| // | |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. | |
| // SPDX-License-Identifier: MIT | |
| // | |
| #pragma once | |
| #include <armnn/utility/IgnoreUnused.hpp> | |
| #include <tensorflow/lite/builtin_ops.h> | |
| #include <tensorflow/lite/c/builtin_op_data.h> | |
| #include <tensorflow/lite/c/common.h> | |
| #include <tensorflow/lite/kernels/internal/tensor_ctypes.h> | |
| #include <tensorflow/lite/minimal_logging.h> | |
| #include <algorithm> | |
| #include <iterator> | |
| #include <string> | |
| #include <vector> | |
| namespace armnnDelegate | |
| { | |
| void SetupConcatViewOrigin(const armnn::TensorInfo& inputTensorInfo, | |
| armnn::OriginsDescriptor& concatDescriptor, | |
| const unsigned int concatAxis, | |
| unsigned int inputIndex, | |
| unsigned int& mergeDimOrigin) | |
| { | |
| const uint32_t inputRank = concatDescriptor.GetNumDimensions(); | |
| // double check dimensions of the tensors | |
| if (inputTensorInfo.GetNumDimensions() != inputRank) | |
| { | |
| throw armnn::ParseException("The number of dimensions for input tensors " | |
| "of the concatenation operator should be: " + std::to_string(inputRank)); | |
| } | |
| for (unsigned int j = 0; j < concatAxis; ++j) | |
| { | |
| concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); | |
| } | |
| concatDescriptor.SetViewOriginCoord(inputIndex, concatAxis, mergeDimOrigin); | |
| mergeDimOrigin += inputTensorInfo.GetShape()[concatAxis]; | |
| for (unsigned int j = concatAxis + 1; j < inputRank; ++j) | |
| { | |
| concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); | |
| } | |
| } | |
| TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData, | |
| TfLiteContext* tfLiteContext, | |
| TfLiteNode* tfLiteNode, | |
| int nodeIndex, | |
| int32_t tfLiteConcatOperatorCode) | |
| { | |
| unsigned int numInputs = tfLiteNode->inputs->size; | |
| if (numInputs < 2) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", | |
| 2, numInputs, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); | |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; | |
| std::vector<armnn::TensorInfo> inputTensorInfos; | |
| for (unsigned int i = 0; i < numInputs; ++i) | |
| { | |
| const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[i]]; | |
| if(!IsValid(&tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", | |
| tfLiteConcatOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| if (IsDynamicTensor(tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", | |
| tfLiteConcatOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); | |
| inputTensorInfos.emplace_back(inputTensorInfo); | |
| } | |
| // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC. | |
| std::vector<const armnn::TensorInfo*> inputConstTensorInfos; | |
| std::transform(inputTensorInfos.begin(), | |
| inputTensorInfos.end(), | |
| std::back_inserter(inputConstTensorInfos), | |
| [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; }); | |
| const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; | |
| if(!IsValid(&tfLiteOutputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", | |
| tfLiteConcatOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| if (IsDynamicTensor(tfLiteOutputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", | |
| tfLiteConcatOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| // Setup OriginsDescriptor, axis and view origin | |
| unsigned int numConcatView = static_cast<unsigned int>(numInputs); | |
| uint32_t inputRank = tfLiteTensors[tfLiteNode->inputs->data[0]].dims->size; | |
| auto* concatenationParameters = reinterpret_cast<TfLiteConcatenationParams*>(tfLiteNode->builtin_data); | |
| const unsigned int concatDimInput = static_cast<unsigned int>( | |
| (static_cast<int>(inputRank) + concatenationParameters->axis) % static_cast<int>(inputRank)); | |
| armnn::OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank); | |
| concatDescriptor.SetConcatAxis(concatDimInput); | |
| unsigned int mergeDimOrigin = 0; | |
| for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) | |
| { | |
| armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor( | |
| tfLiteTensors[tfLiteNode->inputs->data[viewIndex]]); | |
| // Sets up concatDescriptor view origin | |
| SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); | |
| } | |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); | |
| // Check if supported | |
| bool isSupported = false; | |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) | |
| { | |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, | |
| tfLiteContext, | |
| IsConcatSupported, | |
| delegateData.m_Backends, | |
| isSupported, | |
| inputConstTensorInfos, | |
| outputTensorInfo, | |
| concatDescriptor); | |
| }; | |
| if (!delegateData.m_Network) | |
| { | |
| validateFunc(outputTensorInfo, isSupported); | |
