telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4 | // |
| 5 | #include "WorkloadData.hpp" |
| 6 | |
| 7 | #include "CpuTensorHandle.hpp" |
| 8 | #include "WorkloadInfo.hpp" |
| 9 | |
| 10 | #include <algorithm> |
| 11 | #include <string> |
| 12 | #include <sstream> |
| 13 | #include <iomanip> |
| 14 | |
| 15 | #include <boost/format.hpp> |
| 16 | |
| 17 | namespace armnn |
| 18 | { |
| 19 | |
| 20 | //--------------------------------------------------------------- |
| 21 | DataType GetBiasDataType(DataType inputDataType) |
| 22 | { |
| 23 | switch (inputDataType) |
| 24 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 25 | case DataType::Float16: |
| 26 | return DataType::Float16; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 27 | case DataType::Float32: |
| 28 | return DataType::Float32; |
| 29 | case DataType::QuantisedAsymm8: |
| 30 | return DataType::Signed32; |
| 31 | default: |
| 32 | BOOST_ASSERT_MSG(false, "Invalid input data type"); |
| 33 | return DataType::Float32; |
| 34 | } |
| 35 | } |
| 36 | |
| 37 | namespace |
| 38 | { |
| 39 | |
| 40 | //--------------------------------------------------------------- |
| 41 | //android ndk does not support std::to_string function. |
| 42 | template <typename T> |
| 43 | std::string to_string(T value) |
| 44 | { |
| 45 | std::ostringstream os; |
| 46 | os << value; |
| 47 | return os.str(); |
| 48 | } |
| 49 | |
| 50 | //--------------------------------------------------------------- |
| 51 | void ValidatePointer(const void* ptr, std::string const& descName, std::string const& paramName) |
| 52 | { |
| 53 | if (!ptr) |
| 54 | { |
| 55 | throw InvalidArgumentException(descName + ": Invalid null pointer. The " + |
| 56 | paramName + " parameter must be set."); |
| 57 | } |
| 58 | } |
| 59 | |
| 60 | //--------------------------------------------------------------- |
| 61 | void ValidateTensorShapesMatch(const TensorInfo& first, |
| 62 | const TensorInfo& second, |
| 63 | std::string const& descName, |
| 64 | std::string const& firstName, |
| 65 | std::string const& secondName) |
| 66 | { |
| 67 | if (first.GetShape() != second.GetShape()) |
| 68 | { |
| 69 | throw InvalidArgumentException(descName + ": " |
| 70 | + firstName + " & " + secondName + " must have identical shapes"); |
| 71 | } |
| 72 | } |
| 73 | |
| 74 | //--------------------------------------------------------------- |
| 75 | void ValidateNoInputs(const WorkloadInfo& workloadInfo, std::string const& descName) |
| 76 | { |
| 77 | if (workloadInfo.m_InputTensorInfos.size() != 0) |
| 78 | { |
| 79 | throw InvalidArgumentException(descName + |
| 80 | ": Requires no inputs. " + |
| 81 | to_string(workloadInfo.m_InputTensorInfos.size()) + " has been provided."); |
| 82 | } |
| 83 | } |
| 84 | |
| 85 | //--------------------------------------------------------------- |
| 86 | void ValidateSingleInput(const WorkloadInfo& workloadInfo, std::string const& descName) |
| 87 | { |
| 88 | if (workloadInfo.m_InputTensorInfos.size() != 1) |
| 89 | { |
| 90 | throw InvalidArgumentException(descName + |
| 91 | ": Requires exactly one input. " + |
| 92 | to_string(workloadInfo.m_InputTensorInfos.size()) + " has been provided." ); |
| 93 | } |
| 94 | } |
| 95 | |
| 96 | //--------------------------------------------------------------- |
| 97 | void ValidateTwoInputs(const WorkloadInfo& workloadInfo, std::string const& descName) |
| 98 | { |
| 99 | if (workloadInfo.m_InputTensorInfos.size() != 2) |
| 100 | { |
| 101 | throw InvalidArgumentException(descName + |
| 102 | ": Requires exactly two workloadInfo.m_InputTensorInfos. " + |
| 103 | to_string(workloadInfo.m_InputTensorInfos.size()) + " have been provided."); |
| 104 | } |
| 105 | } |
| 106 | |
| 107 | //--------------------------------------------------------------- |
| 108 | void ValidateSingleOutput(const WorkloadInfo& workloadInfo, std::string const& descName) |
| 109 | { |
| 110 | if (workloadInfo.m_OutputTensorInfos.size() != 1) |
| 111 | { |
| 112 | throw InvalidArgumentException(descName + |
| 113 | ": Requires exactly one output. " + |
| 114 | to_string(workloadInfo.m_OutputTensorInfos.size()) + " has been provided."); |
| 115 | } |
| 116 | } |
| 117 | |
| 118 | //--------------------------------------------------------------- |
| 119 | void ValidateTensorNumDimensions(const TensorInfo& tensor, |
| 120 | std::string const& descName, |
| 121 | unsigned int numDimensions, |
| 122 | std::string const& tensorName) |
| 123 | { |
| 124 | if (tensor.GetNumDimensions() != numDimensions) |
| 125 | { |
| 126 | throw InvalidArgumentException(descName + ": Expected " + to_string(numDimensions) + " but got " + |
| 127 | to_string(tensor.GetNumDimensions()) + " dimensions for " + |
| 128 | tensorName + " tensor."); |
| 129 | } |
| 130 | } |
| 131 | |
| 132 | //--------------------------------------------------------------- |
| 133 | void ValidateTensorDataType(const TensorInfo& tensor, DataType dataType, |
| 134 | const std::string& descName, std::string const& tensorName) |
| 135 | { |
| 136 | if (tensor.GetDataType() != dataType) |
| 137 | { |
| 138 | throw InvalidArgumentException(descName + ": Expected data type " + GetDataTypeName(dataType) + " but got " + |
| 139 | GetDataTypeName(tensor.GetDataType()) + " for " + tensorName + " tensor."); |
