DDP `torch.jit.trace()` `--sync-bn` fix (#4615)

* Remove assert

* debug0

* trace=not opt.sync

* sync to sync_bn fix

* Cleanup

train.py

@@ -333,7 +333,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                 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.on_train_batch_end(ni, model, imgs, targets, paths, plots)
+                callbacks.on_train_batch_end(ni, model, imgs, targets, paths, plots, opt.sync_bn)
             # end batch ------------------------------------------------------------------------------------------------
 
         # Scheduler
@@ -499,7 +499,6 @@ def main(opt):
         assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
         assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
         assert not opt.evolve, '--evolve argument is not compatible with DDP training'
-        assert not opt.sync_bn, '--sync-bn known training issue, see https://github.com/ultralytics/yolov5/issues/3998'
         torch.cuda.set_device(LOCAL_RANK)
         device = torch.device('cuda', LOCAL_RANK)
         dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")

utils/loggers/__init__.py

@@ -69,13 +69,14 @@ class Loggers():
         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):
+    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
         # Callback runs on train batch end
         if plots:
             if ni == 0:
-                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), [])
+                if not sync_bn:  # tb.add_graph() --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), [])
             if ni < 3:
                 f = self.save_dir / f'train_batch{ni}.jpg'  # filename
                 Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()