fix neptune logging (fcakyon#69)

* Update neptune_utils.py

* update to v6.0.6

* set defaults of neptune and aws arguments as `None`

* fix typo in neptune utils

yolov5/__init__.py

@@ -1,4 +1,4 @@
 from yolov5.helpers import YOLOv5
 from yolov5.helpers import load_model as load
 
-__version__ = "6.0.5"
+__version__ = "6.0.6"

yolov5/train.py

@@ -565,11 +565,11 @@ def parse_opt(known=False):
     parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
 
     # Neptune AI arguments
-    parser.add_argument('--neptune_token', type=str, default="", help='neptune.ai api token')
-    parser.add_argument('--neptune_project', type=str, default="", help='https://docs.neptune.ai/api-reference/neptune')
+    parser.add_argument('--neptune_token', type=str, default=None, help='neptune.ai api token')
+    parser.add_argument('--neptune_project', type=str, default=None, help='https://docs.neptune.ai/api-reference/neptune')
 
     # AWS arguments
-    parser.add_argument('--s3_upload_dir', type=str, default="", help='aws s3 folder directory to upload best weight and dataset')
+    parser.add_argument('--s3_upload_dir', type=str, default=None, help='aws s3 folder directory to upload best weight and dataset')
     parser.add_argument('--upload_dataset', action='store_true', help='upload dataset to aws s3')
 
     opt = parser.parse_known_args()[0] if known else parser.parse_args()

yolov5/utils/loggers/neptune/neptune_utils.py

@@ -47,8 +47,10 @@ def track_dataset(self, opt):
             yolo_s3_data_dir = data_dict["yolo_s3_data_dir"]
         elif opt.upload_dataset and opt.s3_upload_dir:
             yolo_s3_data_dir = "s3://" + str(Path(opt.s3_upload_dir.replace("s3://","")) / Path(opt.save_dir).name / 'data').replace(os.sep, '/')
-
-        self.neptune_run["data"].track_files(yolo_s3_data_dir)
+        else:
+            yolo_s3_data_dir = None
+        if yolo_s3_data_dir is not None:
+            self.neptune_run["data"].track_files(yolo_s3_data_dir)
 
 
     def setup_training(self, data_dict):