1 /*
2 * Copyright (c) 2016-2020 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 #ifndef __UTILS_UTILS_H__
25 #define __UTILS_UTILS_H__
26
27 /** @dir .
28 * brief Boiler plate code used by examples. Various utilities to print types, load / store assets, etc.
29 */
30
31 #include "arm_compute/core/Helpers.h"
32 #include "arm_compute/core/ITensor.h"
33 #include "arm_compute/core/Types.h"
34 #include "arm_compute/core/Window.h"
35 #include "arm_compute/runtime/Tensor.h"
36 #pragma GCC diagnostic push
37 #pragma GCC diagnostic ignored "-Wunused-parameter"
38 #pragma GCC diagnostic ignored "-Wstrict-overflow"
39 #include "libnpy/npy.hpp"
40 #pragma GCC diagnostic pop
41 #include "support/MemorySupport.h"
42 #include "support/StringSupport.h"
43
44 #ifdef ARM_COMPUTE_CL
45 #include "arm_compute/core/CL/OpenCL.h"
46 #include "arm_compute/runtime/CL/CLDistribution1D.h"
47 #include "arm_compute/runtime/CL/CLTensor.h"
48 #endif /* ARM_COMPUTE_CL */
49 #ifdef ARM_COMPUTE_GC
50 #include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h"
51 #endif /* ARM_COMPUTE_GC */
52
53 #include <cstdlib>
54 #include <cstring>
55 #include <fstream>
56 #include <iostream>
57 #include <random>
58 #include <string>
59 #include <tuple>
60 #include <vector>
61
62 namespace arm_compute
63 {
64 namespace utils
65 {
66 /** Supported image types */
67 enum class ImageType
68 {
69 UNKNOWN,
70 PPM,
71 JPEG
72 };
73
74 /** Abstract Example class.
75 *
76 * All examples have to inherit from this class.
77 */
78 class Example
79 {
80 public:
81 /** Setup the example.
82 *
83 * @param[in] argc Argument count.
84 * @param[in] argv Argument values.
85 *
86 * @return True in case of no errors in setup else false
87 */
do_setup(int argc,char ** argv)88 virtual bool do_setup(int argc, char **argv)
89 {
90 ARM_COMPUTE_UNUSED(argc, argv);
91 return true;
92 };
93 /** Run the example. */
do_run()94 virtual void do_run() {};
95 /** Teardown the example. */
do_teardown()96 virtual void do_teardown() {};
97
98 /** Default destructor. */
99 virtual ~Example() = default;
100 };
101
102 /** Run an example and handle the potential exceptions it throws
103 *
104 * @param[in] argc Number of command line arguments
105 * @param[in] argv Command line arguments
106 * @param[in] example Example to run
107 */
108 int run_example(int argc, char **argv, std::unique_ptr<Example> example);
109
110 template <typename T>
run_example(int argc,char ** argv)111 int run_example(int argc, char **argv)
112 {
113 return run_example(argc, argv, support::cpp14::make_unique<T>());
114 }
115
116 /** Draw a RGB rectangular window for the detected object
117 *
118 * @param[in, out] tensor Input tensor where the rectangle will be drawn on. Format supported: RGB888
119 * @param[in] rect Geometry of the rectangular window
120 * @param[in] r Red colour to use
121 * @param[in] g Green colour to use
122 * @param[in] b Blue colour to use
123 */
124 void draw_detection_rectangle(arm_compute::ITensor *tensor, const arm_compute::DetectionWindow &rect, uint8_t r, uint8_t g, uint8_t b);
125
126 /** Gets image type given a file
127 *
128 * @param[in] filename File to identify its image type
129 *
130 * @return Image type
131 */
132 ImageType get_image_type_from_file(const std::string &filename);
133
134 /** Parse the ppm header from an input file stream. At the end of the execution,
135 * the file position pointer will be located at the first pixel stored in the ppm file
136 *
137 * @param[in] fs Input file stream to parse
138 *
139 * @return The width, height and max value stored in the header of the PPM file
140 */
141 std::tuple<unsigned int, unsigned int, int> parse_ppm_header(std::ifstream &fs);
142
143 /** Parse the npy header from an input file stream. At the end of the execution,
144 * the file position pointer will be located at the first pixel stored in the npy file //TODO
145 *
146 * @param[in] fs Input file stream to parse
147 *
148 * @return The width and height stored in the header of the NPY file
149 */
150 std::tuple<std::vector<unsigned long>, bool, std::string> parse_npy_header(std::ifstream &fs);
151
152 /** Obtain numpy type string from DataType.
