[Refactor] rename `CLASSES` and `PALETTE` to `classes` and `palette` in dataset metainfo (#1932)

* rame CLASS and PALETTE to class and palette

* change mmcv-full to mmcv

* fix comments

mmdet3d/datasets/kitti_dataset.py

@@ -44,7 +44,7 @@ class KittiDataset(Det3DDataset):
     """
     # TODO: use full classes of kitti
     METAINFO = {
-        'CLASSES': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
+        'classes': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
                     'Person_sitting', 'Tram', 'Misc')
     }
 

mmdet3d/datasets/lyft_dataset.py

@@ -42,7 +42,7 @@ class LyftDataset(Det3DDataset):
     """
 
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
          'motorcycle', 'bicycle', 'pedestrian', 'animal')
     }

mmdet3d/datasets/nuscenes_dataset.py

@@ -48,7 +48,7 @@ class NuScenesDataset(Det3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
          'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'),
         'version':

mmdet3d/datasets/s3dis_dataset.py

@@ -43,7 +43,7 @@ class S3DISDataset(Det3DDataset):
         test_mode (bool, optional): Whether the dataset is in test mode.
             Defaults to False.
     """
-    CLASSES = ('table', 'chair', 'sofa', 'bookcase', 'board')
+    classes = ('table', 'chair', 'sofa', 'bookcase', 'board')
 
     def __init__(self,
                  data_root,
@@ -146,7 +146,7 @@ class S3DISDataset(Det3DDataset):
                 use_dim=[0, 1, 2, 3, 4, 5]),
             dict(
                 type='DefaultFormatBundle3D',
-                class_names=self.CLASSES,
+                class_names=self.classes,
                 with_label=False),
             dict(type='Collect3D', keys=['points'])
         ]
@@ -187,10 +187,10 @@ class _S3DISSegDataset(Seg3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
          'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter'),
-        'PALETTE': [[0, 255, 0], [0, 0, 255], [0, 255, 255], [255, 255, 0],
+        'palette': [[0, 255, 0], [0, 0, 255], [0, 255, 255], [255, 255, 0],
                     [255, 0, 255], [100, 100, 255], [200, 200, 100],
                     [170, 120, 200], [255, 0, 0], [200, 100, 100],
                     [10, 200, 100], [200, 200, 200], [50, 50, 50]],

mmdet3d/datasets/scannet_dataset.py

@@ -49,7 +49,7 @@ class ScanNetDataset(Det3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
          'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator',
          'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin'),
@@ -204,12 +204,12 @@ class ScanNetSegDataset(Seg3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
          'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain',
          'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
          'otherfurniture'),
-        'PALETTE': [
+        'palette': [
             [174, 199, 232],
             [152, 223, 138],
             [31, 119, 180],
@@ -278,11 +278,11 @@ class ScanNetSegDataset(Seg3DDataset):
 class ScanNetInstanceSegDataset(Seg3DDataset):
 
     METAINFO = {
-        'CLASSES':
+        'classes':
         ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
          'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator',
          'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin'),
-        'PLATTE': [
+        'palette': [
             [174, 199, 232],
             [152, 223, 138],
             [31, 119, 180],

mmdet3d/datasets/seg3d_dataset.py

@@ -31,7 +31,7 @@ class Seg3DDataset(BaseDataset):
                 - use_lidar: bool
             Defaults to dict(use_lidar=True, use_camera=False).
         ignore_index (int, optional): The label index to be ignored, e.g.
-            unannotated points. If None is given, set to len(self.CLASSES) to
+            unannotated points. If None is given, set to len(self.classes) to
             be consistent with PointSegClassMapping function in pipeline.
             Defaults to None.
         scene_idxs (np.ndarray | str, optional): Precomputed index to load
@@ -50,8 +50,8 @@ class Seg3DDataset(BaseDataset):
             Defaults to dict(backend='disk').
     """
     METAINFO = {
-        'CLASSES': None,  # names of all classes data used for the task
-        'PALETTE': None,  # official color for visualization
+        'classes': None,  # names of all classes data used for the task
+        'palette': None,  # official color for visualization
         'seg_valid_class_ids': None,  # class_ids used for training
         'seg_all_class_ids': None,  # all possible class_ids in loaded seg mask
     }
@@ -81,11 +81,11 @@ class Seg3DDataset(BaseDataset):
 
         # TODO: We maintain the ignore_index attributes,
         # but we may consider to remove it in the future.
-        self.ignore_index = len(self.METAINFO['CLASSES']) if \
+        self.ignore_index = len(self.METAINFO['classes']) if \
             ignore_index is None else ignore_index
 
