Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
Finn Williams | 6f9f990 | 2020-11-13 13:23:15 +0000 | [diff] [blame] | 8 | #include <armnn/utility/IgnoreUnused.hpp> |
| 9 | |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 10 | #include <tensorflow/lite/builtin_ops.h> |
| 11 | #include <tensorflow/lite/c/builtin_op_data.h> |
| 12 | #include <tensorflow/lite/c/common.h> |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 13 | #include <tensorflow/lite/kernels/internal/tensor_ctypes.h> |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 14 | #include <tensorflow/lite/minimal_logging.h> |
| 15 | |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 16 | #include <algorithm> |
| 17 | #include <iterator> |
| 18 | #include <string> |
| 19 | #include <vector> |
| 20 | |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 21 | namespace armnnDelegate |
| 22 | { |
| 23 | |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 24 | void SetupConcatViewOrigin(const armnn::TensorInfo& inputTensorInfo, |
| 25 | armnn::OriginsDescriptor& concatDescriptor, |
| 26 | const unsigned int concatAxis, |
| 27 | unsigned int inputIndex, |
| 28 | unsigned int& mergeDimOrigin) |
| 29 | { |
| 30 | const uint32_t inputRank = concatDescriptor.GetNumDimensions(); |
| 31 | |
| 32 | // double check dimensions of the tensors |
| 33 | if (inputTensorInfo.GetNumDimensions() != inputRank) |
| 34 | { |
| 35 | throw armnn::ParseException("The number of dimensions for input tensors " |
| 36 | "of the concatenation operator should be: " + std::to_string(inputRank)); |
| 37 | } |
| 38 | |
| 39 | for (unsigned int j = 0; j < concatAxis; ++j) |
| 40 | { |
| 41 | concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); |
| 42 | } |
| 43 | |
| 44 | concatDescriptor.SetViewOriginCoord(inputIndex, concatAxis, mergeDimOrigin); |
| 45 | mergeDimOrigin += inputTensorInfo.GetShape()[concatAxis]; |
| 46 | |
| 47 | for (unsigned int j = concatAxis + 1; j < inputRank; ++j) |
| 48 | { |
| 49 | concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); |
| 50 | } |
| 51 | } |
| 52 | |
| 53 | TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData, |
| 54 | TfLiteContext* tfLiteContext, |
| 55 | TfLiteNode* tfLiteNode, |
| 56 | int nodeIndex, |
| 57 | int32_t tfLiteConcatOperatorCode) |
| 58 | { |
| 59 | unsigned int numInputs = tfLiteNode->inputs->size; |
| 60 | if (numInputs < 2) |
| 61 | { |
| 62 | TF_LITE_MAYBE_KERNEL_LOG( |
| 63 | tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 64 | 2, numInputs, nodeIndex); |
| 65 | return kTfLiteError; |
| 66 | } |
| 67 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 68 | |
| 69 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 70 | |
| 71 | std::vector<armnn::TensorInfo> inputTensorInfos; |
| 72 | for (unsigned int i = 0; i < numInputs; ++i) |
| 73 | { |
| 74 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[i]]; |
| 75 | if(!IsValid(&tfLiteInputTensor)) |
| 76 | { |
| 77 | TF_LITE_MAYBE_KERNEL_LOG( |
| 78 | tfLiteContext, |
| 79 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 80 | tfLiteConcatOperatorCode, nodeIndex); |
| 81 | return kTfLiteError; |
| 82 | } |
| 83 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 84 | { |
| 85 | TF_LITE_MAYBE_KERNEL_LOG( |
| 86 | tfLiteContext, |
| 87 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 88 | tfLiteConcatOperatorCode, nodeIndex); |
| 89 | return kTfLiteError; |
| 90 | } |
| 91 | |
| 92 | armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 93 | inputTensorInfos.emplace_back(inputTensorInfo); |
| 94 | } |
| 95 | |
| 96 | // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC. |
| 97 | std::vector<const armnn::TensorInfo*> inputConstTensorInfos; |
| 98 | std::transform(inputTensorInfos.begin(), |
| 99 | inputTensorInfos.end(), |
| 100 | std::back_inserter(inputConstTensorInfos), |
| 101 | [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; }); |
| 102 | |
| 103 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 104 | if(!IsValid(&tfLiteOutputTensor)) |
| 105 | { |
| 106 | TF_LITE_MAYBE_KERNEL_LOG( |
| 107 | tfLiteContext, |
| 108 | "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", |
| 109 | tfLiteConcatOperatorCode, nodeIndex); |
| 110 | return kTfLiteError; |
