| // |
| // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <OpaqueDelegateUtils.hpp> |
| |
| namespace armnnOpaqueDelegate |
| { |
| |
| TfLiteStatus ValidateResizeOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::ResizeDescriptor& descriptor) |
| { |
| bool isSupported = false; |
| FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESIZE", |
| tfLiteContext, |
| IsResizeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| armnn::BackendId(), |
| inputInfo, |
| outputInfo, |
| descriptor); |
| |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| TfLiteStatus VisitResizeOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t resizeOperatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| // Gather input indices and use to get input tensor. |
| auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| const int* inputTensors; |
| if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The first input contains the data of the image that should be resized [batch, height, width, channels] |
| const TfLiteOpaqueTensor* tfLiteInputTensor = |
| TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| if (IsDynamicTensor(tfLiteInputTensor)) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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 TfLiteOpaqueTensor* tfLiteSizeTensor = |
| TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); |
| if (IsDynamicTensor(tfLiteSizeTensor)) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // Gather output indices and use to get output tensors. |
| int numOutputs = 0; |
| const int* outputTensors; |
| if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // The output tensor should have the shape [batch, new_height, new_width, channels] |
| const TfLiteOpaqueTensor* tfLiteOutputTensor = |
| TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| if (IsDynamicTensor(tfLiteOutputTensor)) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = |
| GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = |
| GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| std::string layerName("Resize"); |
| |
| // Fill descriptor |
| armnn::ResizeDescriptor desc; |
| switch (resizeOperatorCode) |
| { |
| case kTfLiteBuiltinResizeBilinear: |
| { |
| desc.m_Method = armnn::ResizeMethod::Bilinear; |
| |
| layerName += "Bilinear:" + std::to_string(nodeIndex); |
| |
| TfLiteResizeBilinearParams* bilinearOptions = |
| reinterpret_cast<TfLiteResizeBilinearParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| |
| desc.m_AlignCorners = bilinearOptions->align_corners; |
| desc.m_HalfPixelCenters = bilinearOptions->half_pixel_centers; |
| break; |
| } |
| case kTfLiteBuiltinResizeNearestNeighbor: |
| { |
| desc.m_Method = armnn::ResizeMethod::NearestNeighbor; |
| layerName += "NearestNeighbor:" + std::to_string(nodeIndex); |
| |
| TfLiteResizeNearestNeighborParams* nearestNeighborOptions = |
| reinterpret_cast<TfLiteResizeNearestNeighborParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| |
| desc.m_AlignCorners = nearestNeighborOptions->align_corners; |
| desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers; |
| break; |
| } |
| default: |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unknown TfLite built in operation for Resize. " |
| "Given operator: #%d node #%d: ", |
| resizeOperatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| } |
| |
| // In Arm NN the values of the size input tensor [new_height, 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 = static_cast<int*>(TfLiteOpaqueTensorData(tfLiteSizeTensor)); |
| auto sizeTensorNumDimensions = TfLiteOpaqueTensorNumDims(tfLiteSizeTensor); |
| // The size tensor is only a 1D tensor -> [new_height, new width] |
| if (sizeTensorNumDimensions != 1) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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 = TfLiteOpaqueTensorDim(tfLiteSizeTensor,0); |
| if (sizeTensorNumValues == 0) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation requires to " |
| "have a dimension of 2 [new_height, 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; |
| layerName += ":"; |
| layerName += nodeIndex; |
| |
| resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str()); |
| |
| armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| |
| // try to connect the Constant Inputs if there are any |
| if (ProcessInputs(resizeLayer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| ARMNN_ASSERT(resizeLayer != nullptr); |
| |
| return Connect(resizeLayer, tfLiteContext, tfLiteNode, delegateData); |
| } |
| |
| } // namespace armnnOpaqueDelegate |