[Refactor] Refactor dataset metainfo to lowercase.

configs/_base_/datasets/voc0712.py

@@ -38,10 +38,10 @@
         times=3,
         dataset=dict(
             type='ConcatDataset',
-            # VOCDataset will add different `DATASET_TYPE` in dataset.metainfo,
+            # VOCDataset will add different `dataset_type` in dataset.metainfo,
             # which will get error if using ConcatDataset. Adding
             # `ignore_keys` can avoid this error.
-            ignore_keys=['DATASET_TYPE'],
+            ignore_keys=['dataset_type'],
             datasets=[
                 dict(
                     type=dataset_type,

configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person-bicycle-car.py

@@ -1,8 +1,8 @@
 _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
 model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
 metainfo = {
-    'CLASSES': ('person', 'bicycle', 'car'),
-    'PALETTE': [
+    'classes': ('person', 'bicycle', 'car'),
+    'palette': [
         (220, 20, 60),
         (119, 11, 32),
         (0, 0, 142),

configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py

@@ -1,8 +1,8 @@
 _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
 model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
 metainfo = {
-    'CLASSES': ('person', ),
-    'PALETTE': [
+    'classes': ('person', ),
+    'palette': [
         (220, 20, 60),
     ]
 }

configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712-cocofmt.py

@@ -5,12 +5,12 @@
 model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
 
 METAINFO = {
-    'CLASSES':
+    'classes':
     ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
      'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
      'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'),
-    # PALETTE is a list of color tuples, which is used for visualization.
-    'PALETTE': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
+    # palette is a list of color tuples, which is used for visualization.
+    'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
                 (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
                 (153, 69, 1), (120, 166, 157), (0, 182, 199), (0, 226, 252),
                 (182, 182, 255), (0, 0, 230), (220, 20, 60), (163, 255, 0),

configs/pascal_voc/ssd300_voc0712.py

@@ -50,10 +50,10 @@
         # so the actual epoch = 12 * 10 = 120.
         times=10,
         dataset=dict(  # ConcatDataset
-            # VOCDataset will add different `DATASET_TYPE` in dataset.metainfo,
+            # VOCDataset will add different `dataset_type` in dataset.metainfo,
             # which will get error if using ConcatDataset. Adding
             # `ignore_keys` can avoid this error.
-            ignore_keys=['DATASET_TYPE'],
+            ignore_keys=['dataset_type'],
             datasets=[
                 dict(
                     type=dataset_type,

configs/pascal_voc/ssd512_voc0712.py

@@ -58,10 +58,10 @@
         # so the actual epoch = 12 * 10 = 120.
         times=10,
         dataset=dict(  # ConcatDataset
-            # VOCDataset will add different `DATASET_TYPE` in dataset.metainfo,
+            # VOCDataset will add different `dataset_type` in dataset.metainfo,
             # which will get error if using ConcatDataset. Adding
             # `ignore_keys` can avoid this error.
-            ignore_keys=['DATASET_TYPE'],
+            ignore_keys=['dataset_type'],
             datasets=[
                 dict(
                     type=dataset_type,

docs/en/advanced_guides/customize_dataset.md

@@ -60,7 +60,7 @@ Here we give an example to show the above two steps, which uses a customized dat
 
 There are two aspects involved in the modification of config file:
 
-1. The `data` field. Specifically, you need to explicitly add the `metainfo=dict(CLASSES=classes)` fields in `train_dataloader.dataset`, `val_dataloader.dataset` and `test_dataloader.dataset` and `classes` must be a tuple type.
+1. The `data` field. Specifically, you need to explicitly add the `metainfo=dict(classes=classes)` fields in `train_dataloader.dataset`, `val_dataloader.dataset` and `test_dataloader.dataset` and `classes` must be a tuple type.
 2. The `num_classes` field in the `model` part. Explicitly over-write all the `num_classes` from default value (e.g. 80 in COCO) to your classes number.
 
