| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "TfLiteParser.hpp" |
| |
| #include <armnn/Descriptors.hpp> |
| #include <armnn/Exceptions.hpp> |
| #include <armnn/Logging.hpp> |
| #include <armnn/TypesUtils.hpp> |
| #include <armnn/utility/Assert.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| #include <armnn/utility/NumericCast.hpp> |
| |
| // armnnUtils: |
| #include <armnnUtils/Permute.hpp> |
| #include <Filesystem.hpp> |
| |
| #include <ParserHelper.hpp> |
| #include <VerificationHelpers.hpp> |
| |
| // The generated code based on the Tf Lite schema: |
| #include <schema_generated.h> |
| |
| #include <flatbuffers/flexbuffers.h> |
| |
| #include <boost/format.hpp> |
| #include <boost/numeric/conversion/cast.hpp> |
| |
| #include <fstream> |
| #include <algorithm> |
| #include <limits> |
| #include <numeric> |
| #include <sstream> |
| |
| #define ARMNN_THROW_PARSE_EXCEPTION(msg) \ |
| { \ |
| throw armnn::ParseException( static_cast<const std::stringstream&>( std::stringstream() << msg \ |
| << ": " \ |
| << CHECK_LOCATION().AsString()).str()); \ |
| } |
| |
| using namespace armnn; |
| using armnn::CheckLocation; |
| namespace armnnTfLiteParser |
| { |
| namespace |
| { |
| |
| const uint32_t VIRTUAL_OPERATOR_ID = std::numeric_limits<uint32_t>::max(); |
| |
| void CheckSubgraph(const TfLiteParser::ModelPtr & model, |
| size_t subgraphIndex, |
| const CheckLocation & location) |
| { |
| if (model.get() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with invalid (null) model. " |
| "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| "subgraph:%2% at %3%") % |
| location.m_Function % |
| subgraphIndex % |
| location.FileLine())); |
| } |
| else if (subgraphIndex >= model->subgraphs.size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid subgraph index. " |
| "subgraph:%2% at %3%") % |
| location.m_Function % |
| subgraphIndex % |
| location.FileLine())); |
| } |
| } |
| |
| #define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX) \ |
| CheckSubgraph(MODEL, SUBGRAPH_INDEX, CHECK_LOCATION()) |
| |
| void CheckModel(const TfLiteParser::ModelPtr & model, |
| size_t subgraphIndex, |
| size_t operatorIndex, |
| const CheckLocation & location) |
| { |
| if (model.get() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with invalid (null) model. " |
| "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| "subgraph:%2% operator:%3% at %4%") % |
| location.m_Function % |
| subgraphIndex % |
| operatorIndex % |
| location.FileLine())); |
| } |
| else if (subgraphIndex >= model->subgraphs.size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid subgraph index. " |
| "subgraph:%2% operator:%3% at %4%") % |
| location.m_Function % |
| subgraphIndex % |
| operatorIndex % |
| location.FileLine())); |
| } |
| else if (operatorIndex >= model->subgraphs[subgraphIndex]->operators.size() && |
| operatorIndex != VIRTUAL_OPERATOR_ID) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid operator index. " |
| "subgraph:%2% operator:%3% at %4%") % |
| location.m_Function % |
| subgraphIndex % |
| operatorIndex % |
| location.FileLine())); |
| } |
| } |
| |
| #define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX) \ |
| CheckModel(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX, CHECK_LOCATION()) |
| |
| void CheckTensor(const TfLiteParser::ModelPtr & model, |
| size_t subgraphIndex, |
| size_t tensorIndex, |
| const CheckLocation & location) |
| { |
| // not checking model, because I assume CHECK_MODEL already run |
| // and checked that. An assert would do. |
| ARMNN_ASSERT_MSG(model.get() != nullptr, "Expecting a valid model in this function"); |
| |
| // also subgraph index should be checked by CHECK_MODEL so |
| // I only add an assert here |
| ARMNN_ASSERT_MSG(subgraphIndex < model->subgraphs.size(), "Expecting a valid subgraph index"); |
| |
| // the tensor index is the only one to check here |
| if (tensorIndex >= model->subgraphs[subgraphIndex]->tensors.size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid tensor index. " |
| "subgraph:%2% tensor:%3% at %4%") % |
| location.m_Function % |
| subgraphIndex % |
| tensorIndex % |
| location.FileLine())); |
| } |
| } |
| |
| #define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX) \ |
| CheckTensor(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX, CHECK_LOCATION()) |
| |
| void CheckTensorPtr(TfLiteParser::TensorRawPtr rawPtr, |
| const CheckLocation & location) |
| { |
| if (rawPtr == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with a null tensor pointer. " |
| "at %2%") % |
| location.m_Function % |
| location.FileLine())); |
| |
| } |
| } |
| |
| #define CHECK_TENSOR_PTR(TENSOR_PTR) \ |
| CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION()) |
| |
| void CheckBuffer(const TfLiteParser::ModelPtr & model, |
| size_t bufferIndex, |
| const CheckLocation & location) |
| { |
| if (model.get() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with invalid (null) model. " |
| "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| "buffer:%2% at %3%") % |
| location.m_Function % |
| bufferIndex % |
| location.FileLine())); |
| } |
| else if (bufferIndex >= model->buffers.size()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("%1% was called with an invalid buffer index. " |
| "buffer index:%2% at %3%") % |
| location.m_Function % |
| bufferIndex % |
| location.FileLine())); |
| } |
| else if (model->buffers[bufferIndex].get() == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("The buffer #%1% is null. %3%") % |
| bufferIndex % |
| location.AsString())); |
| } |
| } |
| |
| #define CHECK_BUFFER(MODEL, BUFFER_INDEX) \ |
| CheckBuffer(MODEL, BUFFER_INDEX, CHECK_LOCATION()) |
| |
| void CheckBufferSize(TfLiteParser::BufferRawPtr bufferPtr, |
| const armnn::TensorInfo & tensorInfo, |
| uint32_t bufferId, |
| const CheckLocation & location) |
| { |
| if (bufferPtr == nullptr) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("BufferPtr is null for buffer:%1%. %2%") % |
| bufferId % |
| location.AsString())); |
| } |
| else if(tensorInfo.GetNumElements() > bufferPtr->data.size() || |
| tensorInfo.GetNumBytes() > bufferPtr->data.size()) |
| { |
| std::stringstream ss; |
| ss << "Buffer #" << bufferId << " has " << bufferPtr->data.size() << " bytes. " |
| << "For tensor: " << tensorInfo.GetShape() |
| << " expecting: " << tensorInfo.GetNumBytes() << " bytes and " |
| << tensorInfo.GetNumElements() << " elements. " << location.AsString(); |
| throw ParseException(ss.str()); |
| } |
| } |
| |
| #define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID) \ |
| CheckBufferSize(BUFFER_PTR, TENSOR_INFO, BUFFER_ID, CHECK_LOCATION()) |
| |
| bool IsActivationSupported(tflite::ActivationFunctionType activationType) |
| { |
| switch(activationType) |
| { |
| case tflite::ActivationFunctionType_NONE: |
| case tflite::ActivationFunctionType_RELU: |
| case tflite::ActivationFunctionType_RELU6: |
| case tflite::ActivationFunctionType_TANH: |
| { |
| return true; |
| } |
| default: |
| { |
| return false; |
| } |
| } |
| } |
| |
| #define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX) \ |
| do { \ |
| if (IsActivationSupported(OPTION->fused_activation_function) == false) \ |
| { \ |
| throw ParseException( \ |
| boost::str( \ |
| boost::format("TfLite parser doesn't suppport fused activation: " \ |
| "%1%/%2% in %3% subgraph:%4% operator:%5% at %6%") % \ |
| OPTION->fused_activation_function % \ |
| tflite::EnumNameActivationFunctionType(\ |
| OPTION->fused_activation_function) % \ |
| __func__ % \ |
| SUBGRAPH_INDEX % \ |
| OPERATOR_INDEX % \ |
| CHECK_LOCATION().FileLine())); \ |
| } \ |
| } while(false) |
| |
| |
| std::vector<unsigned int> AsUnsignedVector(const std::vector<int32_t> & in) |
| { |
| std::vector<unsigned int> result; |
| result.reserve(in.size()); |
| for (auto & i : in) |
| { |
| result.push_back(CHECKED_NON_NEGATIVE(i)); |
| } |
| return result; |
| } |
| |
| void CalcPadding(uint32_t inputSize, |
| uint32_t filterSize, |
| uint32_t stride, |
| uint32_t dilation, |
| uint32_t& paddingFront, |
| uint32_t& paddingBack, |
| tflite::Padding padding) |
| { |
| paddingFront = 0; |
| paddingBack = 0; |
| if (padding == tflite::Padding_SAME) |
| { |
| uint32_t outputSize = (inputSize + stride - 1) / stride; |
| uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1); |
| uint32_t temp = (outputSize - 1) * stride + dilatedSize; |
| if (temp > inputSize) |
| { |
| paddingFront = (temp - inputSize) / 2; |
| paddingBack = (temp - inputSize) - paddingFront; |
| } |
| } |
| } |
| |
| armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, const std::vector<unsigned int>& shapes, |
| const armnn::PermutationVector& dimensionMappings = {0, 1, 2, 3}) |
| { |
| armnn::DataType type; |
| CHECK_TENSOR_PTR(tensorPtr); |
| |
| switch (tensorPtr->type) |
| { |
| case tflite::TensorType_UINT8: |
| type = armnn::DataType::QAsymmU8; |
| break; |
| case tflite::TensorType_FLOAT32: |
| type = armnn::DataType::Float32; |
| break; |
| case tflite::TensorType_INT8: |
| if (tensorPtr->quantization->zero_point.size() == 1) |
| { |
| // Per-tensor |
| type = armnn::DataType::QAsymmS8; |
| } |
| else |
| { |
| // Per-channel |
| type = armnn::DataType::QSymmS8; |
| } |
| break; |
| case tflite::TensorType_INT16: |
| type = armnn::DataType::QSymmS16; |
| break; |
| case tflite::TensorType_INT32: |
| type = armnn::DataType::Signed32; |
| break; |
| |
| default: |
| { |
| CheckLocation location = CHECK_LOCATION(); |
| throw ParseException( |
| boost::str( |
| boost::format("Unsupported data type %1% = %2% for tensor: %3%. %4%") % |
| tensorPtr->type % |
| tflite::EnumNameTensorType(tensorPtr->type) % |
| tensorPtr->name % |
| location.AsString())); |
| } |
| } |
| std::vector<unsigned int> safeShape = shapes; |
| if (safeShape.size() == 0) |
| { |
| safeShape.push_back(1); |
| } |
| |
| float quantizationScale = 0.0f; |
| int32_t quantizationOffset = 0; |
| |
| if (tensorPtr->quantization.get()) |
| { |
| if (tensorPtr->quantization->scale.size() <= 1) |
| { |
| CHECK_VALID_SIZE(tensorPtr->quantization->zero_point.size(), 0, 1); |
| CHECK_VALID_SIZE(tensorPtr->quantization->zero_point.size(), 0, 1); |
| |
| if (tensorPtr->quantization->scale.size() == 1) |
| { |
| quantizationScale = tensorPtr->quantization->scale[0]; |
| } |
| if (tensorPtr->quantization->zero_point.size() == 1) |
| { |
| // NOTE: we lose precision here when converting from 64 bit to 32 |
| // but this is what we support at the moment in ArmNN |
| quantizationOffset = boost::numeric_cast<int32_t>(tensorPtr->quantization->zero_point[0]); |
| } |
| |
| armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()), |
| safeShape.data(), |
| type, |
| quantizationScale, |
| quantizationOffset); |
| |
| return result; |
| } |
| else |
| { |
| std::vector<float> quantizationScales; |
| std::vector<int32_t> quantizationOffsets; |
| |
| // Scale |
| std::copy(tensorPtr->quantization->scale.begin(), |
| tensorPtr->quantization->scale.end(), |
| std::back_inserter(quantizationScales)); |
| |
| // QSymmS8 Per-axis |
| armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()), |
| safeShape.data(), |
| type, |
| quantizationScales, |
| dimensionMappings[boost::numeric_cast<unsigned int>( |
| tensorPtr->quantization->quantized_dimension)]); |
| return result; |
| } |
| } |
| else |
| { |
| armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()), |
| safeShape.data(), |
| type, |
| quantizationScale, |
| quantizationOffset); |
| return result; |
| } |
| } |
| |
| armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, |