         # Get label mapping for custom classes
-        new_classes = metainfo.get('CLASSES', None)
+        new_classes = metainfo.get('classes', None)
 
         self.label_mapping, self.label2cat, seg_valid_class_ids = \
             self.get_label_mapping(new_classes)
@@ -98,10 +98,10 @@ class Seg3DDataset(BaseDataset):
         # generate palette if it is not defined based on
         # label mapping, otherwise directly use palette
         # defined in dataset config.
-        palette = metainfo.get('PALETTE', None)
+        palette = metainfo.get('palette', None)
         updated_palette = self._update_palette(new_classes, palette)
 
-        metainfo['PALETTE'] = updated_palette
+        metainfo['palette'] = updated_palette
 
         # construct seg_label_mapping for semantic mask
         seg_max_cat_id = len(self.METAINFO['seg_all_class_ids'])
@@ -150,13 +150,13 @@ class Seg3DDataset(BaseDataset):
             tuple: The mapping from old classes in cls.METAINFO to
             new classes in metainfo
         """
-        old_classes = self.METAINFO.get('CLASSES', None)
+        old_classes = self.METAINFO.get('classes', None)
         if (new_classes is not None and old_classes is not None
                 and list(new_classes) != list(old_classes)):
             if not set(new_classes).issubset(old_classes):
                 raise ValueError(
                     f'new classes {new_classes} is not a '
-                    f'subset of CLASSES {old_classes} in METAINFO.')
+                    f'subset of classes {old_classes} in METAINFO.')
 
             # obtain true id from valid_class_ids
             valid_class_ids = [
@@ -184,7 +184,7 @@ class Seg3DDataset(BaseDataset):
             # map label to category name
             label2cat = {
                 i: cat_name
-                for i, cat_name in enumerate(self.METAINFO['CLASSES'])
+                for i, cat_name in enumerate(self.METAINFO['classes'])
             }
             valid_class_ids = self.METAINFO['seg_valid_class_ids']
 
@@ -203,10 +203,10 @@ class Seg3DDataset(BaseDataset):
         """
         if palette is None:
             # If palette is not defined, it generate a palette according
-            # to the original PALETTE and classes.
-            old_classes = self.METAINFO.get('CLASSES', None)
+            # to the original palette and classes.
+            old_classes = self.METAINFO.get('classes', None)
             palette = [
-                self.METAINFO['PALETTE'][old_classes.index(cls_name)]
+                self.METAINFO['palette'][old_classes.index(cls_name)]
                 for cls_name in new_classes
             ]
             return palette
@@ -215,8 +215,8 @@ class Seg3DDataset(BaseDataset):
         if len(palette) == len(new_classes):
             return palette
         else:
-            raise ValueError('Once PLATTE in set in metainfo, it should'
-                             'match CLASSES in metainfo')
+            raise ValueError('Once palette in set in metainfo, it should'
+                             'match classes in metainfo')
 
     def parse_data_info(self, info: dict) -> dict:
         """Process the raw data info.

mmdet3d/datasets/semantickitti_dataset.py

@@ -41,7 +41,7 @@ class SemanticKITTIDataset(Seg3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES': ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck',
+        'classes': ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck',
                     'bus', 'person', 'bicyclist', 'motorcyclist', 'road',
                     'parking', 'sidewalk', 'other-ground', 'building', 'fence',
                     'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'),

mmdet3d/datasets/sunrgbd_dataset.py

@@ -46,7 +46,7 @@ class SUNRGBDDataset(Det3DDataset):
             Defaults to False.
     """
     METAINFO = {
-        'CLASSES': ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
+        'classes': ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
                     'dresser', 'night_stand', 'bookshelf', 'bathtub')
     }
 

mmdet3d/datasets/waymo_dataset.py

@@ -63,7 +63,7 @@ class WaymoDataset(KittiDataset):
             Defaults to 'lidar_det'.
         max_sweeps (int): max sweep for each frame. Defaults to 0.
     """
-    METAINFO = {'CLASSES': ('Car', 'Pedestrian', 'Cyclist')}
+    METAINFO = {'classes': ('Car', 'Pedestrian', 'Cyclist')}
 
     def __init__(self,
                  data_root: str,
@@ -91,7 +91,7 @@ class WaymoDataset(KittiDataset):
         # set loading mode for different task settings
         self.cam_sync_instances = cam_sync_instances
         # construct self.cat_ids for vision-only anns parsing
-        self.cat_ids = range(len(self.METAINFO['CLASSES']))
+        self.cat_ids = range(len(self.METAINFO['classes']))
         self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
         self.max_sweeps = max_sweeps
         # we do not provide file_client_args to custom_3d init

mmdet3d/evaluation/metrics/indoor_metric.py

@@ -86,7 +86,7 @@ class IndoorMetric(BaseMetric):
             ann_infos,
             pred_results,
             self.iou_thr,
-            self.dataset_meta['CLASSES'],
+            self.dataset_meta['classes'],
             logger=logger,
             box_mode_3d=box_mode_3d)
 
