |
| |
| bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis. |
| if (operation.inputs.size() <= 1) |
| { |
| return Fail("%s: Operation has insufficient arguments", __func__); |
| } |
| |
| // Get inputs and outputs |
| const std::size_t numInputTensors = operation.inputs.size() - 1; |
| |
| int32_t concatDim; |
| if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* const outputOperand = GetOutputOperand(operation, 0, model); |
| if (!outputOperand) |
| { |
| return Fail("%s: Operation has no outputs", __func__); |
| } |
| |
| |
| armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*outputOperand); |
| armnn::TensorShape outputShape = outputInfo.GetShape(); |
| |
| // |
| // handle negative concat dims along the lines of tensorflow as described here: |
| // https://www.tensorflow.org/api_docs/python/tf/concat |
| // "negative axis refers to axis + rank(values)-th dimension" |
| // |
| if (concatDim < 0) |
| { |
| concatDim += outputShape.GetNumDimensions(); |
| } |
| |
| if (concatDim >= static_cast<int32_t>(outputShape.GetNumDimensions()) || concatDim < 0) |
| { |
| return Fail("%s: Operation has invalid concat axis: %d", __func__, concatDim); |
| } |
| |
| std::vector<LayerInputHandle> inputHandles; |
| std::vector<armnn::TensorShape> inputShapes; |
| |
| inputHandles.reserve(numInputTensors); |
| inputShapes.reserve(numInputTensors); |
| |
| bool inputsHaveBeenReshaped = false; |
| unsigned int tensorDimensionsAdded = 0; |
| |
| for (uint32_t i = 0; i < numInputTensors; ++i) |
| { |
| const Operand* const operand = GetInputOperand(operation, i, model); |
| if (!operand) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::TensorShape operandShape = GetTensorShapeForOperand(*operand); |
| LayerInputHandle operandInputHandle = ConvertToLayerInputHandle(operation, i, model, data); |
| |
| if (operandShape.GetNumDimensions() == 0) |
| { |
| return Fail("%s: Operands with rank 0 are not supported", __func__); |
| } |
| |
| if (RequiresReshape(operandShape)) |
| { |
| inputsHaveBeenReshaped = true; |
| |
| armnn::TensorInfo reshapeInfo = operandInputHandle.GetTensorInfo(); |
| |
| // Expand the tensor to three dimensions |
| if (operandShape.GetNumDimensions() == 2) |
| { |
| reshapeInfo.SetShape(armnn::TensorShape({1, operandShape[0], operandShape[1]})); |
| tensorDimensionsAdded = 1; |
| } |
| else |
| { |
| reshapeInfo.SetShape(armnn::TensorShape({1, 1, operandShape[0]})); |
| tensorDimensionsAdded = 2; |
| } |
| |
| armnn::IConnectableLayer& newReshape = AddReshapeLayer( |
| *data.m_Network, |
| operandInputHandle, |
| reshapeInfo |
| ); |
| |
| // Point to the reshape operation rather then the input operation |
| operandShape = reshapeInfo.GetShape(); |
| operandInputHandle = LayerInputHandle(true, &newReshape.GetOutputSlot(0), reshapeInfo); |
| } |
| |
| inputShapes.emplace_back(operandShape); |
| inputHandles.emplace_back(operandInputHandle); |
| |
| if (!inputHandles.back().IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| } |
| |
| BOOST_ASSERT(inputShapes.size() == inputHandles.size()); |
| |
| if (inputsHaveBeenReshaped) |
| { |
| // Adjust the concatenation dimension by the amount of dimensions added (if any) |
| concatDim += tensorDimensionsAdded; |
| |
| // Add extra dimensions to the output shape to reflect the addition of the reshape layers |
| if (tensorDimensionsAdded == 1) |
| { |
| outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]}); |
| } |
| else if (tensorDimensionsAdded == 2) |
| { |
| outputShape = armnn::TensorShape({1, 1, outputShape[0]}); |
| } |
| } |
| |
| // Check if permutations is required and get the pair of permutations required for the concatenation. |
| // Permutation is required when the concat dimension is 2 for a 4D tensor or 1 for a 3D tensor. |
| std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair = |
| std::make_pair(IdentityPermutation4D, IdentityPermutation4D); |
| |
| bool needPermute = CreateConcatPermutationParameters(inputShapes[0].GetNumDimensions(), concatDim, permutationPair); |
| |
| if (needPermute) |
| { |
| outputShape = armnnUtils::Permuted(outputShape, permutationPair.first); |
| } |
| |
| outputInfo.SetShape(outputShape); |
| |
| // this is no-op for identity swizzles, otherwise it replaces both |
| // the handles and shapes with the swizzled layer output handles and shapes |
| SwizzleInputs(*data.m_Network, inputHandles, inputShapes, permutationPair.first); |
| |
| // Create an armnn merger layer descriptor - this will also perform validation on the input shapes |
| armnn::OriginsDescriptor mergerDescriptor; |
| |
| try |
| { |
| // The merger descriptor is always created across the only supported concat dimension |
| // which is 0, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor. |
| mergerDescriptor = |
| armnn::CreateMergerDescriptorForConcatenation( |
| inputShapes.begin(), inputShapes.end(), concatDim); |
| } |
| catch (const armnn::Exception& error) |
| { |
| return Fail("%s: Error preparing merger descriptor. %s", __func__, error.what()); |
| } |
| |
