delegate/opaque/src/Redefine.hpp - ml/armnn - Gitiles

 //
 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 #pragma once

 #include <OpaqueDelegateUtils.hpp>

 namespace armnnOpaqueDelegate
 {

 TfLiteStatus VisitCastOperator(DelegateData& delegateData,
                                TfLiteOpaqueContext* tfLiteContext,
                                TfLiteOpaqueNode* tfLiteNode,
                                int nodeIndex,
                                int32_t operatorCode)
 {
     TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
     TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
     int numInputs = 0;
     const int* inputTensors;
     if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // This layer only has 1 input, so we can directly assign tensor[0] to a new opaque tensor
     const TfLiteOpaqueTensor*
           tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[numInputs-1]);
     if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     int numOutputs = 0;
     const int* outputTensors;
     if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // This layer only has 1 output, so we can directly assign tensor[0] to a new opaque tensor
     const TfLiteOpaqueTensor*
           tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[numOutputs-1]);
     if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
     const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

     bool             isSupported  = false;
     armnn::BackendId setBackend;
     auto             validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) {
         FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CAST",
                                           tfLiteContext,
                                           IsCastSupported,
                                           delegateData.m_Backends,
                                           isSupported,
                                           setBackend,
                                           inputTensorInfo,
                                           outInfo);
     };

     // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the
     // support for the operator
     // If supported, VisitCastOperator will be called again to add the layer to the network as seen further below
     if (!delegateData.m_Network)
     {
         validateFunc(outputTensorInfo, isSupported);
         return isSupported ? kTfLiteOk : kTfLiteError;
     }

     // Add a Cast layer
     auto layerName = GetName(armnn::LayerType::Cast, nodeIndex);
     armnn::IConnectableLayer* layer = delegateData.m_Network->AddCastLayer(layerName.c_str());
     layer->SetBackendId(setBackend);
     ARMNN_ASSERT(layer != nullptr);

     armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
     outputSlot.SetTensorInfo(outputTensorInfo);

     // try to connect the Constant Inputs if there are any
     if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // Connect
     return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
 }

 TfLiteStatus VisitReshapeOperator(DelegateData& delegateData,
                                   TfLiteOpaqueContext* tfLiteContext,
                                   TfLiteOpaqueNode* tfLiteNode,
                                   int nodeIndex,
                                   int32_t operatorCode)
 {
     auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);

     if (numInputs == 2)
     {
         TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
     }
     else
     {
         TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
     }
     TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

     // Gather input indices and use to get input tensor.
     const int* inputTensors;
     if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     // Gather output indices and use to get output tensors.
     int numOutputs = 0;
     const int* outputTensors;
     if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
     const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

     armnn::ReshapeDescriptor reshapeDesc;
     std::vector<int32_t> targetShape;

     auto* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));

     // The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both.
     // Options might be set without valid data. we need to check the dimensions are in a valid range.
     if (reshapeOptions && reshapeOptions->num_dimensions > 0 && reshapeOptions->num_dimensions <= 8)
     {
         for (int i = 0; i < reshapeOptions->num_dimensions; ++i)
         {
             targetShape.push_back(reshapeOptions->shape[i]);
         }
     }
     else if (numInputs == 2)
     {
         // Get shape from the second input tensor
         const TfLiteOpaqueTensor* tfLiteShapeInputTensor =
                 TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
         if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex))
         {
             return kTfLiteError;
         }

         int32_t numDims = TfLiteOpaqueTensorNumDims(tfLiteShapeInputTensor);
         if (numDims != 1)
         {
             TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                     tfLiteContext,
                     "TfLiteArmnnOpaqueDelegate: Target 'shape' input is not a 1D tensor in "
                     "operator #%d node #%d: Falling back to TfLiteOptions.",
                     operatorCode, nodeIndex);
         }
         else
         {
             // Get the shape data out of the input tensor
             auto* shapeTensorDataPtr = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteShapeInputTensor));
             int32_t shapeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteShapeInputTensor, 0);
             for (int32_t i = 0; i < shapeTensorNumValues; ++i)
             {
                 targetShape.push_back(shapeTensorDataPtr[i]);
             }
         }
     }
     else
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Target shape not defined in reshape parameters or input tensor. "
                 "At least one method required in operator #%d node #%d: ",
                 operatorCode, nodeIndex);
         return kTfLiteError;
     }

