Add `python train.py --freeze N` argument (#4238)

* Add freeze as an argument

I train on different platforms and sometimes I want to freeze some layers. I have to go into the code and change it and also keep track of how many layers I froze on what platform. Please add the number of layers to freeze as an argument in future versions thanks.

* Update train.py

* Update train.py

* Cleanup

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

train.py

@@ -53,9 +53,9 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
           opt,
           device,
           ):
-    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, = \
+    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
-        opt.resume, opt.noval, opt.nosave, opt.workers
+        opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
 
     # Directories
     w = save_dir / 'weights'  # weights dir
@@ -111,7 +111,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
 
     # Freeze
-    freeze = []  # parameter names to freeze (full or partial)
+    freeze = [f'model.{x}.' for x in range(freeze)]  # layers to freeze
     for k, v in model.named_parameters():
         v.requires_grad = True  # train all layers
         if any(x in k for x in freeze):
@@ -442,6 +442,7 @@ def parse_opt(known=False):
     parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
     parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
+    parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
     opt = parser.parse_known_args()[0] if known else parser.parse_args()
     return opt