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); |
Nattapat Chaimanowong | 81a6834 | 2018-11-05 14:04:47 +0000 | [diff] [blame] | 36 | case V1_1::OperationType::SPACE_TO_BATCH_ND: |
| 37 | return ConvertSpaceToBatchNd(operation, model, data); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 38 | case V1_1::OperationType::SQUEEZE: |
| 39 | return ConvertSqueeze(operation, model, data); |
Sadik Armagan | 758eee8 | 2018-11-15 15:34:49 +0000 | [diff] [blame^] | 40 | case V1_1::OperationType::STRIDED_SLICE: |
| 41 | return ConvertStridedSlice(operation, model, data); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 42 | case V1_1::OperationType::TRANSPOSE: |
| 43 | return ConvertTranspose(operation, model, data); |
Éanna Ó Catháin | 2cd99b9 | 2018-11-14 14:33:52 +0000 | [diff] [blame] | 44 | case V1_1::OperationType::BATCH_TO_SPACE_ND: |
| 45 | return ConvertBatchToSpaceNd(operation, model, data); |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 46 | default: |
| 47 | return Fail("%s: Operation type %s not supported in ArmnnDriver", |
| 48 | __func__, toString(operation.type).c_str()); |
| 49 | } |
| 50 | } |
| 51 | } |
| 52 | |
| 53 | bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data) |
| 54 | { |
| 55 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 56 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 57 | |
| 58 | if (!input0.IsValid() || !input1.IsValid()) |
| 59 | { |
| 60 | return Fail("%s: Operation has invalid inputs", __func__); |
| 61 | } |
| 62 | |
| 63 | // The FuseActivation parameter is always the input index 2 |
| 64 | // and it should be optional |
| 65 | ActivationFn activationFunction; |
| 66 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 67 | { |
| 68 | return Fail("%s: Operation has invalid inputs", __func__); |
| 69 | } |
| 70 | |
| 71 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 72 | if (!outputOperand) |
| 73 | { |
| 74 | return false; |
| 75 | } |
| 76 | |
| 77 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 78 | |
| 79 | if (!IsLayerSupported(__func__, |
| 80 | armnn::IsDivisionSupported, |
| 81 | data.m_Compute, |
| 82 | input0.GetTensorInfo(), |
| 83 | input1.GetTensorInfo(), |
| 84 | outInfo)) |
| 85 | { |
| 86 | return false; |
| 87 | } |
| 88 | |
| 89 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddDivisionLayer(); |
| 90 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 91 | |
| 92 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 93 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 94 | |
| 95 | if (endLayer) |
| 96 | { |
| 97 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 98 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 99 | } |
| 100 | |
| 101 | return Fail("%s: ProcessActivation failed", __func__); |
| 102 | } |
| 103 | |
David Beck | 38e1294 | 2018-09-12 16:02:24 +0100 | [diff] [blame] | 104 | bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data) |
| 105 | { |
| 106 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 107 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 108 | |
| 109 | if (!input0.IsValid() || !input1.IsValid()) |
| 110 | { |
| 111 | return Fail("%s: Operation has invalid inputs", __func__); |
| 112 | } |
| 113 | |
| 114 | // The FuseActivation parameter is always the input index 2 |
| 115 | // and it should be optional |
| 116 | ActivationFn activationFunction; |
| 117 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 118 | { |
| 119 | return Fail("%s: Operation has invalid inputs", __func__); |
| 120 | } |
| 121 | |
| 122 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 123 | if (!outputOperand) |
| 124 | { |
| 125 | return false; |
| 126 | } |
| 127 | |
| 128 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 129 | |
| 130 | if (!IsLayerSupported(__func__, |
| 131 | armnn::IsSubtractionSupported, |
| 132 | data.m_Compute, |
| 133 | input0.GetTensorInfo(), |
| 134 | input1.GetTensorInfo(), |
| 135 | outInfo)) |
| 136 | { |
| 137 | return false; |
| 138 | } |
| 139 | |
| 140 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddSubtractionLayer(); |
| 141 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 142 | |
| 143 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 144 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 145 | |