| 140 | } |
| 141 | } |
| 142 | |
| 143 | //--------------------------------------------------------------- |
| 144 | void ValidateBiasTensorQuantization(const TensorInfo& biasTensor, const TensorInfo& inputTensorInfo, |
| 145 | const TensorInfo& weightsTensorInfo, const std::string& descName) |
| 146 | { |
| 147 | if (biasTensor.GetQuantizationOffset() != 0) |
| 148 | { |
| 149 | throw InvalidArgumentException(descName + ": Expected zero quantization offset for bias tensor but got " + |
| 150 | to_string(biasTensor.GetQuantizationOffset())); |
| 151 | } |
| 152 | const float expectedScale = inputTensorInfo.GetQuantizationScale() * weightsTensorInfo.GetQuantizationScale(); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 153 | if (std::abs(biasTensor.GetQuantizationScale() - expectedScale) > 0.000000001f) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 154 | { |
| 155 | // Print the float values with extra precision to see very small differences |
| 156 | std::stringstream msg; |
| 157 | msg << std::setprecision(10) << descName << ": Expected " << expectedScale << |
| 158 | " quantization scale for bias tensor (the product of the input and weight scales), but got " << |
| 159 | biasTensor.GetQuantizationScale(); |
| 160 | throw InvalidArgumentException(msg.str()); |
| 161 | } |
| 162 | } |
| 163 | |
| 164 | //--------------------------------------------------------------- |
| 165 | void ValidateTensors(const std::vector<ITensorHandle*>& vec, |
| 166 | unsigned int numExpected, |
| 167 | const std::string& descName, |
| 168 | const std::string& varName) |
| 169 | { |
| 170 | if (vec.empty() && numExpected > 0) |
| 171 | { |
| 172 | throw InvalidArgumentException(descName + ": Invalid empty " + varName + " array."); |
| 173 | } |
| 174 | |
| 175 | for (unsigned int i = 0; i < numExpected; ++i) |
| 176 | { |
| 177 | if (!vec[i]) |
| 178 | { |
| 179 | throw InvalidArgumentException(descName + ": Invalid NULL for " + varName + to_string(i)); |
| 180 | } |
| 181 | } |
| 182 | } |
| 183 | |
| 184 | //--------------------------------------------------------------- |
| 185 | void ValidateBroadcastTensorShapesMatch(const TensorInfo& first, |
| 186 | const TensorInfo& second, |
| 187 | const TensorInfo& output, |
| 188 | std::string const& descName, |
| 189 | std::string const& firstName, |
| 190 | std::string const& secondName) |
| 191 | { |
| 192 | // Tensors must have the same number of dimensions in order to be explicit about which dimensions will get |
| 193 | // broadcasted. |
| 194 | if (first.GetNumDimensions() != second.GetNumDimensions()) |
| 195 | { |
| 196 | throw InvalidArgumentException(descName + ": Tensors " |
| 197 | + firstName + " & " + secondName |
| 198 | + " must have the same number of dimensions in order to be broadcasted"); |
| 199 | } |
| 200 | uint32_t numDims = first.GetNumDimensions(); |
| 201 | std::vector<uint32_t> outputDims(numDims, 0u); |
| 202 | for (uint32_t i = 0; i < numDims; i++) |
| 203 | { |
| 204 | const bool dimsNotEqual = first.GetShape()[i] != second.GetShape()[i]; |
| 205 | const bool dimsNotOne = (first.GetShape()[i] != 1) && (second.GetShape()[i] != 1); |
| 206 | if (dimsNotEqual && dimsNotOne) |
| 207 | { |
| 208 | throw InvalidArgumentException("Broadcasting is not possible for incompatible shapes"); |
| 209 | } |
| 210 | outputDims[i] = std::max(first.GetShape()[i], second.GetShape()[i]); |
| 211 | } |
| 212 | TensorShape broadcastShape = TensorShape(boost::numeric_cast<unsigned int>(outputDims.size()), outputDims.data()); |
| 213 | if (broadcastShape != output.GetShape()) |
| 214 | { |
| 215 | throw InvalidArgumentException(descName + ": The tensor shape resulting from adding " |
| 216 | + firstName + " & " + secondName |
| 217 | + " does not match the output shape"); |
| 218 | } |
| 219 | } |
| 220 | |
| 221 | //--------------------------------------------------------------- |
| 222 | /// Validates that the output tensor's quantization scale is greater than the product |
| 223 | /// of the two input tensors' quantization scales. This is a requirement of the implementation of |
| 224 | /// the quantized multiplication. |
| 225 | void ValidateTensorQuantizationMultiplier(const TensorInfo& inputTensor1, const TensorInfo& inputTensor2, |
| 226 | const TensorInfo& outputTensorInfo, std::string const& descName, |
| 227 | const std::string& inputTensor1Name, const std::string& inputTensor2Name, const std::string& outputTensorName) |
| 228 | { |
| 229 | if (outputTensorInfo.GetDataType() == DataType::QuantisedAsymm8) |
| 230 | { |
| 231 | if (outputTensorInfo.GetQuantizationScale() <= |
| 232 | inputTensor1.GetQuantizationScale() * inputTensor2.GetQuantizationScale()) |
| 233 | { |
| 234 | std::stringstream msg; |
| 235 | msg << descName << ": Quantization scale of " << outputTensorName << " is not greater than " << |
| 236 | "the product of the " << inputTensor1Name << " and " << inputTensor2Name << " tensors"; |
| 237 | throw InvalidArgumentException(msg.str()); |
| 238 | } |
| 239 | } |
| 240 | } |
| 241 | |
| 242 | } //namespace |
| 243 | |
| 244 | void QueueDescriptor::ValidateInputsOutputs(const std::string& descName, |
| 245 | unsigned int numExpectedIn, unsigned int numExpectedOut) const |
| 246 | { |
| 247 | ValidateTensors(m_Inputs, numExpectedIn, descName, "input"); |
