[x]W&B: Don't resume transfer learning runs (#3604)

* Allow config cahnge

* Allow val change in wandb config

* Don't resume transfer learning runs

* Add entity in log dataset

train.py

         # W&B
         opt.hyp = hyp  # add hyperparameters
         run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
+        run_id = run_id if opt.resume else None # start fresh run if transfer learning
         wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
         loggers['wandb'] = wandb_logger.wandb
         if loggers['wandb']:

utils/wandb_logging/log_dataset.py

     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
+    parser.add_argument('--entity', default=None, help='W&B entity')
+
     opt = parser.parse_args()
     opt.resume = False  # Explicitly disallow resume check for dataset upload job
 

utils/wandb_logging/wandb_utils.py

                 if not opt.resume:
                     wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
                     # Info useful for resuming from artifacts
-                    self.wandb_run.config.opt = vars(opt)
-                    self.wandb_run.config.data_dict = wandb_data_dict
+                    self.wandb_run.config.update({'opt': vars(opt), 'data_dict': data_dict}, allow_val_change=True)
                 self.data_dict = self.setup_training(opt, data_dict)
             if self.job_type == 'Dataset Creation':
                 self.data_dict = self.check_and_upload_dataset(opt)