Fix yaml saving (don't sort keys), reorder --opt keys, bug fix hyp dict accessor

train.py

             else:
                 pg0.append(v)  # all else
 
-    if hyp.optimizer =='adam':
+    if hyp['optimizer'] =='adam':
         optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) #use default beta2, adjust beta1 for Adam momentum per momentum adjustments in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
     else:
         optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
 
     #save hyperparamter and training options in run folder
     with open(os.path.join(log_dir, 'hyp.yaml'), 'w') as f:
-        yaml.dump(hyp, f)
+        yaml.dump(hyp, f, sort_keys=False)
 
     with open(os.path.join(log_dir, 'opt.yaml'), 'w') as f:
-        yaml.dump(vars(opt), f)
+        yaml.dump(vars(opt), f, sort_keys=False)
     
     # Class frequency
     labels = np.concatenate(dataset.labels, 0)
 if __name__ == '__main__':
     check_git_status()
     parser = argparse.ArgumentParser()
-    parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16)
     parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model cfg path[*.yaml]')
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data cfg path [*.yaml]')
+    parser.add_argument('--hyp', type=str, default='',help='hyp cfg path [*.yaml].')
+    parser.add_argument('--epochs', type=int, default=300)
+    parser.add_argument('--batch-size', type=int, default=16)
     parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes. Assumes square imgs.')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--hyp', type=str, default='', help ='hyp cfg path [*.yaml].')
+
     opt = parser.parse_args()
 
     opt.cfg = check_file(opt.cfg)  # check file