1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
|
- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- General utils
- """
- import contextlib
- import glob
- import logging
- import math
- import os
- import platform
- import random
- import re
- import signal
- import time
- import urllib
- from itertools import repeat
- from multiprocessing.pool import ThreadPool
- from pathlib import Path
- from subprocess import check_output
- import cv2
- import numpy as np
- import pandas as pd
- import pkg_resources as pkg
- import torch
- import torchvision
- import yaml
- from utils.downloads import gsutil_getsize
- from utils.metrics import box_iou, fitness
- # Settings
- torch.set_printoptions(linewidth=320, precision=5, profile='long')
- np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
- pd.options.display.max_columns = 10
- cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
- os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads
- FILE = Path(__file__).resolve()
- ROOT = FILE.parents[1] # YOLOv5 root directory
- class Profile(contextlib.ContextDecorator):
- # Usage: @Profile() decorator or 'with Profile():' context manager
- def __enter__(self):
- self.start = time.time()
- def __exit__(self, type, value, traceback):
- print(f'Profile results: {time.time() - self.start:.5f}s')
- class Timeout(contextlib.ContextDecorator):
- # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
- def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
- self.seconds = int(seconds)
- self.timeout_message = timeout_msg
- self.suppress = bool(suppress_timeout_errors)
- def _timeout_handler(self, signum, frame):
- raise TimeoutError(self.timeout_message)
- def __enter__(self):
- signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
- signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
- def __exit__(self, exc_type, exc_val, exc_tb):
- signal.alarm(0) # Cancel SIGALRM if it's scheduled
- if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
- return True
- def try_except(func):
- # try-except function. Usage: @try_except decorator
- def handler(*args, **kwargs):
- try:
- func(*args, **kwargs)
- except Exception as e:
- print(e)
- return handler
- def methods(instance):
- # Get class/instance methods
- return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
- def set_logging(rank=-1, verbose=True):
- logging.basicConfig(
- format="%(message)s",
- level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN)
- def print_args(name, opt):
- # Print argparser arguments
- print(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
- def init_seeds(seed=0):
- # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
- # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
- import torch.backends.cudnn as cudnn
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
- def get_latest_run(search_dir='.'):
- # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
- last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
- return max(last_list, key=os.path.getctime) if last_list else ''
- def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
- # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
- env = os.getenv(env_var)
- if env:
- path = Path(env) # use environment variable
- else:
- cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
- path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
- path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
- path.mkdir(exist_ok=True) # make if required
- return path
- def is_writeable(dir, test=False):
- # Return True if directory has write permissions, test opening a file with write permissions if test=True
- if test: # method 1
- file = Path(dir) / 'tmp.txt'
- try:
- with open(file, 'w'): # open file with write permissions
- pass
- file.unlink() # remove file
- return True
- except IOError:
- return False
- else: # method 2
- return os.access(dir, os.R_OK) # possible issues on Windows
- def is_docker():
- # Is environment a Docker container?
- return Path('/workspace').exists() # or Path('/.dockerenv').exists()
- def is_colab():
- # Is environment a Google Colab instance?
- try:
- import google.colab
- return True
- except Exception as e:
- return False
- def is_pip():
- # Is file in a pip package?
- return 'site-packages' in Path(__file__).resolve().parts
- def is_ascii(s=''):
- # Is string composed of all ASCII (no UTF) characters?
