arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "HalPolicy.hpp" |
| 7 | |
| 8 | #include "../1.0/HalPolicy.hpp" |
| 9 | |
| 10 | namespace armnn_driver |
| 11 | { |
| 12 | namespace hal_1_1 |
| 13 | { |
| 14 | |
| 15 | bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data) |
| 16 | { |
| 17 | if (compliantWithV1_0(operation)) |
| 18 | { |
| 19 | hal_1_0::HalPolicy::Operation v10Operation = convertToV1_0(operation); |
| 20 | hal_1_0::HalPolicy::Model v10Model = convertToV1_0(model); |
| 21 | |
| 22 | return hal_1_0::HalPolicy::ConvertOperation(v10Operation, v10Model, data); |
| 23 | } |
| 24 | else |
| 25 | { |
| 26 | switch (operation.type) |
| 27 | { |
| 28 | case V1_1::OperationType::DIV: |
| 29 | return ConvertDiv(operation, model, data); |
David Beck | 38e1294 | 2018-09-12 16:02:24 +0100 | [diff] [blame] | 30 | case V1_1::OperationType::SUB: |
| 31 | return ConvertSub(operation, model, data); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 32 | case V1_1::OperationType::MEAN: |
| 33 | return ConvertMean(operation, model, data); |
Nina Drozd | 62a4a9f | 2018-10-01 14:20:25 +0100 | [diff] [blame] | 34 | case V1_1::OperationType::PAD: |
| 35 | return ConvertPad(operation, model, data); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 36 | case V1_1::OperationType::SQUEEZE: |
| 37 | return ConvertSqueeze(operation, model, data); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 38 | case V1_1::OperationType::TRANSPOSE: |
| 39 | return ConvertTranspose(operation, model, data); |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 40 | default: |
| 41 | return Fail("%s: Operation type %s not supported in ArmnnDriver", |
| 42 | __func__, toString(operation.type).c_str()); |
| 43 | } |
| 44 | } |
| 45 | } |
| 46 | |
| 47 | bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data) |
| 48 | { |
| 49 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 50 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 51 | |
| 52 | if (!input0.IsValid() || !input1.IsValid()) |
| 53 | { |
| 54 | return Fail("%s: Operation has invalid inputs", __func__); |
| 55 | } |
| 56 | |
| 57 | // The FuseActivation parameter is always the input index 2 |
| 58 | // and it should be optional |
| 59 | ActivationFn activationFunction; |
| 60 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 61 | { |
| 62 | return Fail("%s: Operation has invalid inputs", __func__); |
| 63 | } |
| 64 | |
| 65 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 66 | if (!outputOperand) |
| 67 | { |
| 68 | return false; |
| 69 | } |
| 70 | |
| 71 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 72 | |
| 73 | if (!IsLayerSupported(__func__, |
| 74 | armnn::IsDivisionSupported, |
| 75 | data.m_Compute, |
| 76 | input0.GetTensorInfo(), |
| 77 | input1.GetTensorInfo(), |
| 78 | outInfo)) |
| 79 | { |
| 80 | return false; |
| 81 | } |
| 82 | |
| 83 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddDivisionLayer(); |
| 84 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 85 | |
| 86 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 87 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 88 | |
| 89 | if (endLayer) |
| 90 | { |
| 91 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 92 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 93 | } |
| 94 | |
| 95 | return Fail("%s: ProcessActivation failed", __func__); |
| 96 | } |
| 97 | |
David Beck | 38e1294 | 2018-09-12 16:02:24 +0100 | [diff] [blame] | 98 | bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data) |
| 99 | { |
| 100 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 101 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 102 | |
| 103 | if (!input0.IsValid() || !input1.IsValid()) |
| 104 | { |
| 105 | return Fail("%s: Operation has invalid inputs", __func__); |
| 106 | } |
| 107 | |
| 108 | // The FuseActivation parameter is always the input index 2 |
| 109 | // and it should be optional |
| 110 | ActivationFn activationFunction; |
| 111 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 112 | { |
| 113 | return Fail("%s: Operation has invalid inputs", __func__); |
| 114 | } |
| 115 | |