| // Validate the output shape is correct given the input shapes based on the |
| // only valid concat dimension which is 0, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor. |
| if (!ValidateConcatOutputShape(inputShapes, outputShape, concatDim)) |
| { |
| return Fail("%s: Error validating the output shape for concat", __func__); |
| } |
| |
| std::vector<const armnn::TensorInfo*> inputTensorInfos; |
| std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos), |
| [](const LayerInputHandle& h) -> const armnn::TensorInfo*{ return &h.GetTensorInfo(); }); |
| if (!IsLayerSupported(__func__, |
| armnn::IsMergerSupported, |
| data.m_Compute, |
| inputTensorInfos, |
| outputInfo, |
| mergerDescriptor)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddMergerLayer(mergerDescriptor); |
| assert(layer != nullptr); |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| |
| // Connect inputs to the layer |
| const int numInputSlots = layer->GetNumInputSlots(); |
| assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size()); |
| for (int i = 0; i < numInputSlots; ++i) |
| { |
| // connect the input directly to the merge (concat) layer |
| inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i)); |
| } |
| |
| if (needPermute) |
| { |
| // Add permutation layer and connect the output to it, the permutation becomes the output layer |
| armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(*data.m_Network, |
| layer->GetOutputSlot(0), |
| permutationPair.second); |
| layer = &deswizzleLayer; |
| } |
| |
| if (inputsHaveBeenReshaped) |
| { |
| armnn::TensorInfo afterConcatInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| |
| // Undo the reshape knowing the amount of dimensions added |
| if (tensorDimensionsAdded == 1) |
| { |
| afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[1], |
| afterConcatInfo.GetShape()[2] })); |
| } |
| else if (tensorDimensionsAdded == 2) |
| { |
| afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[2] })); |
| } |
| |
| layer = &AddReshapeLayer( |
| *data.m_Network, |
| layer->GetOutputSlot(0), |
| afterConcatInfo |
| ); |
| } |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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(operation, 1, model, data); |
| const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| |
| if (!weightsPin.IsValid() || !biasPin.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::ConstTensor weights = weightsPin.GetConstTensor(); |
| armnn::ConstTensor bias = biasPin.GetConstTensor(); |
| SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); |
| |
| armnn::Convolution2dDescriptor desc; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| ActivationFn activation; |
| |
| if (operation.inputs.size() == 10) |
| { |
| if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || |
| !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || |
| !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || |
| !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || |
| !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || |
| !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || |
| !GetInputActivationFunction(operation, 9, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| } |
| else if (operation.inputs.size() == 7) |
| { |
| android::nn::PaddingScheme paddingScheme; |
| if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) || |
| !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || |
| !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || |
| !GetInputActivationFunction(operation, 6, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const uint32_t kernelX = weights.GetShape()[2]; |
| const uint32_t kernelY = weights.GetShape()[1]; |
| const uint32_t inputX = inputInfo.GetShape()[2]; |
| const uint32_t inputY = inputInfo.GetShape()[1]; |
| |
| CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); |
| CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); |
| } |
| else |
| { |
| return Fail("%s: Unsupported number of operation inputs", __func__); |
| } |
| |
| desc.m_BiasEnabled = true; |
| armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo()); |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsConvolution2dSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| desc, |
| weights.GetInfo(), |
| biases)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* startLayer = data.m_Network->AddConvolution2dLayer(desc, weights, 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(operation, 0, *endLayer, model, data); |
| } |
| |
| bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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 ] |
| // which is equal to [ M, H, W, I ] |
| const Operand* weightsOperand = GetInputOperand(operation, 1, model); |
| |
| if (weightsOperand == nullptr) |
| { |
| return Fail("%s: Operand is invalid", __func__); |
| } |
| |
| // Reinterpret weight data as [ H, W, I, M ] |
| armnn::TensorShape weightsShape({ weightsOperand->dimensions[1], weightsOperand->dimensions[2], |
| inputInfo.GetShape()[3], |
| weightsOperand->dimensions[3] / inputInfo.GetShape()[3] }); |
| |