| 146 | if (endLayer) |
| 147 | { |
| 148 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 149 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 150 | } |
| 151 | |
| 152 | return Fail("%s: ProcessActivation failed", __func__); |
| 153 | } |
| 154 | |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 155 | bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data) |
| 156 | { |
| 157 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 158 | if (!input.IsValid()) |
| 159 | { |
| 160 | return Fail("%s: Operation has invalid inputs", __func__); |
| 161 | } |
| 162 | |
Matteo Martincigh | ae622b7 | 2018-10-23 18:25:38 +0100 | [diff] [blame] | 163 | const Operand* axisOperand = GetInputOperand(operation, 1, model); |
| 164 | if (!axisOperand) |
| 165 | { |
| 166 | return Fail("%s: Could not read input 1", __func__); |
| 167 | } |
| 168 | |
| 169 | std::vector<int32_t> axis; |
| 170 | if (!GetTensorInt32Values(*axisOperand, axis, model, data)) |
| 171 | { |
| 172 | return Fail("%s: Input 1 has invalid values", __func__); |
| 173 | } |
| 174 | |
| 175 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 176 | |
| 177 | // Convert the axis to unsigned int and remove duplicates. |
| 178 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 179 | std::set<unsigned int> uniqueAxis; |
| 180 | std::transform(axis.begin(), axis.end(), |
| 181 | std::inserter(uniqueAxis, uniqueAxis.begin()), |
| 182 | [rank](int i) -> unsigned int { return (i + rank) % rank; }); |
| 183 | |
| 184 | // Get the "keep dims" flag. |
| 185 | int32_t keepDims = 0; |
| 186 | if (!GetInputInt32(operation, 2, keepDims, model, data)) |
| 187 | { |
| 188 | return Fail("%s: Could not read input 2", __func__); |
| 189 | } |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 190 | |
| 191 | armnn::MeanDescriptor descriptor; |
Matteo Martincigh | ae622b7 | 2018-10-23 18:25:38 +0100 | [diff] [blame] | 192 | descriptor.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); |
| 193 | descriptor.m_KeepDims = keepDims > 0; |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 194 | |
| 195 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 196 | if (!output) |
| 197 | { |
| 198 | return Fail("%s: Could not read output 0", __func__); |
| 199 | } |
| 200 | |
| 201 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 202 | |
| 203 | if (!IsLayerSupported(__func__, |
| 204 | armnn::IsMeanSupported, |
| 205 | data.m_Compute, |
| 206 | inputInfo, |
| 207 | outputInfo, |
| 208 | descriptor)) |
| 209 | { |
| 210 | return false; |
| 211 | } |
| 212 | |
| 213 | armnn::IConnectableLayer* const layer = data.m_Network->AddMeanLayer(descriptor); |
narpra01 | 96bedf0 | 2018-09-26 16:57:28 +0100 | [diff] [blame] | 214 | assert(layer != nullptr); |
| 215 | input.Connect(layer->GetInputSlot(0)); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 216 | |
| 217 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 218 | } |
| 219 | |
Nina Drozd | 62a4a9f | 2018-10-01 14:20:25 +0100 | [diff] [blame] | 220 | bool HalPolicy::ConvertPad(const Operation& operation, const Model& model, ConversionData& data) |
| 221 | { |
| 222 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 223 | |
| 224 | if (!input.IsValid()) |
| 225 | { |
| 226 | return Fail("%s: Operation has invalid inputs", __func__); |
| 227 | } |
| 228 | |
| 229 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 230 | |
| 231 | const Operand* paddingsOperand = GetInputOperand(operation, 1, model); |
| 232 | |
| 233 | if (!paddingsOperand) |
| 234 | { |
| 235 | return Fail("%s: Could not read paddings operand", __func__); |
| 236 | } |
| 237 | |
| 238 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 239 | armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand); |
| 240 | if (paddingsOperandShape.GetNumDimensions() != rank || paddingsOperandShape.GetNumElements() != 2) |
| 241 | { |
| 242 | return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, rank); |
| 243 | } |
| 244 | |
| 245 | std::vector<int32_t> paddings; |
| 246 | GetTensorInt32Values(*paddingsOperand, paddings, model, data); |
| 247 | |
| 248 | // add padding for each dimension of input tensor. |
| 249 | armnn::PadDescriptor descriptor; |
| 250 | for (unsigned int i = 0; i < paddings.size() - 1; i += 2) |
