fitness reverted to original

train.py

@@ -405,21 +405,21 @@ def train(hyp, opt, device, tb_writer=None):
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Log
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
+            # fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                     'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
                     'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                    'x/lr0', 'x/lr1', 'x/lr2',
+                    'x/lr0', 'x/lr1', 'x/lr2']
                     # 'metrics/fitness'
-                    ]  # params
-            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):# + list(fi), tags):
+                    # ]  # params
+            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):  # + list(fi), tags):
                 if tb_writer:
                     tb_writer.add_scalar(tag, x, epoch)  # tensorboard
                 if wandb_logger.wandb:
                     wandb_logger.log({tag: x})  # W&B
 
             # Update best mAP
-            # fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
+            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
             wandb_logger.end_epoch(best_result=best_fitness == fi)