 In `configs/my_custom_config.py`:
@@ -81,7 +81,7 @@ train_dataloader = dict(
     dataset=dict(
         type=dataset_type,
         # explicitly add your class names to the field `metainfo`
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='train/annotation_data',
         data_prefix=dict(img='train/image_data')
@@ -95,7 +95,7 @@ val_dataloader = dict(
         type=dataset_type,
         test_mode=True,
         # explicitly add your class names to the field `metainfo`
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='val/annotation_data',
         data_prefix=dict(img='val/image_data')
@@ -109,7 +109,7 @@ test_dataloader = dict(
         type=dataset_type,
         test_mode=True,
         # explicitly add your class names to the field `metainfo`
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='test/annotation_data',
         data_prefix=dict(img='test/image_data')
@@ -273,8 +273,8 @@ from mmdet.registry import DATASETS
 class MyDataset(BaseDetDataset):
 
     METAINFO = {
-       'CLASSES': ('person', 'bicycle', 'car', 'motorcycle'),
-        'PALETTE': [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230)]
+       'classes': ('person', 'bicycle', 'car', 'motorcycle'),
+        'palette': [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230)]
     }
 
     def load_data_list(self, ann_file):
@@ -341,15 +341,15 @@ The dataset will filter out the ground truth boxes of other classes automaticall
 classes = ('person', 'bicycle', 'car')
 train_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 val_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 test_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 ```
 

docs/en/advanced_guides/customize_runtime.md

@@ -334,7 +334,7 @@ If the hook is already implemented in MMDectection, you can directly modify the
 
 #### Example: `NumClassCheckHook`
 
-We implement a customized hook named [NumClassCheckHook](../../../mmdet/engine/hooks/num_class_check_hook.py) to check whether the `num_classes` in head matches the length of `CLASSES` in `dataset`.
+We implement a customized hook named [NumClassCheckHook](../../../mmdet/engine/hooks/num_class_check_hook.py) to check whether the `num_classes` in head matches the length of `classes` in the metainfo of `dataset`.
 
 We set it in [default_runtime.py](../../../configs/_base_/default_runtime.py).
 

docs/en/user_guides/train.md

@@ -399,8 +399,8 @@ model = dict(
 # Modify dataset related settings
 data_root = 'data/balloon/'
 metainfo = {
-    'CLASSES': ('balloon', ),
-    'PALETTE': [
+    'classes': ('balloon', ),
+    'palette': [
         (220, 20, 60),
     ]
 }

docs/zh_cn/advanced_guides/customize_dataset.md

@@ -62,7 +62,7 @@ COCO 格式的 JSON 标注文件有如下必要的字段：
 
 配置文件的修改涉及两个方面：
 
-1. `dataloaer` 部分。需要在 `train_dataloader.dataset`、`val_dataloader.dataset` 和 `test_dataloader.dataset` 中添加 `metainfo=dict(CLASSES=classes)`, 其中 classes 必须是 tuple 类型。
+1. `dataloaer` 部分。需要在 `train_dataloader.dataset`、`val_dataloader.dataset` 和 `test_dataloader.dataset` 中添加 `metainfo=dict(classes=classes)`, 其中 classes 必须是 tuple 类型。
 2. `model` 部分中的 `num_classes`。需要将默认值（COCO 数据集中为 80）修改为自定义数据集中的类别数。
 
 `configs/my_custom_config.py` 内容如下：
@@ -83,7 +83,7 @@ train_dataloader = dict(
     dataset=dict(
         type=dataset_type,
         # 将类别名字添加至 `metainfo` 字段中
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='train/annotation_data',
         data_prefix=dict(img='train/image_data')
@@ -97,7 +97,7 @@ val_dataloader = dict(
         type=dataset_type,
         test_mode=True,
         # 将类别名字添加至 `metainfo` 字段中
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='val/annotation_data',
         data_prefix=dict(img='val/image_data')
@@ -110,7 +110,7 @@ test_dataloader = dict(
         type=dataset_type,
         test_mode=True,
         # 将类别名字添加至 `metainfo` 字段中
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         data_root=data_root,
         ann_file='test/annotation_data',
         data_prefix=dict(img='test/image_data')
@@ -270,8 +270,8 @@ from mmdet.registry import DATASETS
 class MyDataset(BaseDetDataset):
 