| const armnn::PermutationVector& dimensionMappings = {0, 1, 2, 3}) |
| { |
| auto const & dimensions = AsUnsignedVector(tensorPtr->shape); |
| return ToTensorInfo(tensorPtr, dimensions, dimensionMappings); |
| } |
| |
| template<typename T> |
| std::pair<armnn::ConstTensor, std::unique_ptr<T[]>> |
| CreateConstTensorImpl(TfLiteParser::BufferRawPtr bufferPtr, |
| TfLiteParser::TensorRawPtr tensorPtr, |
| armnn::TensorInfo& tensorInfo, |
| armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| IgnoreUnused(tensorPtr); |
| ARMNN_ASSERT_MSG(tensorPtr != nullptr, "tensorPtr is null"); |
| ARMNN_ASSERT_MSG(bufferPtr != nullptr, |
| boost::str( |
| boost::format("Buffer for buffer:%1% is null") % tensorPtr->buffer).c_str()); |
| |
| std::unique_ptr<T[]> data(new T[tensorInfo.GetNumElements()]); |
| |
| if (permutationVector.has_value() && permutationVector.value().GetSize() > 0) |
| { |
| tensorInfo = armnnUtils::Permuted(tensorInfo, permutationVector.value()); |
| armnnUtils::Permute(tensorInfo.GetShape(), permutationVector.value(), |
| reinterpret_cast<const T*>(bufferPtr->data.data()), data.get(), sizeof(T)); |
| } |
| else |
| { |
| ::memcpy(data.get(), bufferPtr->data.data(), tensorInfo.GetNumBytes()); |
| } |
| |
| return std::make_pair(ConstTensor(tensorInfo, data.get()), std::move(data)); |
| } |
| |
| armnn::LayerBindingId GenerateLayerBindingId(size_t subgraphIndex, size_t tensorIndex) |
| { |
| // generate the binding id by shifting the tensor id by 8 bit |
| // and add the subgraph id, which allows 256 subgraphs |
| return static_cast<armnn::LayerBindingId>((tensorIndex<<8)+subgraphIndex); |
| } |
| |
| bool CheckShape(const armnn::TensorShape& actual, const std::vector<int32_t>& expected) |
| { |
| const unsigned int actualSize = actual.GetNumDimensions(); |
| if (actualSize != expected.size()) |
| { |
| return false; |
| } |
| |
| for (unsigned int i = 0u; i < actualSize; i++) |
| { |
| if (expected[i] < 0 || |
| actual[i] != static_cast<unsigned int>(expected[i])) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| |
| } // <anonymous> |
| |
| TfLiteParser::TfLiteParser(const Optional<ITfLiteParser::TfLiteParserOptions>& options) |
| : m_Options(options) |
| , m_Network(nullptr, nullptr) |
| , m_ParserFunctions(tflite::BuiltinOperator_MAX+1, &TfLiteParser::ParseUnsupportedOperator) |
| { |
| // register supported operators |
| m_ParserFunctions[tflite::BuiltinOperator_ADD] = &TfLiteParser::ParseAdd; |
| m_ParserFunctions[tflite::BuiltinOperator_AVERAGE_POOL_2D] = &TfLiteParser::ParseAveragePool2D; |
| m_ParserFunctions[tflite::BuiltinOperator_BATCH_TO_SPACE_ND] = &TfLiteParser::ParseBatchToSpaceND; |
| m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParser::ParseConcatenation; |
| m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParser::ParseConv2D; |
| m_ParserFunctions[tflite::BuiltinOperator_CUSTOM] = &TfLiteParser::ParseCustomOperator; |
| m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParser::ParseDepthwiseConv2D; |
| m_ParserFunctions[tflite::BuiltinOperator_DEQUANTIZE] = &TfLiteParser::ParseDequantize; |
| m_ParserFunctions[tflite::BuiltinOperator_EXP] = &TfLiteParser::ParseExp; |
| m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED] = &TfLiteParser::ParseFullyConnected; |
| m_ParserFunctions[tflite::BuiltinOperator_LEAKY_RELU] = &TfLiteParser::ParseLeakyRelu; |
| m_ParserFunctions[tflite::BuiltinOperator_LOGISTIC] = &TfLiteParser::ParseLogistic; |
| m_ParserFunctions[tflite::BuiltinOperator_L2_NORMALIZATION] = &TfLiteParser::ParseL2Normalization; |
| m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D] = &TfLiteParser::ParseMaxPool2D; |
| m_ParserFunctions[tflite::BuiltinOperator_MAXIMUM] = &TfLiteParser::ParseMaximum; |
| m_ParserFunctions[tflite::BuiltinOperator_MEAN] = &TfLiteParser::ParseMean; |
| m_ParserFunctions[tflite::BuiltinOperator_MINIMUM] = &TfLiteParser::ParseMinimum; |
| m_ParserFunctions[tflite::BuiltinOperator_MUL] = &TfLiteParser::ParseMul; |
| m_ParserFunctions[tflite::BuiltinOperator_NEG] = &TfLiteParser::ParseNeg; |
| m_ParserFunctions[tflite::BuiltinOperator_PACK] = &TfLiteParser::ParsePack; |
| m_ParserFunctions[tflite::BuiltinOperator_PAD] = &TfLiteParser::ParsePad; |
| m_ParserFunctions[tflite::BuiltinOperator_QUANTIZE] = &TfLiteParser::ParseQuantize; |
| m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParser::ParseRelu; |
| m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParser::ParseRelu6; |
| m_ParserFunctions[tflite::BuiltinOperator_RESHAPE] = &TfLiteParser::ParseReshape; |
| m_ParserFunctions[tflite::BuiltinOperator_RESIZE_BILINEAR] = &TfLiteParser::ParseResizeBilinear; |
| m_ParserFunctions[tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = &TfLiteParser::ParseResizeNearestNeighbor; |
| m_ParserFunctions[tflite::BuiltinOperator_SLICE] = &TfLiteParser::ParseSlice; |
| m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX] = &TfLiteParser::ParseSoftmax; |
| m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_BATCH_ND] = &TfLiteParser::ParseSpaceToBatchND; |
| m_ParserFunctions[tflite::BuiltinOperator_SPLIT] = &TfLiteParser::ParseSplit; |
| m_ParserFunctions[tflite::BuiltinOperator_SPLIT_V] = &TfLiteParser::ParseSplitV; |
| m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE] = &TfLiteParser::ParseSqueeze; |
| m_ParserFunctions[tflite::BuiltinOperator_STRIDED_SLICE] = &TfLiteParser::ParseStridedSlice; |
| m_ParserFunctions[tflite::BuiltinOperator_SUB] = &TfLiteParser::ParseSub; |
| m_ParserFunctions[tflite::BuiltinOperator_TANH] = &TfLiteParser::ParseTanH; |
| m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE] = &TfLiteParser::ParseTranspose; |
| m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE_CONV] = &TfLiteParser::ParseTransposeConv; |
| m_ParserFunctions[tflite::BuiltinOperator_UNPACK] = &TfLiteParser::ParseUnpack; |
| m_ParserFunctions[tflite::BuiltinOperator_DIV] = &TfLiteParser::ParseDiv; |
| // register supported custom operators |
| m_CustomParserFunctions["TFLite_Detection_PostProcess"] = &TfLiteParser::ParseDetectionPostProcess; |
| } |
| |
| void TfLiteParser::ResetParser() |
| { |
| m_Network = armnn::INetworkPtr(nullptr, nullptr); |
| m_Model = nullptr; |
| m_SubgraphConnections.clear(); |
| } |
| |
| void TfLiteParser::AddBroadcastReshapeLayer(size_t subgraphIndex, |
| size_t operatorIndex, |
| IConnectableLayer *layer) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| const auto & subgraphPtr = m_Model->subgraphs[subgraphIndex]; |
| const auto & operatorPtr = subgraphPtr->operators[operatorIndex]; |
| |
| ARMNN_ASSERT(operatorPtr->inputs.size() > 1); |
| |
| uint32_t reshapedInputId = CHECKED_NON_NEGATIVE(operatorPtr->inputs[0]); |
| TensorRawPtr tensorPtr = subgraphPtr->tensors[reshapedInputId].get(); |
| uint32_t inputId = CHECKED_NON_NEGATIVE(operatorPtr->inputs[1]); |
| TensorRawPtr tensorPtr1 = subgraphPtr->tensors[inputId].get(); |
| |
| armnn::TensorInfo reshapedTensorInfo = ToTensorInfo(tensorPtr); |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(tensorPtr1); |
| |
| if (inputTensorInfo.GetNumDimensions() < reshapedTensorInfo.GetNumDimensions()) |
| { |
| uint32_t id = reshapedInputId; |
| reshapedInputId = inputId; |
| inputId = id; |
| |
| reshapedTensorInfo = ToTensorInfo(tensorPtr1); |
| inputTensorInfo = ToTensorInfo(tensorPtr); |
| } |
| |
| uint32_t numDimensions = inputTensorInfo.GetNumDimensions(); |
| |
| std::vector<unsigned> reshapedDim; |
| for (unsigned int i = 0; i < reshapedTensorInfo.GetNumDimensions(); ++i) |
| { |
| reshapedDim.push_back(reshapedTensorInfo.GetShape()[i]); |
| } |
| |
| std::vector<unsigned int> reshapedDimensions(numDimensions, 1); |
| std::copy_backward (reshapedDim.begin(), reshapedDim.end(), reshapedDimensions.end()); |
| |
| reshapedTensorInfo.SetShape(armnn::TensorShape{ numDimensions, reshapedDimensions.data() }); |
| |
| std::string layerName = boost::str(boost::format("Reshape_for:%1%") % layer->GetName()); |
| armnn::ReshapeDescriptor desc; |
| desc.m_TargetShape = reshapedTensorInfo.GetShape(); |
| armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str()); |
| |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); |
| reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); |
| |
| RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {reshapedInputId}); |
| |
| armnn::IInputSlot* input1Slot = &(layer->GetInputSlot(1)); |
| RegisterConsumerOfTensor(subgraphIndex, inputId, input1Slot); |
| } |
| |
| INetworkPtr TfLiteParser::CreateNetworkFromBinaryFile(const char* graphFile) |
| { |
| ResetParser(); |
| m_Model = LoadModelFromFile(graphFile); |
| return CreateNetworkFromModel(); |
| } |
| |
| INetworkPtr TfLiteParser::CreateNetworkFromBinary(const std::vector<uint8_t> & binaryContent) |
| { |
| ResetParser(); |
| m_Model = LoadModelFromBinary(binaryContent.data(), binaryContent.size()); |
| return CreateNetworkFromModel(); |
| } |
| |
| INetworkPtr TfLiteParser::CreateNetworkFromModel() |
| { |
| m_Network = INetwork::Create(); |
| ARMNN_ASSERT(m_Model.get() != nullptr); |
| |
| if (m_Model->subgraphs.size() != 1) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("Current TfLite parser only supports 1 subgraph. Current one has: %1% %2%") % |
| m_Model->subgraphs.size() % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| size_t subgraphIndex = 0; |
| size_t operatorIndex = 0; |
| try |
| { |
| for (SubgraphPtr const& subgraph : m_Model->subgraphs) |
| { |
| m_SubgraphConnections.emplace_back(subgraph->tensors.size()); |
| for (OperatorPtr const& op : subgraph->operators) |
| { |
| auto const& opCodePtr = m_Model->operator_codes[op->opcode_index]; |
| auto builtinCode = opCodePtr->builtin_code; |
| |
| if (builtinCode > tflite::BuiltinOperator_MAX) |
| { |
| throw ParseException(boost::str(boost::format("Operator code %1% is out of range 0-%2%. " |
| "subgraph:%3% operator idx:%4%. %5%") % |
| builtinCode % tflite::BuiltinOperator_MAX % subgraphIndex % |
| operatorIndex % CHECK_LOCATION().AsString())); |
| } |
| |
| // lookup and call the parser function |
| auto& parserFunction = m_ParserFunctions[builtinCode]; |
| (this->*parserFunction)(subgraphIndex, operatorIndex); |
| ++operatorIndex; |
| } |
| |
| SetupInputLayers(subgraphIndex); |
| SetupOutputLayers(subgraphIndex); |
| SetupConstantLayers(subgraphIndex); |
| |
| ++subgraphIndex; |
| operatorIndex = 0; |
| } |
| } |
| catch (const ParseException& e) |
| { |
| std::stringstream errorString; |
| errorString << "Failed to parse operator #" << operatorIndex << " within subgraph #" |
| << subgraphIndex << " error: " << e.what(); |
| ARMNN_LOG(error) << errorString.str(); |
| std::stringstream errors; |
| errors << errorString.str() << "\n"; |
| throw ParseException(errors.str()); |
| } |
| |
| // establish the connections from the layer outputs to the inputs of the subsequent layers |
| for (subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex) |
| { |
| for (size_t tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex) |
| { |
| if (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot != nullptr) |
| { |
| for (size_t inputSlotIdx = 0; |
| inputSlotIdx < m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size(); |
| ++inputSlotIdx) |
| { |
| m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot->Connect( |
| *(m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots[inputSlotIdx])); |
| } |
| } |
| } |
| } |
| |
| return std::move(m_Network); |
| } |
| |
| void TfLiteParser::RegisterProducerOfTensor(size_t subgraphIndex, |
| size_t tensorIndex, |
| armnn::IOutputSlot* slot) |
| { |
