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Diffstat (limited to 'delegate/opaque/src/Resize.hpp')
| -rw-r--r-- | delegate/opaque/src/Resize.hpp | 218 |
1 files changed, 218 insertions, 0 deletions
diff --git a/delegate/opaque/src/Resize.hpp b/delegate/opaque/src/Resize.hpp index e16969768e..509ae62524 100644 --- a/delegate/opaque/src/Resize.hpp +++ b/delegate/opaque/src/Resize.hpp @@ -2,3 +2,221 @@ // 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; + 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) != kTfLiteOk ) + { + return kTfLiteError; + } + + ARMNN_ASSERT(resizeLayer != nullptr); + + return Connect(resizeLayer, tfLiteContext, tfLiteNode, delegateData); +} + +} // namespace armnnOpaqueDelegate |