| // |
| // 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; |
| } |
| } |
| |
| // 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); |
| } |
| |
| auto layerName = GetName(armnn::LayerType::ReverseV2, nodeIndex); |
| 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| ARMNN_ASSERT(reverseV2Layer != nullptr); |
| |
| return Connect(reverseV2Layer, tfLiteContext, tfLiteNode, delegateData); |
| } |
| |
| } // namespace armnnOpaqueDelegate |