utils/GraphUtils.cpp

   1 /*
   2  * Copyright (c) 2017-2018 ARM Limited.
   3  *
   4  * SPDX-License-Identifier: MIT
   5  *
   6  * Permission is hereby granted, free of charge, to any person obtaining a copy
   7  * of this software and associated documentation files (the "Software"), to
   8  * deal in the Software without restriction, including without limitation the
   9  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  10  * sell copies of the Software, and to permit persons to whom the Software is
  11  * furnished to do so, subject to the following conditions:
  12  *
  13  * The above copyright notice and this permission notice shall be included in all
  14  * copies or substantial portions of the Software.
  15  *
  16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  18  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  19  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  20  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  21  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  22  * SOFTWARE.
  23  */
  24
  25 #include "utils/GraphUtils.h"
  26
  27 #include "arm_compute/core/Helpers.h"
  28 #include "arm_compute/core/Types.h"
  29 #include "arm_compute/runtime/SubTensor.h"
  30 #include "utils/Utils.h"
  31
  32 #include <iomanip>
  33
  34 using namespace arm_compute::graph_utils;
  35
  36 namespace
  37 {
  38 std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_permutation_paramaters(const arm_compute::TensorShape &shape,
  39                                                                                                    arm_compute::DataLayout data_layout)
  40 {
  41     // Set permutation parameters if needed
  42     arm_compute::TensorShape       permuted_shape = shape;
  43     arm_compute::PermutationVector perm;
  44     // Permute only if num_dimensions greater than 2
  45     if(shape.num_dimensions() > 2)
  46     {
  47         perm = (data_layout == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
  48
  49         arm_compute::PermutationVector perm_shape = (data_layout == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
  50         arm_compute::permute(permuted_shape, perm_shape);
  51     }
  52
  53     return std::make_pair(permuted_shape, perm);
  54 }
  55 } // namespace
  56
  57 void TFPreproccessor::preprocess(ITensor &tensor)
  58 {
  59     Window window;
  60     window.use_tensor_dimensions(tensor.info()->tensor_shape());
  61
  62     execute_window_loop(window, [&](const Coordinates & id)
  63     {
  64         const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id));
  65         float       res                                       = value / 255.f;      // Normalize to [0, 1]
  66         res                                                   = (res - 0.5f) * 2.f; // Map to [-1, 1]
  67         *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = res;
  68     });
  69 }
  70
  71 CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr)
  72     : _mean(mean), _bgr(bgr)
  73 {
  74     if(_bgr)
  75     {
  76         std::swap(_mean[0], _mean[2]);
  77     }
  78 }
  79
  80 void CaffePreproccessor::preprocess(ITensor &tensor)
  81 {
  82     Window window;
  83     window.use_tensor_dimensions(tensor.info()->tensor_shape());
  84
  85     execute_window_loop(window, [&](const Coordinates & id)
  86     {
  87         const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - _mean[id.z()];
  88         *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value;
  89     });
  90 }
  91
  92 PPMWriter::PPMWriter(std::string name, unsigned int maximum)
  93     : _name(std::move(name)), _iterator(0), _maximum(maximum)
  94 {
  95 }
  96
  97 bool PPMWriter::access_tensor(ITensor &tensor)
  98 {
  99     std::stringstream ss;
 100     ss << _name << _iterator << ".ppm";
 101
 102     arm_compute::utils::save_to_ppm(tensor, ss.str());
 103
 104     _iterator++;
 105     if(_maximum == 0)
 106     {
 107         return true;
 108     }
 109     return _iterator < _maximum;
 110 }
 111
 112 DummyAccessor::DummyAccessor(unsigned int maximum)
 113     : _iterator(0), _maximum(maximum)
 114 {
 115 }
 116
 117 bool DummyAccessor::access_tensor(ITensor &tensor)
 118 {
 119     ARM_COMPUTE_UNUSED(tensor);
 120     bool ret = _maximum == 0 || _iterator < _maximum;
 121     if(_iterator == _maximum)
 122     {
 123         _iterator = 0;
 124     }
 125     else
 126     {
 127         _iterator++;
 128     }
 129     return ret;
 130 }
 131