| 251 | { |
| 252 | int paddingBeforeInput = paddings[i]; |
| 253 | int paddingAfterInput = paddings[i + 1]; |
| 254 | if (paddingBeforeInput < 0 || paddingAfterInput < 0) |
| 255 | { |
| 256 | return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__); |
| 257 | } |
| 258 | descriptor.m_PadList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput); |
| 259 | } |
| 260 | |
| 261 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 262 | if (!output) |
| 263 | { |
| 264 | return Fail("%s: Could not read output 0", __func__); |
| 265 | } |
| 266 | |
| 267 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 268 | |
| 269 | if (!IsLayerSupported(__func__, |
| 270 | armnn::IsPadSupported, |
| 271 | data.m_Compute, |
| 272 | inputInfo, |
| 273 | outputInfo, |
| 274 | descriptor)) |
| 275 | { |
| 276 | return false; |
| 277 | } |
| 278 | |
| 279 | armnn::IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); |
| 280 | assert(layer != nullptr); |
| 281 | input.Connect(layer->GetInputSlot(0)); |
| 282 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 283 | |
| 284 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 285 | } |
| 286 | |
Nattapat Chaimanowong | 81a6834 | 2018-11-05 14:04:47 +0000 | [diff] [blame] | 287 | bool HalPolicy::ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data) |
| 288 | { |
| 289 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 290 | |
| 291 | if (!input.IsValid()) |
| 292 | { |
| 293 | return Fail("%s: Operation has invalid inputs", __func__); |
| 294 | } |
| 295 | |
| 296 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 297 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 298 | unsigned int spatialDim = rank - 2; |
| 299 | |
| 300 | if (rank != 4) |
| 301 | { |
| 302 | Fail("%s: Only inputs with rank 4 are supported", __func__); |
| 303 | } |
| 304 | |
| 305 | armnn::SpaceToBatchNdDescriptor descriptor; |
| 306 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 307 | |
| 308 | const Operand* blockShapeOperand = GetInputOperand(operation, 1, model); |
| 309 | const Operand* paddingsOperand = GetInputOperand(operation, 2, model); |
| 310 | |
| 311 | armnn::TensorShape blockShapeOperandShape = GetTensorShapeForOperand(*blockShapeOperand); |
| 312 | if (blockShapeOperandShape.GetNumDimensions() != 1 || blockShapeOperandShape.GetNumElements() != spatialDim) |
| 313 | { |
| 314 | return Fail("%s: Operation has invalid block shape operand: expected shape [%d]", __func__, spatialDim); |
| 315 | } |
| 316 | |
| 317 | std::vector<int32_t> blockShape; |
| 318 | GetTensorInt32Values(*blockShapeOperand, blockShape, model, data); |
| 319 | for (unsigned int i = 0; i < blockShape.size(); i++) |
| 320 | { |
| 321 | if (blockShape[i] < 1) |
| 322 | { |
| 323 | return Fail("%s: Block shape must be at least 1 in all dimensions.", __func__); |
| 324 | } |
| 325 | |
| 326 | descriptor.m_BlockShape.push_back((unsigned int) blockShape[i]); |
| 327 | } |
| 328 | |
| 329 | armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand); |
| 330 | if (paddingsOperandShape.GetNumDimensions() != 2 || paddingsOperandShape.GetNumElements() != 2 * spatialDim) |
| 331 | { |
| 332 | return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, spatialDim); |
| 333 | } |
| 334 | |
| 335 | std::vector<int32_t> paddings; |
| 336 | GetTensorInt32Values(*paddingsOperand, paddings, model, data); |
| 337 | for (unsigned int i = 0; i < paddings.size() - 1; i += 2) |
| 338 | { |
| 339 | int paddingBeforeInput = paddings[i]; |
| 340 | int paddingAfterInput = paddings[i + 1]; |
| 341 | if (paddingBeforeInput < 0 || paddingAfterInput < 0) |
| 342 | { |
| 343 | return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__); |
| 344 | } |
| 345 | |
| 346 | descriptor.m_PadList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput); |
| 347 | } |
| 348 | |
| 349 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 350 | if (!output) |
| 351 | { |
| 352 | return Fail("%s: Could not read output 0", __func__); |
| 353 | } |
| 354 | |
| 355 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 356 | if (!IsLayerSupported(__func__, |
| 357 | armnn::IsSpaceToBatchNdSupported, |
| 358 | data.m_Compute, |
| 359 | inputInfo, |
| 360 | outputInfo, |
| 361 | descriptor)) |
| 362 | { |