| CHECK_TENSOR(m_Model, subgraphIndex, tensorIndex); |
| ARMNN_ASSERT(m_SubgraphConnections.size() > subgraphIndex); |
| ARMNN_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex); |
| |
| TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex]; |
| |
| // assuming there is only one producer for that tensor |
| if (tensorSlots.outputSlot != nullptr) |
| { |
| throw ParseException(boost::str( |
| boost::format("Another layer has already registered itself as the producer of " |
| "subgraph:%1% tensor:%2% %3%") % |
| subgraphIndex % |
| tensorIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| tensorSlots.outputSlot = slot; |
| } |
| |
| void TfLiteParser::RegisterConsumerOfTensor(size_t subgraphIndex, |
| size_t tensorIndex, |
| armnn::IInputSlot* slot) |
| { |
| CHECK_TENSOR(m_Model, subgraphIndex, tensorIndex); |
| ARMNN_ASSERT(m_SubgraphConnections.size() > subgraphIndex); |
| ARMNN_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex); |
| |
| TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex]; |
| tensorSlots.inputSlots.push_back(slot); |
| } |
| |
| void TfLiteParser::ParseCustomOperator(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| // NOTE: By default we presume the custom operator is not supported |
| auto customParserFunction = &TfLiteParser::ParseUnsupportedOperator; |
| |
| // Identify custom code defined for custom operator |
| const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto& customCode = m_Model->operator_codes[operatorPtr->opcode_index]->custom_code; |
| |
| // Find parser function that correspondes to custom code (if any) |
| auto iterator = m_CustomParserFunctions.find(customCode); |
| if (iterator != m_CustomParserFunctions.end()) |
| { |
| customParserFunction = iterator->second; |
| } |
| |
| // Run parser function |
| (this->*customParserFunction)(subgraphIndex, operatorIndex); |
| } |
| |
| void TfLiteParser::ParseUnsupportedOperator(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| |
| auto opcodeIndex = operatorPtr->opcode_index; |
| auto opcode = m_Model->operator_codes[opcodeIndex]->builtin_code; |
| |
| if (!m_Options || !m_Options.value().m_StandInLayerForUnsupported) |
| { |
| // Do not add StandInLayer, throw ParseException instead |
| throw ParseException( |
| boost::str( |
| boost::format("Operator not supported. " |
| "subgraph:%1% operator:%2% " |
| "opcode_index:%3% opcode:%4% / %5% %6%") % |
| subgraphIndex % |
| operatorIndex % |
| opcodeIndex % |
| opcode % |
| tflite::EnumNameBuiltinOperator(opcode) % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| |
| const unsigned int numInputs = boost::numeric_cast<unsigned int>(inputs.size()); |
| const unsigned int numOutputs = boost::numeric_cast<unsigned int>(outputs.size()); |
| |
| StandInDescriptor descriptor(numInputs, numOutputs); |
| auto layerName = boost::str(boost::format("StandIn:%1%:%2%:%3%") % subgraphIndex % operatorIndex % opcode); |
| |
| // Add a non-executable StandInLayer as a placeholder for any unsupported operator |
| IConnectableLayer* layer = m_Network->AddStandInLayer(descriptor, layerName.c_str()); |
| for (unsigned int i = 0u; i < numOutputs; ++i) |
| { |
| layer->GetOutputSlot(i).SetTensorInfo(ToTensorInfo(outputs[i])); |
| } |
| |
| auto inputTensorIds = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| auto outputTensorIds = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, inputTensorIds); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIds); |
| } |
| |
| void TfLiteParser::ParseConv2D(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsConv2DOptions(); |
| |
| CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| |
| Convolution2dDescriptor desc; |
| desc.m_BiasEnabled = false; |
| desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| desc.m_DilationX = CHECKED_NON_NEGATIVE(options->dilation_w_factor); |
| desc.m_DilationY = CHECKED_NON_NEGATIVE(options->dilation_h_factor); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2, 3); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| |
| // assuming input is NHWC |
| unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| |
| // assuming the filter is OHWI : Output, H, W, Input |
| // which is essentially the same as NHWC |
| unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| |
| CalcPadding(inputHeight, filterHeight, desc.m_StrideY, |
| desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, options->padding); |
| CalcPadding(inputWidth, filterWidth, desc.m_StrideX, |
| desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding); |
| |
| auto filterTensorAndData = CreateConstTensor(inputs[1], |
| filterTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| armnn::IConnectableLayer* layer = nullptr; |
| |
| auto layerName = boost::str(boost::format("Conv2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| if (inputs.size() == 3) |
| { |
| desc.m_BiasEnabled = true; |
| armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| auto biasTensorAndData = CreateConstTensor(inputs[2], |
| biasTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| layer = m_Network->AddConvolution2dLayer(desc, |
| filterTensorAndData.first, |
| Optional<ConstTensor>(biasTensorAndData.first), |
| layerName.c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddConvolution2dLayer(desc, |
| filterTensorAndData.first, |
| EmptyOptional(), |
| layerName.c_str()); |
| } |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsDepthwiseConv2DOptions(); |
| |
| CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| |
| DepthwiseConvolution2dDescriptor desc; |
| desc.m_BiasEnabled = false; |
| desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| CHECKED_NON_NEGATIVE(options->depth_multiplier); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2, 3); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| desc.m_DilationX = CHECKED_NON_NEGATIVE(options->dilation_w_factor); |
| desc.m_DilationY = CHECKED_NON_NEGATIVE(options->dilation_h_factor); |
| |
| // Mappings from TensorflowLite filter tensors to the ArmNN filter tensors (ArmNN weights have to be [M, I, H, W]) |
| PermutationVector permutationVector{ 2, 3, 1, 0 }; // [H, W, I, M] -> [M, I, H, W] |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1], permutationVector); |
| |
| // Assuming input is NHWC |
| unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| |
| // TensorflowLite weights come in the format [1, H, W, I * M] |
| unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| |
| // Reshape weights as [ H, W, I, M ] |
| filterTensorInfo.SetShape({ filterHeight, |
| filterWidth, |
| inputTensorInfo.GetShape()[3], |
| filterTensorInfo.GetShape()[3] / inputTensorInfo.GetShape()[3] }); |
| |
| CalcPadding(inputHeight, filterHeight, desc.m_StrideY, |
| desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, options->padding); |
| CalcPadding(inputWidth, filterWidth, desc.m_StrideX, |
| desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding); |
| |
| auto filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, permutationVector); |
| armnn::IConnectableLayer* layer = nullptr; |
| auto layerName = boost::str(boost::format("DepthwiseConv2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| if (inputs.size() == 3) |
| { |
| desc.m_BiasEnabled = true; |
| TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| auto biasTensorAndData = CreateConstTensor(inputs[2], |
| biasTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| filterTensorAndData.first, |
| Optional<ConstTensor>(biasTensorAndData.first), |
| layerName.c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| filterTensorAndData.first, |
| EmptyOptional(), |
| layerName.c_str()); |
| } |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseDequantize(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = boost::str(boost::format("Dequantize:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| IConnectableLayer* layer = m_Network->AddDequantizeLayer(layerName.c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| void TfLiteParser::ParseExp(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = boost::str(boost::format("Exp:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| ElementwiseUnaryDescriptor desc; |
| desc.m_Operation = UnaryOperation::Exp; |
| IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(desc, layerName.c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| void TfLiteParser::ParseTranspose(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1, 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::IConnectableLayer* layer = nullptr; |
| auto layerName = boost::str(boost::format("Transpose:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| TransposeDescriptor desc; |
| |
| if (inputs.size() == 2) |
| { |
| armnn::TensorInfo permuteTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr permuteBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| auto numPermVecElements = permuteTensorInfo.GetNumElements(); |
| std::vector<unsigned int> permuteShape(numPermVecElements); |
| ::memcpy(permuteShape.data(), permuteBufferPtr->data.data(), permuteTensorInfo.GetNumBytes()); |
| PermutationVector permutationVector(permuteShape.data(), permuteTensorInfo.GetNumElements()); |
| |
| desc = TransposeDescriptor(permutationVector); |
| } |
| |
| layer = m_Network->AddTransposeLayer(desc, layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseTransposeConv(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsTransposeConvOptions(); |
| |
| TransposeConvolution2dDescriptor desc; |
| desc.m_BiasEnabled = false; |
| desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 3); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| if (inputs[0]) |
| { |
| armnn::TensorInfo tensorInfo = ToTensorInfo(inputs[0]); |
| std::vector<int> output_shape(tensorInfo.GetNumElements()); |
| if (tensorInfo.GetDataType() == DataType::Signed32) |
| { |
| ::memcpy(output_shape.data(), GetBuffer(m_Model, inputs[0]->buffer)->data.data(), tensorInfo.GetNumBytes()); |
| } |
| if (tensorInfo.GetDataType() == DataType::QAsymmU8) |
| { |
| for(unsigned int i=0; i < tensorInfo.GetNumElements(); i++) |
| { |
| output_shape[i] = GetBuffer(m_Model, inputs[0]->buffer)->data.data()[i]; |
| } |
| } |
| // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor. |
| for (int dimension : output_shape) |
| { |
| desc.m_OutputShape.push_back(static_cast<unsigned int>(dimension)); |
| } |
| desc.m_OutputShapeEnabled = true; |
| } |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[2]); |
| armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| |
| // TfLite uses NHWC tensors |
| const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| |
| const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| |
| CalcPadding(inputHeight, |
| filterHeight, |
| desc.m_StrideY, |
| 1, // DilationY |
| desc.m_PadTop, |
| desc.m_PadBottom, |
| options->padding); |
| |
| CalcPadding(inputWidth, |
| filterWidth, |
| desc.m_StrideX, |
| 1, // DilationX |
| desc.m_PadLeft, |
| desc.m_PadRight, |
| options->padding); |
| |
| auto filterTensorAndData = CreateConstTensor(inputs[1], |
| filterTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| |
| armnn::IConnectableLayer* layer = nullptr; |
| auto layerName = boost::str(boost::format("TransposeConv:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| layer = m_Network->AddTransposeConvolution2dLayer(desc, |
| filterTensorAndData.first, |
| EmptyOptional(), |
| layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // only the tensors for the inputs are relevant, exclude the const (filter) tensor |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[2]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseAveragePool2D(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Average); |
| } |
| |
| void TfLiteParser::ParseBatchToSpaceND(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 3); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo blockShapeTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr blockShapeBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| armnn::TensorInfo cropsTensorInfo = ToTensorInfo(inputs[2]); |
| BufferRawPtr cropsBufferPtr = GetBuffer(m_Model, inputs[2]->buffer); |
| |
| std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements()); |
| ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes()); |
| |
| std::vector<unsigned int> cropsVector(cropsTensorInfo.GetNumElements()); |
| ::memcpy(cropsVector.data(), cropsBufferPtr->data.data(), cropsTensorInfo.GetNumBytes()); |
| |
| size_t step = 2; |
| std::vector<std::pair<unsigned int, unsigned int>> crops; |
| for (unsigned int i = 0; i < cropsTensorInfo.GetNumElements() / step; ++i) |
| { |
| crops.emplace_back(cropsVector[i * step], cropsVector[i * step + 1]); |
| } |
| |
| armnn::BatchToSpaceNdDescriptor desc; |
| desc.m_BlockShape = blockShape; |
| desc.m_Crops = crops; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| |
| auto layerName = boost::str(boost::format("BatchToSpaceND:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddBatchToSpaceNdLayer(desc, layerName.c_str()); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseL2Normalization(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| L2NormalizationDescriptor desc; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| auto layerName = boost::str(boost::format("L2Normalization:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddL2NormalizationLayer(desc, layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseMaxPool2D(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Max); |
| } |
| |
| void TfLiteParser::ParseMaximum(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Maximum:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseMinimum(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Minimum:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParsePool(size_t subgraphIndex, |
| size_t operatorIndex, |
| PoolingAlgorithm algorithm) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsPool2DOptions(); |
| |
| CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| |
| std::string layerName; |
| |
| switch (algorithm) |
| { |
| case PoolingAlgorithm::Average: |
| layerName = |
| boost::str(boost::format("AveragePool2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| break; |
| case PoolingAlgorithm::Max: |
| layerName = |
| boost::str(boost::format("MaxPool2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| break; |
| default: |
| ARMNN_ASSERT_MSG(false, "Unsupported Pooling Algorithm"); |
| } |
| |
| Pooling2dDescriptor desc; |
| |
| desc.m_PoolType = algorithm; |
| desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| desc.m_PoolWidth = CHECKED_NON_NEGATIVE(options->filter_width); |
| desc.m_PoolHeight = CHECKED_NON_NEGATIVE(options->filter_height); |
| desc.m_PaddingMethod = PaddingMethod::Exclude; |
| desc.m_OutputShapeRounding = OutputShapeRounding::Floor; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| |
| // assuming input is NHWC |
| unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| |
| CalcPadding(inputHeight, desc.m_PoolHeight, desc.m_StrideY, 1u, |
| desc.m_PadTop, desc.m_PadBottom, options->padding); |
| CalcPadding(inputWidth, desc.m_PoolWidth, desc.m_StrideX, 1u, |
| desc.m_PadLeft, desc.m_PadRight, options->padding); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseSlice(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 3); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| SliceDescriptor desc; |
| |
| // set begin tensor info for slice descriptor |
| armnn::TensorInfo beginTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr beginBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| std::vector<unsigned int> begin(beginTensorInfo.GetNumElements()); |
| ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.GetNumBytes()); |
| |
| // set size tensor info for slice descriptor |
| armnn::TensorInfo sizeTensorInfo = ToTensorInfo(inputs[2]); |
| BufferRawPtr sizeBufferPtr = GetBuffer(m_Model, inputs[2]->buffer); |
| |
| std::vector<unsigned int> size(sizeTensorInfo.GetNumElements()); |
| ::memcpy(size.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes()); |
| desc = SliceDescriptor(begin, size); |
| |
| auto layerName = boost::str(boost::format("Slice:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* const layer = m_Network->AddSliceLayer(desc, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseSoftmax(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsSoftmaxOptions(); |
| |
| SoftmaxDescriptor desc; |
| desc.m_Beta = options->beta; |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = boost::str(boost::format("Softmax:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* const layer = m_Network->AddSoftmaxLayer(desc, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseSpaceToBatchND(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 3); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo blockShapeTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr blockShapeBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| armnn::TensorInfo padListTensorInfo = ToTensorInfo(inputs[2]); |
| BufferRawPtr padListBufferPtr = GetBuffer(m_Model, inputs[2]->buffer); |
| |
| std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements()); |
| ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes()); |
| |
| std::vector<unsigned int> padListVector(padListTensorInfo.GetNumElements()); |
| ::memcpy(padListVector.data(), padListBufferPtr->data.data(), padListTensorInfo.GetNumBytes()); |
| |
| size_t step = 2; |
| std::vector<std::pair<unsigned int, unsigned int>> padList; |
| for (unsigned int i = 0; i < padListTensorInfo.GetNumElements() / step; ++i) |
| { |
| padList.emplace_back(padListVector[i * step], padListVector[i * step + 1]); |
| } |
| |
| armnn::SpaceToBatchNdDescriptor desc; |
| desc.m_BlockShape = blockShape; |
| desc.m_PadList = padList; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| |
| auto layerName = boost::str(boost::format("SpaceToBatchND:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddSpaceToBatchNdLayer(desc, layerName.c_str()); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| armnn::TensorInfo TfLiteParser::OutputShapeOfSqueeze(const std::vector<uint32_t> & squeezeDimsIn, |
| const armnn::TensorInfo & inputTensorInfo) |
| { |
| CHECK_VALID_SIZE(squeezeDimsIn.size(), 0, 1, 2, 3, 4); |
| std::vector<uint32_t> squeezeDims = squeezeDimsIn; |
| static const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| |
| if (inputTensorInfo.GetNumDimensions() > 4) |
| { |
| std::stringstream ss; |
| ss << "Input tensor has unexpected number of dimensions:" << inputTensorInfo.GetNumDimensions() |
| << " shape:" << inputTensorInfo.GetShape() << " " |
| << CHECK_LOCATION().AsString(); |
| throw ParseException(ss.str()); |
| } |
| |
| if (squeezeDims.empty()) |
| { |
| squeezeDims.assign(dimensionSequence, |
| dimensionSequence+inputTensorInfo.GetNumDimensions()); |
| } |
| |
| std::vector<uint32_t> outputDims; |
| for(unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); i++) |
| { |
| bool skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end()); |
| auto currentDimension = inputTensorInfo.GetShape()[i]; |
| if (skipSqueeze || currentDimension != 1) |
| { |
| outputDims.push_back(currentDimension); |
| } |
| } |
| |
| if (outputDims.size() > 4) |
| { |
| std::stringstream ss; |
| ss << "Output tensor has unexpected number of dimensions:" << inputTensorInfo.GetNumDimensions() |
| << " shape:" << inputTensorInfo.GetShape() << " " |
| << CHECK_LOCATION().AsString(); |
| throw ParseException(ss.str()); |
| } |
| |
| TensorShape outShape = TensorShape(static_cast<unsigned int>(outputDims.size()), |
| outputDims.data()); |
| |
| // we need to preserve the tensor type and the quantization data as well |
| TensorInfo outTensorInfo = inputTensorInfo; |
| outTensorInfo.SetShape(outShape); |
| |
| return outTensorInfo; |
| } |
| |
| void TfLiteParser::ParseSqueeze(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsSqueezeOptions(); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo outputTensorInfo = |
| TfLiteParser::OutputShapeOfSqueeze(AsUnsignedVector(options->squeeze_dims), |
| inputTensorInfo); |
| |
| ReshapeDescriptor reshapeDesc; |
| reshapeDesc.m_TargetShape = outputTensorInfo.GetShape(); |
| |
| auto layerName = boost::str(boost::format("Squeeze:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseStridedSlice(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 4); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsStridedSliceOptions(); |
| |
| StridedSliceDescriptor desc; |
| desc.m_BeginMask = options->begin_mask; |
| desc.m_EllipsisMask = options->ellipsis_mask; |
| desc.m_EndMask = options->end_mask; |
| desc.m_NewAxisMask = options->new_axis_mask; |
| desc.m_ShrinkAxisMask = options->shrink_axis_mask; |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| armnn::TensorInfo beginTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr beginBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| std::vector<int> begin(beginTensorInfo.GetNumElements()); |
| ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.GetNumBytes()); |
| |
| armnn::TensorInfo endTensorInfo = ToTensorInfo(inputs[2]); |
| BufferRawPtr endBufferPtr = GetBuffer(m_Model, inputs[2]->buffer); |
| |
| std::vector<int> end(endTensorInfo.GetNumElements()); |
| ::memcpy(end.data(), endBufferPtr->data.data(), endTensorInfo.GetNumBytes()); |
| |
| armnn::TensorInfo strideTensorInfo = ToTensorInfo(inputs[3]); |
| BufferRawPtr strideBufferPtr = GetBuffer(m_Model, inputs[3]->buffer); |
| |
| std::vector<int> stride(strideTensorInfo.GetNumElements()); |
| ::memcpy(stride.data(), strideBufferPtr->data.data(), strideTensorInfo.GetNumBytes()); |
| |
| desc.m_Begin = begin; |
| desc.m_End = end; |
| desc.m_Stride = stride; |
| |
| auto layerName = boost::str(boost::format("StridedSlice:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddStridedSliceLayer(desc, layerName.c_str()); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseSub(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsSubOptions(); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Sub:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddSubtractionLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseDiv(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsDivOptions(); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Div:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseAdd(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsAddOptions(); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Add:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseMul(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsMulOptions(); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]); |
| |
| auto layerName = boost::str(boost::format("Mul:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| if (inputTensorInfo.GetNumDimensions() != input1TensorInfo.GetNumDimensions()) |
| { |
| AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer); |
| } |
| else |
| { |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| } |
