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//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "DelegateUtils.hpp"
#include <armnn/utility/IgnoreUnused.hpp>
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
#include <tensorflow/lite/minimal_logging.h>
namespace armnnDelegate
{
TfLiteStatus VisitComparisonOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t tfLiteComparisonOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]];
if (IsDynamicTensor(tfLiteInputTensor0))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
tfLiteComparisonOperatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]];
if (IsDynamicTensor(tfLiteInputTensor1))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
tfLiteComparisonOperatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
tfLiteComparisonOperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0);
const armnn::TensorInfo& inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
armnn::ComparisonOperation comparisonOperation = armnn::ComparisonOperation::Equal;
switch(tfLiteComparisonOperatorCode)
{
case kTfLiteBuiltinEqual:
comparisonOperation = armnn::ComparisonOperation::Equal;
break;
case kTfLiteBuiltinGreater:
comparisonOperation = armnn::ComparisonOperation::Greater;
break;
case kTfLiteBuiltinGreaterEqual:
comparisonOperation = armnn::ComparisonOperation::GreaterOrEqual;
break;
case kTfLiteBuiltinLess:
comparisonOperation = armnn::ComparisonOperation::Less;
break;
case kTfLiteBuiltinLessEqual:
comparisonOperation = armnn::ComparisonOperation::LessOrEqual;
break;
case kTfLiteBuiltinNotEqual:
comparisonOperation = armnn::ComparisonOperation::NotEqual;
break;
default:
return kTfLiteError;
}
armnn::ComparisonDescriptor descriptor(comparisonOperation);
bool isSupported = false;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC("COMPARISON",
tfLiteContext,
IsComparisonSupported,
delegateData.m_Backends,
isSupported,
setBackend,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
armnn::IConnectableLayer* comparisonLayer = delegateData.m_Network->AddComparisonLayer(descriptor);
comparisonLayer->SetBackendId(setBackend);
ARMNN_ASSERT(comparisonLayer != nullptr);
armnn::IOutputSlot& outputSlot = comparisonLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// try to connect the Constant Inputs if there are any
if(ProcessInputs(comparisonLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
{
return kTfLiteError;
}
auto reshapeLayer = BroadcastTensor(inputTensorInfo0,
inputTensorInfo1,
comparisonLayer,
tfLiteContext,
tfLiteNode,
delegateData);
if (!reshapeLayer)
{
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace armnnDelegate
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