| 248 | ValidateTensors(m_Outputs, numExpectedOut, descName, "output"); |
| 249 | } |
| 250 | |
| 251 | //--------------------------------------------------------------- |
| 252 | void MemCopyQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 253 | { |
| 254 | ValidateSingleInput(workloadInfo, "MemCopyQueueDescriptor"); |
| 255 | ValidateSingleOutput(workloadInfo, "MemCopyQueueDescriptor"); |
| 256 | |
| 257 | if (workloadInfo.m_InputTensorInfos.size() != workloadInfo.m_OutputTensorInfos.size()) |
| 258 | { |
| 259 | throw InvalidArgumentException(boost::str( |
| 260 | boost::format("Number of input infos (%1%) does not match the number of output infos (%2%)") |
| 261 | % workloadInfo.m_InputTensorInfos.size() % workloadInfo.m_OutputTensorInfos.size())); |
| 262 | } |
| 263 | |
| 264 | for (std::size_t i = 0; i < workloadInfo.m_InputTensorInfos.size(); ++i) |
| 265 | { |
| 266 | if (workloadInfo.m_InputTensorInfos[i].GetNumElements() != |
| 267 | workloadInfo.m_OutputTensorInfos[i].GetNumElements()) |
| 268 | { |
| 269 | throw InvalidArgumentException(boost::str( |
| 270 | boost::format("Number of elements for tensor input and output %1% does not match") |
| 271 | % i )); |
| 272 | } |
| 273 | } |
| 274 | |
| 275 | if (m_Inputs.size() != m_Outputs.size()) |
| 276 | { |
| 277 | throw InvalidArgumentException(boost::str( |
| 278 | boost::format("Number of inputs (%1%) does not match the number of outputs (%2%)") |
| 279 | % m_Inputs.size() % m_Outputs.size())); |
| 280 | } |
| 281 | |
| 282 | for (unsigned int i = 0; i < m_Inputs.size(); ++i) |
| 283 | { |
| 284 | if (!m_Inputs[i]) |
| 285 | { |
| 286 | throw InvalidArgumentException(boost::str(boost::format("Invalid null input %1%") % i)); |
| 287 | } |
| 288 | |
| 289 | if (!m_Outputs[i]) |
| 290 | { |
| 291 | throw InvalidArgumentException(boost::str(boost::format("Invalid null output %1%") % i)); |
| 292 | } |
| 293 | } |
| 294 | } |
| 295 | |
| 296 | //--------------------------------------------------------------- |
| 297 | void ActivationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 298 | { |
| 299 | ValidateSingleInput(workloadInfo, "ActivationQueueDescriptor"); |
| 300 | ValidateSingleOutput(workloadInfo, "ActivationQueueDescriptor"); |
| 301 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 302 | workloadInfo.m_OutputTensorInfos[0], |
| 303 | "ActivationQueueDescriptor", |
| 304 | "input", |
| 305 | "output"); |
| 306 | } |
| 307 | |
| 308 | //--------------------------------------------------------------- |
| 309 | void SoftmaxQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 310 | { |
| 311 | ValidateSingleInput(workloadInfo, "SoftmaxQueueDescriptor"); |
| 312 | ValidateSingleOutput(workloadInfo, "SoftmaxQueueDescriptor"); |
| 313 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "SoftmaxQueueDescriptor", 2, "input"); |
| 314 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "SoftmaxQueueDescriptor", 2, "output"); |
| 315 | |
| 316 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 317 | workloadInfo.m_OutputTensorInfos[0], |
| 318 | "SoftmaxQueueDescriptor", |
| 319 | "input", |
| 320 | "output"); |
| 321 | } |
| 322 | |
| 323 | //--------------------------------------------------------------- |
| 324 | void SplitterQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 325 | { |
| 326 | ValidateSingleInput(workloadInfo, "SplitterQueueDescriptor"); |
| 327 | |
| 328 | if (workloadInfo.m_OutputTensorInfos.size() <= 0) |
| 329 | { |
| 330 | throw InvalidArgumentException("SplitterQueueDescriptor: At least one output needs to be provided."); |
| 331 | } |
| 332 | |
| 333 | if (workloadInfo.m_OutputTensorInfos.size() != m_ViewOrigins.size()) |
| 334 | { |
| 335 | throw InvalidArgumentException( |
| 336 | "SplitterQueueDescriptor: Number of split windows " |
| 337 | "has to match number of workloadInfo.m_OutputTensorInfos. " |
| 338 | "Number of windows: " + |
| 339 | to_string(m_ViewOrigins.size()) + |
| 340 | ". Number of workloadInfo.m_OutputTensorInfos: " + to_string(workloadInfo.m_OutputTensorInfos.size())); |
| 341 | } |
| 342 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 343 | //The dimensionality of all the windows has to match the dimensionality (not shape) of the input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 344 | std::size_t inputDims = workloadInfo.m_InputTensorInfos[0].GetNumDimensions(); |
| 345 | for(unsigned int w = 0; w < m_ViewOrigins.size(); ++w ) |
| 346 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 347 | //Checks that the dimensionality of input is same as the split windows. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 348 | ViewOrigin const& e = m_ViewOrigins[w]; |
| 349 | if (e.m_Origin.size() != inputDims) |
| 350 | { |
| 351 | throw InvalidArgumentException("SplitterQueueDescriptor: Window origin have to " |
| 352 | "have the same dimensionality as the input tensor. " |
| 353 | "Window origin (index: " + |
| 354 | to_string(w) + ") has " + to_string(e.m_Origin.size()) + |
| 355 | " dimensions, the input " |
| 356 | "tensor has " + |
| 357 | to_string(inputDims) + " dimensions."); |
| 358 | } |
| 359 | for (unsigned int i = 0; i < e.m_Origin.size(); ++i) |
| 360 | { |
| 361 | if (e.m_Origin[i] + workloadInfo.m_OutputTensorInfos[w].GetShape()[i] > |