153 *
154 * @param[in] data_type Data type.
155 *
156 * @return numpy type string.
157 */
get_typestring(DataType data_type)158 inline std::string get_typestring(DataType data_type)
159 {
160 // Check endianness
161 const unsigned int i = 1;
162 const char *c = reinterpret_cast<const char *>(&i);
163 std::string endianness;
164 if(*c == 1)
165 {
166 endianness = std::string("<");
167 }
168 else
169 {
170 endianness = std::string(">");
171 }
172 const std::string no_endianness("|");
173
174 switch(data_type)
175 {
176 case DataType::U8:
177 case DataType::QASYMM8:
178 return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
179 case DataType::S8:
180 case DataType::QSYMM8:
181 case DataType::QSYMM8_PER_CHANNEL:
182 return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
183 case DataType::U16:
184 case DataType::QASYMM16:
185 return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
186 case DataType::S16:
187 case DataType::QSYMM16:
188 return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
189 case DataType::U32:
190 return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
191 case DataType::S32:
192 return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
193 case DataType::U64:
194 return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
195 case DataType::S64:
196 return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
197 case DataType::F16:
198 return endianness + "f" + support::cpp11::to_string(sizeof(half));
199 case DataType::F32:
200 return endianness + "f" + support::cpp11::to_string(sizeof(float));
201 case DataType::F64:
202 return endianness + "f" + support::cpp11::to_string(sizeof(double));
203 case DataType::SIZET:
204 return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
205 default:
206 ARM_COMPUTE_ERROR("Data type not supported");
207 }
208 }
209
210 /** Maps a tensor if needed
211 *
212 * @param[in] tensor Tensor to be mapped
213 * @param[in] blocking Specified if map is blocking or not
214 */
215 template <typename T>
map(T & tensor,bool blocking)216 inline void map(T &tensor, bool blocking)
217 {
218 ARM_COMPUTE_UNUSED(tensor);
219 ARM_COMPUTE_UNUSED(blocking);
220 }
221
222 /** Unmaps a tensor if needed
223 *
224 * @param tensor Tensor to be unmapped
225 */
226 template <typename T>
unmap(T & tensor)227 inline void unmap(T &tensor)
228 {
229 ARM_COMPUTE_UNUSED(tensor);
230 }
231
232 #ifdef ARM_COMPUTE_CL
233 /** Maps a tensor if needed
234 *
235 * @param[in] tensor Tensor to be mapped
236 * @param[in] blocking Specified if map is blocking or not
237 */
map(CLTensor & tensor,bool blocking)238 inline void map(CLTensor &tensor, bool blocking)
239 {
240 tensor.map(blocking);
241 }
242
243 /** Unmaps a tensor if needed
244 *
245 * @param tensor Tensor to be unmapped
246 */
unmap(CLTensor & tensor)247 inline void unmap(CLTensor &tensor)
248 {
249 tensor.unmap();
250 }
251
252 /** Maps a distribution if needed
253 *
254 * @param[in] distribution Distribution to be mapped
255 * @param[in] blocking Specified if map is blocking or not
256 */
map(CLDistribution1D & distribution,bool blocking)257 inline void map(CLDistribution1D &distribution, bool blocking)
258 {
259 distribution.map(blocking);
260 }
261
262 /** Unmaps a distribution if needed
263 *
264 * @param distribution Distribution to be unmapped
265 */
unmap(CLDistribution1D & distribution)266 inline void unmap(CLDistribution1D &distribution)
267 {
268 distribution.unmap();
269 }
270 #endif /* ARM_COMPUTE_CL */
271
272 #ifdef ARM_COMPUTE_GC
273 /** Maps a tensor if needed
274 *
275 * @param[in] tensor Tensor to be mapped
276 * @param[in] blocking Specified if map is blocking or not
277 */
map(GCTensor & tensor,bool blocking)278 inline void map(GCTensor &tensor, bool blocking)
279 {
280 tensor.map(blocking);
281 }
282
283 /** Unmaps a tensor if needed
284 *
285 * @param tensor Tensor to be unmapped
286 */
unmap(GCTensor & tensor)287 inline void unmap(GCTensor &tensor)
288 {
289 tensor.unmap();
290 }
291 #endif /* ARM_COMPUTE_GC */
292
293 /** Specialized class to generate random non-zero FP16 values.
294 * uniform_real_distribution<half> generates values that get rounded off to zero, causing
295 * differences between ACL and reference implementation
296 */
297 class uniform_real_distribution_fp16
298 {
299 half min{ 0.0f }, max{ 0.0f };
300 std::uniform_real_distribution<float> neg{ min, -0.3f };
301 std::uniform_real_distribution<float> pos{ 0.3f, max };
302 std::uniform_int_distribution<uint8_t> sign_picker{ 0, 1 };
303
304 public:
305 using result_type = half;
306 /** Constructor
307 *
308 * @param[in] a Minimum value of the distribution
309 * @param[in] b Maximum value of the distribution
310 */
311 explicit uniform_real_distribution_fp16(half a = half(0.0), half b = half(1.0))
min(a)312 : min(a), max(b)
313 {
314 }
315
316 /** () operator to generate next value
317 *
318 * @param[in] gen an uniform random bit generator object
319 */
operator()320 half operator()(std::mt19937 &gen)
321 {
322 if(sign_picker(gen))
323 {
324 return (half)neg(gen);
325 }
326 return (half)pos(gen);
327 }
328 };
329
330 /** Numpy data loader */
331 class NPYLoader
332 {
333 public:
334 /** Default constructor */
NPYLoader()335 NPYLoader()
336 : _fs(), _shape(), _fortran_order(false), _typestring(), _file_layout(DataLayout::NCHW)
337 {
338 }
339
340 /** Open a NPY file and reads its metadata
341 *
342 * @param[in] npy_filename File to open
343 * @param[in] file_layout (Optional) Layout in which the weights are stored in the file.
344 */
345 void open(const std::string &npy_filename, DataLayout file_layout = DataLayout::NCHW)
346 {
347 ARM_COMPUTE_ERROR_ON(is_open());
348 try
349 {
350 _fs.open(npy_filename, std::ios::in | std::ios::binary);
351 ARM_COMPUTE_EXIT_ON_MSG_VAR(!_fs.good(), "Failed to load binary data from %s", npy_filename.c_str());
352 _fs.exceptions(std::ifstream::failbit | std::ifstream::badbit);
353 _file_layout = file_layout;
354
355 std::tie(_shape, _fortran_order, _typestring) = parse_npy_header(_fs);
356 }
catch(const std::ifstream::failure & e)357 catch(const std::ifstream::failure &e)
358 {
359 ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", npy_filename.c_str(), e.what());
360 }
361 }
362 /** Return true if a NPY file is currently open */
is_open()363 bool is_open()
364 {
365 return _fs.is_open();
366 }
367
368 /** Return true if a NPY file is in fortran order */
is_fortran()369 bool is_fortran()
370 {
371 return _fortran_order;
372 }
373
374 /** Initialise the tensor's metadata with the dimensions of the NPY file currently open
375 *
376 * @param[out] tensor Tensor to initialise
377 * @param[in] dt Data type to use for the tensor
378 */
379 template <typename T>
init_tensor(T & tensor,arm_compute::DataType dt)380 void init_tensor(T &tensor, arm_compute::DataType dt)
381 {
382 ARM_COMPUTE_ERROR_ON(!is_open());
383 ARM_COMPUTE_ERROR_ON(dt != arm_compute::DataType::F32);
384
385 // Use the size of the input NPY tensor
386 TensorShape shape;
387 shape.set_num_dimensions(_shape.size());
388 for(size_t i = 0; i < _shape.size(); ++i)
389 {
390 size_t src = i;
391 if(_fortran_order)
392 {
393 src = _shape.size() - 1 - i;
394 }
395 shape.set(i, _shape.at(src));
396 }
397
398 arm_compute::TensorInfo tensor_info(shape, 1, dt);
399 tensor.allocator()->init(tensor_info);
400 }
401
402 /** Fill a tensor with the content of the currently open NPY file.
403 *
404 * @note If the tensor is a CLTensor, the function maps and unmaps the tensor
405 *
406 * @param[in,out] tensor Tensor to fill (Must be allocated, and of matching dimensions with the opened NPY).
407 */
408 template <typename T>
fill_tensor(T & tensor)409 void fill_tensor(T &tensor)
410 {
411 ARM_COMPUTE_ERROR_ON(!is_open());
412 ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, arm_compute::DataType::F32, arm_compute::DataType::F16);
413 try
414 {
415 // Map buffer if creating a CLTensor
416 map(tensor, true);
417
418 // Check if the file is large enough to fill the tensor
419 const size_t current_position = _fs.tellg();
420 _fs.seekg(0, std::ios_base::end);
421 const size_t end_position = _fs.tellg();
422 _fs.seekg(current_position, std::ios_base::beg);
423
424 ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(),
425 "Not enough data in file");
426 ARM_COMPUTE_UNUSED(end_position);
427
428 // Check if the typestring matches the given one
429 std::string expect_typestr = get_typestring(tensor.info()->data_type());
430 ARM_COMPUTE_ERROR_ON_MSG(_typestring != expect_typestr, "Typestrings mismatch");
431
432 bool are_layouts_different = (_file_layout != tensor.info()->data_layout());
433 // Correct dimensions (Needs to match TensorShape dimension corrections)
434 if(_shape.size() != tensor.info()->tensor_shape().num_dimensions())
435 {
436 for(int i = static_cast<int>(_shape.size()) - 1; i > 0; --i)
437 {
438 if(_shape[i] == 1)
439 {
440 _shape.pop_back();
441 }
442 else
443 {
444 break;
445 }
446 }
447 }
448
449 TensorShape permuted_shape = tensor.info()->tensor_shape();
450 arm_compute::PermutationVector perm;
451 if(are_layouts_different && tensor.info()->tensor_shape().num_dimensions() > 2)
452 {
453 perm = (tensor.info()->data_layout() == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
454 arm_compute::PermutationVector perm_vec = (tensor.info()->data_layout() == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
455
456 arm_compute::permute(permuted_shape, perm_vec);
457 }
458
459 // Validate tensor shape
460 ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.info()->tensor_shape().num_dimensions(), "Tensor ranks mismatch");
461 for(size_t i = 0; i < _shape.size(); ++i)
462 {
463 ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != _shape[i], "Tensor dimensions mismatch");
464 }
465
466 switch(tensor.info()->data_type())
467 {
468 case arm_compute::DataType::QASYMM8:
469 case arm_compute::DataType::S32:
470 case arm_compute::DataType::F32:
471 case arm_compute::DataType::F16:
472 {
473 // Read data
474 if(!are_layouts_different && !_fortran_order && tensor.info()->padding().empty())
475 {
476 // If tensor has no padding read directly from stream.
477 _fs.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
478 }
479 else
480 {
481 // If tensor has padding or is in fortran order accessing tensor elements through execution window.
482 Window window;
483 const unsigned int num_dims = _shape.size();
484 if(_fortran_order)
485 {
486 for(unsigned int dim = 0; dim < num_dims; dim++)
487 {
488 permuted_shape.set(dim, _shape[num_dims - dim - 1]);
489 perm.set(dim, num_dims - dim - 1);
490 }
491 if(are_layouts_different)
492 {
493 // Permute only if num_dimensions greater than 2
494 if(num_dims > 2)
495 {
496 if(_file_layout == DataLayout::NHWC) // i.e destination is NCHW --> permute(1,2,0)
497 {
498 arm_compute::permute(perm, arm_compute::PermutationVector(1U, 2U, 0U));
499 }
500 else
501 {
502 arm_compute::permute(perm, arm_compute::PermutationVector(2U, 0U, 1U));
503 }
504 }
505 }
506 }
507 window.use_tensor_dimensions(permuted_shape);
508
509 execute_window_loop(window, [&](const Coordinates & id)
510 {
511 Coordinates dst(id);
512 arm_compute::permute(dst, perm);
513 _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(dst)), tensor.info()->element_size());
514 });
515 }
516
517 break;
518 }
519 default:
520 ARM_COMPUTE_ERROR("Unsupported data type");
521 }
522
523 // Unmap buffer if creating a CLTensor
524 unmap(tensor);
525 }
526 catch(const std::ifstream::failure &e)
527 {
528 ARM_COMPUTE_ERROR_VAR("Loading NPY file: %s", e.what());
529 }
530 }
531
532 private:
533 std::ifstream _fs;
534 std::vector<unsigned long> _shape;
535 bool _fortran_order;
536 std::string _typestring;
537 DataLayout _file_layout;
538 };
539
540 /** Template helper function to save a tensor image to a PPM file.
541 *
542 * @note Only U8 and RGB888 formats supported.
543 * @note Only works with 2D tensors.
544 * @note If the input tensor is a CLTensor, the function maps and unmaps the image
545 *
546 * @param[in] tensor The tensor to save as PPM file
547 * @param[in] ppm_filename Filename of the file to create.
548 */
549 template <typename T>
save_to_ppm(T & tensor,const std::string & ppm_filename)550 void save_to_ppm(T &tensor, const std::string &ppm_filename)
551 {
552 ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::RGB888, arm_compute::Format::U8);
553 ARM_COMPUTE_ERROR_ON(tensor.info()->num_dimensions() > 2);
554
555 std::ofstream fs;
556
557 try
558 {
559 fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
560 fs.open(ppm_filename, std::ios::out | std::ios::binary);
561
562 const unsigned int width = tensor.info()->tensor_shape()[0];
563 const unsigned int height = tensor.info()->tensor_shape()[1];
564
565 fs << "P6\n"
566 << width << " " << height << " 255\n";
567
568 // Map buffer if creating a CLTensor/GCTensor
569 map(tensor, true);
570
571 switch(tensor.info()->format())
572 {
573 case arm_compute::Format::U8:
574 {
575 arm_compute::Window window;
576 window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, 1));
577 window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1));
578
579 arm_compute::Iterator in(&tensor, window);
580
581 arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
582 {
583 const unsigned char value = *in.ptr();
584
585 fs << value << value << value;
586 },
587 in);
588
589 break;
590 }
591 case arm_compute::Format::RGB888:
592 {
593 arm_compute::Window window;
594 window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, width));
595 window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1));
596
597 arm_compute::Iterator in(&tensor, window);
598
599 arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
600 {
601 fs.write(reinterpret_cast<std::fstream::char_type *>(in.ptr()), width * tensor.info()->element_size());
602 },
603 in);
604
605 break;
606 }
607 default:
608 ARM_COMPUTE_ERROR("Unsupported format");
609 }
610
611 // Unmap buffer if creating a CLTensor/GCTensor
612 unmap(tensor);
613 }
614 catch(const std::ofstream::failure &e)
615 {
616 ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", ppm_filename.c_str(), e.what());
617 }
618 }
619
620 /** Template helper function to save a tensor image to a NPY file.
621 *
622 * @note Only F32 data type supported.
623 * @note If the input tensor is a CLTensor, the function maps and unmaps the image
624 *
625 * @param[in] tensor The tensor to save as NPY file
626 * @param[in] npy_filename Filename of the file to create.
627 * @param[in] fortran_order If true, save matrix in fortran order.
628 */
629 template <typename T, typename U = float>
save_to_npy(T & tensor,const std::string & npy_filename,bool fortran_order)630 void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
631 {
632 ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::F32, arm_compute::DataType::QASYMM8);
633
634 std::ofstream fs;
635 try
636 {
637 fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
638 fs.open(npy_filename, std::ios::out | std::ios::binary);
639
640 std::vector<npy::ndarray_len_t> shape(tensor.info()->num_dimensions());
641
642 for(unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j)
643 {
644 shape[i] = tensor.info()->tensor_shape()[!fortran_order ? j : i];
645 }
646
647 // Map buffer if creating a CLTensor
648 map(tensor, true);
649
650 using typestring_type = typename std::conditional<std::is_floating_point<U>::value, float, qasymm8_t>::type;
651
652 std::vector<typestring_type> tmp; /* Used only to get the typestring */
653 npy::Typestring typestring_o{ tmp };
654 std::string typestring = typestring_o.str();
655
656 std::ofstream stream(npy_filename, std::ofstream::binary);
657 npy::write_header(stream, typestring, fortran_order, shape);
658
659 arm_compute::Window window;
660 window.use_tensor_dimensions(tensor.info()->tensor_shape());
661
662 arm_compute::Iterator in(&tensor, window);
663
664 arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
665 {
666 stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type));
667 },
668 in);
669
670 // Unmap buffer if creating a CLTensor
671 unmap(tensor);
672 }
673 catch(const std::ofstream::failure &e)
674 {
675 ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", npy_filename.c_str(), e.what());
676 }
677 }
678
679 /** Load the tensor with pre-trained data from a binary file
680 *
681 * @param[in] tensor The tensor to be filled. Data type supported: F32.
682 * @param[in] filename Filename of the binary file to load from.