- s = str(s) # convert list, tuple, None, etc. to str
- return len(s.encode().decode('ascii', 'ignore')) == len(s)
- def emojis(str=''):
- # Return platform-dependent emoji-safe version of string
- return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
- def file_size(path):
- # Return file/dir size (MB)
- path = Path(path)
- if path.is_file():
- return path.stat().st_size / 1E6
- elif path.is_dir():
- return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
- else:
- return 0.0
- def check_online():
- # Check internet connectivity
- import socket
- try:
- socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
- return True
- except OSError:
- return False
- @try_except
- def check_git_status():
- # Recommend 'git pull' if code is out of date
- msg = ', for updates see https://github.com/ultralytics/yolov5'
- print(colorstr('github: '), end='')
- assert Path('.git').exists(), 'skipping check (not a git repository)' + msg
- assert not is_docker(), 'skipping check (Docker image)' + msg
- assert check_online(), 'skipping check (offline)' + msg
- cmd = 'git fetch && git config --get remote.origin.url'
- url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch
- branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
- n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
- if n > 0:
- s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
- else:
- s = f'up to date with {url} ✅'
- print(emojis(s)) # emoji-safe
- def check_python(minimum='3.6.2'):
- # Check current python version vs. required python version
- check_version(platform.python_version(), minimum, name='Python ')
- def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False):
- # Check version vs. required version
- current, minimum = (pkg.parse_version(x) for x in (current, minimum))
- result = (current == minimum) if pinned else (current >= minimum)
- assert result, f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed'
- @try_except
- def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True):
- # Check installed dependencies meet requirements (pass *.txt file or list of packages)
- prefix = colorstr('red', 'bold', 'requirements:')
- check_python() # check python version
- if isinstance(requirements, (str, Path)): # requirements.txt file
- file = Path(requirements)
- assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
- requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
- else: # list or tuple of packages
- requirements = [x for x in requirements if x not in exclude]
- n = 0 # number of packages updates
- for r in requirements:
- try:
- pkg.require(r)
- except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
- s = f"{prefix} {r} not found and is required by YOLOv5"
- if install:
- print(f"{s}, attempting auto-update...")
- try:
- assert check_online(), f"'pip install {r}' skipped (offline)"
- print(check_output(f"pip install '{r}'", shell=True).decode())
- n += 1
- except Exception as e:
- print(f'{prefix} {e}')
- else:
- print(f'{s}. Please install and rerun your command.')
- if n: # if packages updated
- source = file.resolve() if 'file' in locals() else requirements
- s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
- f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
- print(emojis(s))
- def check_img_size(imgsz, s=32, floor=0):
- # Verify image size is a multiple of stride s in each dimension
- if isinstance(imgsz, int): # integer i.e. img_size=640
- new_size = max(make_divisible(imgsz, int(s)), floor)
- else: # list i.e. img_size=[640, 480]
- new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
- if new_size != imgsz:
- print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
- return new_size
- def check_imshow():
- # Check if environment supports image displays
- try:
- assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
- assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
- cv2.imshow('test', np.zeros((1, 1, 3)))
- cv2.waitKey(1)
- cv2.destroyAllWindows()
- cv2.waitKey(1)
- return True
- except Exception as e:
- print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
- return False
- def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
- # Check file(s) for acceptable suffixes
- if file and suffix:
- if isinstance(suffix, str):
- suffix = [suffix]
- for f in file if isinstance(file, (list, tuple)) else [file]:
- assert Path(f).suffix.lower() in suffix, f"{msg}{f} acceptable suffix is {suffix}"
- def check_yaml(file, suffix=('.yaml', '.yml')):
- # Search/download YAML file (if necessary) and return path, checking suffix
- return check_file(file, suffix)
- def check_file(file, suffix=''):
- # Search/download file (if necessary) and return path
- check_suffix(file, suffix) # optional
- file = str(file) # convert to str()
- if Path(file).is_file() or file == '': # exists
- return file
- elif file.startswith(('http:/', 'https:/')): # download
- url = str(Path(file)).replace(':/', '://') # Pathlib turns :// -> :/
- file = Path(urllib.parse.unquote(file)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
- print(f'Downloading {url} to {file}...')
- torch.hub.download_url_to_file(url, file)
- assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
- return file
- else: # search
- files = glob.glob('./**/' + file, recursive=True) # find file
- assert len(files), f'File not found: {file}' # assert file was found
- assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
- return files[0] # return file
- def check_dataset(data, autodownload=True):
- # Download and/or unzip dataset if not found locally
- # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
- # Download (optional)
- extract_dir = ''
- if isinstance(data, (str, Path)) and str(data).endswith('.zip'): # i.e. gs://bucket/dir/coco128.zip
- download(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1)
- data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml'))
- extract_dir, autodownload = data.parent, False
- # Read yaml (optional)
- if isinstance(data, (str, Path)):
- with open(data, errors='ignore') as f:
- data = yaml.safe_load(f) # dictionary
- # Parse yaml
- path = extract_dir or Path(data.get('path') or '') # optional 'path' default to '.'
