| // |
| // Copyright © 2022 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(tfLiteContext, tfLiteInputTensor, 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(tfLiteContext, tfLiteOutputTensor, 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, true); |
| |
| // Check if supported |
| bool isSupported = false; |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_SUPPORT_FUNC("CONCATENATION", |
| 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); |
| |
| // Connect the Constant Inputs |
| auto inputsTensorsProcess = ProcessInputs(concatenationLayer, |
| delegateData, |
| tfLiteContext, |
| tfLiteNode); |
| if (inputsTensorsProcess == kTfLiteError) |
| { |
| return inputsTensorsProcess; |
| } |
| |
| 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, true); |
| |
| 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("MEAN", |
| 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 |