     METAINFO = {
-       'CLASSES': ('person', 'bicycle', 'car', 'motorcycle'),
-        'PALETTE': [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230)]
+       'classes': ('person', 'bicycle', 'car', 'motorcycle'),
+        'palette': [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230)]
     }
 
     def load_data_list(self, ann_file):
@@ -335,15 +335,15 @@ MMEngine 也支持非常多的数据集包装器（wrapper）来混合数据集
 classes = ('person', 'bicycle', 'car')
 train_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 val_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 test_dataloader = dict(
     dataset=dict(
-        metainfo=dict(CLASSES=classes))
+        metainfo=dict(classes=classes))
     )
 ```
 

docs/zh_cn/advanced_guides/customize_runtime.md

@@ -330,7 +330,7 @@ custom_hooks = [
 
 #### 例子: `NumClassCheckHook`
 
-我们实现了一个名为 [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/engine/hooks/num_class_check_hook.py) 的自定义钩子来检查 `num_classes` 是否在 head 中和 `dataset` 中的 `CLASSES` 的长度相匹配。
+我们实现了一个名为 [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/engine/hooks/num_class_check_hook.py) 的自定义钩子来检查 `num_classes` 是否在 head 中和 `dataset` 中的 `classes` 的长度相匹配。
 
 我们在 [default_runtime.py](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/configs/_base_/default_runtime.py) 中设置它。
 

docs/zh_cn/user_guides/train.md

@@ -401,8 +401,8 @@ model = dict(
 # 修改数据集相关配置
 data_root = 'data/balloon/'
 metainfo = {
-    'CLASSES': ('balloon', ),
-    'PALETTE': [
+    'classes': ('balloon', ),
+    'palette': [
         (220, 20, 60),
     ]
 }

mmdet/apis/inference.py

@@ -65,15 +65,19 @@ def init_detector(
         elif 'CLASSES' in checkpoint_meta:
             # < mmdet 3.x
             classes = checkpoint_meta['CLASSES']
-            model.dataset_meta = {'CLASSES': classes, 'PALETTE': palette}
+            model.dataset_meta = {'classes': classes, 'palette': palette}
+        elif 'classes' in checkpoint_meta:
+            # < mmdet 3.x
+            classes = checkpoint_meta['classes']
+            model.dataset_meta = {'classes': classes, 'palette': palette}
         else:
             warnings.simplefilter('once')
             warnings.warn(
                 'dataset_meta or class names are not saved in the '
                 'checkpoint\'s meta data, use COCO classes by default.')
             model.dataset_meta = {
-                'CLASSES': get_classes('coco'),
-                'PALETTE': palette
+                'classes': get_classes('coco'),
+                'palette': palette
             }
 
     model.cfg = config  # save the config in the model for convenience

mmdet/datasets/cityscapes.py

@@ -13,9 +13,9 @@ class CityscapesDataset(CocoDataset):
     """Dataset for Cityscapes."""
 
     METAINFO = {
-        'CLASSES': ('person', 'rider', 'car', 'truck', 'bus', 'train',
+        'classes': ('person', 'rider', 'car', 'truck', 'bus', 'train',
                     'motorcycle', 'bicycle'),
-        'PALETTE': [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
+        'palette': [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
                     (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32)]
     }
 

mmdet/datasets/coco.py

@@ -13,7 +13,7 @@ class CocoDataset(BaseDetDataset):
     """Dataset for COCO."""
 
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
          'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
          'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
@@ -27,8 +27,8 @@ class CocoDataset(BaseDetDataset):
          'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
          'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
          'scissors', 'teddy bear', 'hair drier', 'toothbrush'),
-        # PALETTE is a list of color tuples, which is used for visualization.
-        'PALETTE':
+        # palette is a list of color tuples, which is used for visualization.
+        'palette':
         [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230), (106, 0, 228),
          (0, 60, 100), (0, 80, 100), (0, 0, 70), (0, 0, 192), (250, 170, 30),
          (100, 170, 30), (220, 220, 0), (175, 116, 175), (250, 0, 30),
@@ -63,9 +63,9 @@ def load_data_list(self) -> List[dict]:
         with self.file_client.get_local_path(self.ann_file) as local_path:
             self.coco = self.COCOAPI(local_path)
         # The order of returned `cat_ids` will not
