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 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 8 | #include "DelegateUtils.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> |
| 13 | #include <tensorflow/lite/minimal_logging.h> |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 14 | #include "tensorflow/lite/kernels/internal/tensor.h" |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 15 | |
| 16 | namespace armnnDelegate |
| 17 | { |
| 18 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 19 | TfLiteStatus VisitConv2dOperator(DelegateData& delegateData, |
| 20 | TfLiteContext* tfLiteContext, |
| 21 | TfLiteNode* tfLiteNode, |
| 22 | int nodeIndex, |
| 23 | int32_t operatorCode) |
| 24 | { |
| 25 | auto numInputs = tfLiteNode->inputs->size; |
| 26 | if (numInputs < 2) |
| 27 | { |
| 28 | TF_LITE_MAYBE_KERNEL_LOG( |
| 29 | tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 30 | 2, numInputs, nodeIndex); |
| 31 | return kTfLiteError; |
| 32 | } |
| 33 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 34 | |
| 35 | armnn::Convolution2dDescriptor descriptor; |
| 36 | const auto params = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); |
| 37 | |
Mike Kelly | 84d6378 | 2022-05-06 12:14:16 +0100 | [diff] [blame] | 38 | bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 39 | descriptor.m_BiasEnabled = biasEnabled; |
| 40 | descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); |
| 41 | descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); |
| 42 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 43 | descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); |
| 44 | descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); |
| 45 | |
| 46 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 47 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| 48 | if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]])) |
| 49 | { |
| 50 | TF_LITE_MAYBE_KERNEL_LOG( |
| 51 | tfLiteContext, |
| 52 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 53 | operatorCode, nodeIndex); |
| 54 | return kTfLiteError; |
| 55 | } |
| 56 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 57 | { |
| 58 | TF_LITE_MAYBE_KERNEL_LOG( |
| 59 | tfLiteContext, |
| 60 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 61 | operatorCode, nodeIndex); |
| 62 | return kTfLiteError; |
| 63 | } |
| 64 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 65 | if(!IsValid(&tfLiteOutputTensor)) |
| 66 | { |
| 67 | TF_LITE_MAYBE_KERNEL_LOG( |
| 68 | tfLiteContext, |
| 69 | "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", |
| 70 | operatorCode, nodeIndex); |
| 71 | return kTfLiteError; |
| 72 | } |
| 73 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 74 | { |
| 75 | TF_LITE_MAYBE_KERNEL_LOG( |
| 76 | tfLiteContext, |
| 77 | "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 78 | operatorCode, nodeIndex); |
| 79 | return kTfLiteError; |
| 80 | } |
| 81 | |
| 82 | const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| 83 | if(!IsValid(&tfLiteFilterTensor)) |
| 84 | { |
| 85 | TF_LITE_MAYBE_KERNEL_LOG( |
| 86 | tfLiteContext, |
| 87 | "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", |
| 88 | operatorCode, nodeIndex); |
| 89 | return kTfLiteError; |
| 90 | } |
| 91 | if (IsDynamicTensor(tfLiteFilterTensor)) |
| 92 | { |
| 93 | TF_LITE_MAYBE_KERNEL_LOG( |
| 94 | tfLiteContext, |
| 95 | "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", |
| 96 | nodeIndex); |
| 97 | return kTfLiteError; |
| 98 | } |
| 99 | |
| 100 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 101 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 102 | |
| 103 | armnn::TensorInfo filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); |
| 104 | |
| 105 | armnn::TensorInfo biasTensorInfo; |
| 106 | if(biasEnabled) |
| 107 | { |
| 108 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 109 | if(!IsValid(&tfLiteBiasTensor)) |
| 110 | { |
| 111 | TF_LITE_MAYBE_KERNEL_LOG( |
| 112 | tfLiteContext, |
| 113 | "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", |
| 114 | operatorCode, nodeIndex); |
| 115 | return kTfLiteError; |
| 116 | } |
| 117 | if (IsDynamicTensor(tfLiteBiasTensor)) |
| 118 | { |
| 119 | TF_LITE_MAYBE_KERNEL_LOG( |
| 120 | tfLiteContext, |
| 121 | "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", |
| 122 | nodeIndex); |
| 123 | return kTfLiteError; |
| 124 | } |
| 125 | biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); |
| 126 | } |
| 127 | else |
