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-rw-r--r--delegate/src/Pooling.hpp327
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diff --git a/delegate/src/Pooling.hpp b/delegate/src/Pooling.hpp
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-//
-// Copyright © 2022-2023 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>
-#include <flatbuffers/flexbuffers.h>
-
-namespace armnnDelegate
-{
-
-TfLiteStatus VisitPooling2dOperator(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, true);
-
- auto* tfLiteNodeParameters = reinterpret_cast<TfLitePoolParams*>(tfLiteNode->builtin_data);
- TfLiteFusedActivation activationType = kTfLiteActNone;
- if (tfLiteNodeParameters)
- {
- activationType = tfLiteNodeParameters->activation;
- TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo,
- outputTensorInfo, activationType);
- if(activationStatus != kTfLiteOk)
- {
- return kTfLiteError;
- }
-
- }
-
- 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;
-
- descriptor.m_PoolWidth = tfLiteNodeParameters->filter_width;
- descriptor.m_PoolHeight = tfLiteNodeParameters->filter_height;
- descriptor.m_StrideX = tfLiteNodeParameters->stride_width;
- descriptor.m_StrideY = tfLiteNodeParameters->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, tfLiteNodeParameters->padding);
- CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u,
- descriptor.m_PadLeft, descriptor.m_PadRight, tfLiteNodeParameters->padding);
-
- bool isSupported = false;
- armnn::BackendId setBackend;
- auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
- {
- FORWARD_LAYER_SUPPORT_FUNC("POOLING_2D",
- tfLiteContext,
- IsPooling2dSupported,
- delegateData.m_Backends,
- isSupported,
- setBackend,
- inputTensorInfo,
- outputTensorInfo,
- descriptor);
- };
-
- if (!delegateData.m_Network)
- {
- validateFunc(outputTensorInfo, isSupported);
- return isSupported ? kTfLiteOk : kTfLiteError;
- }
-
- armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling2dLayer(descriptor);
- poolingLayer->SetBackendId(setBackend);
- ARMNN_ASSERT(poolingLayer != nullptr);
-
- armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0);
- outputSlot.SetTensorInfo(outputTensorInfo);
-
- // try to connect the Constant Inputs if there are any
- if(ProcessInputs(poolingLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
- {
- return kTfLiteError;
- }
-
- if(Connect(poolingLayer, tfLiteNode, delegateData) != kTfLiteOk)
- {
- return kTfLiteError;
- }
-
- // Check and create activation
- return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
-}
-
-TfLiteStatus VisitPooling3dOperator(DelegateData& delegateData,
- TfLiteContext* tfLiteContext,
- TfLiteNode* tfLiteNode,
- int nodeIndex,
- std::string customOperatorName)
-{
- 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: ",
- customOperatorName.c_str(), 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: ",
- customOperatorName.c_str(), nodeIndex);
- return kTfLiteError;
- }
- // Set the input and output info
- const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
- const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
-
- // Custom Operators are defined by the name string associated to the operator. Use this to determine
- // which pooling algorithm to create the armnn operator with. L2 Pooling3D is unsupported in TfLite.
- armnn::PoolingAlgorithm poolingAlgorithm;
- if (customOperatorName == "MaxPool3D")
- {
- poolingAlgorithm = armnn::PoolingAlgorithm::Max;
- }
- else if (customOperatorName == "AveragePool3D")
- {
- poolingAlgorithm = armnn::PoolingAlgorithm::Average;
- }
- else
- {
- return kTfLiteError;
- }
- // Create the armnn pool3d descriptor and set the algorithm parsed above.
- armnn::Pooling3dDescriptor descriptor;
- descriptor.m_PoolType = poolingAlgorithm;
-
- // custom_initial_data and custom_initial_data_size are void* variables defined in the tflite registration
- // used to access the custom option buffer for the operator.
- auto custom_data = tfLiteNode->custom_initial_data;
- auto custom_data_size = tfLiteNode->custom_initial_data_size;
- // Reinterpret the void* to a byte buffer to access the options data in the flexbuffers map.
