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`--evolve 300` generations CLI argument (#3863) 

* evolve command accepts argument for number of generations

* evolve generations argument used in evolve for loop

* evolve argument boolean fixes

* default to 300 evolve generations

* Update train.py

Co-authored-by: John San Soucie <jsansoucie@whoi.edu>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
pull/3875/head
san-soucie 2021-07-04 06:14:35 -04:00 committed by GitHub
parent 831773f5a2
commit d3e9d69850
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1 changed files with 3 additions and 3 deletions
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train.py
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@ -494,7 +494,7 @@ def parse_opt(known=False):
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
@ -542,7 +542,7 @@ def main(opt):
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
opt.name = 'evolve' if opt.evolve else opt.name
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve))
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
# DDP mode
device = select_device(opt.device, batch_size=opt.batch_size)
@ -603,7 +603,7 @@ def main(opt):
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
for _ in range(300): # generations to evolve
for _ in range(opt.evolve): # generations to evolve
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'