| // |
| // 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/minimal_logging.h> |
| #include <tensorflow/lite/kernels/internal/tensor_ctypes.h> |
| |
| namespace armnnDelegate |
| { |
| |
| TfLiteStatus VisitTransposeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t tfliteTransposeOperatorCode) |
| { |
| 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& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| if (IsDynamicTensor(tfLiteInputTensor0)) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " |
| "operator #%d node #%d: ", |
| tfliteTransposeOperatorCode, nodeIndex); |
| |
| return kTfLiteError; |
| } |
| |
| const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| if (IsDynamicTensor(tfLiteInputTensor1)) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " |
| "operator #%d node #%d: ", |
| tfliteTransposeOperatorCode, 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: ", |
| tfliteTransposeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); |
| const armnn::TensorInfo& inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1); //permutation tensor |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| |
| auto* permTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteInputTensor1); |
| unsigned int numEl = tfLiteInputTensor1.dims->data[0]; |
| |
| ARMNN_ASSERT( numEl <= static_cast<int>(armnn::MaxNumOfTensorDimensions)); |
| ARMNN_ASSERT( tfLiteInputTensor1.dims->size == 1); // ensure only single dimension to the permutation tensor |
| |
| armnn::TransposeDescriptor descriptor(armnn::PermutationVector( |
| reinterpret_cast<const armnn::PermutationVector::ValueType *> (permTensorDataPtr), |
| static_cast<armnn::PermutationVector::SizeType>(numEl))); |
| |
| bool isSupported = false; |
| |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| tfLiteContext, |
| IsTransposeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| inputTensorInfo0, |
| outputTensorInfo, |
| descriptor); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| armnn::IConnectableLayer* transposeLayer = delegateData.m_Network->AddTransposeLayer(descriptor); |
| ARMNN_ASSERT(transposeLayer != nullptr); |
| ARMNN_ASSERT(transposeLayer->GetNumInputSlots() == 1); // permutation vector given to descriptor object |
| |
| armnn::IOutputSlot& outputSlot = transposeLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| |
| return Connect(transposeLayer, tfLiteNode, delegateData); |
| } |
| } // namespace armnnDelegate |