     // Check the target shape to check if there is zero in the shape.
     if (std::find(targetShape.begin(), targetShape.end(), 0) != targetShape.end() &&
         inputTensorInfo0.GetNumElements() != 0)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Input to reshape is a tensor with elements, "
                 "but the requested shape has 0. "
                 "operator #%d node #%d: ",
                 operatorCode, nodeIndex);
         return kTfLiteError;
     }

     // Use the data to create the required tensor shape.
     if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: At most one component of shape can be -1 in: "
                 "operator #%d node #%d: ",
                 operatorCode, nodeIndex);
         return kTfLiteError;
     }

     if (reshapeDesc.m_TargetShape.GetNumElements() != inputTensorInfo0.GetNumElements())
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Reshape, number of elements in output shape does not match input "
                 "operator #%d node #%d: ",
                 operatorCode, nodeIndex);
         return kTfLiteError;
     }

     bool isSupported = false;
     armnn::BackendId setBackend;
     auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
     {
         FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESHAPE",
                                           tfLiteContext,
                                           IsReshapeSupported,
                                           delegateData.m_Backends,
                                           isSupported,
                                           setBackend,
                                           inputTensorInfo0,
                                           outInfo,
                                           reshapeDesc);
     };

     if (!delegateData.m_Network)
     {
         validateFunc(outputTensorInfo, isSupported);
         return isSupported ? kTfLiteOk : kTfLiteError;
     }

     auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex);
     armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
     layer->SetBackendId(setBackend);
     ARMNN_ASSERT(layer != nullptr);

     armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
     outputSlot.SetTensorInfo(outputTensorInfo);

     // try to connect the Constant Inputs if there are any
     if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // Connect
     return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
 }

 TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData,
                                   TfLiteOpaqueContext* tfLiteContext,
                                   TfLiteOpaqueNode* tfLiteNode,
                                   int nodeIndex,
                                   int32_t operatorCode)
 {
     TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
     TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

     // Gather input indices and use to get input tensor.
     int numInputs = 0;
     const int* inputTensors;
     if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     // Gather output indices and use to get output tensors.
     int numOutputs = 0;
     const int* outputTensors;
     if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     auto* options = reinterpret_cast<TfLiteSqueezeParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));

     const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);

     std::vector<uint32_t> squeezeDim;
     // A single negative dim index is interpreted as a negative index in python
     // Meaning the index will be the shape size plus the negative index value
     if (options->num_squeeze_dims == 1 && options->squeeze_dims[0] < 0)
     {
         int32_t dim = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions()) + options->squeeze_dims[0];
         squeezeDim.push_back(static_cast<uint32_t>(dim));
     }
     else
     {
         for (int32_t i = 0; i < options->num_squeeze_dims; ++i)
         {
             squeezeDim.push_back(static_cast<uint32_t>(options->squeeze_dims[i]));
         }
     }

     armnn::TensorInfo outputTensorInfo = OutputShapeOfSqueeze(squeezeDim, inputTensorInfo);

     armnn::ReshapeDescriptor reshapeDesc;
     reshapeDesc.m_TargetShape = outputTensorInfo.GetShape();

     bool isSupported = false;
     armnn::BackendId setBackend;
     auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
     {
         FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("SQUEEZE",
                                           tfLiteContext,
                                           IsReshapeSupported,
                                           delegateData.m_Backends,
                                           isSupported,
                                           setBackend,
                                           inputTensorInfo,
                                           outInfo,
                                           reshapeDesc);
     };

     if (!delegateData.m_Network)
     {
         validateFunc(outputTensorInfo, isSupported);
         return isSupported ? kTfLiteOk : kTfLiteError;
     }

     auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex, "Squeeze");
     armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
     layer->SetBackendId(setBackend);
     ARMNN_ASSERT(layer != nullptr);

     armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
     outputSlot.SetTensorInfo(outputTensorInfo);

     // try to connect the Constant Inputs if there are any
     if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // Connect
     return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
 }

 TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData,
                                      TfLiteOpaqueContext* tfLiteContext,
                                      TfLiteOpaqueNode* tfLiteNode,
                                      int nodeIndex,
                                      int32_t operatorCode)
 {
     TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
     TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

     // Gather input indices and use to get input tensor.
     int numInputs = 0;
     const int* inputTensors;
     if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     const TfLiteOpaqueTensor* tfLiteAxisTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
     if (!IsValid(tfLiteContext, tfLiteAxisTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     // Gather output indices and use to get output tensors.
     int numOutputs = 0;
     const int* outputTensors;
     if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
                 nodeIndex);
         return kTfLiteError;
     }

     TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
     if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
     {
         return kTfLiteError;
     }

     const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
     armnn::TensorInfo outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor);

     auto* axisTensorData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteAxisTensor));
     int32_t axis = axisTensorData[0];

     int32_t inputDimSize = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions());
     if (axis > inputDimSize || axis < 0 - (inputDimSize + 1))
     {
         TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
                 tfLiteContext,
                 "TfLiteArmnnOpaqueDelegate: Axis must be in range "
                 "[0 - (inputDimSize + 1), inputDimSize] inclusive.");
         return kTfLiteError;
     }

     if(axis < 0)
     {
         axis = inputDimSize + axis + 1;
     }

     std::vector<unsigned int> shape(static_cast<unsigned int>(inputDimSize) + 1);
     unsigned int inputShapeIndex = 0;
     for (unsigned int i = 0; i < static_cast<unsigned int>(inputDimSize + 1); ++i)
     {
         if (i == static_cast<unsigned int>(axis))
         {
             shape[i] = 1;
         }
         else
         {
             shape[i] = inputTensorInfo.GetShape()[inputShapeIndex];
             ++inputShapeIndex;
         }
     }

     armnn::ReshapeDescriptor reshapeDesc;
     reshapeDesc.m_TargetShape = armnn::TensorShape(static_cast<unsigned int>(inputDimSize + 1), shape.data());

     bool isSupported = false;
     armnn::BackendId setBackend;
     auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
     {
         FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("EXPAND_DIMS",
                                           tfLiteContext,
                                           IsReshapeSupported,
                                           delegateData.m_Backends,
                                           isSupported,
                                           setBackend,
                                           inputTensorInfo,
                                           outInfo,
                                           reshapeDesc);
     };

     if (!delegateData.m_Network)
     {
         validateFunc(outputTensorInfo, isSupported);
         return isSupported ? kTfLiteOk : kTfLiteError;
     }

     auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex, "ExpandDims");
     armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
     layer->SetBackendId(setBackend);
     ARMNN_ASSERT(layer != nullptr);

     armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
     outputTensorInfo.SetShape(reshapeDesc.m_TargetShape);
     outputSlot.SetTensorInfo(outputTensorInfo);

     // try to connect the Constant Inputs if there are any
     if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
     {
         return kTfLiteError;
     }

     // Connect
     return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
 }

 }
	//
	// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
	// SPDX-License-Identifier: MIT
	//
	#pragma once

	#include <OpaqueDelegateUtils.hpp>

	namespace armnnOpaqueDelegate
	{

	TfLiteStatus VisitCastOperator(DelegateData& delegateData,
	TfLiteOpaqueContext* tfLiteContext,
	TfLiteOpaqueNode* tfLiteNode,
	int nodeIndex,
	int32_t operatorCode)
	{
	TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
	TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
	int numInputs = 0;
	const int* inputTensors;
	if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// This layer only has 1 input, so we can directly assign tensor[0] to a new opaque tensor
	const TfLiteOpaqueTensor*
	tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[numInputs-1]);
	if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	int numOutputs = 0;
	const int* outputTensors;
	if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// This layer only has 1 output, so we can directly assign tensor[0] to a new opaque tensor
	const TfLiteOpaqueTensor*
	tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[numOutputs-1]);
	if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
	const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

	bool isSupported = false;
	armnn::BackendId setBackend;
	auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) {
	FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CAST",
	tfLiteContext,
	IsCastSupported,
	delegateData.m_Backends,
	isSupported,
	setBackend,
	inputTensorInfo,
	outInfo);
	};

	// If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the
	// support for the operator
	// If supported, VisitCastOperator will be called again to add the layer to the network as seen further below
	if (!delegateData.m_Network)
	{
	validateFunc(outputTensorInfo, isSupported);
	return isSupported ? kTfLiteOk : kTfLiteError;
	}

	// Add a Cast layer
	auto layerName = GetName(armnn::LayerType::Cast, nodeIndex);
	armnn::IConnectableLayer* layer = delegateData.m_Network->AddCastLayer(layerName.c_str());
	layer->SetBackendId(setBackend);
	ARMNN_ASSERT(layer != nullptr);

	armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
	outputSlot.SetTensorInfo(outputTensorInfo);

	// try to connect the Constant Inputs if there are any
	if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// Connect
	return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
	}