| return isSupported ? kTfLiteOk : kTfLiteError; | |
| } | |
| // Setup layer and connect. | |
| armnn::IConnectableLayer* concatenationLayer = delegateData.m_Network->AddConcatLayer(concatDescriptor); | |
| ARMNN_ASSERT(concatenationLayer != nullptr); | |
| armnn::IOutputSlot& outputSlot = concatenationLayer->GetOutputSlot(0); | |
| outputSlot.SetTensorInfo(outputTensorInfo); | |
| Connect(concatenationLayer, tfLiteNode, delegateData); | |
| if (!concatenationParameters) | |
| { | |
| // No Activation | |
| return kTfLiteOk; | |
| } | |
| // Check activation | |
| TfLiteFusedActivation activationType = concatenationParameters->activation; | |
| return FusedActivation(tfLiteContext, tfLiteNode, activationType, concatenationLayer, 0, delegateData); | |
| } | |
| TfLiteStatus VisitMeanOperator(DelegateData& delegateData, | |
| TfLiteContext* tfLiteContext, | |
| TfLiteNode* tfLiteNode, | |
| int nodeIndex, | |
| int32_t tfLiteMeanOperatorCode) | |
| { | |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); | |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); | |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; | |
| const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; | |
| if(!IsValid(&tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", | |
| tfLiteMeanOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| if (IsDynamicTensor(tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", | |
| tfLiteMeanOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; | |
| if(!IsValid(&tfLiteAxisTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Invalid axis tensor in operator #%d node #%d: ", | |
| tfLiteMeanOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| if (IsDynamicTensor(tfLiteAxisTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic axis tensors are not supported in operator #%d node #%d: ", | |
| tfLiteMeanOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; | |
| if(!IsValid(&tfLiteOutputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", | |
| tfLiteAxisTensor, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| if (IsDynamicTensor(tfLiteOutputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", | |
| tfLiteMeanOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); | |
| const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); | |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); | |
| auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); | |
| std::vector<int32_t> axis; | |
| // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis. | |
| for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i) | |
| { | |
| axis.emplace_back(axisTensorData[i]); | |
| } | |
| // Convert the axis to unsigned int and remove duplicates. | |
| unsigned int rank = inputTensorInfo.GetNumDimensions(); | |
| std::set<unsigned int> uniqueAxis; | |
| std::transform(axis.begin(), | |
| axis.end(), | |
| std::inserter(uniqueAxis, uniqueAxis.begin()), | |
| [rank](int i)->unsigned int{ return (i + rank) % rank; }); | |
| // Setup MeanDescriptor and assign axis and keepDims | |
| armnn::MeanDescriptor desc; | |
| desc.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); | |
| desc.m_KeepDims = inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? true : false; | |
| // Check if supported | |
| bool isSupported = false; | |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) | |
| { | |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, | |
| tfLiteContext, | |
| IsMeanSupported, | |
| delegateData.m_Backends, | |
| isSupported, | |
| inputTensorInfo, | |
| outputTensorInfo, | |
| desc); | |
| }; | |
| if (!delegateData.m_Network) | |
| { | |
| validateFunc(outputTensorInfo, isSupported); | |
| return isSupported ? kTfLiteOk : kTfLiteError; | |
| } | |
| // Setup layer and connect. | |
| armnn::IConnectableLayer* meanLayer = delegateData.m_Network->AddMeanLayer(desc); | |
| ARMNN_ASSERT(meanLayer != nullptr); | |
| armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0); | |
| outputSlot.SetTensorInfo(outputTensorInfo); | |
| return Connect(meanLayer, tfLiteNode, delegateData); | |
| } | |
| TfLiteStatus VisitControlOperator(DelegateData& delegateData, | |
| TfLiteContext* tfLiteContext, | |
| TfLiteNode* tfLiteNode, | |
| int nodeIndex, | |
| int32_t operatorCode) | |
| { | |
| armnn::IgnoreUnused(delegateData, | |
| tfLiteContext, | |
| tfLiteNode, | |
| nodeIndex, | |
| operatorCode); | |
| switch(operatorCode) | |
| { | |
| case kTfLiteBuiltinConcatenation: | |
| return VisitConcatenationOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); | |
| case kTfLiteBuiltinMean: | |
| return VisitMeanOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); | |
| default: | |
| return kTfLiteError; | |
| } | |
| } | |
| } // namespace armnnDelegate |