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Diffstat (limited to 'delegate/classic/src/Tile.hpp')
| -rw-r--r-- | delegate/classic/src/Tile.hpp | 169 |
1 files changed, 169 insertions, 0 deletions
diff --git a/delegate/classic/src/Tile.hpp b/delegate/classic/src/Tile.hpp new file mode 100644 index 0000000000..974c771a7e --- /dev/null +++ b/delegate/classic/src/Tile.hpp @@ -0,0 +1,169 @@ +// +// 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
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