Add --noplots flag to suppress figures and images logging (#7534)

* support nomedia

* support nomedia for validation

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update train.py

* Revert no plot evolve

evolve plots do not contain any images

* Revert plot_results

contains no media

* Update wandb_utils.py

* sync-bn cleanup

* Cleanup

* Rename nomedia -> noplots

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

train.py

@@ -100,7 +100,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
             callbacks.register_action(k, callback=getattr(loggers, k))
 
     # Config
-    plots = not evolve  # create plots
+    plots = not evolve and not opt.noplots  # create plots
     cuda = device.type != 'cpu'
     init_seeds(1 + RANK)
     with torch_distributed_zero_first(LOCAL_RANK):
@@ -373,7 +373,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                 pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
                                      (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
-                callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
+                callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
                 if callbacks.stop_training:
                     return
             # end batch ------------------------------------------------------------------------------------------------
@@ -488,6 +488,7 @@ def parse_opt(known=False):
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
     parser.add_argument('--noval', action='store_true', help='only validate final epoch')
     parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
+    parser.add_argument('--noplots', action='store_true', help='save no plot files')
     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', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')

utils/loggers/__init__.py

@@ -99,11 +99,11 @@ def on_pretrain_routine_end(self):
         if self.wandb:
             self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
 
-    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
+    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
         # Callback runs on train batch end
         if plots:
             if ni == 0:
-                if not sync_bn:  # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754
+                if not self.opt.sync_bn:  # --sync known issue https://github.com/ultralytics/yolov5/issues/3754
                     with warnings.catch_warnings():
                         warnings.simplefilter('ignore')  # suppress jit trace warning
                         self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])

utils/loggers/wandb/wandb_utils.py

@@ -250,8 +250,8 @@ def setup_training(self, opt):
                 self.map_val_table_path()
         if opt.bbox_interval == -1:
             self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-            if opt.evolve:
-                self.bbox_interval = opt.bbox_interval = opt.epochs + 1
+            if opt.evolve or opt.noplots:
+                self.bbox_interval = opt.bbox_interval = opt.epochs + 1  # disable bbox_interval
         train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
         # Update the the data_dict to point to local artifacts dir
         if train_from_artifact: