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/runtime/SubTensor.h"
  28 #include "utils/Utils.h"
  29
  30 #ifdef ARM_COMPUTE_CL
  31 #include "arm_compute/core/CL/OpenCL.h"
  32 #include "arm_compute/runtime/CL/CLTensor.h"
  33 #endif /* ARM_COMPUTE_CL */
  34
  35 #include <iomanip>
  36
  37 using namespace arm_compute::graph_utils;
  38
  39 void TFPreproccessor::preprocess(ITensor &tensor)
  40 {
  41     Window window;
  42     window.use_tensor_dimensions(tensor.info()->tensor_shape());
  43
  44     execute_window_loop(window, [&](const Coordinates & id)
  45     {
  46         const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id));
  47         float       res                                       = value / 255.f;      // Normalize to [0, 1]
  48         res                                                   = (res - 0.5f) * 2.f; // Map to [-1, 1]
  49         *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = res;
  50     });
  51 }
  52
  53 CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr)
  54     : _mean(mean), _bgr(bgr)
  55 {
  56     if(_bgr)
  57     {
  58         std::swap(_mean[0], _mean[2]);
  59     }
  60 }
  61
  62 void CaffePreproccessor::preprocess(ITensor &tensor)
  63 {
  64     Window window;
  65     window.use_tensor_dimensions(tensor.info()->tensor_shape());
  66
  67     execute_window_loop(window, [&](const Coordinates & id)
  68     {
  69         const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - _mean[id.z()];
  70         *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value;
  71     });
  72 }
  73
  74 PPMWriter::PPMWriter(std::string name, unsigned int maximum)
  75     : _name(std::move(name)), _iterator(0), _maximum(maximum)
  76 {
  77 }
  78
  79 bool PPMWriter::access_tensor(ITensor &tensor)
  80 {
  81     std::stringstream ss;
  82     ss << _name << _iterator << ".ppm";
  83
  84     arm_compute::utils::save_to_ppm(tensor, ss.str());
  85
  86     _iterator++;
  87     if(_maximum == 0)
  88     {
  89         return true;
  90     }
  91     return _iterator < _maximum;
  92 }
  93
  94 DummyAccessor::DummyAccessor(unsigned int maximum)
  95     : _iterator(0), _maximum(maximum)
  96 {
  97 }
  98
  99 bool DummyAccessor::access_tensor(ITensor &tensor)
 100 {
 101     ARM_COMPUTE_UNUSED(tensor);
 102     bool ret = _maximum == 0 || _iterator < _maximum;
 103     if(_iterator == _maximum)
 104     {
 105         _iterator = 0;
 106     }
 107     else
 108     {
 109         _iterator++;
 110     }
 111     return ret;
 112 }
 113
 114 PPMAccessor::PPMAccessor(std::string ppm_path, bool bgr, std::unique_ptr<IPreprocessor> preprocessor)
 115     : _ppm_path(std::move(ppm_path)), _bgr(bgr), _preprocessor(std::move(preprocessor))
 116 {
 117 }
 118
 119 bool PPMAccessor::access_tensor(ITensor &tensor)
 120 {
 121     utils::PPMLoader ppm;
 122
 123     // Open PPM file
 124     ppm.open(_ppm_path);
 125
 126     ARM_COMPUTE_ERROR_ON_MSG(ppm.width() != tensor.info()->dimension(0) || ppm.height() != tensor.info()->dimension(1),
 127                              "Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d].", ppm.width(), ppm.height(), tensor.info()->dimension(0), tensor.info()->dimension(1));
 128
 129     // Fill the tensor with the PPM content (BGR)
 130     ppm.fill_planar_tensor(tensor, _bgr);
 131
 132     // Preprocess tensor
 133     if(_preprocessor)
 134     {
 135         _preprocessor->preprocess(tensor);
 136     }
 137
 138     return true;
 139 }
 140
 141 TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream)
 142     : _labels(), _output_stream(output_stream), _top_n(top_n)
 143 {
 144     _labels.clear();
 145
 146     std::ifstream ifs;
 147
 148     try
 149     {
 150         ifs.exceptions(std::ifstream::badbit);
 151         ifs.open(labels_path, std::ios::in | std::ios::binary);
 152
 153         for(std::string line; !std::getline(ifs, line).fail();)
 154         {
 155             _labels.emplace_back(line);
 156         }
 157     }
 158     catch(const std::ifstream::failure &e)
 159     {
 160         ARM_COMPUTE_ERROR("Accessing %s: %s", labels_path.c_str(), e.what());
 161     }
 162 }
 163
 164 template <typename T>
 165 void TopNPredictionsAccessor::access_predictions_tensor(ITensor &tensor)
 166 {
 167     // Get the predicted class
 168     std::vector<T>      classes_prob;
 169     std::vector<size_t> index;
 170
 171     const auto   output_net  = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
 172     const size_t num_classes = tensor.info()->dimension(0);
 173
 174     classes_prob.resize(num_classes);
 175     index.resize(num_classes);
 176
 177     std::copy(output_net, output_net + num_classes, classes_prob.begin());
 178
 179     // Sort results
 180     std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));
 181     std::sort(std::begin(index), std::end(index),
 182               [&](size_t a, size_t b)
 183     {
 184         return classes_prob[a] > classes_prob[b];
 185     });
 186
 187     _output_stream << "---------- Top " << _top_n << " predictions ----------" << std::endl
 188                    << std::endl;
 189     for(size_t i = 0; i < _top_n; ++i)
 190     {
 191         _output_stream << std::fixed << std::setprecision(4)
 192                        << +classes_prob[index.at(i)]
 193                        << " - [id = " << index.at(i) << "]"
 194                        << ", " << _labels[index.at(i)] << std::endl;
 195     }
 196 }
 197
 198 bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
 199 {
 200     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8);
 201     ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0));
 202
 203     switch(tensor.info()->data_type())
 204     {
 205         case DataType::QASYMM8:
 206             access_predictions_tensor<uint8_t>(tensor);
 207             break;
 208         case DataType::F32:
 209             access_predictions_tensor<float>(tensor);
 210             break;
 211         default:
 212             ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 213     }
 214
 215     return false;
 216 }
 217
 218 RandomAccessor::RandomAccessor(PixelValue lower, PixelValue upper, std::random_device::result_type seed)
 219     : _lower(lower), _upper(upper), _seed(seed)
 220 {
 221 }
 222
 223 template <typename T, typename D>
 224 void RandomAccessor::fill(ITensor &tensor, D &&distribution)
 225 {
 226     std::mt19937 gen(_seed);
 227
 228     if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 229     {
 230         for(size_t offset = 0; offset < tensor.info()->total_size(); offset += tensor.info()->element_size())
 231         {
 232             const T value                                    = distribution(gen);
 233             *reinterpret_cast<T *>(tensor.buffer() + offset) = value;
 234         }
 235     }
 236     else
 237     {
 238         // If tensor has padding accessing tensor elements through execution window.
 239         Window window;
 240         window.use_tensor_dimensions(tensor.info()->tensor_shape());
 241
 242         execute_window_loop(window, [&](const Coordinates & id)
 243         {
 244             const T value                                     = distribution(gen);
 245             *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value;
 246         });
 247     }
 248 }
 249
 250 bool RandomAccessor::access_tensor(ITensor &tensor)
 251 {
 252     switch(tensor.info()->data_type())
 253     {
 254         case DataType::U8:
 255         {
 256             std::uniform_int_distribution<uint8_t> distribution_u8(_lower.get<uint8_t>(), _upper.get<uint8_t>());
 257             fill<uint8_t>(tensor, distribution_u8);
 258             break;
 259         }
 260         case DataType::S8:
 261         case DataType::QS8:
 262         {
 263             std::uniform_int_distribution<int8_t> distribution_s8(_lower.get<int8_t>(), _upper.get<int8_t>());
 264             fill<int8_t>(tensor, distribution_s8);
 265             break;
 266         }
 267         case DataType::U16:
 268         {
 269             std::uniform_int_distribution<uint16_t> distribution_u16(_lower.get<uint16_t>(), _upper.get<uint16_t>());
 270             fill<uint16_t>(tensor, distribution_u16);
 271             break;
 272         }
 273         case DataType::S16:
 274         case DataType::QS16:
 275         {
 276             std::uniform_int_distribution<int16_t> distribution_s16(_lower.get<int16_t>(), _upper.get<int16_t>());
 277             fill<int16_t>(tensor, distribution_s16);
 278             break;
 279         }
 280         case DataType::U32:
 281         {
 282             std::uniform_int_distribution<uint32_t> distribution_u32(_lower.get<uint32_t>(), _upper.get<uint32_t>());
 283             fill<uint32_t>(tensor, distribution_u32);
 284             break;
 285         }
 286         case DataType::S32:
 287         {
 288             std::uniform_int_distribution<int32_t> distribution_s32(_lower.get<int32_t>(), _upper.get<int32_t>());
 289             fill<int32_t>(tensor, distribution_s32);
 290             break;
 291         }
 292         case DataType::U64:
 293         {
 294             std::uniform_int_distribution<uint64_t> distribution_u64(_lower.get<uint64_t>(), _upper.get<uint64_t>());
 295             fill<uint64_t>(tensor, distribution_u64);
 296             break;
 297         }
 298         case DataType::S64:
 299         {
 300             std::uniform_int_distribution<int64_t> distribution_s64(_lower.get<int64_t>(), _upper.get<int64_t>());
 301             fill<int64_t>(tensor, distribution_s64);
 302             break;
 303         }
 304         case DataType::F16:
 305         {
 306             std::uniform_real_distribution<float> distribution_f16(_lower.get<float>(), _upper.get<float>());
 307             fill<float>(tensor, distribution_f16);
 308             break;
 309         }
 310         case DataType::F32:
 311         {
 312             std::uniform_real_distribution<float> distribution_f32(_lower.get<float>(), _upper.get<float>());
 313             fill<float>(tensor, distribution_f32);
 314             break;
 315         }
 316         case DataType::F64:
 317         {
 318             std::uniform_real_distribution<double> distribution_f64(_lower.get<double>(), _upper.get<double>());
 319             fill<double>(tensor, distribution_f64);
 320             break;
 321         }
 322         default:
 323             ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 324     }
 325     return true;
 326 }
 327
 328 NumPyBinLoader::NumPyBinLoader(std::string filename)
 329     : _filename(std::move(filename))
 330 {
 331 }
 332
 333 bool NumPyBinLoader::access_tensor(ITensor &tensor)
 334 {
 335     const TensorShape          tensor_shape = tensor.info()->tensor_shape();
 336     std::vector<unsigned long> shape;
 337
 338     // Open file
 339     std::ifstream stream(_filename, std::ios::in | std::ios::binary);
 340     ARM_COMPUTE_ERROR_ON_MSG(!stream.good(), "Failed to load binary data");
 341     std::string header = npy::read_header(stream);
 342
 343     // Parse header
 344     bool        fortran_order = false;
 345     std::string typestr;
 346     npy::parse_header(header, typestr, fortran_order, shape);
 347
 348     // Check if the typestring matches the given one
 349     std::string expect_typestr = arm_compute::utils::get_typestring(tensor.info()->data_type());
 350     ARM_COMPUTE_ERROR_ON_MSG(typestr != expect_typestr, "Typestrings mismatch");
 351
 352     // Reverse vector in case of non fortran order
 353     if(!fortran_order)
 354     {
 355         std::reverse(shape.begin(), shape.end());
 356     }
 357
 358     // Correct dimensions (Needs to match TensorShape dimension corrections)
 359     if(shape.size() != tensor_shape.num_dimensions())
 360     {
 361         for(int i = static_cast<int>(shape.size()) - 1; i > 0; --i)
 362         {
 363             if(shape[i] == 1)
 364             {
 365                 shape.pop_back();
 366             }
 367             else
 368             {
 369                 break;
 370             }
 371         }
 372     }
 373
 374     // Validate tensor ranks
 375     ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch");
 376
 377     // Validate shapes
 378     for(size_t i = 0; i < shape.size(); ++i)
 379     {
 380         ARM_COMPUTE_ERROR_ON_MSG(tensor_shape[i] != shape[i], "Tensor dimensions mismatch");
 381     }
 382
 383     // Read data
 384     if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 385     {
 386         // If tensor has no padding read directly from stream.
 387         stream.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
 388     }
 389     else
 390     {
 391         // If tensor has padding accessing tensor elements through execution window.
 392         Window window;
 393         window.use_tensor_dimensions(tensor_shape);
 394
 395         execute_window_loop(window, [&](const Coordinates & id)
 396         {
 397             stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size());
 398         });
 399     }
 400     return true;
 401 }