 132 NumPyAccessor::NumPyAccessor(std::string npy_path, TensorShape shape, DataType data_type, std::ostream &output_stream)
 133     : _npy_tensor(), _filename(std::move(npy_path)), _output_stream(output_stream)
 134 {
 135     NumPyBinLoader loader(_filename);
 136
 137     TensorInfo info(shape, 1, data_type);
 138     _npy_tensor.allocator()->init(info);
 139     _npy_tensor.allocator()->allocate();
 140
 141     loader.access_tensor(_npy_tensor);
 142 }
 143
 144 template <typename T>
 145 void NumPyAccessor::access_numpy_tensor(ITensor &tensor)
 146 {
 147     const int num_elements          = tensor.info()->total_size();
 148     int       num_mismatches        = utils::compare_tensor<T>(tensor, _npy_tensor);
 149     float     percentage_mismatches = static_cast<float>(num_mismatches) / num_elements;
 150
 151     _output_stream << "Results: " << 100.f - (percentage_mismatches * 100) << " % matches with the provided output[" << _filename << "]." << std::endl;
 152 }
 153
 154 bool NumPyAccessor::access_tensor(ITensor &tensor)
 155 {
 156     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
 157     ARM_COMPUTE_ERROR_ON(_npy_tensor.info()->dimension(0) != tensor.info()->dimension(0));
 158
 159     switch(tensor.info()->data_type())
 160     {
 161         case DataType::F32:
 162             access_numpy_tensor<float>(tensor);
 163             break;
 164         default:
 165             ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 166     }
 167
 168     return false;
 169 }
 170
 171 PPMAccessor::PPMAccessor(std::string ppm_path, bool bgr, std::unique_ptr<IPreprocessor> preprocessor)
 172     : _ppm_path(std::move(ppm_path)), _bgr(bgr), _preprocessor(std::move(preprocessor))
 173 {
 174 }
 175
 176 bool PPMAccessor::access_tensor(ITensor &tensor)
 177 {
 178     utils::PPMLoader ppm;
 179
 180     // Open PPM file
 181     ppm.open(_ppm_path);
 182
 183     // Get permutated shape and permutation parameters
 184     TensorShape                    permuted_shape = tensor.info()->tensor_shape();
 185     arm_compute::PermutationVector perm;
 186     if(tensor.info()->data_layout() != DataLayout::NCHW)
 187     {
 188         std::tie(permuted_shape, perm) = compute_permutation_paramaters(tensor.info()->tensor_shape(), tensor.info()->data_layout());
 189     }
 190     ARM_COMPUTE_ERROR_ON_MSG(ppm.width() != permuted_shape.x() || ppm.height() != permuted_shape.y(),
 191                              "Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d].", ppm.width(), ppm.height(), permuted_shape.x(), permuted_shape.y());
 192
 193     // Fill the tensor with the PPM content (BGR)
 194     ppm.fill_planar_tensor(tensor, _bgr);
 195
 196     // Preprocess tensor
 197     if(_preprocessor)
 198     {
 199         _preprocessor->preprocess(tensor);
 200     }
 201
 202     return true;
 203 }
 204
 205 TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream)
 206     : _labels(), _output_stream(output_stream), _top_n(top_n)
 207 {
 208     _labels.clear();
 209
 210     std::ifstream ifs;
 211
 212     try
 213     {
 214         ifs.exceptions(std::ifstream::badbit);
 215         ifs.open(labels_path, std::ios::in | std::ios::binary);
 216
 217         for(std::string line; !std::getline(ifs, line).fail();)
 218         {
 219             _labels.emplace_back(line);
 220         }
 221     }
 222     catch(const std::ifstream::failure &e)
 223     {
 224         ARM_COMPUTE_ERROR("Accessing %s: %s", labels_path.c_str(), e.what());
 225     }
 226 }
 227
 228 template <typename T>
 229 void TopNPredictionsAccessor::access_predictions_tensor(ITensor &tensor)
 230 {
 231     // Get the predicted class
 232     std::vector<T>      classes_prob;
 233     std::vector<size_t> index;
 234
 235     const auto   output_net  = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
 236     const size_t num_classes = tensor.info()->dimension(0);
 237
 238     classes_prob.resize(num_classes);
 239     index.resize(num_classes);
 240
 241     std::copy(output_net, output_net + num_classes, classes_prob.begin());
 242
 243     // Sort results
 244     std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));
 245     std::sort(std::begin(index), std::end(index),
 246               [&](size_t a, size_t b)
 247     {
 248         return classes_prob[a] > classes_prob[b];
 249     });
 250
 251     _output_stream << "---------- Top " << _top_n << " predictions ----------" << std::endl
 252                    << std::endl;
 253     for(size_t i = 0; i < _top_n; ++i)
 254     {
 255         _output_stream << std::fixed << std::setprecision(4)