| 362 | workloadInfo.m_InputTensorInfos[0].GetShape()[i]) |
| 363 | { |
| 364 | throw InvalidArgumentException("SplitterQueueDescriptor: Window extent coordinates have to " |
| 365 | "be smaller or equal than the size of the input in that coord."); |
| 366 | } |
| 367 | } |
| 368 | } |
| 369 | } |
| 370 | |
| 371 | //--------------------------------------------------------------- |
| 372 | void MergerQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 373 | { |
| 374 | ValidateSingleOutput(workloadInfo, "MergerQueueDescriptor"); |
| 375 | |
| 376 | if (m_Inputs.size() <= 0) |
| 377 | { |
| 378 | throw InvalidArgumentException("MergerQueueDescriptor: At least one input needs to be provided."); |
| 379 | } |
| 380 | if (m_Outputs.size() <= 0) |
| 381 | { |
| 382 | throw InvalidArgumentException("MergerQueueDescriptor: At least one output needs to be provided."); |
| 383 | } |
| 384 | |
| 385 | if (workloadInfo.m_InputTensorInfos.size() <= 0) |
| 386 | { |
| 387 | throw InvalidArgumentException("MergerQueueDescriptor: At least one TensorInfo input needs to be provided."); |
| 388 | } |
| 389 | if (workloadInfo.m_OutputTensorInfos.size() <= 0) |
| 390 | { |
| 391 | throw InvalidArgumentException("MergerQueueDescriptor: At least one TensorInfo output needs to be provided."); |
| 392 | } |
| 393 | |
| 394 | if (workloadInfo.m_InputTensorInfos.size() != m_ViewOrigins.size()) |
| 395 | { |
| 396 | throw InvalidArgumentException( |
| 397 | "MergerQueueDescriptor: Number of split windows " |
| 398 | "has to match number of workloadInfo.m_InputTensorInfos. " |
| 399 | "Number of windows: " + |
| 400 | to_string(m_ViewOrigins.size()) + |
| 401 | ". Number of workloadInfo.m_InputTensorInfos: " + to_string(workloadInfo.m_InputTensorInfos.size())); |
| 402 | } |
| 403 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 404 | //The dimensionality of all the windows has to match the dimensionality (not shape) of the output. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 405 | std::size_t outputDims = workloadInfo.m_OutputTensorInfos[0].GetNumDimensions(); |
| 406 | for(unsigned int w = 0; w < m_ViewOrigins.size(); ++w ) |
| 407 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 408 | //Checks that the dimensionality of output is same as the split windows. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 409 | ViewOrigin const& e = m_ViewOrigins[w]; |
| 410 | if (e.m_Origin.size() != outputDims) |
| 411 | { |
| 412 | throw InvalidArgumentException("MergerQueueDescriptor: Window origin have to " |
| 413 | "have the same dimensionality as the output tensor. " |
| 414 | "Window origin (index: " + |
| 415 | to_string(w) + ") has " + to_string(e.m_Origin.size()) + |
| 416 | " dimensions, the output " |
| 417 | "tensor has " + |
| 418 | to_string(outputDims) + " dimensions."); |
| 419 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 420 | //Checks that the merge windows are within the output tensor. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 421 | for (unsigned int i = 0; i < e.m_Origin.size(); ++i) |
| 422 | { |
| 423 | if (e.m_Origin[i] + workloadInfo.m_InputTensorInfos[w].GetShape()[i] |
| 424 | > workloadInfo.m_OutputTensorInfos[0].GetShape()[i]) |
| 425 | { |
| 426 | throw InvalidArgumentException("MergerQueueDescriptor: Window extent coordinates have to " |
| 427 | "be smaller or equal than the size of the output in that coord."); |
| 428 | } |
| 429 | } |
| 430 | } |
| 431 | } |
| 432 | |
| 433 | //--------------------------------------------------------------- |
| 434 | void FullyConnectedQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 435 | { |
| 436 | ValidateSingleInput(workloadInfo, "FullyConnectedQueueDescriptor"); |
| 437 | ValidateSingleOutput(workloadInfo, "FullyConnectedQueueDescriptor"); |
| 438 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "FullyConnectedQueueDescriptor", 2, "output"); |
| 439 | |
| 440 | if (!(workloadInfo.m_InputTensorInfos[0].GetNumDimensions() == 2 || |
| 441 | workloadInfo.m_InputTensorInfos[0].GetNumDimensions() == 4)) |
| 442 | { |
| 443 | throw InvalidArgumentException("FullyConnectedQueueDescriptor: Input tensor must have 2 or 4 dimensions."); |
| 444 | } |
| 445 | |
| 446 | if (m_Weight == nullptr) |
| 447 | { |
| 448 | throw InvalidArgumentException("FullyConnectedQueueDescriptor: Weight tensor descriptor is missing."); |
| 449 | } |
| 450 | |
| 451 | ValidateTensorNumDimensions(m_Weight->GetTensorInfo(), "FullyConnectedQueueDescriptor", 2, "weight"); |
| 452 | |
| 453 | if (m_Parameters.m_BiasEnabled) |
| 454 | { |
| 455 | if (m_Bias == nullptr) |
| 456 | { |
| 457 | throw InvalidArgumentException("FullyConnectedQueueDescriptor: Bias is enabled but " |
| 458 | "bias value tensor descriptor is missing."); |
| 459 | } |
| 460 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 461 | // Validates type and quantization values. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 462 | ValidateBiasTensorQuantization(m_Bias->GetTensorInfo(), |
| 463 | workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), "FullyConnectedQueueDescriptor"); |
| 464 | |
| 465 | ValidateTensorDataType(m_Bias->GetTensorInfo(), |
| 466 | GetBiasDataType(workloadInfo.m_InputTensorInfos[0].GetDataType()), |
| 467 | "FullyConnectedQueueDescriptor", "bias"); |
