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Diffstat (limited to 'delegate/opaque/src/ReverseV2.hpp')
| -rw-r--r-- | delegate/opaque/src/ReverseV2.hpp | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/delegate/opaque/src/ReverseV2.hpp b/delegate/opaque/src/ReverseV2.hpp new file mode 100644 index 0000000000..e5714f4576 --- /dev/null +++ b/delegate/opaque/src/ReverseV2.hpp @@ -0,0 +1,174 @@ +// +// Copyright © 2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <OpaqueDelegateUtils.hpp> + +namespace armnnOpaqueDelegate +{ + +TfLiteStatus ValidateReverseV2Operator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + const armnn::TensorInfo& inputInfo0, + const armnn::TensorInfo& inputInfo1, + const armnn::TensorInfo& outputInfo) +{ + bool isSupported = false; + FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("REVERSEV2", + tfLiteContext, + IsReverseV2Supported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo0, + inputInfo1, + outputInfo); + + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus VisitReverseV2Operator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t reverseV2OperatorCode) +{ + 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 to be reversed + 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: ", + reverseV2OperatorCode, nodeIndex); + return kTfLiteError; + } + + // The second input contains the axis tensor + const TfLiteOpaqueTensor* tfLiteAxisTensor = + TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); + if (IsDynamicTensor(tfLiteAxisTensor)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + reverseV2OperatorCode, 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; + } + + // Get the output tensor + 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: ", + reverseV2OperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo0 = + GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); + const armnn::TensorInfo& inputTensorInfo1 = + GetTensorInfoForTfLiteOpaqueTensor(tfLiteAxisTensor); + const armnn::TensorInfo& outputTensorInfo = + GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + if (inputTensorInfo0.GetNumDimensions() != outputTensorInfo.GetNumDimensions()) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: input tensor dimension and output tensor dimension differ #%d node #%d: ", + reverseV2OperatorCode, nodeIndex); + return kTfLiteError; + } + + for (unsigned i=0; i < inputTensorInfo0.GetNumDimensions(); i++) + { + if (inputTensorInfo0.GetShape()[i] != outputTensorInfo.GetShape()[i]) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: input tensor dimension and output tensor differ #%d node #%d: ", + reverseV2OperatorCode, nodeIndex); + return kTfLiteError; + } + } + + std::string layerName("ReverseV2"); + + // Get axis tensor data + auto axisTensorNumValues = static_cast<unsigned int>(TfLiteOpaqueTensorDim(tfLiteAxisTensor,0)); + + const auto maxDimension = 4; + + if (axisTensorNumValues > maxDimension) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: The Axis-Input-Tensor of the ReverseV2 operation requires a " + "dimension of <= %d but a tensor with a dimension of %d was given. " + "Operator: #%d node #%d: ", + maxDimension, axisTensorNumValues, reverseV2OperatorCode, nodeIndex); + return kTfLiteError; + } + + // No network pointer indicates that only support for this operator should be checked + if (!delegateData.m_Network) + { + return ValidateReverseV2Operator(delegateData, + tfLiteContext, + inputTensorInfo0, + inputTensorInfo1, + outputTensorInfo); + } + + armnn::IConnectableLayer* reverseV2Layer = delegateData.m_Network->AddReverseV2Layer(layerName.c_str()); + + armnn::IOutputSlot& outputSlot = reverseV2Layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + // try to connect the Constant Inputs if there are any + if(ProcessInputs(reverseV2Layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) + { + return kTfLiteError; + } + + ARMNN_ASSERT(reverseV2Layer != nullptr); + + return Connect(reverseV2Layer, tfLiteContext, tfLiteNode, delegateData); +} + +} // namespace armnnOpaqueDelegate |