diff options
Diffstat (limited to 'delegate/src/Redefine.hpp')
| -rw-r--r-- | delegate/src/Redefine.hpp | 160 |
1 files changed, 154 insertions, 6 deletions
diff --git a/delegate/src/Redefine.hpp b/delegate/src/Redefine.hpp index 755bb97494..a9c27fb9e6 100644 --- a/delegate/src/Redefine.hpp +++ b/delegate/src/Redefine.hpp @@ -7,27 +7,175 @@ #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) { - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - operatorCode); + auto numInputs = tfLiteNode->inputs->size; - return kTfLiteError; + 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 (IsDynamicTensor(tfLiteInputTensor0)) + { + TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " + "operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + 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: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + armnn::ReshapeDescriptor reshapeDesc; + + // 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) + { + const TfLiteTensor& tfLiteShapeInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (IsDynamicTensor(tfLiteShapeInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " + "operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + // Get the shape data out of the input tensor + std::vector<int32_t> targetShape; + 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)); + } + + // 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; + } + } + else if (tfLiteNode->builtin_data) + { + std::vector<int32_t> targetShape; + TfLiteReshapeParams* reshapeOptions = + reinterpret_cast<TfLiteReshapeParams*>(tfLiteNode->builtin_data); + for (int i=0; i < reshapeOptions->num_dimensions; ++i) + { + targetShape.push_back(reshapeOptions->shape[i]); + } + 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; + } + } + 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); + } + + 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, |