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); |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 38 | default: |
| 39 | return Fail("%s: Operation type %s not supported in ArmnnDriver", |
| 40 | __func__, toString(operation.type).c_str()); |
| 41 | } |
| 42 | } |
| 43 | } |
| 44 | |
| 45 | bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data) |
| 46 | { |
| 47 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 48 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 49 | |
| 50 | if (!input0.IsValid() || !input1.IsValid()) |
| 51 | { |
| 52 | return Fail("%s: Operation has invalid inputs", __func__); |
| 53 | } |
| 54 | |
| 55 | // The FuseActivation parameter is always the input index 2 |
| 56 | // and it should be optional |
| 57 | ActivationFn activationFunction; |
| 58 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 59 | { |
| 60 | return Fail("%s: Operation has invalid inputs", __func__); |
| 61 | } |
| 62 | |
| 63 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 64 | if (!outputOperand) |
| 65 | { |
| 66 | return false; |
| 67 | } |
| 68 | |
| 69 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 70 | |
| 71 | if (!IsLayerSupported(__func__, |
| 72 | armnn::IsDivisionSupported, |
| 73 | data.m_Compute, |
| 74 | input0.GetTensorInfo(), |
| 75 | input1.GetTensorInfo(), |
| 76 | outInfo)) |
| 77 | { |
| 78 | return false; |
| 79 | } |
| 80 | |
| 81 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddDivisionLayer(); |
| 82 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 83 | |
| 84 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 85 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 86 | |
| 87 | if (endLayer) |
| 88 | { |
| 89 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 90 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 91 | } |
| 92 | |
| 93 | return Fail("%s: ProcessActivation failed", __func__); |
| 94 | } |
| 95 | |
David Beck | 38e1294 | 2018-09-12 16:02:24 +0100 | [diff] [blame] | 96 | bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data) |
| 97 | { |
| 98 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 99 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 100 | |
| 101 | if (!input0.IsValid() || !input1.IsValid()) |
| 102 | { |
| 103 | return Fail("%s: Operation has invalid inputs", __func__); |
| 104 | } |
| 105 | |
| 106 | // The FuseActivation parameter is always the input index 2 |
| 107 | // and it should be optional |
| 108 | ActivationFn activationFunction; |
| 109 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 110 | { |
| 111 | return Fail("%s: Operation has invalid inputs", __func__); |
| 112 | } |
| 113 | |
| 114 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 115 | if (!outputOperand) |
| 116 | { |
| 117 | return false; |
| 118 | } |
| 119 | |
| 120 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 121 | |
| 122 | if (!IsLayerSupported(__func__, |
| 123 | armnn::IsSubtractionSupported, |
| 124 | data.m_Compute, |
| 125 | input0.GetTensorInfo(), |
| 126 | input1.GetTensorInfo(), |
| 127 | outInfo)) |
| 128 | { |
| 129 | return false; |
| 130 | } |
| 131 | |
| 132 | armnn::IConnectableLayer* const startLayer = data.m_Network->AddSubtractionLayer(); |
| 133 | armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data); |
| 134 | |
| 135 | const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo(); |
| 136 | const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo(); |
| 137 | |
| 138 | if (endLayer) |
| 139 | { |
| 140 | BroadcastTensor(input0, input1, startLayer, *data.m_Network); |
| 141 | return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data); |
| 142 | } |
| 143 | |
| 144 | return Fail("%s: ProcessActivation failed", __func__); |
| 145 | } |
| 146 | |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 147 | bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data) |