| 363 | return false; |
| 364 | } |
| 365 | |
| 366 | armnn::IConnectableLayer* const layer = data.m_Network->AddSpaceToBatchNdLayer(descriptor); |
| 367 | assert(layer != nullptr); |
| 368 | input.Connect(layer->GetInputSlot(0)); |
| 369 | |
| 370 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 371 | } |
| 372 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 373 | bool HalPolicy::ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data) |
| 374 | { |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 375 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 376 | |
| 377 | if (!input.IsValid()) |
| 378 | { |
| 379 | return Fail("%s: Operation has invalid inputs", __func__); |
| 380 | } |
| 381 | |
| 382 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 383 | |
| 384 | unsigned int rank = inputInfo.GetNumDimensions(); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 385 | if (rank > 4) |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 386 | { |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 387 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 388 | } |
| 389 | |
| 390 | // NOTE: Axis is an optional parameter to SQUEEZE, therefore we do not want to generate a failure |
| 391 | // if the operand index is out of bounds. |
| 392 | const Operand* axisOperand = GetInputOperand(operation, 1, model, false); |
| 393 | |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 394 | const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 395 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 396 | std::vector<int32_t> axis; |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 397 | if (!axisOperand) |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 398 | { |
| 399 | axis.assign(dimensionSequence, |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 400 | dimensionSequence + rank); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 401 | } |
| 402 | else |
| 403 | { |
| 404 | GetTensorInt32Values(*axisOperand, axis, model, data); |
| 405 | } |
| 406 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 407 | |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame] | 408 | std::vector<uint32_t> outputDims; |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 409 | for (unsigned int i = 0; i < rank; i++) |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame] | 410 | { |
| 411 | bool skipSqueeze = (std::find(axis.begin(), axis.end(), i) == axis.end()); |
| 412 | auto currentDimension = inputInfo.GetShape()[i]; |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 413 | if (skipSqueeze || currentDimension != 1) |
| 414 | { |
| 415 | outputDims.push_back(currentDimension); |
| 416 | } |
| 417 | } |
| 418 | |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 419 | armnn::TensorShape outShape = armnn::TensorShape(outputDims.size(), outputDims.data()); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 420 | |
| 421 | armnn::TensorInfo outputInfo = inputInfo; |
| 422 | outputInfo.SetShape(outShape); |
| 423 | |
| 424 | armnn::ReshapeDescriptor reshapeDesc; |
| 425 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 426 | |
| 427 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 428 | if (!output) |
| 429 | { |
| 430 | return Fail("%s: Could not read output 0", __func__); |
| 431 | } |
| 432 | |
| 433 | if (!IsLayerSupported(__func__, |
| 434 | armnn::IsReshapeSupported, |
| 435 | data.m_Compute, |
| 436 | inputInfo)) |
| 437 | { |
| 438 | return false; |
| 439 | } |
| 440 | |
| 441 | armnn::IConnectableLayer* const layer = data.m_Network->AddReshapeLayer(reshapeDesc); |
| 442 | assert(layer != nullptr); |
| 443 | input.Connect(layer->GetInputSlot(0)); |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 444 | |
| 445 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 446 | } |
| 447 | |
Sadik Armagan | 758eee8 | 2018-11-15 15:34:49 +0000 | [diff] [blame^] | 448 | bool HalPolicy::ConvertStridedSlice(const Operation& operation, const Model& model, ConversionData& data) |
| 449 | { |
| 450 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 451 | if (!input.IsValid()) |
| 452 | { |
| 453 | return Fail("%s: Operation has invalid inputs", __func__); |
| 454 | } |
| 455 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 456 | |