| |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseMean(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo dimTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| armnn::MeanDescriptor desc; |
| std::vector<unsigned int> axis(dimTensorInfo.GetNumElements()); |
| ::memcpy(axis.data(), bufferPtr->data.data(), dimTensorInfo.GetNumBytes()); |
| desc.m_Axis = axis; |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| |
| desc.m_KeepDims = |
| inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? |
| true : false; |
| |
| auto layerName = boost::str(boost::format("Mean:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddMeanLayer(desc, layerName.c_str()); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseNeg(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = boost::str(boost::format("Neg:%1%:%2%") % subgraphIndex % operatorIndex); |
| armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Neg); |
| IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| void TfLiteParser::ParsePad(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| TfLiteParser::TensorRawPtrVector inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| |
| TfLiteParser::TensorRawPtrVector outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo padTensorInfo = ToTensorInfo(inputs[1]); |
| BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| |
| std::vector<unsigned int> padBuffer(padTensorInfo.GetNumElements()); |
| ::memcpy(padBuffer.data(), bufferPtr->data.data(), padTensorInfo.GetNumBytes()); |
| |
| size_t step = 2; |
| armnn::PadDescriptor desc; |
| for (unsigned int i = 0; i < padTensorInfo.GetNumElements() / step; ++i) |
| { |
| desc.m_PadList.emplace_back(padBuffer[i * step], padBuffer[i * step + 1]); |
| } |
| |
| auto layerName = boost::str(boost::format("Pad:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddPadLayer(desc, layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseQuantize(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = boost::str(boost::format("Quantize:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| IConnectableLayer* layer = m_Network->AddQuantizeLayer(layerName.c_str()); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| void TfLiteParser::ParseRelu(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::ReLu); |
| } |
| |
| void TfLiteParser::ParseRelu6(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::BoundedReLu); |
| } |
| |
| void TfLiteParser::ParseLeakyRelu(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::LeakyReLu); |
| } |
| |
| void TfLiteParser::ParseLogistic(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::Sigmoid); |
| } |
| |
| void TfLiteParser::ParseTanH(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::TanH); |
| } |
| |
| |
| void TfLiteParser::ParseActivation(size_t subgraphIndex, size_t operatorIndex, ActivationFunction activationType) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| IgnoreUnused(operatorPtr); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| auto layerName = str(boost::format("Activation:")); |
| ActivationDescriptor activationDesc; |
| activationDesc.m_Function = activationType; |
| |
| switch (activationType) |
| { |
| case ActivationFunction::ReLu: |
| { |
| layerName += str(boost::format("RELU:%1%:%2%") % subgraphIndex % operatorIndex); |
| break; |
| } |
| case ActivationFunction::BoundedReLu: |
| { |
| layerName += str(boost::format("RELU6:%1%:%2%") % subgraphIndex % operatorIndex); |
| activationDesc.m_A = 6.0f; |
| activationDesc.m_B = 0.0f; |
| break; |
| } |
| case ActivationFunction::Sigmoid: |
| { |
| layerName += str(boost::format("SIGMOID:%1%:%2%") % subgraphIndex % operatorIndex); |
| break; |
| } |
| case ActivationFunction::TanH: |
| { |
| layerName += str(boost::format("TANH:%1%:%2%") % subgraphIndex % operatorIndex); |
| activationDesc.m_A = 1.0f; |
| activationDesc.m_B = 1.0f; |
| break; |
| } |
| case ActivationFunction::LeakyReLu: |
| { |
| layerName += str(boost::format("LEAKYRELU:%1%:%2%") % subgraphIndex % operatorIndex); |
| const auto * options = operatorPtr->builtin_options.AsLeakyReluOptions(); |
| activationDesc.m_A = options->alpha; |
| break; |
| } |
| default: |
| { |
| throw ParseException( |
| boost::str(boost::format("Unexpected ActivationFunction[%1%] when creating layerName " |
| " %2% ") %static_cast<int>(activationType)% CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| IConnectableLayer* const layer = m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| armnn::TensorInfo TfLiteParser::OutputShapeOfReshape(const armnn::TensorInfo & inputTensorInfo, |
| const std::vector<int32_t> & targetDimsIn) |
| { |
| std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end()); |
| const auto stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1); |
| |
| if (stretchDim != targetDimsIn.end()) |
| { |
| if (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("At most one component of shape can be -1 %1%") % CHECK_LOCATION().AsString())); |
| } |
| |
| auto targetNumElements = |
| boost::numeric_cast<unsigned int>( |
| std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>())); |
| |
| auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim)); |
| outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements; |
| } |
| |
| TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data()); |
| |
| TensorInfo reshapeInfo = inputTensorInfo; |
| reshapeInfo.SetShape(outputShape); |
| |
| return reshapeInfo; |
| } |
| |
| void TfLiteParser::ParseReshape(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsReshapeOptions(); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| armnn::TensorInfo actualOutputTensorInfo = ToTensorInfo(outputs[0]); |
| |
| std::vector<int32_t> targetShape; |
| if (inputs.size() > 1 && inputs[1] != nullptr) |
| { |
| if (inputs[1]->is_variable) |
| { |
| ARMNN_THROW_PARSE_EXCEPTION( "Target shapes defined in non-const input tensors is not supported"); |
| } |
| |
| if (inputs[1]->shape.size() != 1) |
| { |
| ARMNN_THROW_PARSE_EXCEPTION("Target 'shape' input is not a 1D tensor"); |
| } |
| |
| if (inputs[1]->type != tflite::TensorType_INT32) |
| { |
| ARMNN_THROW_PARSE_EXCEPTION("Target 'shape' input is not an int32 type"); |
| } |
| |
| auto bufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| auto vals = reinterpret_cast<const int32_t*>(bufferPtr->data.data()); |
| for (int i=0; i < inputs[1]->shape[0]; i++) |
| { |
| targetShape.push_back(vals[i]); |
| } |
| |
| if (options != nullptr && |
| options->new_shape.empty() == false && |
| options->new_shape != targetShape) |
| { |
| ARMNN_THROW_PARSE_EXCEPTION("Target shape defined in reshape parameters and as input tensor but " |
| "the values do not match"); |
| } |
| } |
| else |
| { |
| if (options == nullptr) |
| { |
| ARMNN_THROW_PARSE_EXCEPTION("Target shape not defined in reshape parameters or input tensor. " |
| "At least one method required"); |
| } |
| |
| targetShape = options->new_shape; |
| } |
| |
| armnn::TensorInfo reshapeOutputTensorInfo = |
| TfLiteParser::OutputShapeOfReshape(inputTensorInfo, targetShape); |
| |
| // Check for valid input size and that reshape parameters equal output shape |
| const armnn::TensorShape& reshapeOutputTensorShape = reshapeOutputTensorInfo.GetShape(); |
| if (inputs.size() > 1 && !CheckShape(reshapeOutputTensorShape, outputs[0]->shape)) |
| { |
| std::stringstream ss; |
| ss << "New shape defined in reshape parameters " |
| << reshapeOutputTensorShape |
| << " does not equal output shape " |
| << actualOutputTensorInfo.GetShape() |
| << ": " |
| << CHECK_LOCATION().AsString(); |
| throw ParseException(ss.str()); |
| } |
| |
| ReshapeDescriptor reshapeDesc; |
| reshapeDesc.m_TargetShape = reshapeOutputTensorInfo.GetShape(); |
| |
| auto layerName = boost::str(boost::format("Reshape:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| layer->GetOutputSlot(0).SetTensorInfo(reshapeOutputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseResizeBilinear(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseResize(subgraphIndex, operatorIndex, ResizeMethod::Bilinear); |
| } |
| |
| void TfLiteParser::ParseResizeNearestNeighbor(size_t subgraphIndex, size_t operatorIndex) |
| { |
| ParseResize(subgraphIndex, operatorIndex, ResizeMethod::NearestNeighbor); |
| } |
| |
| void TfLiteParser::ParseResize(size_t subgraphIndex, size_t operatorIndex, ResizeMethod resizeMethod) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo sizeTensorInfo = ToTensorInfo(inputs[1]); |
| |
| // Data for the parsed tensor args (size) must be stored locally. |
| std::vector<int32_t> sizeTensorData(sizeTensorInfo.GetNumElements()); |
| |
| BufferRawPtr sizeBufferPtr = GetBuffer(m_Model, inputs[1]->buffer); |
| ::memcpy(sizeTensorData.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes()); |
| |
| ResizeDescriptor desc; |
| desc.m_Method = resizeMethod; |
| desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]); |
| desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]); |
| desc.m_DataLayout = armnn::DataLayout::NHWC; |
| |
| auto layerName = str(boost::format("Resize:")); |
| |
| switch (resizeMethod) |
| { |
| case ResizeMethod::Bilinear: |
| { |
| layerName += str(boost::format("BILINEAR:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsResizeBilinearOptions(); |
| |
| desc.m_AlignCorners = options->align_corners; |
| break; |
| } |
| case ResizeMethod::NearestNeighbor: |
| { |
| layerName += str(boost::format("NEARESTNEIGHBOR:%1%:%2%") % subgraphIndex % operatorIndex); |
| break; |
| } |
| default: |
| { |
| throw ParseException( |
| boost::str(boost::format("Unexpected ResizeMethod[%1%] when creating layerName " |
| " %2% ") %static_cast<int>(resizeMethod)% CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| IConnectableLayer* layer = m_Network->AddResizeLayer(desc, layerName.c_str()); |
| |
| TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| void TfLiteParser::ParseConcatenation(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsConcatenationOptions(); |
| |
| CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| unsigned int numConcatView = static_cast<unsigned int>(inputs.size()); |
| uint32_t inputRank = ToTensorInfo(inputs[0]).GetNumDimensions(); |
| |
| const unsigned int concatDimInput = static_cast<unsigned int>( |
| (static_cast<int>(inputRank) + options->axis) % static_cast<int>(inputRank)); |
| |
| OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank); |
| concatDescriptor.SetConcatAxis(concatDimInput); |
| |
| unsigned int mergeDimOrigin = 0; |
| |
| for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| { |
| TensorInfo inputTensorInfo = ToTensorInfo(inputs[viewIndex]); |
| |
| // This set up concatDescriptor view origin |
| armnnUtils::ProcessConcatInputTensorInfo( |
| inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); |
| } |
| |
| auto layerName = boost::str(boost::format("Concatenation:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddConcatLayer(concatDescriptor, layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes}); |
| |
| // add fused activation layer |
| layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseFullyConnected(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorRfr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto options = operatorRfr->builtin_options.AsFullyConnectedOptions(); |
| |
| CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| |
| FullyConnectedDescriptor desc; |
| desc.m_BiasEnabled = false; |
| desc.m_TransposeWeightMatrix = true; |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| |
| // Fully Connected Layer accepts two dimensional weights input |