| 468 | |
| 469 | ValidateTensorNumDimensions(m_Bias->GetTensorInfo(), "FullyConnectedQueueDescriptor", 1, "bias"); |
| 470 | } |
| 471 | |
| 472 | ValidateTensorQuantizationMultiplier(workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), |
| 473 | workloadInfo.m_OutputTensorInfos[0], "FullyConnectedQueueDescriptor", "input", "weights", "output"); |
| 474 | } |
| 475 | |
| 476 | //--------------------------------------------------------------- |
| 477 | void NormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 478 | { |
| 479 | ValidateSingleInput(workloadInfo, "NormalizationQueueDescriptor"); |
| 480 | ValidateSingleOutput(workloadInfo, "NormalizationQueueDescriptor"); |
| 481 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 482 | workloadInfo.m_OutputTensorInfos[0], |
| 483 | "NormalizationQueueDescriptor", |
| 484 | "input", |
| 485 | "output"); |
| 486 | } |
| 487 | |
| 488 | void AdditionQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 489 | { |
| 490 | ValidateTwoInputs(workloadInfo, "AdditionQueueDescriptor"); |
| 491 | ValidateSingleOutput(workloadInfo, "AdditionQueueDescriptor"); |
| 492 | |
| 493 | ValidateBroadcastTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 494 | workloadInfo.m_InputTensorInfos[1], |
| 495 | workloadInfo.m_OutputTensorInfos[0], |
| 496 | "AdditionQueueDescriptor", |
| 497 | "first input", |
| 498 | "second input"); |
| 499 | |
| 500 | } |
| 501 | |
| 502 | //--------------------------------------------------------------- |
| 503 | void MultiplicationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 504 | { |
| 505 | ValidateTwoInputs(workloadInfo, "MultiplicationQueueDescriptor"); |
| 506 | ValidateSingleOutput(workloadInfo, "MultiplicationQueueDescriptor"); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 507 | |
| 508 | ValidateBroadcastTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 509 | workloadInfo.m_InputTensorInfos[1], |
| 510 | workloadInfo.m_OutputTensorInfos[0], |
| 511 | "MultiplicationQueueDescriptor", |
| 512 | "first input", |
| 513 | "second input"); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 514 | } |
| 515 | |
| 516 | void BatchNormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 517 | { |
| 518 | ValidateSingleInput(workloadInfo, "BatchNormalizationQueueDescriptor"); |
| 519 | ValidateSingleOutput(workloadInfo, "BatchNormalizationQueueDescriptor"); |
| 520 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 521 | workloadInfo.m_OutputTensorInfos[0], |
| 522 | "BatchNormalizationQueueDescriptor", |
| 523 | "input", |
| 524 | "output"); |
| 525 | ValidatePointer(m_Mean, "BatchNormalizationQueueDescriptor", "mean"); |
| 526 | ValidatePointer(m_Variance, "BatchNormalizationQueueDescriptor", "variance"); |
| 527 | ValidatePointer(m_Beta, "BatchNormalizationQueueDescriptor", "beta"); |
| 528 | ValidatePointer(m_Gamma, "BatchNormalizationQueueDescriptor", "gamma"); |
| 529 | |
| 530 | |
| 531 | ValidateTensorNumDimensions(m_Mean->GetTensorInfo(), "BatchNormalizationQueueDescriptor", 1, "mean"); |
| 532 | ValidateTensorNumDimensions(m_Variance->GetTensorInfo(), "BatchNormalizationQueueDescriptor", 1, "variance"); |
| 533 | ValidateTensorNumDimensions(m_Beta->GetTensorInfo(), "BatchNormalizationQueueDescriptor", 1, "beta"); |
| 534 | ValidateTensorNumDimensions(m_Gamma->GetTensorInfo(), "BatchNormalizationQueueDescriptor", 1, "gamma"); |
| 535 | |
| 536 | ValidateTensorShapesMatch( |
| 537 | m_Mean->GetTensorInfo(), m_Variance->GetTensorInfo(), "BatchNormalizationQueueDescriptor", "mean", "variance"); |
| 538 | ValidateTensorShapesMatch( |
| 539 | m_Mean->GetTensorInfo(), m_Beta->GetTensorInfo(), "BatchNormalizationQueueDescriptor", "mean", "beta"); |
| 540 | ValidateTensorShapesMatch( |
| 541 | m_Mean->GetTensorInfo(), m_Gamma->GetTensorInfo(), "BatchNormalizationQueueDescriptor", "mean", "gamma"); |
| 542 | } |
| 543 | |
| 544 | void Convolution2dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 545 | { |
| 546 | ValidateSingleInput(workloadInfo, "Convolution2dQueueDescriptor"); |
| 547 | ValidateSingleOutput(workloadInfo, "Convolution2dQueueDescriptor"); |
| 548 | |
| 549 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "Convolution2dQueueDescriptor", 4, "input"); |
| 550 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "Convolution2dQueueDescriptor", 4, "output"); |
| 551 | |
| 552 | ValidatePointer(m_Weight, "Convolution2dQueueDescriptor", "weight"); |
| 553 | ValidateTensorNumDimensions(m_Weight->GetTensorInfo(), "Convolution2dQueueDescriptor", 4, "weight"); |
| 554 | ValidateTensorDataType(m_Weight->GetTensorInfo(), workloadInfo.m_InputTensorInfos[0].GetDataType(), |
| 555 | "Convolution2dQueueDescriptor", "weight"); |
| 556 | if (m_Parameters.m_BiasEnabled) |
| 557 | { |
| 558 | ValidateTensorNumDimensions(m_Bias->GetTensorInfo(), "Convolution2dQueueDescriptor", 1, "bias"); |
| 559 | ValidateTensorDataType(m_Bias->GetTensorInfo(), |
| 560 | GetBiasDataType(workloadInfo.m_InputTensorInfos[0].GetDataType()), |
| 561 | "Convolution2dQueueDescriptor", "bias"); |
| 562 | ValidateBiasTensorQuantization(m_Bias->GetTensorInfo(), |
| 563 | workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), "Convolution2dQueueDescriptor"); |
| 564 | } |
| 565 | |
