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Diffstat (limited to 'delegate/classic/src/Pooling.hpp')
| -rw-r--r-- | delegate/classic/src/Pooling.hpp | 327 |
1 files changed, 327 insertions, 0 deletions
diff --git a/delegate/classic/src/Pooling.hpp b/delegate/classic/src/Pooling.hpp new file mode 100644 index 0000000000..2de40613fb --- /dev/null +++ b/delegate/classic/src/Pooling.hpp @@ -0,0 +1,327 @@ +// +// 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 |