- const flexbuffers::Map& m = flexbuffers::GetRoot(reinterpret_cast<const uint8_t*>(custom_data),
- custom_data_size).AsMap();
- // poolDims is a vector of [ 1, Depth, Height, Width, 1 ]
- const auto poolDims = m["ksize"].AsTypedVector();
- descriptor.m_PoolWidth = poolDims[3].AsInt32();
- descriptor.m_PoolHeight = poolDims[2].AsInt32();
- descriptor.m_PoolDepth = poolDims[1].AsInt32();
-
- // strideDimes is a vector of [ 1, Z, Y, X, 1]
- const auto strideDims = m["strides"].AsTypedVector();
- descriptor.m_StrideX = strideDims[3].AsInt32();
- descriptor.m_StrideY = strideDims[2].AsInt32();
- descriptor.m_StrideZ = strideDims[1].AsInt32();
- descriptor.m_DataLayout = armnn::DataLayout::NDHWC;
-
- unsigned int inputDepth = inputTensorInfo.GetShape()[1];
- unsigned int inputHeight = inputTensorInfo.GetShape()[2];
- unsigned int inputWidth = inputTensorInfo.GetShape()[3];
-
- // CalcPadding expects a TfLitePadding type. Parse flexbuffers to extract padding string and create TfLitePadding.
- std::string paddingStr = m["padding"].AsString().str();
- TfLitePadding padding;
- if (paddingStr == "VALID")
- {
- padding = kTfLitePaddingValid;
- }
- else if (paddingStr == "SAME")
- {
- padding = kTfLitePaddingSame;
- }
- else
- {
- padding = kTfLitePaddingUnknown;
- }
- // Calculates padding for each pooling dimension separately
- CalcPadding(inputHeight, descriptor.m_PoolHeight, descriptor.m_StrideY, 1u,
- descriptor.m_PadTop, descriptor.m_PadBottom, padding);
- CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u,
- descriptor.m_PadLeft, descriptor.m_PadRight, padding);
- CalcPadding(inputDepth, descriptor.m_PoolDepth, descriptor.m_StrideZ, 1u,
- descriptor.m_PadFront, descriptor.m_PadBack, padding);
-
-
- // Check activation by parsing the string from the flexbuffer map
- std::string activationTypeStr = m["activation"].AsString().str();
- TfLiteFusedActivation activationType = kTfLiteActNone;
-
- if (activationTypeStr == "kTfLiteActRelu")
- {
- activationType = kTfLiteActRelu;
- }
- else if (activationTypeStr == "kTfLiteActReluN1To1")
- {
- activationType = kTfLiteActReluN1To1;
- }
- else if (activationTypeStr == "kTfLiteActRelu6")
- {
- activationType = kTfLiteActRelu6;
- }
- else if (activationTypeStr == "kTfLiteActTanh")
- {
- activationType = kTfLiteActTanh;
- }
- else if (activationTypeStr == "kTfLiteActSignBit")
- {
- activationType = kTfLiteActSignBit;
- }
- else if (activationTypeStr == "kTfLiteActSigmoid")
- {
- activationType = kTfLiteActSigmoid;
- }
- else
- {
- activationType = kTfLiteActNone;
- }
-
- TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo,
- outputTensorInfo, activationType);
- if(activationStatus != kTfLiteOk)
- {
- return kTfLiteError;
- }
-
-
- // Validate the output info.
- bool isSupported = false;
- armnn::BackendId setBackend;
- auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) {
- FORWARD_LAYER_SUPPORT_FUNC("POOLING_3D",
- tfLiteContext,
- IsPooling3dSupported,
- delegateData.m_Backends,
- isSupported,
- setBackend,
- inputTensorInfo,
- outputTensorInfo,
- descriptor);
- };
-
- if (!delegateData.m_Network)
- {
- validateFunc(outputTensorInfo, isSupported);
- return isSupported ? kTfLiteOk : kTfLiteError;
- }
-
- // Create the Layer
- armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling3dLayer(descriptor);
- poolingLayer->SetBackendId(setBackend);
- ARMNN_ASSERT(poolingLayer != nullptr);
-
- // Create and set output slots
- armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0);
- outputSlot.SetTensorInfo(outputTensorInfo);
-
- // try to connect the Constant Inputs if there are any
- if(ProcessInputs(poolingLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
- {
- return kTfLiteError;
- }
-
- if(Connect(poolingLayer, tfLiteNode, delegateData) != kTfLiteOk)
- {
- return kTfLiteError;
- }
-
- return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
-}
-
-} // namespace armnnDelegate
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