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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "DelegateUtils.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 VisitPoolingOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t tfLitePoolingOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
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: ",
tfLitePoolingOperatorCode, 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: ",
tfLitePoolingOperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
armnn::PoolingAlgorithm poolingAlgorithm;
switch(tfLitePoolingOperatorCode)
{
case kTfLiteBuiltinAveragePool2d:
poolingAlgorithm = armnn::PoolingAlgorithm::Average;
break;
case kTfLiteBuiltinL2Pool2d:
poolingAlgorithm = armnn::PoolingAlgorithm::L2;
break;
case kTfLiteBuiltinMaxPool2d:
poolingAlgorithm = armnn::PoolingAlgorithm::Max;
break;
default:
return kTfLiteError;
}
armnn::Pooling2dDescriptor descriptor;
descriptor.m_PoolType = poolingAlgorithm;
auto* params = reinterpret_cast<TfLitePoolParams*>(tfLiteNode->builtin_data);
descriptor.m_PoolWidth = params->filter_width;
descriptor.m_PoolHeight = params->filter_height;
descriptor.m_StrideX = params->stride_width;
descriptor.m_StrideY = params->stride_height;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
unsigned int inputHeight = inputTensorInfo.GetShape()[1];
unsigned int inputWidth = inputTensorInfo.GetShape()[2];
CalcPadding(inputHeight, descriptor.m_PoolHeight, descriptor.m_StrideY, 1u,
descriptor.m_PadTop, descriptor.m_PadBottom, params->padding);
CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u,
descriptor.m_PadLeft, descriptor.m_PadRight, params->padding);
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC("POOLING_2D",
tfLiteContext,
IsPooling2dSupported,
delegateData.m_Backends,
isSupported,
inputTensorInfo,
outputTensorInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling2dLayer(descriptor);
ARMNN_ASSERT(poolingLayer != nullptr);
armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
Connect(poolingLayer, tfLiteNode, delegateData);
// Check activation
TfLiteFusedActivation activationType = params->activation;
return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
}
} // namespace armnnDelegate
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