| // |
| // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <OpaqueDelegateUtils.hpp> |
| #include <SharedFunctions.hpp> |
| |
| #include <flatbuffers/flexbuffers.h> |
| |
| namespace armnnOpaqueDelegate |
| { |
| |
| TfLiteStatus VisitPooling2dOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t tfLitePoolingOperatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| // Gather input indices and use to get input tensors. |
| int numInputs = 0; |
| 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLitePoolingOperatorCode, 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLitePoolingOperatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| auto* tfLiteNodeParameters = reinterpret_cast<TfLitePoolParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 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_OPAQUE_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; |
| } |
| |
| auto layerName = GetName(armnn::LayerType::Pooling2d, nodeIndex); |
| armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling2dLayer(descriptor, layerName.c_str()); |
| 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| if (Connect(poolingLayer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Check and create activation |
| return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData, nodeIndex); |
| } |
| |
| TfLiteStatus VisitPooling3dOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| std::string customOperatorName) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| // Gather input indices and use to get input tensors. |
| int numInputs = 0; |
| 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, kTfLiteBuiltinCustom, 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, kTfLiteBuiltinCustom, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // Set the input and output info |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(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. |
| const void* customData = nullptr; |
| int customDataSize = 0; |
| if (TfLiteOpaqueNodeGetCustomInitialData(tfLiteNode, &customData, &customDataSize) != kTfLiteOk) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unable to initialise initial custom data from node #%d: ", |
| nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // 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*>(customData), |
| customDataSize).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_OPAQUE_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 |
| auto layerName = GetName(armnn::LayerType::Pooling3d, nodeIndex); |
| armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling3dLayer(descriptor, layerName.c_str()); |
| 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| if (Connect(poolingLayer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData, nodeIndex); |
| } |
| |
| } // namespace armnnOpaqueDelegate |