683 */
684 template <typename T>
load_trained_data(T & tensor,const std::string & filename)685 void load_trained_data(T &tensor, const std::string &filename)
686 {
687 ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
688
689 std::ifstream fs;
690
691 try
692 {
693 fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
694 // Open file
695 fs.open(filename, std::ios::in | std::ios::binary);
696
697 if(!fs.good())
698 {
699 throw std::runtime_error("Could not load binary data: " + filename);
700 }
701
702 // Map buffer if creating a CLTensor/GCTensor
703 map(tensor, true);
704
705 Window window;
706
707 window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, 1, 1));
708
709 for(unsigned int d = 1; d < tensor.info()->num_dimensions(); ++d)
710 {
711 window.set(d, Window::Dimension(0, tensor.info()->tensor_shape()[d], 1));
712 }
713
714 arm_compute::Iterator in(&tensor, window);
715
716 execute_window_loop(window, [&](const Coordinates &)
717 {
718 fs.read(reinterpret_cast<std::fstream::char_type *>(in.ptr()), tensor.info()->tensor_shape()[0] * tensor.info()->element_size());
719 },
720 in);
721
722 // Unmap buffer if creating a CLTensor/GCTensor
723 unmap(tensor);
724 }
725 catch(const std::ofstream::failure &e)
726 {
727 ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", filename.c_str(), e.what());
728 }
729 }
730
731 template <typename T>
fill_random_tensor(T & tensor,float lower_bound,float upper_bound)732 void fill_random_tensor(T &tensor, float lower_bound, float upper_bound)
733 {
734 std::random_device rd;
735 std::mt19937 gen(rd());
736
737 Window window;
738 window.use_tensor_dimensions(tensor.info()->tensor_shape());
739
740 map(tensor, true);
741
742 Iterator it(&tensor, window);
743
744 switch(tensor.info()->data_type())
745 {
746 case arm_compute::DataType::F16:
747 {
748 std::uniform_real_distribution<float> dist(lower_bound, upper_bound);
749
750 execute_window_loop(window, [&](const Coordinates &)
751 {
752 *reinterpret_cast<half *>(it.ptr()) = (half)dist(gen);
753 },
754 it);
755
756 break;
757 }
758 case arm_compute::DataType::F32:
759 {
760 std::uniform_real_distribution<float> dist(lower_bound, upper_bound);
761
762 execute_window_loop(window, [&](const Coordinates &)
763 {
764 *reinterpret_cast<float *>(it.ptr()) = dist(gen);
765 },
766 it);
767
768 break;
769 }
770 default:
771 {
772 ARM_COMPUTE_ERROR("Unsupported format");
773 }
774 }
775
776 unmap(tensor);
777 }
778
779 template <typename T>
init_sgemm_output(T & dst,T & src0,T & src1,arm_compute::DataType dt)780 void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::DataType dt)
781 {
782 dst.allocator()->init(TensorInfo(TensorShape(src1.info()->dimension(0), src0.info()->dimension(1), src0.info()->dimension(2)), 1, dt));
783 }
784 /** This function returns the amount of memory free reading from /proc/meminfo
785 *
786 * @return The free memory in kB
787 */
788 uint64_t get_mem_free_from_meminfo();
789
790 /** Compare two tensors
791 *
792 * @param[in] tensor1 First tensor to be compared.
793 * @param[in] tensor2 Second tensor to be compared.
794 * @param[in] tolerance Tolerance used for the comparison.
795 *
796 * @return The number of mismatches
797 */
798 template <typename T>
compare_tensor(ITensor & tensor1,ITensor & tensor2,T tolerance)799 int compare_tensor(ITensor &tensor1, ITensor &tensor2, T tolerance)
800 {
801 ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&tensor1, &tensor2);
802 ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(&tensor1, &tensor2);
803
804 int num_mismatches = 0;
805 Window window;
806 window.use_tensor_dimensions(tensor1.info()->tensor_shape());
807
808 map(tensor1, true);
809 map(tensor2, true);
810
811 Iterator itensor1(&tensor1, window);
812 Iterator itensor2(&tensor2, window);
813
814 execute_window_loop(window, [&](const Coordinates &)
815 {
816 if(std::abs(*reinterpret_cast<T *>(itensor1.ptr()) - *reinterpret_cast<T *>(itensor2.ptr())) > tolerance)
817 {
818 ++num_mismatches;
819 }
820 },
821 itensor1, itensor2);
822
823 unmap(itensor1);
824 unmap(itensor2);
825
826 return num_mismatches;
827 }
828 } // namespace utils
829 } // namespace arm_compute
830 #endif /* __UTILS_UTILS_H__*/
831