Single-source training update (#680)

train.py

     parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')
     parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
-    parser.add_argument('--hyp', type=str, default='data/hyp.finetune.yaml', help='hyperparameters path')
+    parser.add_argument('--hyp', type=str, default='', help='hyperparameters path, i.e. data/hyp.scratch.yaml')
     parser.add_argument('--epochs', type=int, default=300)
     parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
     parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
     opt = parser.parse_args()
 
     # Resume
-    last = get_latest_run() if opt.resume == 'get_last' else opt.resume  # resume from most recent run
-    if last and not opt.weights:
-        print(f'Resuming training from {last}')
-    opt.weights = last if opt.resume and not opt.weights else opt.weights
+    if opt.resume:
+        last = get_latest_run() if opt.resume == 'get_last' else opt.resume  # resume from most recent run
+        if last and not opt.weights:
+            print(f'Resuming training from {last}')
+        opt.weights = last if opt.resume and not opt.weights else opt.weights
     if opt.local_rank == -1 or ("RANK" in os.environ and os.environ["RANK"] == "0"):
         check_git_status()
 
+    opt.hyp = opt.hyp or ('data/hyp.finetune.yaml' if opt.weights else 'data/hyp.scratch.yaml')
     opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check files
     assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-    assert len(opt.hyp), '--hyp must be specified'
 
     opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
     device = select_device(opt.device, batch_size=opt.batch_size)