Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #define LOG_TAG "arm-armnn-sl" |
| 7 | |
| 8 | #include "ModelToINetworkTransformer.hpp" |
| 9 | #include "CanonicalUtils.hpp" |
| 10 | #include "Converter.hpp" |
| 11 | |
| 12 | #include <log/log.h> |
| 13 | #include <type_traits> |
| 14 | |
| 15 | namespace armnn_driver |
| 16 | { |
| 17 | |
| 18 | ModelToINetworkTransformer::ModelToINetworkTransformer( |
| 19 | const std::vector<armnn::BackendId>& backends, |
| 20 | const Model& model, |
| 21 | const std::set<unsigned int>& forcedUnsupportedOperations) |
| 22 | : m_Data(backends) |
| 23 | , m_Model(model) |
| 24 | , m_ForcedUnsupportedOperations(forcedUnsupportedOperations) |
| 25 | , m_ConversionResult(ConversionResult::Success) |
| 26 | { |
| 27 | try |
| 28 | { |
| 29 | Convert(); |
| 30 | } |
| 31 | catch (std::exception& e) |
| 32 | { |
| 33 | m_ConversionResult = ConversionResult::UnsupportedFeature; |
| 34 | VLOG(DRIVER) << "ModelToINetworkTransformer: Unexpected exception: " << e.what(); |
| 35 | assert(false); |
| 36 | } |
| 37 | } |
| 38 | |
| 39 | void ModelToINetworkTransformer::Convert() |
| 40 | { |
| 41 | VLOG(DRIVER) << "ModelToINetworkTransformer: Convert()"; |
| 42 | //VLOG(DRIVER) << "ModelToINetworkTransformer: Convert(): " << GetModelSummary(m_Model).c_str(); |
| 43 | |
| 44 | // map the memory pool into shared pointers |
| 45 | m_Data.m_MemPools.clear(); |
| 46 | if (!setRunTimePoolInfosFromCanonicalMemories(&m_Data.m_MemPools, m_Model.pools)) |
| 47 | { |
| 48 | VLOG(DRIVER) << "Setting of run time pool infos from Hidl Memories has failed." << __func__; |
| 49 | m_ConversionResult = ConversionResult::ErrorMappingPools; |
| 50 | return; |
| 51 | } |
| 52 | |
| 53 | using NetworkOptions = std::vector<armnn::BackendOptions>; |
| 54 | NetworkOptions networkOptions; |
| 55 | armnn::BackendOptions shapeInferenceMethodOption("ShapeInferenceMethod", |
| 56 | { |
| 57 | { "InferAndValidate", true } |
| 58 | }); |
| 59 | |
| 60 | networkOptions.push_back(shapeInferenceMethodOption); |
| 61 | |
| 62 | // Create armnn::INetwork |
| 63 | m_Data.m_Network = armnn::INetwork::Create(networkOptions); |
| 64 | |
| 65 | // add operations to it |
| 66 | // track which layer outputs each operand |
| 67 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): m_OutputSlotForOperand"; |
| 68 | m_Data.m_OutputSlotForOperand = std::vector<armnn::IOutputSlot*>(m_Model.main.operands.size(), nullptr); |
| 69 | try |
| 70 | { |
| 71 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): for m_Model.inputIndexes.size()"; |
| 72 | for (uint32_t i = 0; i < m_Model.main.inputIndexes.size(); i++) |
| 73 | { |
| 74 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): m_Model.inputIndexes[i]"; |
| 75 | // inputs in android nn are represented by operands |
| 76 | uint32_t inputIndex = m_Model.main.inputIndexes[i]; |
| 77 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): m_Model.operands[inputIndex]"; |
| 78 | const Operand& operand = m_Model.main.operands[inputIndex]; |
| 79 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): GetTensorInfoForOperand(operand)"; |
| 80 | |
| 81 | const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand); |
| 82 | const std::string layerName = "Input_" + std::to_string(i); |
| 83 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): m_Data.m_Network->AddInputLayer(...)"; |
| 84 | armnn::IConnectableLayer* layer = m_Data.m_Network->AddInputLayer(i, layerName.c_str()); |
| 85 | |
| 86 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): layer->GetOutputSlot(0)"; |
| 87 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 88 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): outputSlot.SetTensorInfo(...)"; |
| 89 | outputSlot.SetTensorInfo(GetTensorInfoForOperand(operand)); |
| 90 | |
| 91 | VLOG(DRIVER) << "ModelToINetworkTransformer::Convert(): store for later layers"; |
| 92 | // store for later layers |
| 93 | m_Data.m_OutputSlotForOperand[inputIndex] = &outputSlot; |
| 94 | } |
| 95 | } |
| 96 | catch (UnsupportedOperand<OperandType>& e) |
| 97 | { |
| 98 | VLOG(DRIVER) << __func__ << "Operand type: " << e.m_type << " is not supported in ArmnnDriver"; |
| 99 | m_ConversionResult = ConversionResult::UnsupportedFeature; |