-        # change with the order of the CLASSES
+        # change with the order of the `classes`
         self.cat_ids = self.coco.get_cat_ids(
-            cat_names=self.metainfo['CLASSES'])
+            cat_names=self.metainfo['classes'])
         self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
         self.cat_img_map = copy.deepcopy(self.coco.cat_img_map)
 

mmdet/datasets/coco_panoptic.py

@@ -67,7 +67,7 @@ class CocoPanopticDataset(CocoDataset):
     """
 
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
          'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
          'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
@@ -92,7 +92,7 @@ class CocoPanopticDataset(CocoDataset):
          'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
          'paper-merged', 'food-other-merged', 'building-other-merged',
          'rock-merged', 'wall-other-merged', 'rug-merged'),
-        'THING_CLASSES':
+        'thing_classes':
         ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
          'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
          'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
@@ -106,7 +106,7 @@ class CocoPanopticDataset(CocoDataset):
          'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
          'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
          'scissors', 'teddy bear', 'hair drier', 'toothbrush'),
-        'STUFF_CLASSES':
+        'stuff_classes':
         ('banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain',
          'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house',
          'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield',
@@ -118,7 +118,7 @@ class CocoPanopticDataset(CocoDataset):
          'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
          'paper-merged', 'food-other-merged', 'building-other-merged',
          'rock-merged', 'wall-other-merged', 'rug-merged'),
-        'PALETTE':
+        'palette':
         [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230), (106, 0, 228),
          (0, 60, 100), (0, 80, 100), (0, 0, 70), (0, 0, 192), (250, 170, 30),
          (100, 170, 30), (220, 220, 0), (175, 116, 175), (250, 0, 30),

mmdet/datasets/crowdhuman.py

@@ -30,9 +30,9 @@ class CrowdHumanDataset(BaseDetDataset):
     """
 
     METAINFO = {
-        'CLASSES': ('person', ),
-        # PALETTE is a list of color tuples, which is used for visualization.
-        'PALETTE': [(220, 20, 60)]
+        'classes': ('person', ),
+        # palette is a list of color tuples, which is used for visualization.
+        'palette': [(220, 20, 60)]
     }
 
     def __init__(self, data_root, ann_file, extra_ann_file=None, **kwargs):
@@ -122,11 +122,11 @@ def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]:
         instances = []
         for i, ann in enumerate(raw_data_info['gtboxes']):
             instance = {}
-            if ann['tag'] not in self.metainfo['CLASSES']:
+            if ann['tag'] not in self.metainfo['classes']:
                 instance['bbox_label'] = -1
                 instance['ignore_flag'] = 1
             else:
-                instance['bbox_label'] = self.metainfo['CLASSES'].index(
+                instance['bbox_label'] = self.metainfo['classes'].index(
                     ann['tag'])
                 instance['ignore_flag'] = 0
             if 'extra' in ann:

mmdet/datasets/deepfashion.py

@@ -8,11 +8,11 @@ class DeepFashionDataset(CocoDataset):
     """Dataset for DeepFashion."""
 
     METAINFO = {
-        'CLASSES': ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants',
+        'classes': ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants',
                     'bag', 'neckwear', 'headwear', 'eyeglass', 'belt',
                     'footwear', 'hair', 'skin', 'face'),
-        # PALETTE is a list of color tuples, which is used for visualization.
-        'PALETTE': [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64),
+        # palette is a list of color tuples, which is used for visualization.
+        'palette': [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64),
                     (0, 192, 224), (0, 192, 192), (128, 192, 64), (0, 192, 96),
                     (128, 32, 192), (0, 0, 224), (0, 0, 64), (0, 160, 192),
                     (128, 0, 96), (128, 0, 192), (0, 32, 192)]