--evolve 300 generations CLI argument (ultralytics#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>

train.py

@@ -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'