W&B artifacts feature addition (ultralytics#1712)

* Log artifacts

* cleanup

train.py

@@ -386,10 +386,12 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     if rank in [-1, 0]:
         # Strip optimizers
+        final = best if best.exists() else last  # final model
         for f in [last, best]:
-            if f.exists():  # is *.pt
-                strip_optimizer(f)  # strip optimizer
-                os.system('gsutil cp %s gs://%s/weights' % (f, opt.bucket)) if opt.bucket else None  # upload
+            if f.exists():
+                strip_optimizer(f)  # strip optimizers
+        if opt.bucket:
+            os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
 
         # Plots
         if plots:
@@ -398,17 +400,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
                 wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
                                        if (save_dir / f).exists()]})
-        logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
+                if opt.log_artifacts:
+                    wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem)
 
         # Test best.pt
+        logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
         if opt.data.endswith('coco.yaml') and nc == 80:  # if COCO
             for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]):  # speed, mAP tests
                 results, _, _ = test.test(opt.data,
                                           batch_size=total_batch_size,
                                           imgsz=imgsz_test,
                                           conf_thres=conf,
                                           iou_thres=iou,
-                                          model=attempt_load(best if best.exists() else last, device).half(),
+                                          model=attempt_load(final, device).half(),
                                           single_cls=opt.single_cls,
                                           dataloader=testloader,
                                           save_dir=save_dir,
@@ -448,6 +452,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
     parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
+    parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
     parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
     parser.add_argument('--project', default='runs/train', help='save to project/name')
     parser.add_argument('--name', default='exp', help='save to project/name')