| 116 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 117 | if (!outputOperand) |
| 118 | { |
| 119 | return false; |
| 120 | } |
| 121 | |
| 122 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 123 | |
| 124 | if (!IsLayerSupported(__func__, |
| 125 | armnn::IsSubtractionSupported, |
| 126 | data.m_Compute, |
| 127 | input0.GetTensorInfo(), |
| 128 | input1.GetTensorInfo(), |
| 129 | outInfo)) |
| 130 | { |
| 131 | return false; |
| 132 | } |
| 133 | |
| 134 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddSubtractionLayer(); |
| 135 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 136 | |
| 137 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 138 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 139 | |
| 140 | if (endLayer) |
| 141 | { |
| 142 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 143 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 144 | } |
| 145 | |
| 146 | return Fail("%s: ProcessActivation failed", __func__); |
| 147 | } |
| 148 | |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 149 | bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data) |
| 150 | { |
| 151 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 152 | if (!input.IsValid()) |
| 153 | { |
| 154 | return Fail("%s: Operation has invalid inputs", __func__); |
| 155 | } |
| 156 | |
Matteo Martincigh | ae622b7 | 2018-10-23 18:25:38 +0100 | [diff] [blame^] | 157 | const Operand* axisOperand = GetInputOperand(operation, 1, model); |
| 158 | if (!axisOperand) |
| 159 | { |
| 160 | return Fail("%s: Could not read input 1", __func__); |
| 161 | } |
| 162 | |
| 163 | std::vector<int32_t> axis; |
| 164 | if (!GetTensorInt32Values(*axisOperand, axis, model, data)) |
| 165 | { |
| 166 | return Fail("%s: Input 1 has invalid values", __func__); |
| 167 | } |
| 168 | |
| 169 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 170 | |
| 171 | // Convert the axis to unsigned int and remove duplicates. |
| 172 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 173 | std::set<unsigned int> uniqueAxis; |
| 174 | std::transform(axis.begin(), axis.end(), |
| 175 | std::inserter(uniqueAxis, uniqueAxis.begin()), |
| 176 | [rank](int i) -> unsigned int { return (i + rank) % rank; }); |
| 177 | |
| 178 | // Get the "keep dims" flag. |
| 179 | int32_t keepDims = 0; |
| 180 | if (!GetInputInt32(operation, 2, keepDims, model, data)) |
| 181 | { |
| 182 | return Fail("%s: Could not read input 2", __func__); |
| 183 | } |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 184 | |
| 185 | armnn::MeanDescriptor descriptor; |
Matteo Martincigh | ae622b7 | 2018-10-23 18:25:38 +0100 | [diff] [blame^] | 186 | descriptor.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); |
| 187 | descriptor.m_KeepDims = keepDims > 0; |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 188 | |
| 189 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 190 | if (!output) |
| 191 | { |
| 192 | return Fail("%s: Could not read output 0", __func__); |
| 193 | } |
| 194 | |
| 195 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 196 | |
| 197 | if (!IsLayerSupported(__func__, |
| 198 | armnn::IsMeanSupported, |
| 199 | data.m_Compute, |
| 200 | inputInfo, |
| 201 | outputInfo, |
| 202 | descriptor)) |
| 203 | { |
| 204 | return false; |
| 205 | } |
| 206 | |
| 207 | armnn::IConnectableLayer* const layer = data.m_Network->AddMeanLayer(descriptor); |
narpra01 | 96bedf0 | 2018-09-26 16:57:28 +0100 | [diff] [blame] | 208 | assert(layer != nullptr); |
| 209 | input.Connect(layer->GetInputSlot(0)); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 210 | |
| 211 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 212 | } |
| 213 | |
Nina Drozd | 62a4a9f | 2018-10-01 14:20:25 +0100 | [diff] [blame] | 214 | bool HalPolicy::ConvertPad(const Operation& operation, const Model& model, ConversionData& data) |
| 215 | { |
| 216 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 217 | |
| 218 | if (!input.IsValid()) |
| 219 | { |
| 220 | return Fail("%s: Operation has invalid inputs", __func__); |
| 221 | } |
| 222 | |
| 223 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 224 | |
| 225 | const Operand* paddingsOperand = GetInputOperand(operation, 1, model); |