	TfLiteStatus VisitReshapeOperator(DelegateData& delegateData,
	TfLiteOpaqueContext* tfLiteContext,
	TfLiteOpaqueNode* tfLiteNode,
	int nodeIndex,
	int32_t operatorCode)
	{
	auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);

	if (numInputs == 2)
	{
	TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
	}
	else
	{
	TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
	}
	TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

	// Gather input indices and use to get input tensor.
	const int* inputTensors;
	if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	// Gather output indices and use to get output tensors.
	int numOutputs = 0;
	const int* outputTensors;
	if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
	const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);

	armnn::ReshapeDescriptor reshapeDesc;
	std::vector<int32_t> targetShape;

	auto* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));

	// The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both.
	// Options might be set without valid data. we need to check the dimensions are in a valid range.
	if (reshapeOptions && reshapeOptions->num_dimensions > 0 && reshapeOptions->num_dimensions <= 8)
	{
	for (int i = 0; i < reshapeOptions->num_dimensions; ++i)
	{
	targetShape.push_back(reshapeOptions->shape[i]);
	}
	}
	else if (numInputs == 2)
	{
	// Get shape from the second input tensor
	const TfLiteOpaqueTensor* tfLiteShapeInputTensor =
	TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
	if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	int32_t numDims = TfLiteOpaqueTensorNumDims(tfLiteShapeInputTensor);
	if (numDims != 1)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Target 'shape' input is not a 1D tensor in "
	"operator #%d node #%d: Falling back to TfLiteOptions.",
	operatorCode, nodeIndex);
	}
	else
	{
	// Get the shape data out of the input tensor
	auto* shapeTensorDataPtr = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteShapeInputTensor));
	int32_t shapeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteShapeInputTensor, 0);
	for (int32_t i = 0; i < shapeTensorNumValues; ++i)
	{
	targetShape.push_back(shapeTensorDataPtr[i]);
	}
	}
	}
	else
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Target shape not defined in reshape parameters or input tensor. "
	"At least one method required in operator #%d node #%d: ",
	operatorCode, nodeIndex);
	return kTfLiteError;
	}

	// Check the target shape to check if there is zero in the shape.
	if (std::find(targetShape.begin(), targetShape.end(), 0) != targetShape.end() &&
	inputTensorInfo0.GetNumElements() != 0)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Input to reshape is a tensor with elements, "
	"but the requested shape has 0. "
	"operator #%d node #%d: ",
	operatorCode, nodeIndex);
	return kTfLiteError;
	}

	// Use the data to create the required tensor shape.
	if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: At most one component of shape can be -1 in: "
	"operator #%d node #%d: ",
	operatorCode, nodeIndex);
	return kTfLiteError;
	}

	if (reshapeDesc.m_TargetShape.GetNumElements() != inputTensorInfo0.GetNumElements())
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Reshape, number of elements in output shape does not match input "
	"operator #%d node #%d: ",
	operatorCode, nodeIndex);
	return kTfLiteError;
	}

	bool isSupported = false;
	armnn::BackendId setBackend;
	auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
	{
	FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESHAPE",
	tfLiteContext,
	IsReshapeSupported,
	delegateData.m_Backends,
	isSupported,
	setBackend,
	inputTensorInfo0,
	outInfo,
	reshapeDesc);
	};

	if (!delegateData.m_Network)
	{
	validateFunc(outputTensorInfo, isSupported);
	return isSupported ? kTfLiteOk : kTfLiteError;
	}

	auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex);
	armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
	layer->SetBackendId(setBackend);
	ARMNN_ASSERT(layer != nullptr);

	armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
	outputSlot.SetTensorInfo(outputTensorInfo);

	// try to connect the Constant Inputs if there are any
	if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// Connect
	return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
	}

	TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData,
	TfLiteOpaqueContext* tfLiteContext,
	TfLiteOpaqueNode* tfLiteNode,
	int nodeIndex,
	int32_t operatorCode)
	{
	TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
	TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