| 457 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 458 | if (rank > 4) |
| 459 | { |
| 460 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 461 | } |
| 462 | |
| 463 | const Operand* beginOperand = GetInputOperand(operation, 1, model); |
| 464 | const Operand* endOperand = GetInputOperand(operation, 2, model); |
| 465 | const Operand* stridesOperand = GetInputOperand(operation, 3, model); |
| 466 | |
| 467 | std::vector<int32_t> beginValues; |
| 468 | std::vector<int32_t> endValues; |
| 469 | std::vector<int32_t> stridesValues; |
| 470 | |
| 471 | // The length of the beginOperand, endOperand and stridesOperand must be of a rank(input) |
| 472 | auto ValidateInputOperands = [&] (const Operand& operand, std::vector<int32_t>& operandValues) |
| 473 | { |
| 474 | if (!GetTensorInt32Values(operand, operandValues, model, data)) |
| 475 | { |
| 476 | return false; |
| 477 | } |
| 478 | |
| 479 | if (operandValues.size() != rank) |
| 480 | { |
| 481 | return false; |
| 482 | } |
| 483 | |
| 484 | return true; |
| 485 | }; |
| 486 | |
| 487 | if (!ValidateInputOperands(*beginOperand, beginValues) |
| 488 | || !ValidateInputOperands(*endOperand, endValues) |
| 489 | || !ValidateInputOperands(*stridesOperand, stridesValues)) |
| 490 | { |
| 491 | return Fail("%s: Operation has invalid input operand", __func__); |
| 492 | } |
| 493 | |
| 494 | // Stride cannot have value '0' |
| 495 | if (std::any_of(stridesValues.cbegin(), stridesValues.cend(), [](int32_t i){ return i == 0; })) |
| 496 | { |
| 497 | return Fail("%s: Stride must be non-zero value.", __func__); |
| 498 | } |
| 499 | |
| 500 | armnn::StridedSliceDescriptor descriptor; |
| 501 | descriptor.m_Begin.assign(beginValues.cbegin(), beginValues.cend()); |
| 502 | descriptor.m_End.assign(endValues.cbegin(), endValues.cend()); |
| 503 | descriptor.m_Stride.assign(stridesValues.cbegin(), stridesValues.cend()); |
| 504 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 505 | |
| 506 | // Get the "begin_mask", "end_mask", and "shrink_axis_mask" flags |
| 507 | if (!GetInputInt32(operation, 4, descriptor.m_BeginMask, model, data) |
| 508 | || !GetInputInt32(operation, 5, descriptor.m_EndMask, model, data) |
| 509 | || !GetInputInt32(operation, 6, descriptor.m_ShrinkAxisMask, model, data)) |
| 510 | { |
| 511 | return Fail("%s: Operation has invalid inputs", __func__); |
| 512 | } |
| 513 | |
| 514 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 515 | if (!output) |
| 516 | { |
| 517 | return Fail("%s: Could not read output 0", __func__); |
| 518 | } |
| 519 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 520 | |
| 521 | if (!IsLayerSupported(__func__, |
| 522 | armnn::IsStridedSliceSupported, |
| 523 | data.m_Compute, |
| 524 | inputInfo, |
| 525 | outputInfo, |
| 526 | descriptor)) |
| 527 | { |
| 528 | return false; |
| 529 | } |
| 530 | |
| 531 | armnn::IConnectableLayer* const layer = data.m_Network->AddStridedSliceLayer(descriptor); |
| 532 | assert(layer != nullptr); |
| 533 | input.Connect(layer->GetInputSlot(0)); |
| 534 | |
| 535 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 536 | } |
| 537 | |
saoste01 | fe46315 | 2018-10-18 17:49:56 +0100 | [diff] [blame] | 538 | bool HalPolicy::ConvertTranspose(const Operation& operation, const Model& model, ConversionData& data) |
| 539 | { |
| 540 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 541 | |
| 542 | if (!input.IsValid()) |
| 543 | { |
| 544 | return Fail("%s: Operation has invalid inputs", __func__); |
| 545 | } |
| 546 | |
| 547 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 548 | |
| 549 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 550 | if (rank > 4) |
| 551 | { |
| 552 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 553 | } |
| 554 | |
| 555 | // NOTE: Axis is an optional parameter to TRANSPOSE, therefore we do not want to generate a failure |
| 556 | // if the operand index is out of bounds. |
| 557 | const Operand* permOperand = GetInputOperand(operation, 1, model, false); |
| 558 | |
| 559 | std::vector<int32_t> perm(rank); |
| 560 | if (!permOperand) |
| 561 | { |
| 562 | // NOTE: If perm is not given, it is set to (n-1...0), where n is the rank of the tensor |
| 563 | for (unsigned int i = rank; i > 0; i--) |
| 564 | { |
| 565 | perm[rank - i] = boost::numeric_cast<int> (i - 1); |