- for k in 'train', 'val', 'test':
- if data.get(k): # prepend path
- data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
- assert 'nc' in data, "Dataset 'nc' key missing."
- if 'names' not in data:
- data['names'] = [f'class{i}' for i in range(data['nc'])] # assign class names if missing
- train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')]
- if val:
- val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
- if not all(x.exists() for x in val):
- print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
- if s and autodownload: # download script
- if s.startswith('http') and s.endswith('.zip'): # URL
- f = Path(s).name # filename
- print(f'Downloading {s} ...')
- torch.hub.download_url_to_file(s, f)
- root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
- Path(root).mkdir(parents=True, exist_ok=True) # create root
- r = os.system(f'unzip -q {f} -d {root} && rm {f}') # unzip
- elif s.startswith('bash '): # bash script
- print(f'Running {s} ...')
- r = os.system(s)
- else: # python script
- r = exec(s, {'yaml': data}) # return None
- print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure')) # print result
- else:
- raise Exception('Dataset not found.')
- return data # dictionary
- def url2file(url):
- # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
- url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
- file = Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
- return file
- def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
- # Multi-threaded file download and unzip function, used in data.yaml for autodownload
- def download_one(url, dir):
- # Download 1 file
- f = dir / Path(url).name # filename
- if Path(url).is_file(): # exists in current path
- Path(url).rename(f) # move to dir
- elif not f.exists():
- print(f'Downloading {url} to {f}...')
- if curl:
- os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail
- else:
- torch.hub.download_url_to_file(url, f, progress=True) # torch download
- if unzip and f.suffix in ('.zip', '.gz'):
- print(f'Unzipping {f}...')
- if f.suffix == '.zip':
- s = f'unzip -qo {f} -d {dir}' # unzip -quiet -overwrite
- elif f.suffix == '.gz':
- s = f'tar xfz {f} --directory {f.parent}' # unzip
- if delete: # delete zip file after unzip
- s += f' && rm {f}'
- os.system(s)
- dir = Path(dir)
- dir.mkdir(parents=True, exist_ok=True) # make directory
- if threads > 1:
- pool = ThreadPool(threads)
- pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multi-threaded
- pool.close()
- pool.join()
- else:
- for u in [url] if isinstance(url, (str, Path)) else url:
- download_one(u, dir)
- def make_divisible(x, divisor):
- # Returns x evenly divisible by divisor
- return math.ceil(x / divisor) * divisor
- def clean_str(s):
- # Cleans a string by replacing special characters with underscore _
- return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
- def one_cycle(y1=0.0, y2=1.0, steps=100):
- # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
- return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
- def colorstr(*input):
- # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
- *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
- colors = {'black': '\033[30m', # basic colors
- 'red': '\033[31m',
- 'green': '\033[32m',
- 'yellow': '\033[33m',
- 'blue': '\033[34m',
- 'magenta': '\033[35m',
- 'cyan': '\033[36m',
- 'white': '\033[37m',
- 'bright_black': '\033[90m', # bright colors
- 'bright_red': '\033[91m',
- 'bright_green': '\033[92m',
- 'bright_yellow': '\033[93m',
- 'bright_blue': '\033[94m',
- 'bright_magenta': '\033[95m',
- 'bright_cyan': '\033[96m',
- 'bright_white': '\033[97m',
- 'end': '\033[0m', # misc
- 'bold': '\033[1m',
- 'underline': '\033[4m'}
- return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
- def labels_to_class_weights(labels, nc=80):
- # Get class weights (inverse frequency) from training labels
- if labels[0] is None: # no labels loaded
- return torch.Tensor()
- labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
- classes = labels[:, 0].astype(np.int) # labels = [class xywh]
- weights = np.bincount(classes, minlength=nc) # occurrences per class
- # Prepend gridpoint count (for uCE training)
- # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
- # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
- weights[weights == 0] = 1 # replace empty bins with 1
- weights = 1 / weights # number of targets per class
- weights /= weights.sum() # normalize
- return torch.from_numpy(weights)
- def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
- # Produces image weights based on class_weights and image contents
- class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
- image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
- # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
- return image_weights
- def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
- # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
- # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
- # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
- # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
- # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
- x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