| 128 | { |
| 129 | biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); |
| 130 | } |
| 131 | |
| 132 | armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); |
| 133 | |
| 134 | // TfLite uses NHWC tensors |
| 135 | const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 136 | const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 137 | |
| 138 | const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 139 | const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 140 | |
| 141 | // Calculate padding |
| 142 | CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, |
| 143 | descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); |
| 144 | CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, |
| 145 | descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); |
| 146 | |
| 147 | if (!delegateData.m_Network) |
| 148 | { |
| 149 | bool isSupported = false; |
Sadik Armagan | bfa767c | 2022-02-09 14:58:03 +0000 | [diff] [blame] | 150 | FORWARD_LAYER_SUPPORT_FUNC("CONV2D", |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 151 | tfLiteContext, |
| 152 | IsConvolution2dSupported, |
| 153 | delegateData.m_Backends, |
| 154 | isSupported, |
| 155 | inputTensorInfo, |
| 156 | outputTensorInfo, |
| 157 | descriptor, |
| 158 | filterTensorInfo, |
| 159 | optionalBiasInfo); |
| 160 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 161 | } |
| 162 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 163 | // Set up filter and biases |
Keith Davis | b4dd5cc | 2022-04-07 11:32:00 +0100 | [diff] [blame] | 164 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor); |
| 165 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 166 | auto filter = |
| 167 | CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]], |
| 168 | filterTensorInfo, |
| 169 | armnn::Optional<armnn::PermutationVector&>()); |
| 170 | |
Keith Davis | b4dd5cc | 2022-04-07 11:32:00 +0100 | [diff] [blame] | 171 | armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); |
| 172 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 173 | weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); |
| 174 | |
| 175 | if (biasEnabled) |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 176 | { |
Keith Davis | b4dd5cc | 2022-04-07 11:32:00 +0100 | [diff] [blame] | 177 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 178 | if(tflite::IsConstantTensor(&tfLiteBiasTensor)) |
| 179 | { |
| 180 | auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); |
| 181 | armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); |
| 182 | ARMNN_ASSERT(biasLayer != nullptr); |
| 183 | biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| 184 | biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); |
| 185 | } |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 186 | } |
| 187 | |
| 188 | ARMNN_ASSERT(layer != nullptr); |
| 189 | |
| 190 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 191 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 192 | |
| 193 | Connect(layer, tfLiteNode, delegateData); |
| 194 | |
| 195 | auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); |
| 196 | if (!tfLiteNodeParameters) |
| 197 | { |
| 198 | // No Activation |
| 199 | return kTfLiteOk; |
| 200 | } |
| 201 | // Check activation |
| 202 | TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; |
| 203 | return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); |
| 204 | |
| 205 | } |
| 206 | |
Matthew Sloyan | 81ec994 | 2021-10-12 10:26:30 +0100 | [diff] [blame] | 207 | // Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. |
| 208 | #if defined(ARMNN_POST_TFLITE_2_5) |
| 209 | TfLiteStatus VisitConv3dOperator(DelegateData& delegateData, |
| 210 | TfLiteContext* tfLiteContext, |
| 211 | TfLiteNode* tfLiteNode, |
| 212 | int nodeIndex, |
| 213 | int32_t operatorCode) |
| 214 | { |
| 215 | auto numInputs = tfLiteNode->inputs->size; |
| 216 | if (numInputs < 2) |
| 217 | { |
| 218 | TF_LITE_MAYBE_KERNEL_LOG( |
| 219 | tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 220 | 2, numInputs, nodeIndex); |
| 221 | return kTfLiteError; |
| 222 | } |
| 223 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 224 | |
| 225 | armnn::Convolution3dDescriptor descriptor; |
| 226 | const auto params = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); |
| 227 | |
Mike Kelly | 84d6378 | 2022-05-06 12:14:16 +0100 | [diff] [blame] | 228 | bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); |
