Cleanup (#197)

Co-authored-by: Onuralp Sezer <thunderbirdtr@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/yolo/utils/callbacks/base.py

@@ -143,8 +143,7 @@ def add_integration_callbacks(instance):
     from .comet import callbacks as comet_callbacks
     from .hub import callbacks as hub_callbacks
     from .tensorboard import callbacks as tb_callbacks
-    from .wb import callbacks as wb_callbacks
 
-    for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks, wb_callbacks:
+    for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks:
         for k, v in x.items():
             instance.callbacks[k].append(v)  # callback[name].append(func)

ultralytics/yolo/utils/callbacks/wb.py

@@ -1,48 +0,0 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
-
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')
-except (ImportError, AssertionError):
-    wandb = None
-
-
-def on_pretrain_routine_start(trainer):
-    wandb.init(project=trainer.args.project or "YOLOv8", name=trainer.args.name, config=dict(
-        trainer.args)) if not wandb.run else wandb.run
-
-
-def on_fit_epoch_end(trainer):
-    wandb.run.log(trainer.metrics, step=trainer.epoch + 1)
-    if trainer.epoch == 0:
-        model_info = {
-            "model/parameters": get_num_params(trainer.model),
-            "model/GFLOPs": round(get_flops(trainer.model), 3),
-            "model/speed(ms)": round(trainer.validator.speed[1], 3)}
-        wandb.run.log(model_info, step=trainer.epoch + 1)
-
-
-def on_train_epoch_end(trainer):
-    wandb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
-    wandb.run.log(trainer.lr, step=trainer.epoch + 1)
-    if trainer.epoch == 1:
-        wandb.run.log({f.stem: wandb.Image(str(f))
-                       for f in trainer.save_dir.glob('train_batch*.jpg')},
-                      step=trainer.epoch + 1)
-
-
-def on_train_end(trainer):
-    art = wandb.Artifact(type="model", name=f"run_{wandb.run.id}_model")
-    if trainer.best.exists():
-        art.add_file(trainer.best)
-        wandb.run.log_artifact(art)
-
-
-callbacks = {
-    "on_pretrain_routine_start": on_pretrain_routine_start,
-    "on_train_epoch_end": on_train_epoch_end,
-    "on_fit_epoch_end": on_fit_epoch_end,
-    "on_train_end": on_train_end} if wandb else {}