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Diffstat (limited to 'delegate/src/Control.hpp')
| -rw-r--r-- | delegate/src/Control.hpp | 342 |
1 files changed, 0 insertions, 342 deletions
diff --git a/delegate/src/Control.hpp b/delegate/src/Control.hpp deleted file mode 100644 index a3ea6e92a7..0000000000 --- a/delegate/src/Control.hpp +++ /dev/null @@ -1,342 +0,0 @@ -// -// Copyright © 2022-2023 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); - - if(!concatenationParameters) - { - throw armnn::Exception(&"TfLiteArmnnDelegate: Concat parameters are null in: " [ nodeIndex]); - } - - 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); - - // Verify we support the fused activation before attempting to create a layer - TfLiteFusedActivation activationType = concatenationParameters->activation; - - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("CONCATENATION", - tfLiteContext, - IsConcatSupported, - delegateData.m_Backends, - isSupported, - setBackend, - 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); - concatenationLayer->SetBackendId(setBackend); - 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); - if(Connect(concatenationLayer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (activationType == kTfLiteActNone) - { - // No Activation - return kTfLiteOk; - } - - // Check and Create 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; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("MEAN", - tfLiteContext, - IsMeanSupported, - delegateData.m_Backends, - isSupported, - setBackend, - 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); - meanLayer->SetBackendId(setBackend); - ARMNN_ASSERT(meanLayer != nullptr); - - armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(meanLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - 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 |