Matthew Sloyan | 81ec994 | 2021-10-12 10:26:30 +0100 | [diff] [blame] | 229 | descriptor.m_BiasEnabled = biasEnabled; |
| 230 | descriptor.m_DataLayout = armnn::DataLayout::NDHWC; |
| 231 | descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); |
| 232 | descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); |
| 233 | descriptor.m_StrideZ = NonNegative(params->stride_depth, nodeIndex); |
| 234 | descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); |
| 235 | descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); |
| 236 | descriptor.m_DilationZ = NonNegative(params->dilation_depth_factor, nodeIndex); |
| 237 | |
| 238 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 239 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| 240 | if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| 241 | { |
| 242 | return kTfLiteError; |
| 243 | } |
| 244 | |
| 245 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 246 | if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| 247 | { |
| 248 | return kTfLiteError; |
| 249 | } |
| 250 | |
| 251 | const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| 252 | if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex)) |
| 253 | { |
| 254 | return kTfLiteError; |
| 255 | } |
| 256 | |
| 257 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 258 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 259 | |
| 260 | armnn::TensorInfo filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); |
| 261 | |
| 262 | armnn::TensorInfo biasTensorInfo; |
| 263 | if(biasEnabled) |
| 264 | { |
| 265 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 266 | if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) |
| 267 | { |
| 268 | return kTfLiteError; |
| 269 | } |
| 270 | biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); |
| 271 | } |
| 272 | else |
| 273 | { |
| 274 | biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); |
| 275 | } |
| 276 | |
| 277 | armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); |
| 278 | |
| 279 | // TfLite uses NDHWC tensors |
| 280 | const unsigned int inputDepth = inputTensorInfo.GetShape()[1]; |
| 281 | const unsigned int inputHeight = inputTensorInfo.GetShape()[2]; |
| 282 | const unsigned int inputWidth = inputTensorInfo.GetShape()[3]; |
| 283 | |
| 284 | // Assuming the filter is DHWIO : Depth, Height, Width, OutputChannels, InputChannels |
| 285 | const unsigned int filterDepth = filterTensorInfo.GetShape()[0]; |
| 286 | const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 287 | const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 288 | |
| 289 | // Calculate padding |
| 290 | CalcPadding(inputDepth, filterDepth, descriptor.m_StrideZ, descriptor.m_DilationZ, |
| 291 | descriptor.m_PadFront, descriptor.m_PadBack, params->padding); |
| 292 | CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, |
| 293 | descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); |
| 294 | CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, |
| 295 | descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); |
| 296 | |
| 297 | // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the |
| 298 | // support for the operator |
| 299 | // If supported, VisitConvolutionOperator will be called again to add the layer to the network as seen below. |
| 300 | if (!delegateData.m_Network) |
| 301 | { |
| 302 | bool isSupported = false; |
Sadik Armagan | bfa767c | 2022-02-09 14:58:03 +0000 | [diff] [blame] | 303 | FORWARD_LAYER_SUPPORT_FUNC("CONV3D", |
Matthew Sloyan | 81ec994 | 2021-10-12 10:26:30 +0100 | [diff] [blame] | 304 | tfLiteContext, |
| 305 | IsConvolution3dSupported, |
| 306 | delegateData.m_Backends, |
| 307 | isSupported, |
| 308 | inputTensorInfo, |
| 309 | outputTensorInfo, |
| 310 | descriptor, |
| 311 | filterTensorInfo, |
| 312 | optionalBiasInfo); |
| 313 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 314 | } |
| 315 | |
| 316 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor); |
| 317 | ARMNN_ASSERT(layer != nullptr); |
| 318 | |
| 319 | // Add a constant layer for weights and biases if inputs are constant, |
| 320 | // which are connected to the Convolution3d layer as inputs. |
| 321 | if (tflite::IsConstantTensor(&tfLiteFilterTensor)) |
| 322 | { |
| 323 | auto filter = CreateConstTensor(&tfLiteFilterTensor, |
| 324 | filterTensorInfo, |
| 325 | armnn::Optional<armnn::PermutationVector&>()); |
| 326 | |
| 327 | armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); |
| 328 | ARMNN_ASSERT(weightsLayer != nullptr); |