| 111 | } |
| 112 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 113 | { |
| 114 | TF_LITE_MAYBE_KERNEL_LOG( |
| 115 | tfLiteContext, |
| 116 | "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 117 | tfLiteConcatOperatorCode, nodeIndex); |
| 118 | return kTfLiteError; |
| 119 | } |
| 120 | |
| 121 | // Setup OriginsDescriptor, axis and view origin |
| 122 | unsigned int numConcatView = static_cast<unsigned int>(numInputs); |
| 123 | uint32_t inputRank = tfLiteTensors[tfLiteNode->inputs->data[0]].dims->size; |
| 124 | |
| 125 | auto* concatenationParameters = reinterpret_cast<TfLiteConcatenationParams*>(tfLiteNode->builtin_data); |
| 126 | const unsigned int concatDimInput = static_cast<unsigned int>( |
| 127 | (static_cast<int>(inputRank) + concatenationParameters->axis) % static_cast<int>(inputRank)); |
| 128 | |
| 129 | armnn::OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank); |
| 130 | concatDescriptor.SetConcatAxis(concatDimInput); |
| 131 | |
| 132 | unsigned int mergeDimOrigin = 0; |
| 133 | for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| 134 | { |
| 135 | armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor( |
| 136 | tfLiteTensors[tfLiteNode->inputs->data[viewIndex]]); |
| 137 | |
| 138 | // Sets up concatDescriptor view origin |
| 139 | SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); |
| 140 | } |
| 141 | |
| 142 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 143 | |
| 144 | // Check if supported |
| 145 | bool isSupported = false; |
| 146 | auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| 147 | { |
| 148 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 149 | tfLiteContext, |
| 150 | IsConcatSupported, |
| 151 | delegateData.m_Backends, |
| 152 | isSupported, |
| 153 | inputConstTensorInfos, |
| 154 | outputTensorInfo, |
| 155 | concatDescriptor); |
| 156 | }; |
| 157 | |
| 158 | if (!delegateData.m_Network) |
| 159 | { |
| 160 | validateFunc(outputTensorInfo, isSupported); |
| 161 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 162 | } |
| 163 | |
| 164 | // Setup layer and connect. |
| 165 | armnn::IConnectableLayer* concatenationLayer = delegateData.m_Network->AddConcatLayer(concatDescriptor); |
| 166 | ARMNN_ASSERT(concatenationLayer != nullptr); |
| 167 | |
| 168 | armnn::IOutputSlot& outputSlot = concatenationLayer->GetOutputSlot(0); |
| 169 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 170 | Connect(concatenationLayer, tfLiteNode, delegateData); |
| 171 | |
| 172 | if (!concatenationParameters) |
| 173 | { |
| 174 | // No Activation |
| 175 | return kTfLiteOk; |
| 176 | } |
| 177 | |
| 178 | // Check activation |
| 179 | TfLiteFusedActivation activationType = concatenationParameters->activation; |
| 180 | return FusedActivation(tfLiteContext, tfLiteNode, activationType, concatenationLayer, 0, delegateData); |
| 181 | } |
| 182 | |
| 183 | TfLiteStatus VisitMeanOperator(DelegateData& delegateData, |
| 184 | TfLiteContext* tfLiteContext, |
| 185 | TfLiteNode* tfLiteNode, |
| 186 | int nodeIndex, |
| 187 | int32_t tfLiteMeanOperatorCode) |
| 188 | { |
| 189 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| 190 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 191 | |
| 192 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 193 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| 194 | if(!IsValid(&tfLiteInputTensor)) |
| 195 | { |
| 196 | TF_LITE_MAYBE_KERNEL_LOG( |
| 197 | tfLiteContext, |
| 198 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 199 | tfLiteMeanOperatorCode, nodeIndex); |
| 200 | return kTfLiteError; |
| 201 | } |
| 202 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 203 | { |
| 204 | TF_LITE_MAYBE_KERNEL_LOG( |
| 205 | tfLiteContext, |
| 206 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 207 | tfLiteMeanOperatorCode, nodeIndex); |
| 208 | return kTfLiteError; |
| 209 | } |
| 210 | |
| 211 | const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| 212 | if(!IsValid(&tfLiteAxisTensor)) |
| 213 | { |
| 214 | TF_LITE_MAYBE_KERNEL_LOG( |
| 215 | tfLiteContext, |
| 216 | "TfLiteArmnnDelegate: Invalid axis tensor in operator #%d node #%d: ", |
| 217 | tfLiteMeanOperatorCode, nodeIndex); |
| 218 | return kTfLiteError; |
