hyperparameter printout update

test.py

@@ -19,7 +19,6 @@ def test(data,
          dataloader=None,
          save_dir='',
          merge=False):
-
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py

train.py

@@ -20,7 +20,6 @@ except:
     print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
     mixed_precision = False  # not installed
 
-
 # Hyperparameters
 hyp = {'optimizer': 'SGD',  # ['adam', 'SGD', None] if none, default is SGD
        'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
@@ -44,6 +43,7 @@ hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
 
 
 def train(hyp):
+    print(f'Hyperparameters {hyp}')
     log_dir = tb_writer.log_dir  # run directory
     wdir = str(Path(log_dir) / 'weights') + os.sep  # weights directory
 
@@ -365,7 +365,8 @@ if __name__ == '__main__':
     parser.add_argument('--batch-size', type=int, default=16)
     parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
-    parser.add_argument('--resume', nargs='?', const = 'get_last', default=False, help='resume from given path/to/last.pt, or most recent run if blank.')
+    parser.add_argument('--resume', nargs='?', const='get_last', default=False,
+                        help='resume from given path/to/last.pt, or most recent run if blank.')
     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')
@@ -394,14 +395,12 @@ if __name__ == '__main__':
 
     # Train
     if not opt.evolve:
+        print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
         tb_writer = SummaryWriter(comment=opt.name)
         if opt.hyp:  # update hyps
             with open(opt.hyp) as f:
                 hyp.update(yaml.load(f, Loader=yaml.FullLoader))
 
-        print(f'Beginning training with {hyp}\n\n')
-        print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
-        
         train(hyp)
 
     # Evolve hyperparameters (optional)

utils/utils.py

@@ -37,10 +37,10 @@ def init_seeds(seed=0):
     torch_utils.init_seeds(seed=seed)
 
 
-def get_latest_run(search_dir = './runs'):
+def get_latest_run(search_dir='./runs'):
     # 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)
+    return max(last_list, key=os.path.getctime)
 
 
 def check_git_status():
@@ -1113,7 +1113,7 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_st
     plt.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
-def plot_labels(labels, save_dir= ''):
+def plot_labels(labels, save_dir=''):
     # plot dataset labels
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classees, boxes
 
@@ -1180,7 +1180,8 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
         fig.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
-def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir= ''):  # from utils.utils import *; plot_results()
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
+                 save_dir=''):  # from utils.utils import *; plot_results()
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()