| 329 | |
| 330 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 331 | weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); |
| 332 | } |
| 333 | |
| 334 | if(biasEnabled) |
| 335 | { |
| 336 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 337 | if(tflite::IsConstantTensor(&tfLiteBiasTensor)) |
| 338 | { |
| 339 | auto biases = CreateConstTensor(&tfLiteBiasTensor, |
| 340 | biasTensorInfo, |
| 341 | armnn::Optional<armnn::PermutationVector&>()); |
| 342 | |
| 343 | armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases); |
| 344 | ARMNN_ASSERT(biasLayer != nullptr); |
| 345 | |
| 346 | biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| 347 | biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); |
| 348 | } |
| 349 | } |
| 350 | |
| 351 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 352 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 353 | |
| 354 | Connect(layer, tfLiteNode, delegateData); |
| 355 | |
| 356 | auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); |
| 357 | if (!tfLiteNodeParameters) |
| 358 | { |
| 359 | // No Activation |
| 360 | return kTfLiteOk; |
| 361 | } |
| 362 | |
| 363 | // Check activation |
| 364 | TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; |
| 365 | return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); |
| 366 | } |
| 367 | #endif |
| 368 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 369 | TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, |
| 370 | TfLiteContext* tfLiteContext, |
| 371 | TfLiteNode* tfLiteNode, |
| 372 | int nodeIndex, |
| 373 | int32_t operatorCode) |
| 374 | { |
| 375 | auto numInputs = tfLiteNode->inputs->size; |
| 376 | if (numInputs < 2) |
| 377 | { |
| 378 | TF_LITE_MAYBE_KERNEL_LOG( |
| 379 | tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 380 | 2, numInputs, nodeIndex); |
| 381 | return kTfLiteError; |
| 382 | } |
| 383 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 384 | |
Mike Kelly | 84d6378 | 2022-05-06 12:14:16 +0100 | [diff] [blame] | 385 | bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 386 | |
| 387 | armnn::DepthwiseConvolution2dDescriptor descriptor; |
| 388 | const auto params = reinterpret_cast<TfLiteDepthwiseConvParams*>(tfLiteNode->builtin_data); |
| 389 | |
| 390 | descriptor.m_BiasEnabled = biasEnabled; |
| 391 | descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); |
| 392 | descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); |
| 393 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 394 | descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); |
| 395 | descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); |
| 396 | |
| 397 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 398 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| 399 | if(!IsValid(&tfLiteInputTensor)) |
| 400 | { |
| 401 | TF_LITE_MAYBE_KERNEL_LOG( |
| 402 | tfLiteContext, |
| 403 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 404 | operatorCode, nodeIndex); |
| 405 | return kTfLiteError; |
| 406 | } |
| 407 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 408 | { |
| 409 | TF_LITE_MAYBE_KERNEL_LOG( |
| 410 | tfLiteContext, |
| 411 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 412 | operatorCode, nodeIndex); |
| 413 | return kTfLiteError; |
| 414 | } |
| 415 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 416 | if(!IsValid(&tfLiteOutputTensor)) |
| 417 | { |
| 418 | TF_LITE_MAYBE_KERNEL_LOG( |
| 419 | tfLiteContext, |
| 420 | "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", |
| 421 | operatorCode, nodeIndex); |
| 422 | return kTfLiteError; |
| 423 | } |
| 424 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 425 | { |
| 426 | TF_LITE_MAYBE_KERNEL_LOG( |
| 427 | tfLiteContext, |
| 428 | "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 429 | operatorCode, nodeIndex); |
| 430 | return kTfLiteError; |
| 431 | } |
| 432 | |
| 433 | const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| 434 | if(!IsValid(&tfLiteFilterTensor)) |
| 435 | { |
| 436 | TF_LITE_MAYBE_KERNEL_LOG( |
| 437 | tfLiteContext, |
| 438 | "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", |
| 439 | operatorCode, nodeIndex); |
| 440 | return kTfLiteError; |
| 441 | } |
| 442 | if (IsDynamicTensor(tfLiteFilterTensor)) |
| 443 | { |
| 444 | TF_LITE_MAYBE_KERNEL_LOG( |
| 445 | tfLiteContext, |
| 446 | "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", |
| 447 | nodeIndex); |
| 448 | return kTfLiteError; |
