| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <armnn/utility/IgnoreUnused.hpp> |
| |
| #include "DelegateUtils.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 <numeric> |
| |
| namespace armnnDelegate |
| { |
| |
| TfLiteStatus CreateOutputTensorShape(const armnn::TensorInfo& inputTensorInfo, |
| const std::vector<int32_t>& targetShape, |
| armnn::ReshapeDescriptor& reshapeDesc) |
| { |
| std::vector<unsigned int> outputDims(targetShape.begin(), targetShape.end()); |
| const auto stretchDim = std::find(targetShape.begin(), targetShape.end(), -1); |
| |
| if (stretchDim != targetShape.end()) |
| { |
| if (std::find(std::next(stretchDim), targetShape.end(), -1) != targetShape.end()) |
| { |
| // Return kTfLiteError and log the error after returning |
| return kTfLiteError; |
| } |
| |
| auto targetNumElements = |
| armnn::numeric_cast<unsigned int>( |
| std::accumulate(targetShape.begin(), targetShape.end(), -1, std::multiplies<int32_t>())); |
| |
| auto stretchIndex = static_cast<size_t>(std::distance(targetShape.begin(), stretchDim)); |
| outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements; |
| } |
| |
| armnn::TensorShape outputShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()), |
| outputDims.data()); |
| reshapeDesc.m_TargetShape = outputShape; |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus VisitReshapeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| auto numInputs = tfLiteNode->inputs->size; |
| |
| if (numInputs == 2) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| } |
| else |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| } |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| if (!IsValid(tfLiteContext, tfLiteInputTensor0, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| |
| armnn::ReshapeDescriptor reshapeDesc; |
| std::vector<int32_t> targetShape; |
| bool shapeSet = false; |
| |
| // The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both. |
| if (numInputs == 2) |
| { |
| // Get shape from the second input tensor |
| const TfLiteTensor& tfLiteShapeInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| if (tfLiteShapeInputTensor.dims->size != 1) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: Target 'shape' input is not a 1D tensor in " |
| "operator #%d node #%d: Falling back to TfLiteOptions.", |
| operatorCode, nodeIndex); |
| } |
| else |
| { |
| // Get the shape data out of the input tensor |
| auto* shapeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteShapeInputTensor); |
| auto shapeTensorNumValues = tfLiteShapeInputTensor.dims->data[0]; |
| for (auto i=0; i < shapeTensorNumValues; ++i) |
| { |
| targetShape.push_back(*(shapeTensorDataPtr+i)); |
| } |
| shapeSet = true; |
| } |
| } |
| if (!shapeSet) |
| { |
| // Get shape from the builtin data |
| TfLiteReshapeParams* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(tfLiteNode->builtin_data); |
| |
| if (reshapeOptions != nullptr) |
| { |
| // Options might be set without valid data. we need to check the dimensions are in a valid range. |
| if (reshapeOptions->num_dimensions > 0 && reshapeOptions->num_dimensions <= 8) |
| { |
| for (int i=0; i < reshapeOptions->num_dimensions; ++i) |
| { |
| targetShape.push_back(reshapeOptions->shape[i]); |
| } |
| } |
| } |
| else |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "Target shape not defined in reshape parameters or input tensor. " |
| "At least one method required in operator #%d node #%d: ", |
| operatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| } |
| |
| // Use the data to create the required tensor shape. |
| if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: At most one component of shape can be -1 in: " |
| "operator #%d node #%d: ", |
| operatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| if (reshapeDesc.m_TargetShape.GetNumElements() != inputTensorInfo0.GetNumElements()) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Reshape, number of elements in output shape does not match input " |
| "operator #%d node #%d: ", |
| operatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| bool isSupported = false; |
| auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| tfLiteContext, |
| IsReshapeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| inputTensorInfo0, |
| outInfo, |
| reshapeDesc); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| |
| // Connect |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| armnn::IgnoreUnused(delegateData, |
| tfLiteContext, |
| tfLiteNode, |
| nodeIndex, |
| operatorCode); |
| |
| return kTfLiteError; |
| } |
| |
| TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| armnn::IgnoreUnused(delegateData, |
| tfLiteContext, |
| tfLiteNode, |
| nodeIndex, |
| operatorCode); |
| |
| return kTfLiteError; |
| } |
| |
| } // namespace armnnDelegate |