Copy wandb param dict before training to avoid overwrites (#7317)

* Copy wandb param dict before training to avoid overwrites. Copy the hyperparameter dict retrieved from wandb configuration before passing it to `train()`. Training overwrites parameters in the dictionary (eg scaling obj/box/cls gains), which causes the values reported in wandb to not match the input values. This is confusing as it makes it hard to reproduce a run, and also throws off wandb's Bayesian sweep algorithm. * Cleanup Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Copy wandb param dict before training to avoid overwrites (#7317)
a88a8146 · Nick Martin · GitHub · 245d6459 · a88a8146
--- a/utils/loggers/wandb/sweep.py
+++ b/utils/loggers/wandb/sweep.py
@@ -16,8 +16,8 @@ from utils.torch_utils import select_device

 def sweep():
    wandb.init()
-    # Get hyp dict from sweep agent
-    hyp_dict = vars(wandb.config).get("_items")
+    # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
+    hyp_dict = vars(wandb.config).get("_items").copy()

    # Workaround: get necessary opt args
    opt = parse_opt(known=True)