| 219 | } |
| 220 | if (IsDynamicTensor(tfLiteAxisTensor)) |
| 221 | { |
| 222 | TF_LITE_MAYBE_KERNEL_LOG( |
| 223 | tfLiteContext, |
| 224 | "TfLiteArmnnDelegate: Dynamic axis tensors are not supported in operator #%d node #%d: ", |
| 225 | tfLiteMeanOperatorCode, nodeIndex); |
| 226 | return kTfLiteError; |
| 227 | } |
| 228 | |
| 229 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 230 | if(!IsValid(&tfLiteOutputTensor)) |
| 231 | { |
| 232 | TF_LITE_MAYBE_KERNEL_LOG( |
| 233 | tfLiteContext, |
| 234 | "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", |
| 235 | tfLiteAxisTensor, nodeIndex); |
| 236 | return kTfLiteError; |
| 237 | } |
| 238 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 239 | { |
| 240 | TF_LITE_MAYBE_KERNEL_LOG( |
| 241 | tfLiteContext, |
| 242 | "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 243 | tfLiteMeanOperatorCode, nodeIndex); |
| 244 | return kTfLiteError; |
| 245 | } |
| 246 | |
| 247 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 248 | const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); |
| 249 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 250 | |
| 251 | auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); |
| 252 | |
| 253 | std::vector<int32_t> axis; |
| 254 | // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis. |
| 255 | for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i) |
| 256 | { |
| 257 | axis.emplace_back(axisTensorData[i]); |
| 258 | } |
| 259 | |
| 260 | // Convert the axis to unsigned int and remove duplicates. |
| 261 | unsigned int rank = inputTensorInfo.GetNumDimensions(); |
| 262 | std::set<unsigned int> uniqueAxis; |
| 263 | std::transform(axis.begin(), |
| 264 | axis.end(), |
| 265 | std::inserter(uniqueAxis, uniqueAxis.begin()), |
| 266 | [rank](int i)->unsigned int{ return (i + rank) % rank; }); |
| 267 | |
| 268 | // Setup MeanDescriptor and assign axis and keepDims |
| 269 | armnn::MeanDescriptor desc; |
| 270 | desc.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); |
| 271 | desc.m_KeepDims = inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? true : false; |
| 272 | |
| 273 | // Check if supported |
| 274 | bool isSupported = false; |
| 275 | auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| 276 | { |
| 277 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 278 | tfLiteContext, |
| 279 | IsMeanSupported, |
| 280 | delegateData.m_Backends, |
| 281 | isSupported, |
| 282 | inputTensorInfo, |
| 283 | outputTensorInfo, |
| 284 | desc); |
| 285 | }; |
| 286 | |
| 287 | if (!delegateData.m_Network) |
| 288 | { |
| 289 | validateFunc(outputTensorInfo, isSupported); |
| 290 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 291 | } |
| 292 | |
| 293 | // Setup layer and connect. |
| 294 | armnn::IConnectableLayer* meanLayer = delegateData.m_Network->AddMeanLayer(desc); |
| 295 | ARMNN_ASSERT(meanLayer != nullptr); |
| 296 | |
| 297 | armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0); |
| 298 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 299 | return Connect(meanLayer, tfLiteNode, delegateData); |
| 300 | } |
| 301 | |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 302 | TfLiteStatus VisitControlOperator(DelegateData& delegateData, |
| 303 | TfLiteContext* tfLiteContext, |
| 304 | TfLiteNode* tfLiteNode, |
| 305 | int nodeIndex, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 306 | int32_t operatorCode) |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 307 | { |
Finn Williams | 6f9f990 | 2020-11-13 13:23:15 +0000 | [diff] [blame] | 308 | armnn::IgnoreUnused(delegateData, |
| 309 | tfLiteContext, |
| 310 | tfLiteNode, |
| 311 | nodeIndex, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 312 | operatorCode); |
| 313 | |
| 314 | switch(operatorCode) |
| 315 | { |
| 316 | case kTfLiteBuiltinConcatenation: |
| 317 | return VisitConcatenationOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| 318 | case kTfLiteBuiltinMean: |
| 319 | return VisitMeanOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| 320 | default: |
| 321 | return kTfLiteError; |
| 322 | } |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 323 | } |
| 324 | |
| 325 | } // namespace armnnDelegate |