- 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
- 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
- return x
- def xyxy2xywh(x):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
- y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
- y[:, 2] = x[:, 2] - x[:, 0] # width
- y[:, 3] = x[:, 3] - x[:, 1] # height
- return y
- def xywh2xyxy(x):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
- y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
- y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
- y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
- return y
- def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
- # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
- y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y
- y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x
- y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y
- return y
- def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
- if clip:
- clip_coords(x, (h - eps, w - eps)) # warning: inplace clip
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w # x center
- y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h # y center
- y[:, 2] = (x[:, 2] - x[:, 0]) / w # width
- y[:, 3] = (x[:, 3] - x[:, 1]) / h # height
- return y
- def xyn2xy(x, w=640, h=640, padw=0, padh=0):
- # Convert normalized segments into pixel segments, shape (n,2)
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * x[:, 0] + padw # top left x
- y[:, 1] = h * x[:, 1] + padh # top left y
- return y
- def segment2box(segment, width=640, height=640):
- # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
- x, y = segment.T # segment xy
- inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
- x, y, = x[inside], y[inside]
- return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
- def segments2boxes(segments):
- # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
- boxes = []
- for s in segments:
- x, y = s.T # segment xy
- boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
- return xyxy2xywh(np.array(boxes)) # cls, xywh
- def resample_segments(segments, n=1000):
- # Up-sample an (n,2) segment
- for i, s in enumerate(segments):
- x = np.linspace(0, len(s) - 1, n)
- xp = np.arange(len(s))
- segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
- return segments
- def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
- # Rescale coords (xyxy) from img1_shape to img0_shape
- if ratio_pad is None: # calculate from img0_shape
- gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
- pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
- else:
- gain = ratio_pad[0][0]
- pad = ratio_pad[1]
- coords[:, [0, 2]] -= pad[0] # x padding
- coords[:, [1, 3]] -= pad[1] # y padding
- coords[:, :4] /= gain
- clip_coords(coords, img0_shape)
- return coords
- def clip_coords(boxes, shape):
- # Clip bounding xyxy bounding boxes to image shape (height, width)
- if isinstance(boxes, torch.Tensor): # faster individually
- boxes[:, 0].clamp_(0, shape[1]) # x1
- boxes[:, 1].clamp_(0, shape[0]) # y1
- boxes[:, 2].clamp_(0, shape[1]) # x2
- boxes[:, 3].clamp_(0, shape[0]) # y2
- else: # np.array (faster grouped)
- boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
- boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
- def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
- labels=(), max_det=300):
- """Runs Non-Maximum Suppression (NMS) on inference results
- Returns:
- list of detections, on (n,6) tensor per image [xyxy, conf, cls]
- """
- nc = prediction.shape[2] - 5 # number of classes
- xc = prediction[..., 4] > conf_thres # candidates
- # Checks
- assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
- assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
- # Settings
- min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
- max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
- time_limit = 10.0 # seconds to quit after
- redundant = True # require redundant detections
- multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
- merge = False # use merge-NMS
- t = time.time()
- output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
- for xi, x in enumerate(prediction): # image index, image inference
- # Apply constraints
- # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
- x = x[xc[xi]] # confidence
- # Cat apriori labels if autolabelling
- if labels and len(labels[xi]):
- l = labels[xi]
- v = torch.zeros((len(l), nc + 5), device=x.device)
- v[:, :4] = l[:, 1:5] # box
- v[:, 4] = 1.0 # conf
- v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls
- x = torch.cat((x, v), 0)
- # If none remain process next image
- if not x.shape[0]:
- continue
- # Compute conf
- x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
- # Box (center x, center y, width, height) to (x1, y1, x2, y2)
- box = xywh2xyxy(x[:, :4])
- # Detections matrix nx6 (xyxy, conf, cls)
- if multi_label:
- i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
- x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
- else: # best class only
- conf, j = x[:, 5:].max(1, keepdim=True)
- x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
- # Filter by class
- if classes is not None:
- x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
- # Apply finite constraint
- # if not torch.isfinite(x).all():