 256                        << +classes_prob[index.at(i)]
 257                        << " - [id = " << index.at(i) << "]"
 258                        << ", " << _labels[index.at(i)] << std::endl;
 259     }
 260 }
 261
 262 bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
 263 {
 264     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8);
 265     ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0));
 266
 267     switch(tensor.info()->data_type())
 268     {
 269         case DataType::QASYMM8:
 270             access_predictions_tensor<uint8_t>(tensor);
 271             break;
 272         case DataType::F32:
 273             access_predictions_tensor<float>(tensor);
 274             break;
 275         default:
 276             ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 277     }
 278
 279     return false;
 280 }
 281
 282 RandomAccessor::RandomAccessor(PixelValue lower, PixelValue upper, std::random_device::result_type seed)
 283     : _lower(lower), _upper(upper), _seed(seed)
 284 {
 285 }
 286
 287 template <typename T, typename D>
 288 void RandomAccessor::fill(ITensor &tensor, D &&distribution)
 289 {
 290     std::mt19937 gen(_seed);
 291
 292     if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 293     {
 294         for(size_t offset = 0; offset < tensor.info()->total_size(); offset += tensor.info()->element_size())
 295         {
 296             const T value                                    = distribution(gen);
 297             *reinterpret_cast<T *>(tensor.buffer() + offset) = value;
 298         }
 299     }
 300     else
 301     {
 302         // If tensor has padding accessing tensor elements through execution window.
 303         Window window;
 304         window.use_tensor_dimensions(tensor.info()->tensor_shape());
 305
 306         execute_window_loop(window, [&](const Coordinates & id)
 307         {
 308             const T value                                     = distribution(gen);
 309             *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value;
 310         });
 311     }
 312 }
 313
 314 bool RandomAccessor::access_tensor(ITensor &tensor)
 315 {
 316     switch(tensor.info()->data_type())
 317     {
 318         case DataType::U8:
 319         {
 320             std::uniform_int_distribution<uint8_t> distribution_u8(_lower.get<uint8_t>(), _upper.get<uint8_t>());
 321             fill<uint8_t>(tensor, distribution_u8);
 322             break;
 323         }
 324         case DataType::S8:
 325         case DataType::QS8:
 326         {
 327             std::uniform_int_distribution<int8_t> distribution_s8(_lower.get<int8_t>(), _upper.get<int8_t>());
 328             fill<int8_t>(tensor, distribution_s8);
 329             break;
 330         }
 331         case DataType::U16:
 332         {
 333             std::uniform_int_distribution<uint16_t> distribution_u16(_lower.get<uint16_t>(), _upper.get<uint16_t>());
 334             fill<uint16_t>(tensor, distribution_u16);
 335             break;
 336         }
 337         case DataType::S16:
 338         case DataType::QS16:
 339         {
 340             std::uniform_int_distribution<int16_t> distribution_s16(_lower.get<int16_t>(), _upper.get<int16_t>());
 341             fill<int16_t>(tensor, distribution_s16);
 342             break;
 343         }
 344         case DataType::U32:
 345         {
 346             std::uniform_int_distribution<uint32_t> distribution_u32(_lower.get<uint32_t>(), _upper.get<uint32_t>());
 347             fill<uint32_t>(tensor, distribution_u32);
 348             break;
 349         }
 350         case DataType::S32:
 351         {
 352             std::uniform_int_distribution<int32_t> distribution_s32(_lower.get<int32_t>(), _upper.get<int32_t>());
 353             fill<int32_t>(tensor, distribution_s32);
 354             break;
 355         }
 356         case DataType::U64:
 357         {
 358             std::uniform_int_distribution<uint64_t> distribution_u64(_lower.get<uint64_t>(), _upper.get<uint64_t>());
 359             fill<uint64_t>(tensor, distribution_u64);
 360             break;
 361         }
 362         case DataType::S64:
 363         {
 364             std::uniform_int_distribution<int64_t> distribution_s64(_lower.get<int64_t>(), _upper.get<int64_t>());
 365             fill<int64_t>(tensor, distribution_s64);
 366             break;
 367         }
 368         case DataType::F16:
 369         {
 370             std::uniform_real_distribution<float> distribution_f16(_lower.get<float>(), _upper.get<float>());