	// Gather input indices and use to get input tensor.
	int numInputs = 0;
	const int* inputTensors;
	if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	// Gather output indices and use to get output tensors.
	int numOutputs = 0;
	const int* outputTensors;
	if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	auto* options = reinterpret_cast<TfLiteSqueezeParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));

	const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);

	std::vector<uint32_t> squeezeDim;
	// A single negative dim index is interpreted as a negative index in python
	// Meaning the index will be the shape size plus the negative index value
	if (options->num_squeeze_dims == 1 && options->squeeze_dims[0] < 0)
	{
	int32_t dim = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions()) + options->squeeze_dims[0];
	squeezeDim.push_back(static_cast<uint32_t>(dim));
	}
	else
	{
	for (int32_t i = 0; i < options->num_squeeze_dims; ++i)
	{
	squeezeDim.push_back(static_cast<uint32_t>(options->squeeze_dims[i]));
	}
	}

	armnn::TensorInfo outputTensorInfo = OutputShapeOfSqueeze(squeezeDim, inputTensorInfo);

	armnn::ReshapeDescriptor reshapeDesc;
	reshapeDesc.m_TargetShape = outputTensorInfo.GetShape();

	bool isSupported = false;
	armnn::BackendId setBackend;
	auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
	{
	FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("SQUEEZE",
	tfLiteContext,
	IsReshapeSupported,
	delegateData.m_Backends,
	isSupported,
	setBackend,
	inputTensorInfo,
	outInfo,
	reshapeDesc);
	};

	if (!delegateData.m_Network)
	{
	validateFunc(outputTensorInfo, isSupported);
	return isSupported ? kTfLiteOk : kTfLiteError;
	}

	auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex, "Squeeze");
	armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
	layer->SetBackendId(setBackend);
	ARMNN_ASSERT(layer != nullptr);

	armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
	outputSlot.SetTensorInfo(outputTensorInfo);

	// try to connect the Constant Inputs if there are any
	if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// Connect
	return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
	}

	TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData,
	TfLiteOpaqueContext* tfLiteContext,
	TfLiteOpaqueNode* tfLiteNode,
	int nodeIndex,
	int32_t operatorCode)
	{
	TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
	TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));

	// Gather input indices and use to get input tensor.
	int numInputs = 0;
	const int* inputTensors;
	if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	const TfLiteOpaqueTensor* tfLiteAxisTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
	if (!IsValid(tfLiteContext, tfLiteAxisTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	// Gather output indices and use to get output tensors.
	int numOutputs = 0;
	const int* outputTensors;
	if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
	nodeIndex);
	return kTfLiteError;
	}

	TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
	if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
	{
	return kTfLiteError;
	}

	const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
	armnn::TensorInfo outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor);

	auto* axisTensorData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteAxisTensor));
	int32_t axis = axisTensorData[0];

	int32_t inputDimSize = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions());
	if (axis > inputDimSize \|\| axis < 0 - (inputDimSize + 1))
	{
	TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
	tfLiteContext,
	"TfLiteArmnnOpaqueDelegate: Axis must be in range "
	"[0 - (inputDimSize + 1), inputDimSize] inclusive.");
	return kTfLiteError;
	}

	if(axis < 0)
	{
	axis = inputDimSize + axis + 1;
	}

	std::vector<unsigned int> shape(static_cast<unsigned int>(inputDimSize) + 1);
	unsigned int inputShapeIndex = 0;
	for (unsigned int i = 0; i < static_cast<unsigned int>(inputDimSize + 1); ++i)
	{
	if (i == static_cast<unsigned int>(axis))
	{
	shape[i] = 1;
	}
	else
	{
	shape[i] = inputTensorInfo.GetShape()[inputShapeIndex];
	++inputShapeIndex;
	}
	}

	armnn::ReshapeDescriptor reshapeDesc;
	reshapeDesc.m_TargetShape = armnn::TensorShape(static_cast<unsigned int>(inputDimSize + 1), shape.data());

	bool isSupported = false;
	armnn::BackendId setBackend;
	auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
	{
	FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("EXPAND_DIMS",
	tfLiteContext,
	IsReshapeSupported,
	delegateData.m_Backends,
	isSupported,
	setBackend,
	inputTensorInfo,
	outInfo,
	reshapeDesc);
	};

	if (!delegateData.m_Network)
	{
	validateFunc(outputTensorInfo, isSupported);
	return isSupported ? kTfLiteOk : kTfLiteError;
	}

	auto layerName = GetName(armnn::LayerType::Reshape, nodeIndex, "ExpandDims");
	armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
	layer->SetBackendId(setBackend);
	ARMNN_ASSERT(layer != nullptr);

	armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
	outputTensorInfo.SetShape(reshapeDesc.m_TargetShape);
	outputSlot.SetTensorInfo(outputTensorInfo);

	// try to connect the Constant Inputs if there are any
	if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
	{
	return kTfLiteError;
	}

	// Connect
	return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
	}

	}