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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <ClassicDelegateUtils.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>
#include <tensorflow/lite/kernels/internal/tensor_ctypes.h>
namespace armnnDelegate
{
TfLiteStatus ValidateReverseV2Operator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo0,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC("REVERSEV2",
tfLiteContext,
IsReverseV2Supported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo0,
inputInfo1,
outputInfo);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus VisitReverseV2Operator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t reverseV2OperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
// The first input contains the data that should be reversed
const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
if (IsDynamicTensor(tfLiteInputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
// The second input contains an axis tensor.
const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
if (IsDynamicTensor(tfLiteAxisTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
// Get the output tensor
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: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
const armnn::TensorInfo& inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
if (inputTensorInfo0.GetNumDimensions() != outputTensorInfo.GetNumDimensions())
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: input tensor dimension and output tensor dimension differ #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
for (unsigned i=0; i < inputTensorInfo0.GetNumDimensions(); i++)
{
if (inputTensorInfo0.GetShape()[i] != outputTensorInfo.GetShape()[i])
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: input tensor dimension and output tensor differ #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
}
std::string layerName("ReverseV2");
const auto maxDimension = 4;
const auto axisTensorNumValues = static_cast<unsigned int>(tfLiteAxisTensor.dims->size);
if (axisTensorNumValues > maxDimension)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: The Axis-Input-Tensor of the ReverseV2 operation requires a "
"dimension of <= %d but a tensor with a dimension of %d was given. "
"Operator: #%d node #%d: ",
maxDimension, axisTensorNumValues, reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
// No network pointer indicates that only support for this operator should be checked
if (!delegateData.m_Network)
{
return ValidateReverseV2Operator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
}
armnn::IConnectableLayer* reverseV2Layer = delegateData.m_Network->AddReverseV2Layer(layerName.c_str());
armnn::IOutputSlot& outputSlot = reverseV2Layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// Try to connect the Constant Inputs if there are any
if(ProcessInputs(reverseV2Layer, delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
{
return kTfLiteError;
}
ARMNN_ASSERT(reverseV2Layer != nullptr);
return Connect(reverseV2Layer, tfLiteNode, delegateData);
}
} // namespace armnnDelegate
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