| // | |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. | |
| // SPDX-License-Identifier: MIT | |
| // | |
| #pragma once | |
| #include <armnn/utility/IgnoreUnused.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> | |
| namespace armnnDelegate | |
| { | |
| TfLiteStatus VisitDequantizeOperator(DelegateData& delegateData, | |
| TfLiteContext* tfLiteContext, | |
| TfLiteNode* tfLiteNode, | |
| int nodeIndex, | |
| int32_t tfLiteDequantizeOperatorCode) | |
| { | |
| 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& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; | |
| if (IsDynamicTensor(tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", | |
| tfLiteDequantizeOperatorCode, 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: ", | |
| tfLiteDequantizeOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); | |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); | |
| bool isSupported = false; | |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) | |
| { | |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, | |
| tfLiteContext, | |
| IsDequantizeSupported, | |
| delegateData.m_Backends, | |
| isSupported, | |
| inputTensorInfo, | |
| outputTensorInfo); | |
| }; | |
| if (!delegateData.m_Network) | |
| { | |
| validateFunc(outputTensorInfo, isSupported); | |
| return isSupported ? kTfLiteOk : kTfLiteError; | |
| } | |
| armnn::IConnectableLayer* dequantizeLayer = delegateData.m_Network->AddDequantizeLayer(); | |
| ARMNN_ASSERT(dequantizeLayer != nullptr); | |
| armnn::IOutputSlot& outputSlot = dequantizeLayer->GetOutputSlot(0); | |
| outputSlot.SetTensorInfo(outputTensorInfo); | |
| auto inputsTensorsProcess = ProcessInputs(dequantizeLayer, | |
| delegateData, | |
| tfLiteContext, | |
| tfLiteNode); | |
| if (inputsTensorsProcess == kTfLiteError) | |
| { | |
| return inputsTensorsProcess; | |
| } | |
| return Connect(dequantizeLayer, tfLiteNode, delegateData); | |
| } | |
| TfLiteStatus VisitQuantizeOperator(DelegateData& delegateData, | |
| TfLiteContext* tfLiteContext, | |
| TfLiteNode* tfLiteNode, | |
| int nodeIndex, | |
| int32_t tfLiteQuantizeOperatorCode) | |
| { | |
| 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& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; | |
| if (IsDynamicTensor(tfLiteInputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", | |
| tfLiteQuantizeOperatorCode, 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: ", | |
| tfLiteQuantizeOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| // Only affine per-layer quantization is supported. | |
| if (!IsAffineQuantization(tfLiteOutputTensor)) | |
| { | |
| TF_LITE_MAYBE_KERNEL_LOG( | |
| tfLiteContext, | |
| "TfLiteArmnnDelegate: Only affine per-layer quantization is supported in operator #%d node #%d: ", | |
| tfLiteQuantizeOperatorCode, nodeIndex); | |
| return kTfLiteError; | |
| } | |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); | |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); | |
| bool isSupported = false; | |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) | |
| { | |
| FORWARD_LAYER_SUPPORT_FUNC(__func__, | |
| tfLiteContext, | |
| IsQuantizeSupported, | |
| delegateData.m_Backends, | |
| isSupported, | |
| inputTensorInfo, | |
| outputTensorInfo); | |
| }; | |
| if (!delegateData.m_Network) | |
| { | |
| validateFunc(outputTensorInfo, isSupported); | |
| return isSupported ? kTfLiteOk : kTfLiteError; | |
| } | |
| armnn::IConnectableLayer* quantizeLayer = delegateData.m_Network->AddQuantizeLayer(); | |
| ARMNN_ASSERT(quantizeLayer != nullptr); | |
| armnn::IOutputSlot& outputSlot = quantizeLayer->GetOutputSlot(0); | |
| outputSlot.SetTensorInfo(outputTensorInfo); | |
| return Connect(quantizeLayer, tfLiteNode, delegateData); | |
| } | |
| } // namespace armnnDelegate |