| // Swizzle weight data [ H, W, I, M ] -> [ M, H, W, I ] |
| const armnn::PermutationVector HWIMToMHWI = { 1U, 2U, 3U, 0U }; |
| |
| ConstTensorPin weightsPin = |
| ConvertOperationInputToConstTensorPin(operation, 1, model, data, HWIMToMHWI, &weightsShape); |
| |
| // Bias is a 1D tensor |
| ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| |
| if (!weightsPin.IsValid() || !biasPin.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::ConstTensor weights = weightsPin.GetConstTensor(); |
| armnn::ConstTensor bias = biasPin.GetConstTensor(); |
| SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); |
| |
| armnn::DepthwiseConvolution2dDescriptor desc; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| ActivationFn activation; |
| |
| if (operation.inputs.size() == 11) |
| { |
| if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || |
| !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || |
| !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || |
| !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || |
| !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || |
| !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || |
| !GetInputActivationFunction(operation, 10, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| } |
| else if (operation.inputs.size() == 8) |
| { |
| android::nn::PaddingScheme paddingScheme; |
| if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) || |
| !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || |
| !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || |
| !GetInputActivationFunction(operation, 7, activation, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const uint32_t kernelX = weights.GetShape()[2]; |
| const uint32_t kernelY = weights.GetShape()[1]; |
| const uint32_t inputX = inputInfo.GetShape()[2]; |
| const uint32_t inputY = inputInfo.GetShape()[1]; |
| |
| CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); |
| CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); |
| } |
| else |
| { |
| return Fail("%s: Unsupported number of operation inputs", __func__); |
| } |
| |
| desc.m_BiasEnabled = true; |
| armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo()); |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsDepthwiseConvolutionSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| desc, |
| weights.GetInfo(), |
| biases)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* startLayer = data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, 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(operation, 0, *endLayer, model, data); |
| } |
| |
| bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* const outputOperand = GetOutputOperand(operation, 0, model); |
| if (!outputOperand) |
| { |
| return Fail("%s: Operation has invalid outputs", __func__); |
| } |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsFloorSupported, |
| data.m_Compute, |
| input.GetTensorInfo(), |
| GetTensorInfoForOperand(*outputOperand))) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddFloorLayer(); |
| assert(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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 |
| ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data); // 2D |
| ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data); // 1D |
| |
| if (!weightsPin.IsValid() || !biasPin.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::ConstTensor weights = weightsPin.GetConstTensor(); |
| armnn::ConstTensor bias = biasPin.GetConstTensor(); |
| |
| armnn::TensorInfo reshapedInfo = inputInfo; |
| if (inputInfo.GetNumDimensions() > 2U) |
| { |
| unsigned int dim0 = inputInfo.GetShape()[0]; |
| unsigned int dim1 = inputInfo.GetShape()[1]; |
| |
| for (unsigned int i = 2U; i < inputInfo.GetNumDimensions(); ++i) |
| { |
| dim1 *= inputInfo.GetShape()[i]; |
| } |
| |
| unsigned int divisor = weights.GetInfo().GetShape()[1] / dim1; |
| if(dim0 % divisor != 0) |
| { |
| return Fail("%s: Failed to deduce tensor shape", __func__); |
| } |
| |
| reshapedInfo.SetShape(armnn::TensorShape({dim0 / divisor, dim1 * divisor})); |
| } |
| |
| // ensuring that the bias value is within 1% of the weights input (small float differences can exist) |
| SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo); |
| |
| ActivationFn activationFunction; |
| if (!GetInputActivationFunction(operation, 3, activationFunction, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::FullyConnectedDescriptor desc; |
| desc.m_TransposeWeightMatrix = true; |
| desc.m_BiasEnabled = true; |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsFullyConnectedSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| weights.GetInfo(), |
| bias.GetInfo(), |
| desc)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* startLayer = data.m_Network->AddFullyConnectedLayer(desc, weights, bias); |
| armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activationFunction, startLayer, data); |
| |
| if (endLayer != nullptr) |
| { |
| if (inputInfo.GetNumDimensions() > 2U) |
| { |
| armnn::ReshapeDescriptor reshapeDescriptor; |
| reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape(); |
| |
| armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(reshapeDescriptor); |
| assert(reshapeLayer != nullptr); |
| input.Connect(reshapeLayer->GetInputSlot(0)); |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo); |
| reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0)); |
| } |
| else |
| { |
| input.Connect(startLayer->GetInputSlot(0)); |
| } |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| } |
| else |
| { |
| return Fail("%s: ProcessActivation failed", __func__); |
| } |
| } |
| |
| bool HalPolicy::ConvertLocalResponseNormalization(const Operation& operation, |
| const Model& model, |
| ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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::NormalizationDescriptor descriptor; |
| |
| descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across; |
| descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| |
| if (!input.IsValid() || |
| !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize, model, data) || |
| !GetInputFloat32(operation, 2, descriptor.m_K, model, data) || |
| !GetInputFloat32(operation, 3, descriptor.m_Alpha, model, data) || |
| !GetInputFloat32(operation, 4, descriptor.m_Beta, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| // ArmNN expects normSize to be the full size of the normalization |
| // window rather than the radius as in AndroidNN. |
| descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize); |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsNormalizationSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| descriptor)) |
| { |
| return false; |
| } |
| |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddNormalizationLayer(descriptor); |
| assert(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| armnn::ActivationDescriptor desc; |
| desc.m_Function = armnn::ActivationFunction::Sigmoid; |
| |
| return ConvertToActivation(operation, __func__, desc, model, data); |
| } |
| |
| bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| // 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(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(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(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 = ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| const ConstTensorPin inputToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 3, model, data); |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| const ConstTensorPin inputToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 4, model, data); |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| const ConstTensorPin recurrentToForgetWeightsPin = |
| ConvertOperationInputToConstTensorPin(operation, 6, model, data); |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| const ConstTensorPin recurrentToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 7, model, data); |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| const ConstTensorPin recurrentToOutputWeightsPin = |
| ConvertOperationInputToConstTensorPin(operation, 8, model, data); |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 13, model, data); |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin(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(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 = ConvertOperationInputToConstTensorPin(operation, 1, model, data); |
| // 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 = ConvertOperationInputToConstTensorPin(operation, 5, model, data); |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin cellToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 9, model, data); |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin cellToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 10, model, data); |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin cellToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 11, model, data); |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 12, model, data); |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| const ConstTensorPin projectionWeightsPin = ConvertOperationInputToConstTensorPin(operation, 16, model, data); |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin(operation, 17, model, data); |
| |
| 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(operation, 20, activation, model, data) || |
| !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip, model, data) || |
| !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip, model, data)) |
| { |
| return Fail("%s: Operation has invalid scalar inputs", __func__); |
| } |
| |
| // 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(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(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(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(operation, 3, model); |
| if (!output) |
| { |
| return Fail("%s: Could not read output 3: output", __func__); |
| } |
| |
| // set the params structure for the AddLstmLayer call |
| armnn::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(); |