| 449 | } |
| 450 | |
| 451 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 452 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 453 | |
Jan Eilers | 7612bd6 | 2021-04-06 17:29:03 +0100 | [diff] [blame] | 454 | armnn::TensorInfo filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 455 | |
| 456 | // Assuming input is NHWC |
| 457 | unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 458 | unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 459 | |
| 460 | // TensorflowLite weights come in the format [1, H, W, I * M] |
| 461 | unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 462 | unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 463 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 464 | // Calculate padding |
| 465 | CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, |
| 466 | descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); |
| 467 | CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, |
| 468 | descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); |
| 469 | |
| 470 | armnn::TensorInfo biasTensorInfo; |
| 471 | if(biasEnabled) |
| 472 | { |
| 473 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 474 | if(!IsValid(&tfLiteBiasTensor)) |
| 475 | { |
| 476 | TF_LITE_MAYBE_KERNEL_LOG( |
| 477 | tfLiteContext, |
| 478 | "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", |
| 479 | operatorCode, nodeIndex); |
| 480 | return kTfLiteError; |
| 481 | } |
| 482 | if (IsDynamicTensor(tfLiteBiasTensor)) |
| 483 | { |
| 484 | TF_LITE_MAYBE_KERNEL_LOG( |
| 485 | tfLiteContext, |
| 486 | "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", |
| 487 | nodeIndex); |
| 488 | return kTfLiteError; |
| 489 | } |
| 490 | biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); |
| 491 | } |
| 492 | else |
| 493 | { |
| 494 | biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); |
| 495 | } |
| 496 | |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 497 | // For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required. |
| 498 | auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo); |
Narumol Prangnawarat | 1672542 | 2020-11-20 16:17:48 +0000 | [diff] [blame] | 499 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 500 | if (!delegateData.m_Network) |
| 501 | { |
| 502 | bool isSupported = false; |
Sadik Armagan | bfa767c | 2022-02-09 14:58:03 +0000 | [diff] [blame] | 503 | FORWARD_LAYER_SUPPORT_FUNC("DEPTHWISE_CONV2D", |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 504 | tfLiteContext, |
| 505 | IsDepthwiseConvolutionSupported, |
| 506 | delegateData.m_Backends, |
| 507 | isSupported, |
| 508 | inputTensorInfo, |
| 509 | outputTensorInfo, |
| 510 | descriptor, |
Narumol Prangnawarat | 1672542 | 2020-11-20 16:17:48 +0000 | [diff] [blame] | 511 | filter.GetInfo(), |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 512 | armnn::Optional<armnn::TensorInfo>(biasTensorInfo)); |
| 513 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 514 | } |
| 515 | |
Cathal Corbett | 0690265 | 2022-04-14 17:55:11 +0100 | [diff] [blame] | 516 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor); |
Narumol Prangnawarat | 1672542 | 2020-11-20 16:17:48 +0000 | [diff] [blame] | 517 | |
Cathal Corbett | 0690265 | 2022-04-14 17:55:11 +0100 | [diff] [blame] | 518 | armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); |
| 519 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 520 | weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); |
| 521 | |
| 522 | if (biasEnabled) |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 523 | { |
Cathal Corbett | 0690265 | 2022-04-14 17:55:11 +0100 | [diff] [blame] | 524 | const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 525 | if(tflite::IsConstantTensor(&tfLiteBiasTensor)) |
| 526 | { |
| 527 | auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); |
| 528 | armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); |
| 529 | ARMNN_ASSERT(biasLayer != nullptr); |
| 530 | biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| 531 | biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); |
| 532 | } |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 533 | } |
| 534 | |
| 535 | ARMNN_ASSERT(layer != nullptr); |
| 536 | |
| 537 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 538 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 539 | |
| 540 | Connect(layer, tfLiteNode, delegateData); |
| 541 | auto* tfLiteNodeParameters = reinterpret_cast<TfLiteDepthwiseConvParams*>(tfLiteNode->builtin_data); |
