| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "DelegateUtils.hpp" |
| #include <armnn/utility/IgnoreUnused.hpp> |
| |
| #include <armnn/Descriptors.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 ValidateResizeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::ResizeDescriptor& descriptor) |
| { |
| bool isSupported = false; |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| tfLiteContext, |
| IsResizeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| inputInfo, |
| outputInfo, |
| descriptor); |
| |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| TfLiteStatus VisitResizeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t resizeOperatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| |
| // The first input contains the data of the image that should be resized [batch, height, width, channels] |
| 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: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The second input contains a size tensor. The size tensor contains two integer values |
| // that describe the new height and width of the image [new_height, new_width] |
| const TfLiteTensor& tfLiteSizeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| if (IsDynamicTensor(tfLiteSizeTensor)) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The output tensor should have the shape [batch, new_height, new_width, channels] |
| 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: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& sizeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteSizeTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| |
| std::string layerName("Resize"); |
| |
| // Fill descriptor |
| armnn::ResizeDescriptor desc; |
| switch (resizeOperatorCode) |
| { |
| case kTfLiteBuiltinResizeBilinear: |
| { |
| desc.m_Method = armnn::ResizeMethod::Bilinear; |
| |
| layerName += "Bilinear:" + nodeIndex; |
| |
| TfLiteResizeBilinearParams* biliniarOptions = |
| reinterpret_cast<TfLiteResizeBilinearParams*>(tfLiteNode->builtin_data); |
| |
| desc.m_AlignCorners = biliniarOptions->align_corners; |
| desc.m_HalfPixelCenters = biliniarOptions->half_pixel_centers; |
| break; |
| } |
| case kTfLiteBuiltinResizeNearestNeighbor: |
| { |
| desc.m_Method = armnn::ResizeMethod::NearestNeighbor; |
| layerName += "NearestNeighbor:" + nodeIndex; |
| |
| TfLiteResizeNearestNeighborParams* nearestNeighborOptions = |
| reinterpret_cast<TfLiteResizeNearestNeighborParams*>(tfLiteNode->builtin_data); |
| |
| desc.m_AlignCorners = nearestNeighborOptions->align_corners; |
| desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers; |
| break; |
| } |
| default: |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Unknown TfLite built in operation for Resize. Given operator: #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| } |
| |
| // In armnn the values of the size input tensor [new_hight, new_width] is saved in the operator |
| // descriptor. We have to read it from the input tensor and write it to the descriptor. |
| |
| auto* sizeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteSizeTensor); |
| auto sizeTensorNumDimensions = tfLiteSizeTensor.dims->size; |
| // The size tensor is only a 1D tensor -> [new_hight, new width] |
| if (sizeTensorNumDimensions != 1) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " |
| "dynamic tensor. Operator: #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // Get number of values in the size tensor |
| auto sizeTensorNumValues = tfLiteSizeTensor.dims->data[0]; |
| if (sizeTensorNumValues == 0) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " |
| "dynamic tensor. Operator: #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| else if (sizeTensorNumValues != 2) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation requires to " |
| "have a dimension of 2 [new_hight, new width] but a tensor with a dimension of #%d was given. " |
| "Operator: #%d node #%d: ", |
| sizeTensorNumValues, resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| // get size tensor data |
| std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues); |
| |
| desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]); |
| desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]); |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| // No network pointer indicates that only support for this operator should be checked |
| if (!delegateData.m_Network) |
| { |
| return ValidateResizeOperator(delegateData, |
| tfLiteContext, |
| inputTensorInfo, |
| outputTensorInfo, |
| desc); |
| } |
| |
| |
| armnn::IConnectableLayer* resizeLayer = nullptr; |
| resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str()); |
| |
| armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| |
| ARMNN_ASSERT(resizeLayer != nullptr); |
| |
| return Connect(resizeLayer, tfLiteNode, delegateData); |
| } |
| |
| } // namespace armnnDelegate |