| 148 | { |
| 149 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 150 | |
| 151 | if (!input.IsValid()) |
| 152 | { |
| 153 | return Fail("%s: Operation has invalid inputs", __func__); |
| 154 | } |
| 155 | |
| 156 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 157 | |
| 158 | armnn::MeanDescriptor descriptor; |
| 159 | |
| 160 | const Operand* axisOperand = GetInputOperand(operation, 1, model); |
| 161 | if (axisOperand) |
| 162 | { |
| 163 | std::vector<int32_t> axis; |
| 164 | GetTensorInt32Values(*axisOperand, axis, model, data); |
| 165 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 166 | // convert the axis to unsigned int. |
| 167 | for (auto& i : axis) |
| 168 | { |
| 169 | unsigned int unsignedAxis = (i + rank) % rank; |
| 170 | if (std::find(descriptor.m_Axis.begin(), descriptor.m_Axis.end(), unsignedAxis) == descriptor.m_Axis.end()) |
| 171 | { |
| 172 | descriptor.m_Axis.push_back(unsignedAxis); |
| 173 | } |
| 174 | } |
| 175 | } |
| 176 | |
| 177 | int32_t keepDims; |
| 178 | GetInputInt32(operation, 2, keepDims, model, data); |
| 179 | if (keepDims > 0) |
| 180 | { |
| 181 | descriptor.m_KeepDims = true; |
| 182 | } |
| 183 | |
| 184 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 185 | if (!output) |
| 186 | { |
| 187 | return Fail("%s: Could not read output 0", __func__); |
| 188 | } |
| 189 | |
| 190 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 191 | |
| 192 | if (!IsLayerSupported(__func__, |
| 193 | armnn::IsMeanSupported, |
| 194 | data.m_Compute, |
| 195 | inputInfo, |
| 196 | outputInfo, |
| 197 | descriptor)) |
| 198 | { |
| 199 | return false; |
| 200 | } |
| 201 | |
| 202 | armnn::IConnectableLayer* const layer = data.m_Network->AddMeanLayer(descriptor); |
narpra01 | 96bedf0 | 2018-09-26 16:57:28 +0100 | [diff] [blame] | 203 | assert(layer != nullptr); |
| 204 | input.Connect(layer->GetInputSlot(0)); |
| 205 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
narpra01 | 3c05256 | 2018-09-17 14:25:04 +0100 | [diff] [blame] | 206 | |
| 207 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 208 | } |
| 209 | |
Nina Drozd | 62a4a9f | 2018-10-01 14:20:25 +0100 | [diff] [blame] | 210 | bool HalPolicy::ConvertPad(const Operation& operation, const Model& model, ConversionData& data) |
| 211 | { |
| 212 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 213 | |
| 214 | if (!input.IsValid()) |
| 215 | { |
| 216 | return Fail("%s: Operation has invalid inputs", __func__); |
| 217 | } |
| 218 | |
| 219 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 220 | |
| 221 | const Operand* paddingsOperand = GetInputOperand(operation, 1, model); |
| 222 | |
| 223 | if (!paddingsOperand) |
| 224 | { |
| 225 | return Fail("%s: Could not read paddings operand", __func__); |
| 226 | } |
| 227 | |
| 228 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 229 | armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand); |
| 230 | if (paddingsOperandShape.GetNumDimensions() != rank || paddingsOperandShape.GetNumElements() != 2) |
| 231 | { |
| 232 | return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, rank); |
| 233 | } |
| 234 | |
| 235 | std::vector<int32_t> paddings; |
| 236 | GetTensorInt32Values(*paddingsOperand, paddings, model, data); |
| 237 | |
| 238 | // add padding for each dimension of input tensor. |
| 239 | armnn::PadDescriptor descriptor; |
| 240 | for (unsigned int i = 0; i < paddings.size() - 1; i += 2) |
| 241 | { |
| 242 | int paddingBeforeInput = paddings[i]; |
| 243 | int paddingAfterInput = paddings[i + 1]; |
| 244 | if (paddingBeforeInput < 0 || paddingAfterInput < 0) |
| 245 | { |
| 246 | return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__); |
| 247 | } |
| 248 | descriptor.m_PadList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput); |
| 249 | } |
| 250 | |