- # x = x[torch.isfinite(x).all(1)]
- # Check shape
- n = x.shape[0] # number of boxes
- if not n: # no boxes
- continue
- elif n > max_nms: # excess boxes
- x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
- # Batched NMS
- c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
- boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
- i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
- if i.shape[0] > max_det: # limit detections
- i = i[:max_det]
- if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
- # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
- iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
- weights = iou * scores[None] # box weights
- x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
- if redundant:
- i = i[iou.sum(1) > 1] # require redundancy
- output[xi] = x[i]
- if (time.time() - t) > time_limit:
- print(f'WARNING: NMS time limit {time_limit}s exceeded')
- break # time limit exceeded
- return output
- def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer()
- # Strip optimizer from 'f' to finalize training, optionally save as 's'
- x = torch.load(f, map_location=torch.device('cpu'))
- if x.get('ema'):
- x['model'] = x['ema'] # replace model with ema
- for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys
- x[k] = None
- x['epoch'] = -1
- x['model'].half() # to FP16
- for p in x['model'].parameters():
- p.requires_grad = False
- torch.save(x, s or f)
- mb = os.path.getsize(s or f) / 1E6 # filesize
- print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
- def print_mutation(results, hyp, save_dir, bucket):
- evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml'
- keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
- 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
- keys = tuple(x.strip() for x in keys)
- vals = results + tuple(hyp.values())
- n = len(keys)
- # Download (optional)
- if bucket:
- url = f'gs://{bucket}/evolve.csv'
- if gsutil_getsize(url) > (os.path.getsize(evolve_csv) if os.path.exists(evolve_csv) else 0):
- os.system(f'gsutil cp {url} {save_dir}') # download evolve.csv if larger than local
- # Log to evolve.csv
- s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header
- with open(evolve_csv, 'a') as f:
- f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
- # Print to screen
- print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
- print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\n')
- # Save yaml
- with open(evolve_yaml, 'w') as f:
- data = pd.read_csv(evolve_csv)
- data = data.rename(columns=lambda x: x.strip()) # strip keys
- i = np.argmax(fitness(data.values[:, :7])) #
- f.write(f'# YOLOv5 Hyperparameter Evolution Results\n' +
- f'# Best generation: {i}\n' +
- f'# Last generation: {len(data)}\n' +
- f'# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
- f'# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
- yaml.safe_dump(hyp, f, sort_keys=False)
- if bucket:
- os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}') # upload
- def apply_classifier(x, model, img, im0):
- # Apply a second stage classifier to yolo outputs
- im0 = [im0] if isinstance(im0, np.ndarray) else im0
- for i, d in enumerate(x): # per image
- if d is not None and len(d):
- d = d.clone()
- # Reshape and pad cutouts
- b = xyxy2xywh(d[:, :4]) # boxes
- b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
- b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
- d[:, :4] = xywh2xyxy(b).long()
- # Rescale boxes from img_size to im0 size
- scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
- # Classes
- pred_cls1 = d[:, 5].long()
- ims = []
- for j, a in enumerate(d): # per item
- cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
- im = cv2.resize(cutout, (224, 224)) # BGR
- # cv2.imwrite('example%i.jpg' % j, cutout)
- im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
- im /= 255.0 # 0 - 255 to 0.0 - 1.0
- ims.append(im)
- pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
- x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
- return x
- def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
- # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
- xyxy = torch.tensor(xyxy).view(-1, 4)
- b = xyxy2xywh(xyxy) # boxes
- if square:
- b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
- b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
- xyxy = xywh2xyxy(b).long()
- clip_coords(xyxy, im.shape)
- crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
- if save:
- cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop)
- return crop
- def increment_path(path, exist_ok=False, sep='', mkdir=False):
- # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
- path = Path(path) # os-agnostic
- if path.exists() and not exist_ok:
- suffix = path.suffix
- path = path.with_suffix('')
- dirs = glob.glob(f"{path}{sep}*") # similar paths
- matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
- i = [int(m.groups()[0]) for m in matches if m] # indices
- n = max(i) + 1 if i else 2 # increment number
- path = Path(f"{path}{sep}{n}{suffix}") # update path
- dir = path if path.suffix == '' else path.parent # directory
- if not dir.exists() and mkdir:
- dir.mkdir(parents=True, exist_ok=True) # make directory
- return path
|