| int32_t weightsDimension = static_cast<int32_t>(filterTensorInfo.GetNumDimensions()); |
| if (weightsDimension != 2) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format( |
| "Dimension %1% for Fully Connected weights is not supported by Armnn. " |
| "Node %2%") |
| % weightsDimension |
| % CHECK_LOCATION().AsString())); |
| } |
| |
| auto filterTensorAndData = CreateConstTensor(inputs[1], |
| filterTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| armnn::IConnectableLayer* layer = nullptr; |
| auto layerName = boost::str(boost::format("FullyConnected:%1%:%2%") % subgraphIndex % operatorIndex); |
| |
| if (inputs.size() == 3) |
| { |
| desc.m_BiasEnabled = true; |
| TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| auto biasTensorAndData = CreateConstTensor(inputs[2], |
| biasTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| layer = m_Network->AddFullyConnectedLayer(desc, |
| filterTensorAndData.first, |
| Optional<ConstTensor>(biasTensorAndData.first), |
| layerName.c_str()); |
| } |
| else |
| { |
| layer = m_Network->AddFullyConnectedLayer(desc, |
| filterTensorAndData.first, |
| EmptyOptional(), |
| layerName.c_str()); |
| } |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| |
| if (inputTensorInfo.GetNumDimensions() > 2) |
| { |
| // Add reshape to flatten to 2D [batch_size, input_size], |
| // where "input_size" corresponds to the number of inputs to the layer, |
| // matching the second dimension of weights, |
| // and "batch_size" is calculated by dividing the number of elements by "input_size". |
| std::vector<unsigned int> reshapedDimensions(2); |
| reshapedDimensions[1] = filterTensorInfo.GetShape()[1]; |
| reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; |
| |
| if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format( |
| "Failed to deduce input tensor shape from filter size %1%") |
| % reshapedDimensions[1] |
| % CHECK_LOCATION().AsString())); |
| } |
| |
| armnn::TensorInfo reshapedTensorInfo = ToTensorInfo(inputs[0]); |
| reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); |
| |
| std::string reshapeLayerName = boost::str(boost::format("Reshape_for:%1%") % layer->GetName()); |
| armnn::ReshapeDescriptor desc; |
| desc.m_TargetShape = reshapedTensorInfo.GetShape(); |
| armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str()); |
| |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); |
| reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); |
| |
| RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {inputTensorIndexes[0]}); |
| } |
| else |
| { |
| // register the input connection slot for the layer |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| } |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| // we need to add the activation layer and fortunately we don't need to care about the data layout |
| armnn::IConnectableLayer* fusedActivationLayer = AddFusedActivationLayer(layer, 0, |
| options->fused_activation_function); |
| |
| // register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseDetectionPostProcess(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 4); |
| |
| // Obtain custom options from flexbuffers |
| auto custom_options = operatorPtr->custom_options; |
| const flexbuffers::Map& m = flexbuffers::GetRoot(custom_options.data(), custom_options.size()).AsMap(); |
| |
| // Obtain descriptor information from tf lite |
| DetectionPostProcessDescriptor desc; |
| desc.m_MaxDetections = m["max_detections"].AsUInt32(); |
| desc.m_MaxClassesPerDetection = m["max_classes_per_detection"].AsUInt32(); |
| desc.m_NmsScoreThreshold = m["nms_score_threshold"].AsFloat(); |
| desc.m_NmsIouThreshold = m["nms_iou_threshold"].AsFloat(); |
| desc.m_NumClasses = m["num_classes"].AsUInt32(); |
| desc.m_ScaleH = m["h_scale"].AsFloat(); |
| desc.m_ScaleW = m["w_scale"].AsFloat(); |
| desc.m_ScaleX = m["x_scale"].AsFloat(); |
| desc.m_ScaleY = m["y_scale"].AsFloat(); |
| |
| if (!(m["use_regular_nms"].IsNull())) |
| { |
| desc.m_UseRegularNms = m["use_regular_nms"].AsBool(); |
| } |
| if (!(m["detections_per_class"].IsNull())) |
| { |
| desc.m_DetectionsPerClass = m["detections_per_class"].AsUInt32(); |
| } |
| |
| if (desc.m_NmsIouThreshold <= 0.0f || desc.m_NmsIouThreshold > 1.0f) |
| { |
| throw InvalidArgumentException("DetectionPostProcessTFLiteParser: Intersection over union threshold " |
| "must be positive and less than or equal to 1."); |
| } |
| |
| armnn::TensorInfo anchorTensorInfo = ToTensorInfo(inputs[2]); |
| auto anchorTensorAndData = CreateConstTensor(inputs[2], anchorTensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| |
| auto layerName = boost::str(boost::format("DetectionPostProcess:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddDetectionPostProcessLayer(desc, anchorTensorAndData.first, |
| layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| // The model does not specify the output shapes. |
| // The output shapes are calculated from the max_detection and max_classes_per_detection. |
| unsigned int numDetectedBox = desc.m_MaxDetections * desc.m_MaxClassesPerDetection; |
| m_OverridenOutputShapes.push_back({ 1, numDetectedBox, 4 }); |
| m_OverridenOutputShapes.push_back({ 1, numDetectedBox }); |
| m_OverridenOutputShapes.push_back({ 1, numDetectedBox }); |
| m_OverridenOutputShapes.push_back({ 1 }); |
| |
| for (unsigned int i = 0 ; i < outputs.size() ; ++i) |
| { |
| armnn::TensorInfo detectionBoxOutputTensorInfo = ToTensorInfo(outputs[i], m_OverridenOutputShapes[i]); |
| layer->GetOutputSlot(i).SetTensorInfo(detectionBoxOutputTensorInfo); |
| } |
| |
| // Register the input connection slots for the layer, connections are made after all layers have been created |
| // only the tensors for the inputs are relevant, exclude the const tensors |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]}); |
| |
| // Register the output connection slots for the layer, connections are made after all layers have been created |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0], |
| outputTensorIndexes[1], |
| outputTensorIndexes[2], |
| outputTensorIndexes[3]}); |
| } |
| |
| /// The TfLite Pack operator is equivalent to the ArmNN Stack operator |
| void TfLiteParser::ParsePack(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), 1); |
| |
| if (inputs.size() < 1) |
| { |
| throw ParseException("Pack must have at least one input."); |
| } |
| |
| const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto* options = operatorPtr->builtin_options.AsPackOptions(); |
| |
| StackDescriptor desc; |
| desc.m_Axis = static_cast<uint32_t>(options->axis); |
| desc.m_NumInputs = static_cast<uint32_t>(inputs.size()); |
| |
| // Use the tensor shape of the first input as the "correct" input shape in the descriptor |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| desc.m_InputShape = inputTensorInfo.GetShape(); |
| |
| auto layerName = boost::str(boost::format("Pack:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddStackLayer(desc, layerName.c_str()); |
| |
| ARMNN_ASSERT(layer != nullptr); |
| |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes}); |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| } |
| |
| void TfLiteParser::ParseUnpack(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsUnpackOptions(); |
| |
| // This unpackAxis indicates the axis to unpack |
| const unsigned int unpackAxis = CHECKED_NON_NEGATIVE(options->axis); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 1); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| |
| if (unpackAxis >= inputTensorInfo.GetNumDimensions()) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format( |
| "The unpack axis: %1% cannot be greater than or equal to " |
| "the number of input dimension %2% %3%") |
| % unpackAxis |
| % inputTensorInfo.GetNumDimensions() |
| % CHECK_LOCATION().AsString())); |
| } |
| |
| unsigned int unpackNum = CHECKED_NON_NEGATIVE(options->num); |
| // If num is not defined, automatically infer from the length of the dimension axis. |
| if(unpackNum == 0) |
| { |
| unpackNum = inputTensorInfo.GetShape()[unpackAxis]; |
| } |
| |
| // If unpack number cannot be inferred and is still zero, throw ParseException. |
| if(unpackNum == 0) |
| { |
| throw ParseException("Number to unpack must greater than zero."); |
| } |
| |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), unpackNum); |
| |
| auto inputDimSize = inputTensorInfo.GetNumDimensions(); |
| std::vector<unsigned int> unpackDimSizes(inputDimSize); |
| |
| // Add current input shape to unpackDimSizes |
| for (unsigned int i = 0; i < inputDimSize; ++i) |
| { |
| unpackDimSizes[i] = inputTensorInfo.GetShape()[i]; |
| } |
| |
| if (unpackDimSizes[unpackAxis] != unpackNum) |
| { |
| throw ParseException("Number to unpack must be the same as length of the dimension to " |
| "unpack along."); |
| } |
| |
| unpackDimSizes[unpackAxis] /= unpackNum; |
| |
| SplitterDescriptor splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size())); |
| for (unsigned int j = 0; j < unpackNum; ++j) |
| { |
| // Set the size of the views. |
| for (unsigned int dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx) |
| { |
| splitDesc.SetViewSize(j, dimIdx, unpackDimSizes[dimIdx]); |
| } |
| splitDesc.SetViewOriginCoord(j, unpackAxis, unpackDimSizes[unpackAxis] * j); |
| } |
| |
| auto layerName = boost::str(boost::format("Unpack:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str()); |
| |
| TensorShape splitOutShape = TensorShape(static_cast<unsigned int>(unpackDimSizes.size()), |
| unpackDimSizes.data()); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| // Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter. |
| for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) |
| { |
| armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[k]); |
| std::string reshapeLayerName = boost::str(boost::format("Reshape_for:%1%") % layer->GetName()); |
| armnn::ReshapeDescriptor desc; |
| desc.m_TargetShape = outputTensorInfo.GetShape(); |
| armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str()); |
| |
| layer->GetOutputSlot(k).SetTensorInfo(armnn::TensorInfo(splitOutShape, |
| outputTensorInfo.GetDataType(), |
| outputTensorInfo.GetQuantizationScale(), |
| outputTensorInfo.GetQuantizationOffset())); |
| layer->GetOutputSlot(k).Connect(reshapeLayer->GetInputSlot(0)); |
| |
| reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| |
| uint32_t reshapedOutputId = CHECKED_NON_NEGATIVE(operatorPtr->outputs[k]); |
| armnn::IOutputSlot* slot = &(reshapeLayer->GetOutputSlot(0)); |
| RegisterProducerOfTensor(subgraphIndex, reshapedOutputId, slot); |
| } |
| } |
| |
| void TfLiteParser::ParseSplit(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsSplitOptions(); |
| |
| const unsigned int numSplits = CHECKED_NON_NEGATIVE(options->num_splits); |
| |
| // If number of splits cannot be inferred and is zero, throw ParseException. |
| if(numSplits == 0) |
| { |
| throw ParseException("Number to splits must greater than zero."); |
| } |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 2); |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), numSplits); |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[1]); |
| armnn::TensorInfo axisTensorInfo = ToTensorInfo(inputs[0]); |
| |
| BufferRawPtr axisBufferPtr = GetBuffer(m_Model, inputs[0]->buffer); |
| std::vector<unsigned int> axisData(axisTensorInfo.GetNumElements()); |
| ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.GetNumBytes()); |
| |
| ARMNN_ASSERT(axisTensorInfo.GetNumElements() == 1); |
| const unsigned int splitDim = axisData[0]; |