| 566 | } |
| 567 | } |
| 568 | else |
| 569 | { |
| 570 | GetTensorInt32Values(*permOperand, perm, model, data); |
| 571 | } |
| 572 | |
| 573 | std::vector<uint32_t> outputDims(perm.begin(), perm.begin() + rank); |
| 574 | |
| 575 | auto permutationVector = armnn::PermutationVector(outputDims.data(), outputDims.size()); |
| 576 | if (!permutationVector.IsEqual(NHWCToArmNN) |
| 577 | && !permutationVector.IsEqual(ArmNNToNHWC) |
| 578 | && !permutationVector.IsEqual({ 3, 2, 0, 1 })) |
| 579 | { |
| 580 | return Fail("%s: Only [0, 3, 1, 2], [0, 2, 3, 1] and [3, 2, 0, 1] permutations are supported.", __func__); |
| 581 | } |
| 582 | |
| 583 | armnn::PermuteDescriptor permuteDesc; |
| 584 | permuteDesc.m_DimMappings = permutationVector; |
| 585 | |
| 586 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 587 | if (!output) |
| 588 | { |
| 589 | return Fail("%s: Could not read output 0", __func__); |
| 590 | } |
| 591 | |
| 592 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 593 | |
| 594 | if (!IsLayerSupported(__func__, |
| 595 | armnn::IsPermuteSupported, |
| 596 | data.m_Compute, |
| 597 | inputInfo, |
| 598 | outputInfo, |
| 599 | permuteDesc)) |
| 600 | { |
| 601 | return false; |
| 602 | } |
| 603 | |
| 604 | armnn::IConnectableLayer* const layer = data.m_Network->AddPermuteLayer(permuteDesc); |
| 605 | assert(layer != nullptr); |
| 606 | input.Connect(layer->GetInputSlot(0)); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 607 | |
| 608 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 609 | } |
| 610 | |
Éanna Ó Catháin | 2cd99b9 | 2018-11-14 14:33:52 +0000 | [diff] [blame] | 611 | bool HalPolicy::ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data) |
| 612 | { |
| 613 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 614 | if (!input.IsValid()) |
| 615 | { |
| 616 | return Fail("%s: Operation has invalid inputs", __func__); |
| 617 | } |
| 618 | |
| 619 | const Operand* blockOperand = GetInputOperand(operation, 1, model); |
| 620 | if (!blockOperand) |
| 621 | { |
| 622 | return Fail("%s: Could not read input 1", __func__); |
| 623 | } |
| 624 | |
| 625 | // Convert the block operand to int32 |
| 626 | std::vector<int32_t> block; |
| 627 | if (!GetTensorInt32Values(*blockOperand, block, model, data)) |
| 628 | { |
| 629 | return Fail("%s: Input 1 has invalid values", __func__); |
| 630 | } |
| 631 | |
| 632 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 633 | |
| 634 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 635 | if (rank != 4) |
| 636 | { |
| 637 | Fail("%s: Only inputs with rank equal to 4 are supported", __func__); |
| 638 | } |
| 639 | |
| 640 | if (std::any_of(block.cbegin(), block.cend(), [](int32_t i){ return i < 1; })) |
| 641 | { |
| 642 | return Fail("%s: Block sizes for each spatial dimension of the input tensor must be" |
| 643 | " greater than or equal to 1", __func__); |
| 644 | } |
| 645 | |
| 646 | armnn::BatchToSpaceNdDescriptor batchToSpaceNdDesc; |
| 647 | batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend()); |
| 648 | batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC; |
| 649 | |
| 650 | // Setting crops to 0,0 0,0 as it is not supported in Android NN API |
| 651 | batchToSpaceNdDesc.m_Crops = {{0, 0}, {0, 0}}; |
| 652 | |
| 653 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 654 | if (!output) |
| 655 | { |
| 656 | return Fail("%s: Could not read output 0", __func__); |
| 657 | } |
| 658 | |
| 659 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 660 | |
| 661 | if (!IsLayerSupported(__func__, |
| 662 | armnn::IsBatchToSpaceNdSupported, |
| 663 | data.m_Compute, |
| 664 | inputInfo, |
| 665 | outputInfo, |
| 666 | batchToSpaceNdDesc)) |
| 667 | { |
| 668 | return false; |
| 669 | } |
| 670 | |
| 671 | armnn::IConnectableLayer* const layer = data.m_Network->AddBatchToSpaceNdLayer(batchToSpaceNdDesc); |
| 672 | assert(layer != nullptr); |
| 673 | input.Connect(layer->GetInputSlot(0)); |
| 674 | |
| 675 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 676 | } |
| 677 | |
| 678 | |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 679 | } // namespace hal_1_1 |
Éanna Ó Catháin | 2cd99b9 | 2018-11-14 14:33:52 +0000 | [diff] [blame] | 680 | } // namespace armnn_driver |