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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 ValidateReverseV2Operator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
const armnn::TensorInfo& inputInfo0,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("REVERSEV2",
tfLiteContext,
IsReverseV2Supported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo0,
inputInfo1,
outputInfo);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus VisitReverseV2Operator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t reverseV2OperatorCode)
{
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.
auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
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;
}
// The first input contains the data to be reversed
const TfLiteOpaqueTensor* tfLiteInputTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (IsDynamicTensor(tfLiteInputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
// The second input contains the axis tensor
const TfLiteOpaqueTensor* tfLiteAxisTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if (IsDynamicTensor(tfLiteAxisTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
reverseV2OperatorCode, 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;
}
// Get the output tensor
const TfLiteOpaqueTensor* tfLiteOutputTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo0 =
GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& inputTensorInfo1 =
GetTensorInfoForTfLiteOpaqueTensor(tfLiteAxisTensor);
const armnn::TensorInfo& outputTensorInfo =
GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
if (inputTensorInfo0.GetNumDimensions() != outputTensorInfo.GetNumDimensions())
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: 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_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: input tensor dimension and output tensor differ #%d node #%d: ",
reverseV2OperatorCode, nodeIndex);
return kTfLiteError;
}
}
std::string layerName("ReverseV2");
// Get axis tensor data
auto axisTensorNumValues = static_cast<unsigned int>(TfLiteOpaqueTensorDim(tfLiteAxisTensor,0));
const auto maxDimension = 4;
if (axisTensorNumValues > maxDimension)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: 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, tfLiteContext, tfLiteNode, delegateData);
}
} // namespace armnnOpaqueDelegate
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