Add optional dataset.yaml path attribute (#3753)

* Add optional dataset.yaml `path` attribute

@KalenMike

* pass locals to python scripts

* handle lists

* update coco128.yaml

* Capitalize first letter

* add test key

* finalize GlobalWheat2020.yaml

* finalize objects365.yaml

* finalize SKU-110K.yaml

* finalize SKU-110K.yaml

* finalize VisDrone.yaml

* NoneType fix

* update download comment

* voc to VOC

* update

* update VOC.yaml

* update VOC.yaml

* remove dashes

* delete get_voc.sh

* force coco and coco128 to ../datasets

* Capitalize Argoverse_HD.yaml

* Capitalize Objects365.yaml

* update Argoverse_HD.yaml

* coco segments fix

* VOC single-thread

* update Argoverse_HD.yaml

* update data_dict in test handling

* create root

data/GlobalWheat2020.yaml

@@ -1,43 +1,40 @@
 # Global Wheat 2020 dataset http://www.global-wheat.com/
 # Train command: python train.py --data GlobalWheat2020.yaml
 # Default dataset location is next to YOLOv5:
-#   /parent_folder
+#   /parent
 #     /datasets/GlobalWheat2020
 #     /yolov5
 
 
-# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
-train: # 3422 images
-  - ../datasets/GlobalWheat2020/images/arvalis_1
-  - ../datasets/GlobalWheat2020/images/arvalis_2
-  - ../datasets/GlobalWheat2020/images/arvalis_3
-  - ../datasets/GlobalWheat2020/images/ethz_1
-  - ../datasets/GlobalWheat2020/images/rres_1
-  - ../datasets/GlobalWheat2020/images/inrae_1
-  - ../datasets/GlobalWheat2020/images/usask_1
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020  # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
 
-val: # 748 images (WARNING: train set contains ethz_1)
-  - ../datasets/GlobalWheat2020/images/ethz_1
-
-test: # 1276 images
-  - ../datasets/GlobalWheat2020/images/utokyo_1
-  - ../datasets/GlobalWheat2020/images/utokyo_2
-  - ../datasets/GlobalWheat2020/images/nau_1
-  - ../datasets/GlobalWheat2020/images/uq_1
-
-# number of classes
-nc: 1
-
-# class names
-names: [ 'wheat_head' ]
+# Classes
+nc: 1  # number of classes
+names: [ 'wheat_head' ]  # class names
 
 
-# download command/URL (optional) --------------------------------------------------------------------------------------
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from utils.general import download, Path
 
   # Download
-  dir = Path('../datasets/GlobalWheat2020')  # dataset directory
+  dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
   download(urls, dir=dir)