text: //
  // Copyright © 2017 Arm Ltd. All rights reserved.
  // SPDX-License-Identifier: MIT
  //
  
  #include "HalPolicy.hpp"
  
  #include <armnn/Optional.hpp>
  
  #include "FullyConnected.hpp"
  #include "Utils.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::DEPTH_TO_SPACE:
              return ConvertDepthToSpace(operation, model, data);
          case V1_0::OperationType::DEPTHWISE_CONV_2D:
              return ConvertDepthwiseConv2d(operation, model, data);
          case V1_0::OperationType::DEQUANTIZE:
              return ConvertDequantize(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::SPACE_TO_DEPTH:
              return ConvertSpaceToDepth(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)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertAdd()");
      return ::ConvertAdd<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertAveragePool2d()");
      return ConvertPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
  }
  
  bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertConcatenation()");
      return ::ConvertConcatenation<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertConv2d()");
      return ::ConvertConv2d<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertDepthToSpace()");
      return ::ConvertDepthToSpace<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertDepthwiseConv2d()");
      return ::ConvertDepthwiseConv2d<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertDequantize()");
      return ::ConvertDequantize<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertFloor()");
      return ::ConvertFloor<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertFullyConnected()");
      return ::ConvertFullyConnected<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertLocalResponseNormalization(const Operation& operation,
                                                    const Model& model,
                                                    ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertLocalResponseNormalization()");
      return ::ConvertLocalResponseNormalization<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertLogistic()");
      return ::ConvertLogistic<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::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_0::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_0::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_0::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 =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(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<hal_1_0::HalPolicy>(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<hal_1_0::HalPolicy>(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<hal_1_0::HalPolicy>(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<hal_1_0::HalPolicy>(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<hal_1_0::HalPolicy>(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<hal_1_0::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_0::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_0::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 =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    1,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    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 =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    5,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    true);
  
      // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
      const ConstTensorPin cellToInputWeightsPin =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    9,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    true);
  
      // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
      const ConstTensorPin cellToForgetWeightsPin =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    10,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    true);
  
      // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
      const ConstTensorPin cellToOutputWeightsPin =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    11,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    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_0::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 =
          ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation,
                                                                    16,
                                                                    model,
                                                                    data,
                                                                    g_DontPermute,
                                                                    nullptr,
                                                                    true);
  
      // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
      const ConstTensorPin projectionBiasPin =
      ConvertOperationInputToConstTensorPin<hal_1_0::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_0::HalPolicy>(operation, 20, activation, model, data) ||
          !GetInputScalar<hal_1_0::HalPolicy>(operation, 21, OperandType::FLOAT32, cellClip, model, data) ||
          !GetInputScalar<hal_1_0::HalPolicy>(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<hal_1_0::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_0::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_0::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_0::HalPolicy>(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
      armnn::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());
      }
  
      bool isSupported = false;
      FORWARD_LAYER_SUPPORT_FUNC(__func__,
                                 IsLstmSupported,
                                 data.m_Backends,
                                 isSupported,
                                 inputInfo,
                                 outputStateInInfo,
                                 cellStateInInfo,
                                 scratchBufferInfo,
                                 outputStateOutInfo,
                                 cellStateOutInfo,
                                 outputInfo,
                                 desc,
                                 paramsInfo);
      if (!isSupported)
      {
          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<hal_1_0::HalPolicy>(operation, 0, *layer, 0, model, data) &&
              SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 1, *layer, 1, model, data) &&
              SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 2, *layer, 2, model, data) &&
              SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 3, *layer, 3, model, data));
  }
  
  bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertL2Normalization()");
      return ::ConvertL2Normalization<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertL2Pool2d()");
      return ConvertPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::L2, model, data);
  }
  
  bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertMaxPool2d()");
      return ConvertPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::Max, model, data);
  }
  
  bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertMul()");
      return ::ConvertMul<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertReLu()");
      return ::ConvertReLu<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertReLu1()");
      return ::ConvertReLu1<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertReLu6()");
      return ::ConvertReLu6<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertSoftmax()");
  
      LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data);
      if (!input.IsValid())
      {
          return Fail("%s: Operation has invalid inputs", __func__);
      }
  
      const Operand* outputOperand = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model);
      if (!outputOperand)
      {
          return Fail("%s: Operation has no outputs", __func__);
      }
  
      const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand);
      if (IsDynamicTensor(outputInfo))
      {
          return Fail("%s: Dynamic output tensors are not supported", __func__);
      }
  
      armnn::SoftmaxDescriptor desc;
      if (!GetInputFloat32<hal_1_0::HalPolicy>(operation, 1, desc.m_Beta, model, data))
      {
          return Fail("%s: Operation has invalid inputs", __func__);
      }
  
      bool isSupported = false;
      FORWARD_LAYER_SUPPORT_FUNC(__func__,
                                 IsSoftmaxSupported,
                                 data.m_Backends,
                                 isSupported,
                                 input.GetTensorInfo(),
                                 outputInfo,
                                 desc);
      if (!isSupported)
      {
          return false;
      }
  
      armnn::IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc);
      assert(layer != nullptr);
      input.Connect(layer->GetInputSlot(0));
  
      return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data);
  }
  
  bool HalPolicy::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertSpaceToDepth()");
  
      LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data);
      if (!input.IsValid() )
      {
          return Fail("%s: Operation has invalid inputs", __func__);
      }
  
      const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
      unsigned int rank = inputInfo.GetNumDimensions();
  
      if (rank != 4)
      {
          return Fail("%s: Only inputs with rank 4 are supported", __func__);
      }
  
      armnn::SpaceToDepthDescriptor desc;
  
      GetInputScalar<hal_1_0::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");
      }
  
      const Operand* output = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model);
      if (!output)
      {
          return Fail("%s: Could not read output 0", __func__);
      }
  
      const armnn::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__,
                                 IsSpaceToDepthSupported,
                                 data.m_Backends,
                                 isSupported,
                                 inputInfo,
                                 outputInfo,
                                 desc);
      if (!isSupported)
      {
          return false;
      }
  
      armnn::IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc);
      assert(layer != nullptr);
      input.Connect(layer->GetInputSlot(0));
  
      return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data);
  }
  
  bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertTanH()");
      return ::ConvertTanH<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertReshape()");
      return ::ConvertReshape<hal_1_0::HalPolicy>(operation, model, data);
  }
  
  bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data)
  {
      ALOGV("hal_1_0::HalPolicy::ConvertResizeBilinear()");
  
      LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data);
      if (!input.IsValid())
      {
          return Fail("%s: Could not read input 0", __func__);
      }
  
      const Operand* output = GetOutputOperand<hal_1_0::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);
  
      if (IsDynamicTensor(outputInfo))
      {
          return Fail("%s: Dynamic output tensors are not supported", __func__);
      }
  
      armnn::ResizeDescriptor desc;
      desc.m_Method     = armnn::ResizeMethod::Bilinear;
      desc.m_DataLayout = armnn::DataLayout::NHWC;
  
      bool isSupported = false;
      FORWARD_LAYER_SUPPORT_FUNC(__func__,
                                 IsResizeSupported,
                                 data.m_Backends,
                                 isSupported,
                                 inputInfo,
                                 outputInfo,
                                 desc);
      if (!isSupported)
      {
          return false;
      }
  
      if (!GetInputScalar<hal_1_0::HalPolicy>(operation, 1, OperandType::INT32, desc.m_TargetWidth, model, data) ||
          !GetInputScalar<hal_1_0::HalPolicy>(operation, 2, OperandType::INT32, desc.m_TargetHeight, model, data))
      {
          return Fail("%s: Operation has invalid inputs", __func__);
      }
  
      armnn::IConnectableLayer* layer = data.m_Network->AddResizeLayer(desc);
  
      assert(layer != nullptr);
  
      layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
      input.Connect(layer->GetInputSlot(0));
  
      return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data);
  
  }
  
  } // namespace hal_1_0
  } // namespace armnn_driver
  