| 100 | } |
| 101 | catch (const armnn::InvalidArgumentException& e) |
| 102 | { |
| 103 | Fail("%s: Failed to convert input operand to TensorShape: %s", __func__, e.what()); |
| 104 | m_ConversionResult = ConversionResult::UnsupportedFeature; |
| 105 | } |
| 106 | bool UnsupportedDynamicOperation = false; |
| 107 | for (uint32_t operationIdx = 0; operationIdx < m_Model.main.operations.size(); operationIdx++) |
| 108 | { |
| 109 | const auto& operation = m_Model.main.operations[operationIdx]; |
| 110 | |
| 111 | bool ok = true; |
| 112 | if (m_ForcedUnsupportedOperations.find(operationIdx) != m_ForcedUnsupportedOperations.end()) |
| 113 | { |
| 114 | Fail("%s: Operation at index %i has been forced to be unsupported.", __func__, operationIdx); |
| 115 | ok = false; |
| 116 | } |
| 117 | |
| 118 | if (ok) |
| 119 | { |
| 120 | try |
| 121 | { |
| 122 | ok = Converter::ConvertOperation(operation, m_Model, m_Data); |
| 123 | } |
| 124 | catch (UnsupportedOperand<OperandType>& e) |
| 125 | { |
| 126 | VLOG(DRIVER) << __func__ << "Operation type: " << e.m_type << "is not supported in ArmnnDriver"; |
| 127 | ok = false; |
| 128 | } |
| 129 | catch (const armnn::InvalidArgumentException& e) |
| 130 | { |
| 131 | Fail("%s: Failed to convert operation in %s", __func__, e.what()); |
| 132 | ok = false; |
| 133 | } |
| 134 | } |
| 135 | |
| 136 | // Store whether this operation was successfully converted. |
| 137 | m_OperationSupported.emplace(operationIdx, ok); |
| 138 | |
| 139 | // Any single operation failing will fail the entire conversion. |
| 140 | // We still need to continue and check the other ones. |
| 141 | if (!ok) |
| 142 | { |
| 143 | if (m_Data.m_DynamicInputsEncountered) |
| 144 | { |
| 145 | Fail("%s: The unsupported operation at index %i has dynamic inputs.", __func__, operationIdx); |
| 146 | UnsupportedDynamicOperation = true; |
| 147 | } |
| 148 | |
| 149 | m_ConversionResult = ConversionResult::UnsupportedFeature; |
| 150 | } |
| 151 | m_Data.m_DynamicInputsEncountered = false; |
| 152 | } |
| 153 | |
| 154 | // Due to the NNAPI partitioner not supporting partition boundaries of unknown size, |
| 155 | // any operations who's outputs connect to an unsupported operation with with dynamic inputs |
| 156 | // will cause a failure. |
| 157 | |
| 158 | // The simplest solution to this problem is to not support any operations in a model containing |
| 159 | // an unsupported operation with with dynamic inputs. |
| 160 | if (UnsupportedDynamicOperation) |
| 161 | { |
| 162 | Fail("%s: Unsupported operation with dynamic inputs found. Retroactively setting all operations to unsupported", |
| 163 | __func__); |
| 164 | for (auto& operation : m_OperationSupported) |
| 165 | { |
| 166 | operation.second = false; |
| 167 | } |
| 168 | } |
| 169 | |
| 170 | try |
| 171 | { |
| 172 | if (m_ConversionResult == ConversionResult::Success) |
| 173 | { |
| 174 | for (uint32_t i = 0; i < m_Model.main.outputIndexes.size(); i++) |
| 175 | { |
| 176 | // outputs in android nn are represented by operands |
| 177 | uint32_t outputIndex = m_Model.main.outputIndexes[i]; |
| 178 | const auto& operand = m_Model.main.operands[outputIndex]; |
| 179 | const armnn::TensorInfo& tensor = GetTensorInfoForOperand(operand); |
| 180 | const std::string layerName = "Output_" + std::to_string(i); |
| 181 | armnn::IConnectableLayer* layer = m_Data.m_Network->AddOutputLayer(i, layerName.c_str()); |
| 182 | |
| 183 | assert(m_Data.m_OutputSlotForOperand[outputIndex]); |
| 184 | m_Data.m_OutputSlotForOperand[outputIndex]->Connect(layer->GetInputSlot(0)); |
| 185 | } |
| 186 | } |
| 187 | } |
| 188 | catch (const armnn::InvalidArgumentException& e) |
| 189 | { |
| 190 | Fail("%s: Failed to convert output operand to TensorShape: %s", __func__, e.what()); |
| 191 | m_ConversionResult = ConversionResult::UnsupportedFeature; |
| 192 | } |
| 193 | } |
| 194 | |
| 195 | bool ModelToINetworkTransformer::IsOperationSupported(uint32_t operationIndex) const |
| 196 | { |
| 197 | std::map<uint32_t, bool>::const_iterator it = m_OperationSupported.find(operationIndex); |
| 198 | assert(it != m_OperationSupported.end()); |
| 199 | return it->second; |
| 200 | } |
| 201 | |
| 202 | } // armnn_driver |