 371             fill<float>(tensor, distribution_f16);
 372             break;
 373         }
 374         case DataType::F32:
 375         {
 376             std::uniform_real_distribution<float> distribution_f32(_lower.get<float>(), _upper.get<float>());
 377             fill<float>(tensor, distribution_f32);
 378             break;
 379         }
 380         case DataType::F64:
 381         {
 382             std::uniform_real_distribution<double> distribution_f64(_lower.get<double>(), _upper.get<double>());
 383             fill<double>(tensor, distribution_f64);
 384             break;
 385         }
 386         default:
 387             ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 388     }
 389     return true;
 390 }
 391
 392 NumPyBinLoader::NumPyBinLoader(std::string filename, DataLayout file_layout)
 393     : _filename(std::move(filename)), _file_layout(file_layout)
 394 {
 395 }
 396
 397 bool NumPyBinLoader::access_tensor(ITensor &tensor)
 398 {
 399     const TensorShape          tensor_shape = tensor.info()->tensor_shape();
 400     std::vector<unsigned long> shape;
 401
 402     // Open file
 403     std::ifstream stream(_filename, std::ios::in | std::ios::binary);
 404     ARM_COMPUTE_ERROR_ON_MSG(!stream.good(), "Failed to load binary data");
 405     std::string header = npy::read_header(stream);
 406
 407     // Parse header
 408     bool        fortran_order = false;
 409     std::string typestr;
 410     npy::parse_header(header, typestr, fortran_order, shape);
 411
 412     // Check if the typestring matches the given one
 413     std::string expect_typestr = arm_compute::utils::get_typestring(tensor.info()->data_type());
 414     ARM_COMPUTE_ERROR_ON_MSG(typestr != expect_typestr, "Typestrings mismatch");
 415
 416     // Reverse vector in case of non fortran order
 417     if(!fortran_order)
 418     {
 419         std::reverse(shape.begin(), shape.end());
 420     }
 421
 422     // Correct dimensions (Needs to match TensorShape dimension corrections)
 423     if(shape.size() != tensor_shape.num_dimensions())
 424     {
 425         for(int i = static_cast<int>(shape.size()) - 1; i > 0; --i)
 426         {
 427             if(shape[i] == 1)
 428             {
 429                 shape.pop_back();
 430             }
 431             else
 432             {
 433                 break;
 434             }
 435         }
 436     }
 437
 438     bool are_layouts_different = (_file_layout != tensor.info()->data_layout());
 439
 440     // Validate tensor ranks
 441     ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch");
 442
 443     // Set permutation parameters if needed
 444     TensorShape                    permuted_shape = tensor_shape;
 445     arm_compute::PermutationVector perm;
 446     if(are_layouts_different)
 447     {
 448         std::tie(permuted_shape, perm) = compute_permutation_paramaters(tensor_shape, tensor.info()->data_layout());
 449     }
 450
 451     // Validate shapes
 452     for(size_t i = 0; i < shape.size(); ++i)
 453     {
 454         ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != shape[i], "Tensor dimensions mismatch");
 455     }
 456
 457     // Validate shapes and copy tensor
 458     if(!are_layouts_different || perm.num_dimensions() <= 2)
 459     {
 460         // Read data
 461         if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 462         {
 463             // If tensor has no padding read directly from stream.
 464             stream.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
 465         }
 466         else
 467         {
 468             // If tensor has padding accessing tensor elements through execution window.
 469             Window window;
 470             window.use_tensor_dimensions(tensor_shape);
 471
 472             execute_window_loop(window, [&](const Coordinates & id)
 473             {
 474                 stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size());
 475             });
 476         }
 477     }
 478     else
 479     {
 480         // If tensor has padding accessing tensor elements through execution window.
 481         Window window;
 482         window.use_tensor_dimensions(permuted_shape);
 483
 484         execute_window_loop(window, [&](const Coordinates & id)
 485         {
 486             Coordinates coords(id);
 487             arm_compute::permute(coords, perm);
 488             stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(coords)), tensor.info()->element_size());
 489         });
 490     }
 491     return true;
 492 }