| 542 | if (!tfLiteNodeParameters) |
| 543 | { |
| 544 | // No Activation |
| 545 | return kTfLiteOk; |
| 546 | } |
| 547 | // Check activation |
| 548 | TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; |
| 549 | return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); |
| 550 | } |
| 551 | |
| 552 | TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData, |
| 553 | TfLiteContext* tfLiteContext, |
| 554 | TfLiteNode* tfLiteNode, |
| 555 | int nodeIndex, |
| 556 | int32_t operatorCode) |
| 557 | { |
| 558 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); |
| 559 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 560 | |
| 561 | armnn::TransposeConvolution2dDescriptor descriptor; |
| 562 | auto* parameters = reinterpret_cast<TfLiteTransposeConvParams*>(tfLiteNode->builtin_data); |
| 563 | descriptor.m_BiasEnabled = false; |
| 564 | descriptor.m_StrideX = NonNegative(parameters->stride_width, nodeIndex); |
| 565 | descriptor.m_StrideY = NonNegative(parameters->stride_height, nodeIndex); |
| 566 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 567 | |
| 568 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 569 | const TfLiteTensor& tfLiteOutputShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| 570 | if(!IsValid(&tfLiteOutputShapeTensor)) |
| 571 | { |
| 572 | TF_LITE_MAYBE_KERNEL_LOG( |
| 573 | tfLiteContext, |
| 574 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 575 | operatorCode, nodeIndex); |
| 576 | return kTfLiteError; |
| 577 | } |
| 578 | if (IsDynamicTensor(tfLiteOutputShapeTensor)) |
| 579 | { |
| 580 | TF_LITE_MAYBE_KERNEL_LOG( |
| 581 | tfLiteContext, |
| 582 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 583 | operatorCode, nodeIndex); |
| 584 | return kTfLiteError; |
| 585 | } |
| 586 | |
| 587 | armnn::TensorInfo tensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputShapeTensor); |
| 588 | std::vector<int32_t> outputShape(tensorInfo.GetNumElements()); |
| 589 | if (tensorInfo.GetDataType() == armnn::DataType::Signed32) |
| 590 | { |
| 591 | for(unsigned int i=0; i < tensorInfo.GetNumElements(); i++) |
| 592 | { |
| 593 | outputShape[i] = ::tflite::GetTensorData<int32_t>(&tfLiteOutputShapeTensor)[i]; |
| 594 | } |
| 595 | } |
| 596 | |
| 597 | if (tensorInfo.GetDataType() == armnn::DataType::QAsymmU8) |
| 598 | { |
| 599 | for(unsigned int i=0; i < tensorInfo.GetNumElements(); i++) |
| 600 | { |
| 601 | outputShape[i] = ::tflite::GetTensorData<uint8_t>(&tfLiteOutputShapeTensor)[i]; |
| 602 | } |
| 603 | } |
| 604 | // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor. |
| 605 | for (int dimension : outputShape) |
| 606 | { |
| 607 | descriptor.m_OutputShape.push_back(static_cast<unsigned int>(dimension)); |
| 608 | } |
| 609 | descriptor.m_OutputShapeEnabled = true; |
| 610 | |
| 611 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; |
| 612 | if(!IsValid(&tfLiteInputTensor)) |
| 613 | { |
| 614 | TF_LITE_MAYBE_KERNEL_LOG( |
| 615 | tfLiteContext, |
| 616 | "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", |
| 617 | operatorCode, nodeIndex); |
| 618 | return kTfLiteError; |
| 619 | } |
| 620 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 621 | { |
| 622 | TF_LITE_MAYBE_KERNEL_LOG( |
| 623 | tfLiteContext, |
| 624 | "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 625 | operatorCode, nodeIndex); |
| 626 | return kTfLiteError; |
| 627 | } |
| 628 | |
| 629 | const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| 630 | if(!IsValid(&tfLiteOutputTensor)) |
| 631 | { |
| 632 | TF_LITE_MAYBE_KERNEL_LOG( |
| 633 | tfLiteContext, |
| 634 | "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", |
| 635 | operatorCode, nodeIndex); |
| 636 | return kTfLiteError; |
| 637 | } |
| 638 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 639 | { |
| 640 | TF_LITE_MAYBE_KERNEL_LOG( |
| 641 | tfLiteContext, |
| 642 | "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 643 | operatorCode, nodeIndex); |
| 644 | return kTfLiteError; |
| 645 | } |
| 646 | |
| 647 | const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| 648 | if(!IsValid(&tfLiteFilterTensor)) |
| 649 | { |
| 650 | TF_LITE_MAYBE_KERNEL_LOG( |
| 651 | tfLiteContext, |
| 652 | "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", |
| 653 | operatorCode, nodeIndex); |
| 654 | return kTfLiteError; |
| 655 | } |
| 656 | if (IsDynamicTensor(tfLiteFilterTensor)) |
| 657 | { |
| 658 | TF_LITE_MAYBE_KERNEL_LOG( |