| 251 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 252 | if (!output) |
| 253 | { |
| 254 | return Fail("%s: Could not read output 0", __func__); |
| 255 | } |
| 256 | |
| 257 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 258 | |
| 259 | if (!IsLayerSupported(__func__, |
| 260 | armnn::IsPadSupported, |
| 261 | data.m_Compute, |
| 262 | inputInfo, |
| 263 | outputInfo, |
| 264 | descriptor)) |
| 265 | { |
| 266 | return false; |
| 267 | } |
| 268 | |
| 269 | armnn::IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); |
| 270 | assert(layer != nullptr); |
| 271 | input.Connect(layer->GetInputSlot(0)); |
| 272 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 273 | |
| 274 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 275 | } |
| 276 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 277 | bool HalPolicy::ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data) |
| 278 | { |
| 279 | static const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 280 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 281 | |
| 282 | if (!input.IsValid()) |
| 283 | { |
| 284 | return Fail("%s: Operation has invalid inputs", __func__); |
| 285 | } |
| 286 | |
| 287 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 288 | |
| 289 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 290 | if( rank > 4 ) |
| 291 | { |
| 292 | Fail("%s: Inputs with rank greater than: %i are not supported", __func__, rank); |
| 293 | } |
| 294 | |
| 295 | // NOTE: Axis is an optional parameter to SQUEEZE, therefore we do not want to generate a failure |
| 296 | // if the operand index is out of bounds. |
| 297 | const Operand* axisOperand = GetInputOperand(operation, 1, model, false); |
| 298 | |
| 299 | std::vector<int32_t> axis; |
| 300 | if(!axisOperand) |
| 301 | { |
| 302 | axis.assign(dimensionSequence, |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame^] | 303 | dimensionSequence+rank); |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 304 | } |
| 305 | else |
| 306 | { |
| 307 | GetTensorInt32Values(*axisOperand, axis, model, data); |
| 308 | } |
| 309 | |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 310 | |
saoste01 | a893efa | 2018-10-13 11:56:12 +0100 | [diff] [blame^] | 311 | std::vector<uint32_t> outputDims; |
| 312 | for(unsigned int i = 0; i < rank; i++) |
| 313 | { |
| 314 | bool skipSqueeze = (std::find(axis.begin(), axis.end(), i) == axis.end()); |
| 315 | auto currentDimension = inputInfo.GetShape()[i]; |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 316 | if (skipSqueeze || currentDimension != 1) |
| 317 | { |
| 318 | outputDims.push_back(currentDimension); |
| 319 | } |
| 320 | } |
| 321 | |
| 322 | armnn::TensorShape outShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data()); |
| 323 | |
| 324 | armnn::TensorInfo outputInfo = inputInfo; |
| 325 | outputInfo.SetShape(outShape); |
| 326 | |
| 327 | armnn::ReshapeDescriptor reshapeDesc; |
| 328 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 329 | |
| 330 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 331 | if (!output) |
| 332 | { |
| 333 | return Fail("%s: Could not read output 0", __func__); |
| 334 | } |
| 335 | |
| 336 | if (!IsLayerSupported(__func__, |
| 337 | armnn::IsReshapeSupported, |
| 338 | data.m_Compute, |
| 339 | inputInfo)) |
| 340 | { |
| 341 | return false; |
| 342 | } |
| 343 | |
| 344 | armnn::IConnectableLayer* const layer = data.m_Network->AddReshapeLayer(reshapeDesc); |
| 345 | assert(layer != nullptr); |
| 346 | input.Connect(layer->GetInputSlot(0)); |
| 347 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 348 | |
| 349 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 350 | } |
| 351 | |
arovir01 | b0717b5 | 2018-09-05 17:03:25 +0100 | [diff] [blame] | 352 | } // namespace hal_1_1 |
saoste01 | b847148 | 2018-10-10 09:44:51 +0100 | [diff] [blame] | 353 | } // namespace armnn_driver |