| // |
| // Copyright © 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/minimal_logging.h> |
| #include <tensorflow/lite/kernels/internal/tensor_ctypes.h> |
| #include <tensorflow/lite/schema/schema_generated.h> |
| |
| namespace armnnDelegate |
| { |
| TfLiteStatus ValidateTileOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::TileDescriptor& descriptor) |
| { |
| bool isSupported = false; |
| FORWARD_LAYER_SUPPORT_FUNC("TILE", |
| tfLiteContext, |
| IsTileSupported, |
| delegateData.m_Backends, |
| isSupported, |
| armnn::BackendId(), |
| inputInfo, |
| outputInfo, |
| descriptor); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| TfLiteStatus VisitTileOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t tileOperatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| |
| // The input contains the data that should be tiled |
| const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| if (IsDynamicTensor(tfLiteInputTensor)) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The multiples tensor contains the number of copies for each axis |
| const TfLiteTensor& tfLiteMultiplesTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| if (IsDynamicTensor(tfLiteMultiplesTensor)) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The output tensor |
| 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: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& multiplesTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteMultiplesTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| |
| // Multiples length must be the same as the number of dimension in input tensor |
| if (multiplesTensorInfo.GetNumElements() != inputTensorInfo.GetNumDimensions()) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: The Multiples length must be the same as the number of dimension in input tensor", |
| "Operator: #%d node #%d: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // Get the Multiples data: In armnn, the values of the multiples input tensor is saved in the operator descriptor |
| // We have to read it from the input tensor and write it the descriptor |
| auto* multiplesTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteMultiplesTensor); |
| auto multiplesTensorNum = tfLiteMultiplesTensor.dims->data[0]; |
| std::vector<int32_t> multiplesIntData(multiplesTensorDataPtr, multiplesTensorDataPtr + multiplesTensorNum); |
| |
| // The multiples must be positive |
| for (auto multiple : multiplesIntData) |
| { |
| if (multiple < 0) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: The Multiples must be positive values", |
| "Operator: #%d node #%d: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| } |
| |
| // The original input from TFLite is int32, and we have to make it as uint32 for our descriptor |
| std::vector<uint32_t> multiplesUintData; |
| std::transform(multiplesIntData.begin(), |
| multiplesIntData.end(), |
| std::back_inserter(multiplesUintData), |
| [] (const int value) |
| { |
| return static_cast<uint32_t>(value); |
| }); |
| |
| armnn::TileDescriptor tileDescriptor; |
| tileDescriptor.m_Multiples = multiplesUintData; |
| |
| // Check output dimensions |
| if (inputTensorInfo.GetNumDimensions() != outputTensorInfo.GetNumDimensions()) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Input tensor dimension and output tensor dimension differ", |
| "Operator: #%d node #%d: ", |
| tileOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // No network pointer indicates that only support for this operator should be checked |
| if (!delegateData.m_Network) |
| { |
| return ValidateTileOperator(delegateData, |
| tfLiteContext, |
| inputTensorInfo, |
| outputTensorInfo, |
| tileDescriptor); |
| } |
| |
| std::string layerName("Tile"); |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddTileLayer(tileDescriptor, layerName.c_str()); |
| |
| if (layer == nullptr) |
| { |
| return kTfLiteError; |
| } |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| } // namespace armnnDelegate |