| 566 | ValidateTensorQuantizationMultiplier(workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), |
| 567 | workloadInfo.m_OutputTensorInfos[0], "Convolution2dQueueDescriptor", "input", "weights", "output"); |
| 568 | } |
| 569 | |
| 570 | void DepthwiseConvolution2dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 571 | { |
| 572 | ValidateSingleInput(workloadInfo, "DepthwiseConvolution2dQueueDescriptor"); |
| 573 | ValidateSingleOutput(workloadInfo, "DepthwiseConvolution2dQueueDescriptor"); |
| 574 | |
| 575 | ValidateTensorNumDimensions( |
| 576 | workloadInfo.m_InputTensorInfos[0], "DepthwiseConvolution2dQueueDescriptor", 4, "input"); |
| 577 | ValidateTensorNumDimensions( |
| 578 | workloadInfo.m_OutputTensorInfos[0], "DepthwiseConvolution2dQueueDescriptor", 4, "output"); |
| 579 | |
| 580 | ValidatePointer(m_Weight, "DepthwiseConvolution2dQueueDescriptor", "weight"); |
| 581 | ValidateTensorNumDimensions(m_Weight->GetTensorInfo(), "DepthwiseConvolution2dQueueDescriptor", 4, "weight"); |
| 582 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 583 | //inputChannels * channelMultiplier should be equal to outputChannels. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 584 | const unsigned int numWeightChannelMultiplier = m_Weight->GetTensorInfo().GetShape()[0]; |
| 585 | const unsigned int numWeightInputChannels = m_Weight->GetTensorInfo().GetShape()[1]; |
| 586 | const unsigned int numWeightOutputChannels = workloadInfo.m_OutputTensorInfos[0].GetShape()[1]; |
| 587 | if (numWeightChannelMultiplier * numWeightInputChannels != numWeightOutputChannels) |
| 588 | { |
| 589 | throw InvalidArgumentException( |
| 590 | boost::str(boost::format("DepthwiseConvolution2dQueueDescriptor: output_channels (provided %1%) should be " |
| 591 | "equal to input_channels (provided %2%) multiplied by channel_multiplier " |
| 592 | "(provided %3%).") |
| 593 | % numWeightOutputChannels % numWeightInputChannels % numWeightChannelMultiplier)); |
| 594 | } |
| 595 | |
| 596 | if (m_Parameters.m_BiasEnabled) |
| 597 | { |
| 598 | ValidatePointer(m_Bias, "DepthwiseConvolution2dQueueDescriptor", "bias"); |
| 599 | ValidateTensorNumDimensions(m_Bias->GetTensorInfo(), "DepthwiseConvolution2dQueueDescriptor", 1, "bias"); |
| 600 | ValidateBiasTensorQuantization(m_Bias->GetTensorInfo(), |
| 601 | workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), "DepthwiseConvolution2dQueueDescriptor"); |
| 602 | |
| 603 | ValidateTensorDataType(m_Bias->GetTensorInfo(), |
| 604 | GetBiasDataType(workloadInfo.m_InputTensorInfos[0].GetDataType()), |
| 605 | "DepthwiseConvolution2dQueueDescriptor", "bias"); |
| 606 | } |
| 607 | |
| 608 | ValidateTensorQuantizationMultiplier(workloadInfo.m_InputTensorInfos[0], m_Weight->GetTensorInfo(), |
| 609 | workloadInfo.m_OutputTensorInfos[0], "DepthwiseConvolution2dQueueDescriptor", "input", "weights", "output"); |
| 610 | } |
| 611 | |
| 612 | void PermuteQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 613 | { |
| 614 | ValidateSingleInput(workloadInfo, "PermuteQueueDescriptor"); |
| 615 | ValidateSingleOutput(workloadInfo, "PermuteQueueDescriptor"); |
| 616 | |
| 617 | const PermutationVector& mapping = m_Parameters.m_DimMappings; |
| 618 | |
| 619 | const TensorInfo& input = workloadInfo.m_InputTensorInfos[0]; |
| 620 | const TensorInfo& output = workloadInfo.m_OutputTensorInfos[0]; |
| 621 | |
| 622 | ValidateTensorNumDimensions(input, "PermuteQueueDescriptor", mapping.GetSize(), "input"); |
| 623 | ValidateTensorNumDimensions(output, "PermuteQueueDescriptor", mapping.GetSize(), "output"); |
| 624 | |
| 625 | for (unsigned int i = 0; i < mapping.GetSize(); ++i) |
| 626 | { |
| 627 | if (input.GetShape()[i] != output.GetShape()[mapping[i]]) |
| 628 | { |
| 629 | throw InvalidArgumentException("PermuteQueueDescriptor: src dimension " + to_string(i) + |
| 630 | " (=" + to_string(input.GetShape()[i]) + ") " + |
| 631 | "must match dst dimension " + to_string(mapping[i]) + |
| 632 | " (=" + to_string(output.GetShape()[mapping[i]]) + ")"); |
| 633 | } |
| 634 | } |
| 635 | } |
| 636 | |
| 637 | void Pooling2dQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 638 | { |
| 639 | ValidateSingleInput(workloadInfo, "Pooling2dQueueDescriptor"); |
| 640 | ValidateSingleOutput(workloadInfo, "Pooling2dQueueDescriptor"); |
| 641 | |
| 642 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "Pooling2dQueueDescriptor", 4, "input"); |
| 643 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "Pooling2dQueueDescriptor", 4, "output"); |
| 644 | } |
| 645 | |
| 646 | void ResizeBilinearQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 647 | { |
| 648 | ValidateSingleInput(workloadInfo, "ResizeBilinearQueueDescriptor"); |
| 649 | ValidateSingleOutput(workloadInfo, "ResizeBilinearQueueDescriptor"); |
| 650 | |
| 651 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "ResizeBilinearQueueDescriptor", 4, "input"); |
| 652 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "ResizeBilinearQueueDescriptor", 4, "output"); |
| 653 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 654 | // Resizes bilinear only changes width and height: batch and channel count must match. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 655 | { |
| 656 | const unsigned int inputBatchSize = workloadInfo.m_InputTensorInfos[0].GetShape()[0]; |
