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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <OpaqueDelegateUtils.hpp>
namespace armnnOpaqueDelegate
{
TfLiteStatus VisitL2NormalizationOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t tfLiteL2NormalizationOperatorCode)
{
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;
}
// Use input indices to get input tensor.
const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex))
{
return kTfLiteError;
}
// Gather output indices and use to get output tensor.
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;
}
// Use output indices to get output tensor.
const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex))
{
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
armnn::L2NormalizationDescriptor descriptor;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
bool isSupported = false;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("L2_NORMALIZATION",
tfLiteContext,
IsL2NormalizationSupported,
delegateData.m_Backends,
isSupported,
setBackend,
inputTensorInfo,
outInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
// Add a L2Normalization layer
armnn::IConnectableLayer* layer = delegateData.m_Network->AddL2NormalizationLayer(descriptor);
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) != kTfLiteOk )
{
return kTfLiteError;
}
// Connect
return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}
TfLiteStatus VisitLocalResponseNormalizationOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t tfLiteNormalizationOperatorCode)
{
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;
}
// Use input indices to get input tensor.
const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteNormalizationOperatorCode, nodeIndex))
{
return kTfLiteError;
}
// Gather output indices and use to get output tensor.
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;
}
// Use output indices to get output tensor.
const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteNormalizationOperatorCode, nodeIndex))
{
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
armnn::NormalizationDescriptor descriptor;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
auto* nodeParams = reinterpret_cast<TfLiteLocalResponseNormParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
descriptor.m_NormSize = nodeParams->radius;
descriptor.m_K = nodeParams->bias;
descriptor.m_Alpha = nodeParams->alpha;
descriptor.m_Beta = nodeParams->beta;
// ArmNN expects normSize to be the full size of the normalization window
descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);
bool isSupported = false;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("NORMALIZATION",
tfLiteContext,
IsNormalizationSupported,
delegateData.m_Backends,
isSupported,
setBackend,
inputTensorInfo,
outInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
// Add a Normalization layer
armnn::IConnectableLayer* layer = delegateData.m_Network->AddNormalizationLayer(descriptor);
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) != kTfLiteOk )
{
return kTfLiteError;
}
// Connect
return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}
} // namespace armnnOpaqueDelegate
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