| |
| // set the layer descriptor |
| armnn::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); |
| |
| // 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__); |
| } |
| |
| // Check if the layer is supported |
| // Inputs |
| const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| const armnn::TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo(); |
| const armnn::TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo(); |
| |
| // Outputs |
| const armnn::TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer); |
| const armnn::TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut); |
| const armnn::TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); |
| const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| |
| // Basic parameters |
| const armnn::TensorInfo& inputToForgetWeights = params.m_InputToForgetWeights->GetInfo(); |
| const armnn::TensorInfo& inputToCellWeights = params.m_InputToCellWeights->GetInfo(); |
| const armnn::TensorInfo& inputToOutputWeights = params.m_InputToOutputWeights->GetInfo(); |
| const armnn::TensorInfo& recurrentToForgetWeights = params.m_RecurrentToForgetWeights->GetInfo(); |
| const armnn::TensorInfo& recurrentToCellWeights = params.m_RecurrentToCellWeights->GetInfo(); |
| const armnn::TensorInfo& recurrentToOutputWeights = params.m_RecurrentToOutputWeights->GetInfo(); |
| const armnn::TensorInfo& forgetGateBias = params.m_ForgetGateBias->GetInfo(); |
| const armnn::TensorInfo& cellBias = params.m_CellBias->GetInfo(); |
| const armnn::TensorInfo& outputGateBias = params.m_OutputGateBias->GetInfo(); |
| |
| //Optional parameters |
| const armnn::TensorInfo* inputToInputWeights = nullptr; |
| const armnn::TensorInfo* recurrentToInputWeights = nullptr; |
| const armnn::TensorInfo* cellToInputWeights = nullptr; |
| const armnn::TensorInfo* inputGateBias = nullptr; |
| const armnn::TensorInfo* projectionWeights = nullptr; |
| const armnn::TensorInfo* projectionBias = nullptr; |
| const armnn::TensorInfo* cellToForgetWeights = nullptr; |
| const armnn::TensorInfo* cellToOutputWeights = nullptr; |
| |
| if(!desc.m_CifgEnabled) |
| { |
| inputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); |
| recurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); |
| if (params.m_CellToInputWeights != nullptr) |
| { |
| cellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); |
| } |
| inputGateBias = &(params.m_InputGateBias->GetInfo()); |
| } |
| |
| if(desc.m_ProjectionEnabled) |
| { |
| projectionWeights = &(params.m_ProjectionWeights->GetInfo()); |
| if (params.m_ProjectionBias != nullptr) |
| { |
| projectionBias = &(params.m_ProjectionBias->GetInfo()); |
| } |
| } |
| |
| if(desc.m_PeepholeEnabled) |
| { |
| cellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); |
| cellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); |
| } |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsLstmSupported, |
| data.m_Compute, |
| inputInfo, |
| outputStateInInfo, |
| cellStateInInfo, |
| scratchBufferInfo, |
| outputStateOutInfo, |
| cellStateOutInfo, |
| outputInfo, |
| desc, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| inputToInputWeights, |
| recurrentToInputWeights, |
| cellToInputWeights, |
| inputGateBias, |
| projectionWeights, |
| projectionBias, |
| cellToForgetWeights, |
| cellToOutputWeights)) |
| { |
| return false; |
| } |
| |
| // Add the layer |
| armnn::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 (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && |
| SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) && |
| SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) && |
| SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3, model, data)); |
| } |
| |
| bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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::L2NormalizationDescriptor desc; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsL2NormalizationSupported, |
| data.m_Compute, |
| inputInfo, |
| outputInfo, |
| desc)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddL2NormalizationLayer(desc); |
| assert(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2, model, data); |
| } |
| |
| bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max, model, data); |
| } |
| |
| bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| |
| if (!input0.IsValid() || !input1.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| // The FuseActivation parameter is always the input index 2 |
| // and it should be optional |
| ActivationFn activationFunction; |
| if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| |
| if (outputOperand == nullptr) |
| { |
| return false; |
| } |
| |
| const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsMultiplicationSupported, |
| data.m_Compute, |
| input0.GetTensorInfo(), |
| input1.GetTensorInfo(), |
| outInfo)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* const startLayer = data.m_Network->AddMultiplicationLayer(); |
| armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| |