| 657 | const unsigned int outputBatchSize = workloadInfo.m_OutputTensorInfos[0].GetShape()[0]; |
| 658 | if (inputBatchSize != outputBatchSize) |
| 659 | { |
| 660 | throw InvalidArgumentException( |
| 661 | boost::str(boost::format("ResizeBilinearQueueDescriptor: Input batch size (%1%) " |
| 662 | "does not match output batch size (%2%)") % inputBatchSize % outputBatchSize)); |
| 663 | } |
| 664 | } |
| 665 | |
| 666 | { |
| 667 | const unsigned int inputChannelCount = workloadInfo.m_InputTensorInfos[0].GetShape()[1]; |
| 668 | const unsigned int outputChannelCount = workloadInfo.m_OutputTensorInfos[0].GetShape()[1]; |
| 669 | if (inputChannelCount != outputChannelCount) |
| 670 | { |
| 671 | throw InvalidArgumentException( |
| 672 | boost::str(boost::format("ResizeBilinearQueueDescriptor: Input channel count (%1%) " |
| 673 | "does not match output channel count (%2%)") % inputChannelCount % outputChannelCount)); |
| 674 | } |
| 675 | } |
| 676 | } |
| 677 | |
| 678 | void FakeQuantizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 679 | { |
| 680 | ValidateSingleInput(workloadInfo, "FakeQuantizationQueueDescriptor"); |
| 681 | ValidateSingleOutput(workloadInfo, "FakeQuantizationQueueDescriptor"); |
| 682 | |
| 683 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "FakeQuantizationQueueDescriptor", 2, "input"); |
| 684 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "FakeQuantizationQueueDescriptor", 2, "output"); |
| 685 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 686 | workloadInfo.m_OutputTensorInfos[0], |
| 687 | "FakeQuantizationQueueDescriptor", |
| 688 | "input", |
| 689 | "output"); |
| 690 | if (m_Parameters.m_Min > m_Parameters.m_Max) |
| 691 | { |
| 692 | throw InvalidArgumentException("FakeQuantizationQueueDescriptor: min cannot be greater than max"); |
| 693 | } |
| 694 | |
| 695 | } |
| 696 | |
| 697 | void L2NormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 698 | { |
| 699 | ValidateSingleInput(workloadInfo, "L2NormalizationQueueDescriptor"); |
| 700 | ValidateSingleOutput(workloadInfo, "L2NormalizationQueueDescriptor"); |
| 701 | |
| 702 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "L2NormalizationQueueDescriptor", 4, "input"); |
| 703 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "L2NormalizationQueueDescriptor", 4, "output"); |
| 704 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 705 | workloadInfo.m_OutputTensorInfos[0], |
| 706 | "L2NormalizationQueueDescriptor", |
| 707 | "input", |
| 708 | "output"); |
| 709 | } |
| 710 | |
| 711 | void ConstantQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 712 | { |
| 713 | ValidateNoInputs(workloadInfo, "ConstantQueueDescriptor"); |
| 714 | ValidateSingleOutput(workloadInfo, "ConstantQueueDescriptor"); |
| 715 | |
| 716 | if (!m_LayerOutput) |
| 717 | { |
| 718 | throw InvalidArgumentException("ConstantQueueDescriptor: No const input specified"); |
| 719 | } |
| 720 | |
| 721 | ValidateTensorShapesMatch(m_LayerOutput->GetTensorInfo(), |
| 722 | workloadInfo.m_OutputTensorInfos[0], |
| 723 | "ConstantQueueDescriptor", |
| 724 | "constant", |
| 725 | "output"); |
| 726 | } |
| 727 | |
| 728 | void ReshapeQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 729 | { |
| 730 | ValidateSingleInput(workloadInfo, "ReshapeQueueDescriptor"); |
| 731 | ValidateSingleOutput(workloadInfo, "ReshapeQueueDescriptor"); |
| 732 | |
| 733 | if (workloadInfo.m_InputTensorInfos[0].GetNumElements() != workloadInfo.m_OutputTensorInfos[0].GetNumElements()) |
| 734 | { |
| 735 | throw InvalidArgumentException("ReshapeQueueDescriptor: Input tensor has " + |
| 736 | to_string(workloadInfo.m_InputTensorInfos[0].GetNumElements()) + " but output tensor has " + |
| 737 | to_string(workloadInfo.m_OutputTensorInfos[0].GetNumElements()) + " elements."); |
| 738 | } |
| 739 | } |
| 740 | |
| 741 | void FloorQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 742 | { |
| 743 | ValidateSingleInput(workloadInfo, "FloorQueueDescriptor"); |
| 744 | ValidateSingleOutput(workloadInfo, "FlootQueueDescriptor"); |
| 745 | |
| 746 | if (workloadInfo.m_InputTensorInfos[0] != workloadInfo.m_OutputTensorInfos[0]) |
| 747 | { |
| 748 | throw InvalidArgumentException("FloorQueueDescriptor: Input and output tensor infos do not match."); |
| 749 | } |
| 750 | } |
| 751 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 752 | void LstmQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 753 | { |
| 754 | ValidateTensorNumDimensions(workloadInfo.m_InputTensorInfos[0], "LstmQueueDescriptor", 2, "input"); |
| 755 | ValidateTensorNumDimensions(workloadInfo.m_OutputTensorInfos[0], "LstmQueueDescriptor", 2, "output"); |
| 756 | } |
| 757 | |
| 758 | void ConvertFp32ToFp16QueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 759 | { |
| 760 | ValidateSingleInput(workloadInfo, "ConvertFp32ToFp16QueueDescriptor"); |
| 761 | ValidateSingleOutput(workloadInfo, "ConvertFp32ToFp16QueueDescriptor"); |
| 762 | |
| 763 | if (workloadInfo.m_InputTensorInfos[0].GetDataType() != DataType::Float32) |
| 764 | { |
| 765 | throw InvalidArgumentException("ConvertFp32ToFp16QueueDescriptor: Input tensor type must be Float32."); |
| 766 | } |
| 767 | |
| 768 | if (workloadInfo.m_OutputTensorInfos[0].GetDataType() != DataType::Float16) |