| 659 | tfLiteContext, |
| 660 | "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in operator #%d node #%d: ", |
| 661 | operatorCode, nodeIndex); |
| 662 | return kTfLiteError; |
| 663 | } |
| 664 | |
| 665 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 666 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); |
| 667 | armnn::TensorInfo filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); |
| 668 | |
| 669 | // TfLite uses NHWC tensors |
| 670 | const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 671 | const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 672 | |
| 673 | const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 674 | const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 675 | |
| 676 | // Calculate padding |
| 677 | CalcPadding(inputHeight, |
| 678 | filterHeight, |
| 679 | descriptor.m_StrideY, |
| 680 | 1, // dilation y |
| 681 | descriptor.m_PadTop, |
| 682 | descriptor.m_PadBottom, |
| 683 | parameters->padding); |
| 684 | CalcPadding(inputWidth, |
| 685 | filterWidth, |
| 686 | descriptor.m_StrideX, |
| 687 | 1, // dilation x |
| 688 | descriptor.m_PadLeft, |
| 689 | descriptor.m_PadRight, |
| 690 | parameters->padding); |
| 691 | |
| 692 | // Set up filter |
| 693 | auto filterTensor = CreateConstTensor(&tfLiteFilterTensor, |
| 694 | filterTensorInfo, |
| 695 | armnn::Optional<armnn::PermutationVector&>()); |
| 696 | if (!delegateData.m_Network) |
| 697 | { |
| 698 | bool isSupported = false; |
Sadik Armagan | bfa767c | 2022-02-09 14:58:03 +0000 | [diff] [blame] | 699 | FORWARD_LAYER_SUPPORT_FUNC("TRANSPOSE_CONV2D", |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 700 | tfLiteContext, |
| 701 | IsTransposeConvolution2dSupported, |
| 702 | delegateData.m_Backends, |
| 703 | isSupported, |
| 704 | inputTensorInfo, |
| 705 | outputTensorInfo, |
| 706 | descriptor, |
| 707 | filterTensorInfo, |
| 708 | armnn::EmptyOptional()); |
| 709 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 710 | } |
| 711 | |
| 712 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddTransposeConvolution2dLayer(descriptor, |
| 713 | filterTensor, |
| 714 | armnn::EmptyOptional()); |
| 715 | ARMNN_ASSERT(layer != nullptr); |
| 716 | |
| 717 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 718 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 719 | |
| 720 | // Connect |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 721 | if (delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])] != nullptr) |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 722 | { |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 723 | delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])]-> |
| 724 | Connect(layer->GetInputSlot(0)); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 725 | } |
| 726 | |
| 727 | // Prepare output slots |
| 728 | for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) |
| 729 | { |
| 730 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 731 | delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->outputs->data[outputIndex])] = |
| 732 | &outputSlot; |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 733 | } |
| 734 | return kTfLiteOk; |
| 735 | } |
| 736 | |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 737 | TfLiteStatus VisitConvolutionOperator(DelegateData& delegateData, |
| 738 | TfLiteContext* tfLiteContext, |
| 739 | TfLiteNode* tfLiteNode, |
| 740 | int nodeIndex, |
| 741 | int32_t operatorCode) |
| 742 | { |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 743 | switch(operatorCode) |
| 744 | { |
| 745 | case kTfLiteBuiltinConv2d: |
| 746 | return VisitConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
Matthew Sloyan | 81ec994 | 2021-10-12 10:26:30 +0100 | [diff] [blame] | 747 | // Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. |
| 748 | #if defined(ARMNN_POST_TFLITE_2_5) |
| 749 | case kTfLiteBuiltinConv3d: |
| 750 | return VisitConv3dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| 751 | #endif |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 752 | case kTfLiteBuiltinDepthwiseConv2d: |
| 753 | return VisitDepthwiseConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| 754 | case kTfLiteBuiltinTransposeConv: |
| 755 | return VisitTransposeConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| 756 | default: |
| 757 | return kTfLiteError; |
| 758 | } |
Sadik Armagan | 62483be | 2020-10-23 17:14:43 +0100 | [diff] [blame] | 759 | } |
| 760 | |
| 761 | } // namespace armnnDelegate |