| const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| |
| if (endLayer != nullptr) |
| { |
| BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| } |
| else |
| { |
| return Fail("%s: ProcessActivation failed", __func__); |
| } |
| } |
| |
| bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| armnn::ActivationDescriptor desc; |
| desc.m_Function = armnn::ActivationFunction::ReLu; |
| |
| return ConvertToActivation(operation, __func__, desc, model, data); |
| } |
| |
| bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| armnn::ActivationDescriptor desc; |
| desc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| desc.m_A = 1.0f; |
| desc.m_B = -1.0f; |
| |
| return ConvertToActivation(operation, __func__, desc, model, data); |
| } |
| |
| bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| armnn::ActivationDescriptor desc; |
| desc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| desc.m_A = 6.0f; |
| |
| return ConvertToActivation(operation, __func__, desc, model, data); |
| } |
| |
| bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| if (!outputOperand) |
| { |
| return Fail("%s: Operation has no outputs", __func__); |
| } |
| |
| const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand); |
| |
| armnn::SoftmaxDescriptor desc; |
| if (!GetInputFloat32(operation, 1, desc.m_Beta, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsSoftmaxSupported, |
| data.m_Compute, |
| input.GetTensorInfo(), |
| outInfo, |
| desc)) |
| { |
| return false; |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc); |
| assert(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| armnn::ActivationDescriptor desc; |
| desc.m_Function = armnn::ActivationFunction::TanH; |
| desc.m_A = 1.0f; // android nn does not support tanH parameters |
| desc.m_B = 1.0f; // set to 1.0f for unity scaling |
| |
| return ConvertToActivation(operation, __func__, desc, model, data); |
| } |
| |
| bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| const Operand* inputOperand = GetInputOperand(operation, 0, model); |
| const Operand* requestedShapeOperand = GetInputOperand(operation, 1, model); |
| const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| |
| if (inputOperand == nullptr |
| || requestedShapeOperand == nullptr |
| || outputOperand == nullptr) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| |
| if (requestedShapeOperand->dimensions.size() != 1) |
| { |
| return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)", |
| __func__, requestedShapeOperand->dimensions.size()); |
| } |
| |
| std::vector<int32_t> targetDimensions; |
| if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions, model, data)) |
| { |
| return Fail("%s: Could not read values of input 1", __func__); |
| } |
| |
| const Shape inputOperandShape = GetOperandShape(*inputOperand); |
| |
| Shape requestedShape; |
| // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility |
| // function that resolves these values into a fully specified tensor shape. |
| if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape)) |
| { |
| return Fail("%s: Failed to resolve the requested shape", __func__); |
| } |
| |
| const Shape outputOperandShape = GetOperandShape(*outputOperand); |
| if (!SameShape(requestedShape, outputOperandShape)) |
| { |
| return Fail("%s: Shape of output operand does not match resolved requested shape", __func__); |
| } |
| |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Could not read input 0", __func__); |
| } |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsReshapeSupported, |
| data.m_Compute, |
| input.GetTensorInfo())) |
| { |
| return false; |
| } |
| |
| |
| armnn::ReshapeDescriptor reshapeDescriptor; |
| reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(), |
| requestedShape.dimensions.data()); |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor); |
| assert(layer != nullptr); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| } |
| |
| bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data) |
| { |
| LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| if (!input.IsValid()) |
| { |
| return Fail("%s: Could not read input 0", __func__); |
| } |
| |
| const Operand* output = GetOutputOperand(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::ResizeBilinearDescriptor desc; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| if (!IsLayerSupported(__func__, |
| armnn::IsResizeBilinearSupported, |
| data.m_Compute, |
| inputInfo)) |
| { |
| return false; |
| } |
| |
| |
| if ( !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight, model, data) |
| || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth, model, data)) |
| { |
| return Fail("%s: Operation has invalid inputs", __func__); |
| } |
| |
| armnn::IConnectableLayer* layer = data.m_Network->AddResizeBilinearLayer(desc); |
| |
| assert(layer != nullptr); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| input.Connect(layer->GetInputSlot(0)); |
| |
| return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| |
| } |
| |
| } // namespace hal_1_0 |
| } // namespace armnn_driver |