| 769 | { |
| 770 | throw InvalidArgumentException("ConvertFp32ToFp16QueueDescriptor: Output tensor type must be Float16."); |
| 771 | } |
| 772 | |
| 773 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 774 | workloadInfo.m_OutputTensorInfos[0], |
| 775 | "ConvertFp32ToFp16QueueDescriptor", |
| 776 | "input", |
| 777 | "output"); |
| 778 | } |
| 779 | |
| 780 | void ConvertFp16ToFp32QueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 781 | { |
| 782 | ValidateSingleInput(workloadInfo, "ConvertFp16ToFp32QueueDescriptor"); |
| 783 | ValidateSingleOutput(workloadInfo, "ConvertFp16ToFp32QueueDescriptor"); |
| 784 | |
| 785 | if (workloadInfo.m_InputTensorInfos[0].GetDataType() != DataType::Float16) |
| 786 | { |
| 787 | throw InvalidArgumentException("ConvertFp16ToFp32QueueDescriptor: Input tensor type must be Float16."); |
| 788 | } |
| 789 | if (workloadInfo.m_OutputTensorInfos[0].GetDataType() != DataType::Float32) |
| 790 | { |
| 791 | throw InvalidArgumentException("ConvertFp16ToFp32QueueDescriptor: Output tensor type must be Float32."); |
| 792 | } |
| 793 | |
| 794 | ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 795 | workloadInfo.m_OutputTensorInfos[0], |
| 796 | "ConvertFp16ToFp32QueueDescriptor", |
| 797 | "input", |
| 798 | "output"); |
| 799 | } |
| 800 | |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 801 | void DivisionQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 802 | { |
| 803 | ValidateTwoInputs(workloadInfo, "DivisionQueueDescriptor"); |
| 804 | ValidateSingleOutput(workloadInfo, "DivisionQueueDescriptor"); |
| 805 | |
| 806 | ValidateBroadcastTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 807 | workloadInfo.m_InputTensorInfos[1], |
| 808 | workloadInfo.m_OutputTensorInfos[0], |
| 809 | "DivisionQueueDescriptor", |
| 810 | "first input", |
| 811 | "second input"); |
| 812 | } |
| 813 | |
David Beck | c2044fe | 2018-09-05 15:00:38 +0100 | [diff] [blame] | 814 | void SubtractionQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 815 | { |
| 816 | ValidateTwoInputs(workloadInfo, "SubtractionQueueDescriptor"); |
| 817 | ValidateSingleOutput(workloadInfo, "SubtractionQueueDescriptor"); |
| 818 | |
| 819 | ValidateBroadcastTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], |
| 820 | workloadInfo.m_InputTensorInfos[1], |
| 821 | workloadInfo.m_OutputTensorInfos[0], |
| 822 | "SubtractionQueueDescriptor", |
| 823 | "first input", |
| 824 | "second input"); |
| 825 | } |
| 826 | |
narpra01 | a6bf912 | 2018-09-10 09:50:09 +0100 | [diff] [blame] | 827 | void MeanQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 828 | { |
| 829 | ValidateSingleInput(workloadInfo, "MeanQueueDescriptor"); |
| 830 | ValidateSingleOutput(workloadInfo, "MeanQueueDescriptor"); |
narpra01 | eb06191 | 2018-09-10 17:35:27 +0100 | [diff] [blame] | 831 | |
| 832 | const TensorInfo& input = workloadInfo.m_InputTensorInfos[0]; |
| 833 | const TensorInfo& output = workloadInfo.m_OutputTensorInfos[0]; |
| 834 | |
narpra01 | 32b9046 | 2018-09-13 11:07:48 +0100 | [diff] [blame] | 835 | if (m_Parameters.m_KeepDims) |
narpra01 | eb06191 | 2018-09-10 17:35:27 +0100 | [diff] [blame] | 836 | { |
| 837 | ValidateTensorNumDimensions(output, "MeanQueueDescriptor", input.GetNumDimensions(), "output"); |
| 838 | } |
narpra01 | 32b9046 | 2018-09-13 11:07:48 +0100 | [diff] [blame] | 839 | else if (m_Parameters.m_Axis.empty()) |
narpra01 | eb06191 | 2018-09-10 17:35:27 +0100 | [diff] [blame] | 840 | { |
| 841 | ValidateTensorNumDimensions(output, "MeanQueueDescriptor", 1, "output"); |
| 842 | } |
| 843 | else |
| 844 | { |
narpra01 | 32b9046 | 2018-09-13 11:07:48 +0100 | [diff] [blame] | 845 | auto outputDim = input.GetNumDimensions() - boost::numeric_cast<unsigned int>(m_Parameters.m_Axis.size()); |
narpra01 | eb06191 | 2018-09-10 17:35:27 +0100 | [diff] [blame] | 846 | ValidateTensorNumDimensions(output, |
| 847 | "MeanQueueDescriptor", |
| 848 | outputDim > 0 ? outputDim : 1, |
| 849 | "output"); |
| 850 | } |
narpra01 | a6bf912 | 2018-09-10 09:50:09 +0100 | [diff] [blame] | 851 | } |
| 852 | |
jimfly01 | 2c9322a | 2018-09-19 10:59:49 +0100 | [diff] [blame] | 853 | void PadQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const |
| 854 | { |
| 855 | ValidateSingleInput(workloadInfo, "PadQueueDescriptor"); |
| 856 | ValidateSingleOutput(workloadInfo, "PadQueueDescriptor"); |
| 857 | |
| 858 | const TensorInfo& input = workloadInfo.m_InputTensorInfos[0]; |
| 859 | const TensorInfo& output = workloadInfo.m_OutputTensorInfos[1]; |
| 860 | // input and output should have the same number of dimensions |
| 861 | ValidateTensorNumDimensions(output, "PadQueueDescriptor", input.GetNumDimensions(), "output"); |
| 862 | // there should be entry in the pad list for each dimension in the input tensor |
| 863 | if (m_Parameters.m_PadList.size() != input.GetNumDimensions()) { |
| 864 | throw InvalidArgumentException("Pad List should contain the same number of entries as there" |
| 865 | " are dimensions in the input tensor that is " + |
| 866 | to_string(input.GetNumDimensions()) + " entries " + |
| 867 | " not " + to_string(m_Parameters.m_PadList.size()) + " entries."); |
| 868 | } |
| 869 | } |
| 870 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 871 | } //namespace armnn |