| 226 | |
| 227 | if (!paddingsOperand) |
| 228 | { |
| 229 | return Fail("%s: Could not read paddings operand", __func__); |
| 230 | } |
| 231 | |
| 232 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 233 | armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand); |
| 234 | if (paddingsOperandShape.GetNumDimensions() != rank || paddingsOperandShape.GetNumElements() != 2) |
| 235 | { |
| 236 | return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, rank); |
| 237 | } |
| 238 | |
| 239 | std::vector<int32_t> paddings; |
| 240 | GetTensorInt32Values(*paddingsOperand, paddings, model, data); |
| 241 | |
| 242 | // add padding for each dimension of input tensor. |
| 243 | armnn::PadDescriptor descriptor; |
| 244 | for (unsigned int i = 0; i < paddings.size() - 1; i += 2) |
| 245 | { |
| 246 | int paddingBeforeInput = paddings[i]; |
| 247 | int paddingAfterInput = paddings[i + 1]; |
| 248 | if (paddingBeforeInput < 0 || paddingAfterInput < 0) |
| 249 | { |
| 250 | return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__); |
| 251 | } |
| 252 | descriptor.m_PadList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput); |
| 253 | } |
| 254 | |
| 255 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 256 | if (!output) |
| 257 | { |
| 258 | return Fail("%s: Could not read output 0", __func__); |
| 259 | } |
| 260 | |
| 261 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 262 | |
| 263 | if (!IsLayerSupported(__func__, |
| 264 | armnn::IsPadSupported, |
| 265 | data.m_Compute, |
| 266 | inputInfo, |
| 267 | outputInfo, |
| 268 | descriptor)) |
| 269 | { |
| 270 | return false; |
| 271 | } |
| 272 | |
| 273 | armnn::IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); |
| 274 | assert(layer != nullptr); |
| 275 | input.Connect(layer->GetInputSlot(0)); |
| 276 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 277 | |
| 278 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 279 | } |
| 280 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 281 | bool HalPolicy::ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data) |
| 282 | { |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 283 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 284 | |
| 285 | if (!input.IsValid()) |
| 286 | { |
| 287 | return Fail("%s: Operation has invalid inputs", __func__); |
| 288 | } |
| 289 | |
| 290 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 291 | |
| 292 | unsigned int rank = inputInfo.GetNumDimensions(); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 293 | if (rank > 4) |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 294 | { |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 295 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 296 | } |
| 297 | |
| 298 | // NOTE: Axis is an optional parameter to SQUEEZE, therefore we do not want to generate a failure |
| 299 | // if the operand index is out of bounds. |
| 300 | const Operand* axisOperand = GetInputOperand(operation, 1, model, false); |
| 301 | |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 302 | const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 303 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 304 | std::vector<int32_t> axis; |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 305 | if (!axisOperand) |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 306 | { |
| 307 | axis.assign(dimensionSequence, |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 308 | dimensionSequence + rank); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 309 | } |
| 310 | else |
| 311 | { |
| 312 | GetTensorInt32Values(*axisOperand, axis, model, data); |
| 313 | } |
| 314 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 315 | |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame] | 316 | std::vector<uint32_t> outputDims; |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 317 | for (unsigned int i = 0; i < rank; i++) |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame] | 318 | { |
| 319 | bool skipSqueeze = (std::find(axis.begin(), axis.end(), i) == axis.end()); |
