W&B DDP fix (#2574)

train.py

     is_coco = opt.data.endswith('coco.yaml')
 
     # Logging- Doing this before checking the dataset. Might update data_dict
+    loggers = {'wandb': None}  # loggers dict
     if rank in [-1, 0]:
         opt.hyp = hyp  # add hyperparameters
         run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
         wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
+        loggers['wandb'] = wandb_logger.wandb
         data_dict = wandb_logger.data_dict
         if wandb_logger.wandb:
             weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
-    loggers = {'wandb': wandb_logger.wandb}  # loggers dict
+    
     nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
             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)
 
             # Save model
             if (not opt.nosave) or (final_epoch and not opt.evolve):  # if save
                         wandb_logger.log_model(
                             last.parent, opt, epoch, fi, best_model=best_fitness == fi)
                 del ckpt
-        wandb_logger.end_epoch(best_result=best_fitness == fi)
         
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training
             wandb_logger.wandb.log_artifact(str(final), type='model',
                                             name='run_' + wandb_logger.wandb_run.id + '_model',
                                             aliases=['last', 'best', 'stripped'])
+        wandb_logger.finish_run()
     else:
         dist.destroy_process_group()
     torch.cuda.empty_cache()
-    wandb_logger.finish_run()
     return results
 
 

utils/wandb_logging/wandb_utils.py

 
 try:
     import wandb
+    from wandb import init, finish
 except ImportError:
     wandb = None
-    print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
 
 WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
 
                 self.data_dict = self.setup_training(opt, data_dict)
             if self.job_type == 'Dataset Creation':
                 self.data_dict = self.check_and_upload_dataset(opt)
+        else:
+            print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
+
 
     def check_and_upload_dataset(self, opt):
         assert wandb, 'Install wandb to upload dataset'