| |
| auto inputDimSize = inputTensorInfo.GetNumDimensions(); |
| if (inputDimSize > MaxNumOfTensorDimensions) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format( |
| "The number of dimensions: %1% for input tensors of the " |
| "split op cannot be greater than %2% %3%") |
| % inputTensorInfo.GetNumDimensions() |
| % MaxNumOfTensorDimensions |
| % CHECK_LOCATION().AsString())); |
| } |
| |
| std::vector<unsigned int> splitterDimSizes(inputDimSize); |
| |
| // Add current input shape to splitterDimSizes |
| for (unsigned int i = 0; i < inputDimSize; ++i) |
| { |
| splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; |
| } |
| |
| if (splitterDimSizes[splitDim] % numSplits != 0) |
| { |
| throw ParseException("Number of splits must evenly divide the dimension"); |
| } |
| splitterDimSizes[splitDim] /= numSplits; |
| |
| SplitterDescriptor splitDesc(numSplits, inputDimSize); |
| for (unsigned int j = 0; j < numSplits; ++j) |
| { |
| // Set the size of the views. |
| for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) |
| { |
| splitDesc.SetViewSize(j, dimIdx, splitterDimSizes[dimIdx]); |
| } |
| splitDesc.SetViewOriginCoord(j, splitDim, splitterDimSizes[splitDim] * j); |
| } |
| |
| auto layerName = boost::str(boost::format("Split:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str()); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[1]}); |
| |
| for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) |
| { |
| armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k]); |
| layer->GetOutputSlot(k).SetTensorInfo(tensorInfo); |
| } |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| unsigned int ComputeWrappedIndex(int idx, unsigned int numDimsIn) |
| { |
| int numDims = armnn::numeric_cast<int>(numDimsIn); |
| int v = idx < 0 ? numDims + idx : idx; |
| ARMNN_ASSERT(v >= 0); |
| ARMNN_ASSERT(v < numDims); |
| |
| return static_cast<unsigned int>(v); |
| } |
| |
| void TfLiteParser::ParseSplitV(size_t subgraphIndex, size_t operatorIndex) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| |
| const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| const auto * options = operatorPtr->builtin_options.AsSplitVOptions(); |
| |
| auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(inputs.size(), 3); |
| |
| auto& inputTensor = inputs[0]; |
| auto& splitsTensor = inputs[1]; |
| auto& axisTensor = inputs[2]; |
| |
| armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputTensor); |
| armnn::TensorInfo splitsInfo = ToTensorInfo(splitsTensor); |
| armnn::TensorInfo axisTensorInfo = ToTensorInfo(axisTensor); |
| ARMNN_ASSERT(axisTensorInfo.GetNumElements() == 1); |
| |
| // Inputs |
| auto inputDimSize = inputTensorInfo.GetNumDimensions(); |
| if (inputDimSize > MaxNumOfTensorDimensions) |
| { |
| throw ParseException( |
| boost::str( |
| boost::format( |
| "The number of dimensions: %1% for input tensors of the " |
| "SplitV op cannot be greater than %2% %3%") |
| % inputTensorInfo.GetNumDimensions() |
| % MaxNumOfTensorDimensions |
| % CHECK_LOCATION().AsString())); |
| } |
| |
| // Get split axis |
| BufferRawPtr axisBufferPtr = GetBuffer(m_Model, axisTensor->buffer); |
| std::vector<int> axisData(axisTensorInfo.GetNumElements()); |
| ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.GetNumBytes()); |
| const unsigned int splitDim = ComputeWrappedIndex(axisData[0], inputTensorInfo.GetNumDimensions()); |
| |
| // Set split sizes |
| CHECK_VALID_SIZE(splitsInfo.GetNumDimensions(), 1); |
| unsigned int numSplits{0}; |
| |
| if(options) |
| { |
| numSplits = CHECKED_NON_NEGATIVE(options->num_splits); |
| } |
| else |
| { |
| numSplits = splitsInfo.GetNumElements(); |
| } |
| |
| if (numSplits <=0) |
| { |
| throw ParseException("SplitV has invalid number of splits"); |
| } |
| |
| std::vector<int> splitsData(numSplits); |
| BufferRawPtr splitsBufferPtr = GetBuffer(m_Model, splitsTensor->buffer); |
| ::memcpy(splitsData.data(), splitsBufferPtr->data.data(), splitsInfo.GetNumBytes()); |
| |
| unsigned int idx = 0; |
| int numInferred{0}; |
| unsigned int inferIdx{0}; |
| int splitSum{0}; |
| for (auto split : splitsData) |
| { |
| if (split < 0) |
| { |
| numInferred++; |
| inferIdx = idx; |
| } |
| else |
| { |
| splitSum += split; |
| } |
| idx++; |
| } |
| // Check for inferred Axis |
| if (numInferred == 0) |
| { |
| if (splitSum != numeric_cast<int>(inputTensorInfo.GetShape()[splitDim])) |
| { |
| throw ParseException("SplitV split_sizes does not sum to the dimension of value along split_dim."); |
| } |
| } |
| else if (numInferred == 1) |
| { |
| splitsData[inferIdx] = numeric_cast<int>(inputTensorInfo.GetShape()[splitDim]) - splitSum; |
| } |
| else |
| { |
| throw ParseException("Cannot infer split size for more than one split"); |
| } |
| |
| //Ouput size validation |
| auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| CHECK_VALID_SIZE(outputs.size(), numSplits); |
| |
| // Setup Armnn descriptor |
| SplitterDescriptor splitDesc(numSplits, inputDimSize); |
| unsigned int accumSplit = 0; |
| for (unsigned int j = 0; j < numSplits; ++j) |
| { |
| unsigned int splitSize = numeric_cast<unsigned int>(splitsData[j]); |
| |
| // Set the size of the views. |
| for (unsigned int dimIdx = 0; dimIdx < inputTensorInfo.GetNumDimensions(); ++dimIdx) |
| { |
| unsigned int dimSize = inputTensorInfo.GetShape()[dimIdx]; |
| if (dimIdx == splitDim) |
| { |
| dimSize = splitSize; |
| } |
| splitDesc.SetViewSize(j, dimIdx, dimSize); |
| } |
| |
| splitDesc.SetViewOriginCoord(j, splitDim, accumSplit); |
| accumSplit += splitSize; |
| } |
| |
| auto layerName = boost::str(boost::format("SplitV:%1%:%2%") % subgraphIndex % operatorIndex); |
| IConnectableLayer* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str()); |
| |
| auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| |
| for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) |
| { |
| armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k]); |
| layer->GetOutputSlot(k).SetTensorInfo(tensorInfo); |
| } |
| |
| auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes); |
| } |
| |
| armnn::IConnectableLayer* TfLiteParser::AddFusedActivationLayer(armnn::IConnectableLayer* prevLayer, |
| unsigned int outputSlot, |
| tflite::ActivationFunctionType activationType) |
| { |
| ActivationDescriptor activationDesc; |
| std::string layerName = prevLayer->GetName(); |
| |
| switch(activationType) |
| { |
| case tflite::ActivationFunctionType_NONE: |
| { |
| // this is a no-op: return previous layer |
| return prevLayer; |
| } |
| case tflite::ActivationFunctionType_RELU: |
| { |
| activationDesc.m_Function = ActivationFunction::ReLu; |
| layerName += ":RELU"; |
| break; |
| } |
| case tflite::ActivationFunctionType_RELU6: |
| { |
| activationDesc.m_Function = ActivationFunction::BoundedReLu; |
| activationDesc.m_A = 6.0f; |
| activationDesc.m_B = 0.0f; |
| layerName += ":RELU6"; |
| break; |
| } |
| case tflite::ActivationFunctionType_TANH: |
| { |
| activationDesc.m_Function = ActivationFunction::TanH; |
| activationDesc.m_A = 1.0f; |
| activationDesc.m_B = 1.0f; |
| layerName += ":TANH"; |
| break; |
| } |
| |
| // I only put these here as a reminder what others we could support |
| case tflite::ActivationFunctionType_RELU_N1_TO_1: |
| case tflite::ActivationFunctionType_SIGN_BIT: |
| default: |
| { |
| throw ParseException( |
| boost::str( |
| boost::format("TfLite parser doesn't suppport fused activation: " |
| "%1%/%2% %3% ") % |
| activationType % |
| tflite::EnumNameActivationFunctionType(activationType) % |
| CHECK_LOCATION().AsString())); |
| |
| } |
| } |
| |
| IConnectableLayer* activationLayer = |
| m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
| |
| auto & prevOutputSlot = prevLayer->GetOutputSlot(outputSlot); |
| prevOutputSlot.Connect(activationLayer->GetInputSlot(0)); |
| activationLayer->GetOutputSlot(0).SetTensorInfo(prevOutputSlot.GetTensorInfo()); |
| return activationLayer; |
| } |
| |
| TfLiteParser::ModelPtr TfLiteParser::LoadModelFromFile(const char * fileName) |
| { |
| if (fileName == nullptr) |
| { |
| throw InvalidArgumentException(boost::str(boost::format("Invalid (null) file name %1%") % |
| CHECK_LOCATION().AsString())); |
| } |
| std::error_code errorCode; |
| fs::path pathToFile(fileName); |
| if (!fs::exists(pathToFile, errorCode)) |
| { |
| std::string locationString = CHECK_LOCATION().AsString(); |
| std::string msg = boost::str(boost::format("Cannot find the file (%1%) errorCode: %2% %3%") % |
| fileName % |
| errorCode % |
| locationString); |
| throw FileNotFoundException(msg); |
| } |
| std::ifstream file(fileName, std::ios::binary); |
| std::string fileContent((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>()); |
| return LoadModelFromBinary(reinterpret_cast<const uint8_t *>(fileContent.c_str()), |
| fileContent.size()); |
| } |
| |
| TfLiteParser::ModelPtr TfLiteParser::LoadModelFromBinary(const uint8_t * binaryContent, size_t len) |
| { |
| if (binaryContent == nullptr) |
| { |
| throw InvalidArgumentException(boost::str(boost::format("Invalid (null) binary content %1%") % |
| CHECK_LOCATION().AsString())); |
| } |
| flatbuffers::Verifier verifier(binaryContent, len); |
| if (verifier.VerifyBuffer<tflite::Model>() == false) |
| { |
| throw ParseException( |
| boost::str(boost::format("Buffer doesn't conform to the expected Tensorflow Lite " |
| "flatbuffers format. size:%1% %2%") % |
| len % |
| CHECK_LOCATION().AsString())); |
| } |
| return tflite::UnPackModel(binaryContent); |
| } |
| |
| TfLiteParser::TensorRawPtrVector TfLiteParser::GetInputs(const ModelPtr & model, |
| size_t subgraphIndex, |
| size_t operatorIndex) |
| { |
| CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| const auto & operatorPtr = subgraphPtr->operators[operatorIndex]; |
| |
| size_t inputCount = operatorPtr->inputs.size(); |
| TensorRawPtrVector result(inputCount); |
| for (size_t i=0; i<inputCount; ++i) |
| { |
| uint32_t inputId = CHECKED_NON_NEGATIVE(operatorPtr->inputs[i]); |
| result[i] = subgraphPtr->tensors[inputId].get(); |
| } |
| return result; |
| } |
| |
| TfLiteParser::TensorRawPtrVector TfLiteParser::GetOutputs(const ModelPtr & model, |
| size_t subgraphIndex, |
| size_t operatorIndex) |
| { |
| CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| const auto & operatorPtr = subgraphPtr->operators[operatorIndex]; |
| |
| size_t outputCount = operatorPtr->outputs.size(); |
| TensorRawPtrVector result(outputCount); |
| for (size_t i=0; i<outputCount; ++i) |
| { |
| uint32_t outputId = CHECKED_NON_NEGATIVE(operatorPtr->outputs[i]); |
| CHECK_TENSOR(model, subgraphIndex, outputId); |
| result[i] = subgraphPtr->tensors[outputId].get(); |
| } |
| return result; |
| } |
| |
| TfLiteParser::TensorIdRawPtrVector TfLiteParser::GetSubgraphInputs(const ModelPtr & model, |
| size_t subgraphIndex) |
| { |
| CHECK_SUBGRAPH(model, subgraphIndex); |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| |
| size_t inputCount = subgraphPtr->inputs.size(); |
| TensorIdRawPtrVector result(inputCount); |
| for (size_t i=0; i<inputCount; ++i) |
| { |
| uint32_t inputId = CHECKED_NON_NEGATIVE(subgraphPtr->inputs[i]); |
| CHECK_TENSOR(model, subgraphIndex, inputId); |
| result[i] = std::make_pair(inputId, subgraphPtr->tensors[inputId].get()); |
| } |
| return result; |
| } |
| |
| TfLiteParser::TensorIdRawPtrVector TfLiteParser::GetSubgraphOutputs(const ModelPtr & model, |
| size_t subgraphIndex) |
| { |
| CHECK_SUBGRAPH(model, subgraphIndex); |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| |
| size_t outputCount = subgraphPtr->outputs.size(); |
| TensorIdRawPtrVector result(outputCount); |
| for (size_t i=0; i<outputCount; ++i) |
| { |
| uint32_t outputId = CHECKED_NON_NEGATIVE(subgraphPtr->outputs[i]); |
| result[i] = std::make_pair(outputId, subgraphPtr->tensors[outputId].get()); |
| } |
| return result; |
| } |
| |
| std::vector<int32_t>& TfLiteParser::GetInputTensorIds(const ModelPtr& model, |
| size_t subgraphIndex, |
| size_t operatorIndex) |
| { |
| CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| const auto & operatorPtr = subgraphPtr->operators[operatorIndex]; |
| return operatorPtr->inputs; |
| } |
| |
| std::vector<int32_t>& TfLiteParser::GetOutputTensorIds(const ModelPtr& model, |
| size_t subgraphIndex, |
| size_t operatorIndex) |
| { |
| CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| const auto & subgraphPtr = model->subgraphs[subgraphIndex]; |
| const auto & operatorPtr = subgraphPtr->operators[operatorIndex]; |
| return operatorPtr->outputs; |
| } |
| |
| void TfLiteParser::RegisterInputSlots(size_t subgraphIndex, |
| size_t operatorIndex, |
| IConnectableLayer* layer, |
| const std::vector<unsigned int>& tensorIndexes) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| ARMNN_ASSERT(layer != nullptr); |
| if (tensorIndexes.size() != layer->GetNumInputSlots()) |
| { |
| throw ParseException( |
| boost::str(boost::format("The number of tensor inputs (%1%) does not match the number expected (%2%)" |
| " for subgraph:%3% operator index:%4% %5%") % |
| tensorIndexes.size() % |
| layer->GetNumInputSlots() % |
| subgraphIndex % |
| operatorIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex) |
| { |
| unsigned int tensorIndex = tensorIndexes[slotIndex]; |
| armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| RegisterConsumerOfTensor(subgraphIndex, tensorIndex, slot); |
| } |
| } |
| |
| void TfLiteParser::RegisterOutputSlots(size_t subgraphIndex, |
| size_t operatorIndex, |
| IConnectableLayer* layer, |
| const std::vector<unsigned int>& tensorIndexes) |
| { |
| CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| ARMNN_ASSERT(layer != nullptr); |
| if (tensorIndexes.size() != layer->GetNumOutputSlots()) |
| { |
| throw ParseException( |
| boost::str(boost::format("The number of tensor outputs (%1%) does not match the number expected (%2%)" |
| " for subgraph:%3% operator index:%4% %5%") % |
| tensorIndexes.size() % |
| layer->GetNumOutputSlots() % |
| subgraphIndex % |
| operatorIndex % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex) |
| { |
| unsigned int tensorIndex = tensorIndexes[slotIndex]; |
| armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex)); |
| RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot); |
| } |
| } |
| |
| void TfLiteParser::SetupInputLayers(size_t subgraphIndex) |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphIndex); |
| |
| auto inputs = GetSubgraphInputs(m_Model, subgraphIndex); |
| for (auto const & tensorIdAndPtr : inputs) |
| { |
| auto bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first); |
| IConnectableLayer* layer = |
| m_Network->AddInputLayer(bindingId, tensorIdAndPtr.second->name.c_str()); |
| |
| auto tensorInfo = ToTensorInfo(tensorIdAndPtr.second); |
| layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| RegisterOutputSlots(subgraphIndex, |
| VIRTUAL_OPERATOR_ID, |
| layer, |
| { static_cast<uint32_t>(tensorIdAndPtr.first) }); |
| } |
| } |
| |
| void TfLiteParser::SetupOutputLayers(size_t subgraphIndex) |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphIndex); |
| |
| auto outputs = GetSubgraphOutputs(m_Model, subgraphIndex); |
| for (auto const & tensorIdAndPtr : outputs) |
| { |
| auto bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first); |
| IConnectableLayer* layer = |
| m_Network->AddOutputLayer(bindingId, tensorIdAndPtr.second->name.c_str()); |
| |
| RegisterInputSlots(subgraphIndex, |
| VIRTUAL_OPERATOR_ID, |
| layer, |
| { static_cast<uint32_t>(tensorIdAndPtr.first) }); |
| } |
| } |
| |
| void TfLiteParser::SetupConstantLayers(size_t subgraphIndex) |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphIndex); |
| |
| const auto & subgraphPtr = m_Model->subgraphs[subgraphIndex]; |
| for (unsigned int subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex) |
| { |
| for (unsigned int tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex) |
| { |
| if (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot == nullptr && |
| m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size() > 0) |
| { |
| TensorRawPtr tensorPtr = subgraphPtr->tensors[tensorIndex].get(); |
| armnn::TensorInfo tensorInfo = ToTensorInfo(tensorPtr); |
| auto tensorAndData = CreateConstTensor(tensorPtr, |
| tensorInfo, |
| armnn::Optional<armnn::PermutationVector&>()); |
| |
| std::string layerName = boost::str(boost::format("Constant:%1%") % tensorPtr->name); |
| IConnectableLayer *layer = |
| m_Network->AddConstantLayer(tensorAndData.first, layerName.c_str()); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| RegisterOutputSlots(subgraphIndex, |
| VIRTUAL_OPERATOR_ID, |
| layer, |
| { tensorIndex }); |
| |
| } |
| } |
| } |
| } |
| |
| // example usage: BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[0]->buffer); |
| TfLiteParser::BufferRawPtr TfLiteParser::GetBuffer(const ModelPtr& model, size_t bufferIndex) |
| { |
| CHECK_BUFFER(model, bufferIndex); |
| return model->buffers[bufferIndex].get(); |
| } |
| |
| template<typename T> |
| std::pair<armnn::ConstTensor, TfLiteParser::SupportedDataStorage> |
| TfLiteParser::CreateConstTensorAndStoreData(TfLiteParser::BufferRawPtr bufferPtr, |
| TfLiteParser::TensorRawPtr tensorPtr, |
| armnn::TensorInfo& tensorInfo, |
| armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| auto constData = CreateConstTensorImpl<T>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| TfLiteParser::SupportedDataStorage storage(std::move(constData.second)); |
| return std::make_pair(constData.first, std::move(storage)); |
| } |
| |
| std::pair<armnn::ConstTensor, TfLiteParser::SupportedDataStorage> |
| TfLiteParser::CreateConstTensor(TensorRawPtr tensorPtr, |
| armnn::TensorInfo& tensorInfo, |
| armnn::Optional<armnn::PermutationVector&> permutationVector) |
| { |
| CHECK_TENSOR_PTR(tensorPtr); |
| auto bufferPtr = GetBuffer(m_Model, tensorPtr->buffer); |
| CHECK_BUFFER_SIZE(bufferPtr, tensorInfo, tensorPtr->buffer); |
| |
| switch (tensorInfo.GetDataType()) |
| { |
| case armnn::DataType::Float32: |
| return CreateConstTensorAndStoreData<float>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| case armnn::DataType::QAsymmU8: |
| return CreateConstTensorAndStoreData<uint8_t>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| case armnn::DataType::QSymmS8: |
| return CreateConstTensorAndStoreData<int8_t>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| case armnn::DataType::QAsymmS8: |
| return CreateConstTensorAndStoreData<int8_t>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| case armnn::DataType::Signed32: |
| return CreateConstTensorAndStoreData<int32_t>(bufferPtr, |
| tensorPtr, |
| tensorInfo, |
| permutationVector); |
| default: |
| { |
| std::stringstream errString; |
| errString << "Unexpected datatype when creating const tensor: " |
| << armnn::GetDataTypeName(tensorInfo.GetDataType()) |
| << " shape:" << tensorInfo.GetShape() |
| << CHECK_LOCATION().AsString(); |
| throw ParseException(errString.str()); |
| } |
| } |
| } |
| |
| BindingPointInfo TfLiteParser::GetNetworkInputBindingInfo(size_t subgraphId, |
| const std::string& name) const |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphId); |
| auto inputs = GetSubgraphInputs(m_Model, subgraphId); |
| for (auto const & input : inputs) |
| { |
| if (input.second->name == name) |
| { |
| auto bindingId = GenerateLayerBindingId(subgraphId, input.first); |
| return std::make_pair(bindingId, ToTensorInfo(input.second)); |
| } |
| } |
| |
| std::stringstream bindings; |
| for (auto const & input : inputs) |
| { |
| bindings << "'" << input.second->name << "' "; |
| } |
| |
| throw ParseException( |
| boost::str( |
| boost::format("No input binding found for subgraph:%1% and name:%2%. " |
| "Possible inputs are: [%3%] %4%") % |
| subgraphId % |
| name % |
| bindings.str() % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| BindingPointInfo TfLiteParser::GetNetworkOutputBindingInfo(size_t subgraphId, |
| const std::string& name) const |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphId); |
| auto outputs = GetSubgraphOutputs(m_Model, subgraphId); |
| for (unsigned int i = 0; i < outputs.size(); ++i) |
| { |
| auto const output = outputs[i]; |
| if (output.second->name == name) |
| { |
| auto bindingId = GenerateLayerBindingId(subgraphId, output.first); |
| std::vector<unsigned int> shape = m_OverridenOutputShapes.size() > 0 ? |
| m_OverridenOutputShapes[i] : AsUnsignedVector(output.second->shape); |
| return std::make_pair(bindingId, ToTensorInfo(output.second, shape)); |
| } |
| } |
| |
| std::stringstream bindings; |
| for (auto const & output : outputs) |
| { |
| bindings << "'" << output.second->name << "' "; |
| } |
| |
| throw ParseException( |
| boost::str( |
| boost::format("No output binding found for subgraph:%1% and name:%2%. " |
| "Possible outputs are: [%3%] %4%") % |
| subgraphId % |
| name % |
| bindings.str() % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| size_t TfLiteParser::GetSubgraphCount() const |
| { |
| return m_Model->subgraphs.size(); |
| } |
| |
| std::vector<std::string> TfLiteParser::GetSubgraphInputTensorNames(size_t subgraphId) const |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphId); |
| auto inputs = GetSubgraphInputs(m_Model, subgraphId); |
| std::vector<std::string> result; |
| result.reserve(inputs.size()); |
| for (auto const & input : inputs) |
| { |
| result.push_back(input.second->name); |
| } |
| return result; |
| } |
| |
| std::vector<std::string> TfLiteParser::GetSubgraphOutputTensorNames(size_t subgraphId) const |
| { |
| CHECK_SUBGRAPH(m_Model, subgraphId); |
| auto outputs = GetSubgraphOutputs(m_Model, subgraphId); |
| std::vector<std::string> result; |
| result.reserve(outputs.size()); |
| for (auto const & output : outputs) |
| { |
| result.push_back(output.second->name); |
| } |
| return result; |
| } |
| |
| ITfLiteParser* ITfLiteParser::CreateRaw(const Optional<ITfLiteParser::TfLiteParserOptions>& options) |
| { |
| return new TfLiteParser(options); |
| } |
| |
| ITfLiteParserPtr ITfLiteParser::Create(const Optional<ITfLiteParser::TfLiteParserOptions>& options) |
| { |
| return ITfLiteParserPtr(CreateRaw(options), &ITfLiteParser::Destroy); |
| } |
| |
| void ITfLiteParser::Destroy(ITfLiteParser* parser) |
| { |
| delete parser; |
| } |
| |
| TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<float[]> && data) |
| : m_FloatData(std::move(data)) |
| , m_Uint8Data(nullptr) |
| , m_Int8Data(nullptr) |
| , m_Int32Data(nullptr) |
| { |
| } |
| |
| TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<uint8_t[]> && data) |
| : m_FloatData(nullptr) |
| , m_Uint8Data(std::move(data)) |
| , m_Int8Data(nullptr) |
| , m_Int32Data(nullptr) |
| { |
| } |
| |
| TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int8_t[]> && data) |
| : m_FloatData(nullptr) |
| , m_Uint8Data(nullptr) |
| , m_Int8Data(std::move(data)) |
| , m_Int32Data(nullptr) |
| { |
| } |
| |
| TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int32_t[]> && data) |
| : m_FloatData(nullptr) |
| , m_Uint8Data(nullptr) |
| , m_Int8Data(nullptr) |
| , m_Int32Data(std::move(data)) |
| { |
| } |
| |
| } // armnnTfLiteParser |
