| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/BackendHelper.hpp> |
| #include <armnn/utility/Assert.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 |
| { |
| |
| // Macro to call an Is<layer_name>Supported function and log caller name together with reason for lack of support |
| #define FORWARD_LAYER_SUPPORT_FUNC(funcName, tfLiteContext, func, backends, supported, ...) \ |
| try \ |
| { \ |
| for (auto&& backendId : backends) \ |
| { \ |
| auto layerSupportObject = armnn::GetILayerSupportByBackendId(backendId); \ |
| if (layerSupportObject) \ |
| { \ |
| std::string reasonIfUnsupported; \ |
| supported = \ |
| layerSupportObject->func(__VA_ARGS__, armnn::Optional<std::string&>(reasonIfUnsupported)); \ |
| if (supported) \ |
| { \ |
| break; \ |
| } \ |
| else \ |
| { \ |
| if (reasonIfUnsupported.size() > 0) \ |
| { \ |
| TF_LITE_KERNEL_LOG( \ |
| tfLiteContext, "%s: not supported by armnn: %s", funcName, reasonIfUnsupported.c_str()); \ |
| } \ |
| else \ |
| { \ |
| TF_LITE_KERNEL_LOG(tfLiteContext, "%s: not supported by armnn", funcName); \ |
| } \ |
| } \ |
| } \ |
| else \ |
| { \ |
| TF_LITE_KERNEL_LOG(tfLiteContext, "%s: backend not registered: %s", funcName, backendId.Get().c_str()); \ |
| } \ |
| } \ |
| if (!supported) \ |
| { \ |
| TF_LITE_KERNEL_LOG(tfLiteContext, "%s: not supported by any specified backend", funcName); \ |
| } \ |
| } \ |
| catch (const armnn::InvalidArgumentException &e) \ |
| { \ |
| throw armnn::InvalidArgumentException(e, "Failed to check layer support", CHECK_LOCATION()); \ |
| } |
| |
| TfLiteStatus ValidateNumInputs(TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| const unsigned int expectedSize, |
| int nodeIndex) |
| { |
| auto numInputs = tfLiteNode->inputs->size; |
| if (numInputs != expectedSize) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of inputs (%d != %d) in node #%d", |
| numInputs, expectedSize, nodeIndex); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus ValidateNumOutputs(TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| const unsigned int expectedSize, |
| int nodeIndex) |
| { |
| auto numOutputs = tfLiteNode->outputs->size; |
| if (numOutputs != expectedSize) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of outputs (%d != %d) in node #%d", |
| numOutputs, expectedSize, nodeIndex); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| bool IsDynamicTensor(const TfLiteTensor& tfLiteTensor) |
| { |
| auto tensorAllocationType = tfLiteTensor.allocation_type; |
| if (tensorAllocationType == kTfLiteDynamic) |
| { |
| return true; |
| } |
| return false; |
| } |
| |
| armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor) |
| { |
| armnn::DataType type; |
| switch (tfLiteTensor.type) |
| { |
| case kTfLiteBool: |
| type = armnn::DataType::Boolean; |
| break; |
| case kTfLiteFloat32: |
| type = armnn::DataType::Float32; |
| break; |
| case kTfLiteFloat16: |
| type = armnn::DataType::Float16; |
| break; |
| case kTfLiteUInt8: |
| type = armnn::DataType::QAsymmU8; |
| break; |
| case kTfLiteInt8: |
| type = armnn::DataType::QSymmS8; |
| break; |
| case kTfLiteInt16: |
| type = armnn::DataType::QSymmS16; |
| break; |
| case kTfLiteInt32: |
| type = armnn::DataType::Signed32; |
| break; |
| default: |
| throw armnn::Exception("TfLiteArmnnDelegate: Unsupported data type: " + tfLiteTensor.type); |
| } |
| |
| armnn::TensorInfo ret; |
| auto tensorDimensionSize = tfLiteTensor.dims->size; |
| if (tensorDimensionSize == 0) |
| { |
| armnn::TensorShape tensorShape(armnn::Dimensionality::NotSpecified); |
| ret = armnn::TensorInfo(tensorShape, type); |
| } |
| else |
| { |
| std::vector<unsigned int> tensorDims(tensorDimensionSize); |
| bool dimensionsSpecificity[5] = { true, true, true, true, true }; |
| for (unsigned int i = 0; i < tensorDimensionSize; ++i) { |
| auto dim = tfLiteTensor.dims->data[i]; |
| if (dim == 0) |
| { |
| dimensionsSpecificity[i] = false; |
| } |
| tensorDims[i] = dim; |
| } |
| armnn::TensorShape tensorShape(tensorDimensionSize, tensorDims.data(), dimensionsSpecificity); |
| ret = armnn::TensorInfo(tensorShape, type); |
| } |
| |
| auto quantizationInfo = tfLiteTensor.quantization; |
| if (quantizationInfo.type == kTfLiteAffineQuantization) |
| { |
| // get per-channel quantization parameters |
| const auto* affineQuantization = |
| reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params); |
| std::vector<float> quantizationScales; |
| for (unsigned int i = 1; i < affineQuantization->scale->size; ++i) |
| { |
| quantizationScales.push_back(affineQuantization->scale->data[i]); |
| } |
| ret.SetQuantizationScales(quantizationScales); |
| ret.SetQuantizationDim(armnn::MakeOptional<unsigned int>(affineQuantization->quantized_dimension)); |
| } |
| else |
| { |
| auto quantizationParameters = tfLiteTensor.params; |
| ret.SetQuantizationScale(quantizationParameters.scale); |
| ret.SetQuantizationOffset(quantizationParameters.zero_point); |
| } |
| |
| return ret; |
| } |
| |
| TfLiteStatus Connect(armnn::IConnectableLayer& layer, |
| TfLiteNode* tfLiteNode, |
| armnnDelegate::DelegateData& data) |
| { |
| ARMNN_ASSERT(tfLiteNode->inputs->size == layer.GetNumInputSlots()); |
| ARMNN_ASSERT(tfLiteNode->outputs->size == layer.GetNumOutputSlots()); |
| |
| // connect the input slots |
| for (unsigned int inputIndex = 0; inputIndex < layer.GetNumInputSlots(); ++inputIndex) |
| { |
| data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]]->Connect(layer.GetInputSlot(inputIndex)); |
| } |
| |
| // prepare output slots |
| for (unsigned int outputIndex = 0; outputIndex < layer.GetNumOutputSlots(); ++outputIndex) |
| { |
| armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(outputIndex); |
| data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot; |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace anonymous |