| 320 | auto currentDimension = inputInfo.GetShape()[i]; |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 321 | if (skipSqueeze || currentDimension != 1) |
| 322 | { |
| 323 | outputDims.push_back(currentDimension); |
| 324 | } |
| 325 | } |
| 326 | |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 327 | armnn::TensorShape outShape = armnn::TensorShape(outputDims.size(), outputDims.data()); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 328 | |
| 329 | armnn::TensorInfo outputInfo = inputInfo; |
| 330 | outputInfo.SetShape(outShape); |
| 331 | |
| 332 | armnn::ReshapeDescriptor reshapeDesc; |
| 333 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 334 | |
| 335 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 336 | if (!output) |
| 337 | { |
| 338 | return Fail("%s: Could not read output 0", __func__); |
| 339 | } |
| 340 | |
| 341 | if (!IsLayerSupported(__func__, |
| 342 | armnn::IsReshapeSupported, |
| 343 | data.m_Compute, |
| 344 | inputInfo)) |
| 345 | { |
| 346 | return false; |
| 347 | } |
| 348 | |
| 349 | armnn::IConnectableLayer* const layer = data.m_Network->AddReshapeLayer(reshapeDesc); |
| 350 | assert(layer != nullptr); |
| 351 | input.Connect(layer->GetInputSlot(0)); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 352 | |
| 353 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 354 | } |
| 355 | |
| 356 | bool HalPolicy::ConvertTranspose(const Operation& operation, const Model& model, ConversionData& data) |
| 357 | { |
| 358 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 359 | |
| 360 | if (!input.IsValid()) |
| 361 | { |
| 362 | return Fail("%s: Operation has invalid inputs", __func__); |
| 363 | } |
| 364 | |
| 365 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 366 | |
| 367 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 368 | if (rank > 4) |
| 369 | { |
| 370 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 371 | } |
| 372 | |
| 373 | // NOTE: Axis is an optional parameter to TRANSPOSE, therefore we do not want to generate a failure |
| 374 | // if the operand index is out of bounds. |
| 375 | const Operand* permOperand = GetInputOperand(operation, 1, model, false); |
| 376 | |
| 377 | std::vector<int32_t> perm(rank); |
| 378 | if (!permOperand) |
| 379 | { |
| 380 | // NOTE: If perm is not given, it is set to (n-1...0), where n is the rank of the tensor |
| 381 | for (unsigned int i = rank; i > 0; i--) |
| 382 | { |
| 383 | perm[rank - i] = boost::numeric_cast<int> (i - 1); |
| 384 | } |
| 385 | } |
| 386 | else |
| 387 | { |
| 388 | GetTensorInt32Values(*permOperand, perm, model, data); |
| 389 | } |
| 390 | |
| 391 | std::vector<uint32_t> outputDims(perm.begin(), perm.begin() + rank); |
| 392 | |
| 393 | auto permutationVector = armnn::PermutationVector(outputDims.data(), outputDims.size()); |
| 394 | if (!permutationVector.IsEqual(NHWCToArmNN) |
| 395 | && !permutationVector.IsEqual(ArmNNToNHWC) |
| 396 | && !permutationVector.IsEqual({ 3, 2, 0, 1 })) |
| 397 | { |
| 398 | return Fail("%s: Only [0, 3, 1, 2], [0, 2, 3, 1] and [3, 2, 0, 1] permutations are supported.", __func__); |
| 399 | } |
| 400 | |
| 401 | armnn::PermuteDescriptor permuteDesc; |
| 402 | permuteDesc.m_DimMappings = permutationVector; |
| 403 | |
| 404 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 405 | if (!output) |
| 406 | { |
| 407 | return Fail("%s: Could not read output 0", __func__); |
| 408 | } |
| 409 | |
| 410 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 411 | |
| 412 | if (!IsLayerSupported(__func__, |
| 413 | armnn::IsPermuteSupported, |
| 414 | data.m_Compute, |
| 415 | inputInfo, |
| 416 | outputInfo, |
| 417 | permuteDesc)) |
| 418 | { |
| 419 | return false; |
| 420 | } |
| 421 | |
| 422 | armnn::IConnectableLayer* const layer = data.m_Network->AddPermuteLayer(permuteDesc); |
| 423 | assert(layer != nullptr); |
| 424 | input.Connect(layer->GetInputSlot(0)); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 425 | |
| 426 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 427 | } |
| 428 | |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 429 | } // namespace hal_1_1 |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 430 | } // namespace armnn_driver |