@@ -142,7 +142,7 @@ class Indoor2DMetric(BaseMetric):
             pred_labels = pred['labels'].cpu().numpy()
 
             dets = []
-            for label in range(len(self.dataset_meta['CLASSES'])):
+            for label in range(len(self.dataset_meta['classes'])):
                 index = np.where(pred_labels == label)[0]
                 pred_bbox_scores = np.hstack(
                     [pred_bboxes[index], pred_scores[index].reshape((-1, 1))])
@@ -171,7 +171,7 @@ class Indoor2DMetric(BaseMetric):
                 annotations,
                 scale_ranges=None,
                 iou_thr=iou_thr_2d_single,
-                dataset=self.dataset_meta['CLASSES'],
+                dataset=self.dataset_meta['classes'],
                 logger=logger)
             eval_results['mAP_' + str(iou_thr_2d_single)] = mean_ap
         return eval_results

mmdet3d/evaluation/metrics/instance_seg_metric.py

@@ -64,7 +64,7 @@ class InstanceSegMetric(BaseMetric):
         """
         logger: MMLogger = MMLogger.get_current_instance()
 
-        self.classes = self.dataset_meta['CLASSES']
+        self.classes = self.dataset_meta['classes']
         self.valid_class_ids = self.dataset_meta['seg_valid_class_ids']
 
         gt_semantic_masks = []

mmdet3d/evaluation/metrics/kitti_metric.py

@@ -167,7 +167,7 @@ class KittiMetric(BaseMetric):
             the metrics, and the values are corresponding results.
         """
         logger: MMLogger = MMLogger.get_current_instance()
-        self.classes = self.dataset_meta['CLASSES']
+        self.classes = self.dataset_meta['classes']
 
         # load annotations
         pkl_infos = load(self.ann_file, file_client_args=self.file_client_args)

mmdet3d/evaluation/metrics/lyft_metric.py

@@ -110,7 +110,7 @@ class LyftMetric(BaseMetric):
         """
         logger: MMLogger = MMLogger.get_current_instance()
 
-        classes = self.dataset_meta['CLASSES']
+        classes = self.dataset_meta['classes']
         self.version = self.dataset_meta['version']
         # load annotations
 

mmdet3d/evaluation/metrics/nuscenes_metric.py

@@ -151,7 +151,7 @@ class NuScenesMetric(BaseMetric):
         """
         logger: MMLogger = MMLogger.get_current_instance()
 
-        classes = self.dataset_meta['CLASSES']
+        classes = self.dataset_meta['classes']
         self.version = self.dataset_meta['version']
         # load annotations
         self.data_infos = load(

mmdet3d/evaluation/metrics/waymo_metric.py

@@ -100,7 +100,7 @@ class WaymoMetric(KittiMetric):
             the metrics, and the values are corresponding results.
         """
         logger: MMLogger = MMLogger.get_current_instance()
-        self.classes = self.dataset_meta['CLASSES']
+        self.classes = self.dataset_meta['classes']
 
         # load annotations
         self.data_infos = load(self.ann_file)['data_list']
@@ -379,7 +379,7 @@ class WaymoMetric(KittiMetric):
             torch.from_numpy(box_dict['box3d_lidar']).cuda())
         scores = torch.from_numpy(box_dict['scores']).cuda()
         labels = torch.from_numpy(box_dict['label_preds']).long().cuda()
-        nms_scores = scores.new_zeros(scores.shape[0], len(self.CLASSES) + 1)
+        nms_scores = scores.new_zeros(scores.shape[0], len(self.classes) + 1)
         indices = labels.new_tensor(list(range(scores.shape[0])))
         nms_scores[indices, labels] = scores
         lidar_boxes3d_for_nms = xywhr2xyxyr(lidar_boxes3d.bev)

mmdet3d/visualization/local_visualizer.py

@@ -708,9 +708,9 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                 and masks. Defaults to 0.3.
             step (int): Global step value to record. Defaults to 0.
         """
-        classes = self.dataset_meta.get('CLASSES', None)
-        # For object detection datasets, no PALETTE is saved
-        palette = self.dataset_meta.get('PALETTE', None)
+        classes = self.dataset_meta.get('classes', None)
+        # For object detection datasets, no palette is saved
+        palette = self.dataset_meta.get('palette', None)
         ignore_index = self.dataset_meta.get('ignore_index', None)
 
         gt_data_3d = None

tests/test_datasets/test_kitti_dataset.py

@@ -54,7 +54,7 @@ def test_getitem():
             img='training/image_2',
         ),
         pipeline=pipeline,
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         modality=modality)
 
     kitti_dataset.prepare_data(0)
@@ -94,7 +94,7 @@ def test_getitem():
             img='training/image_2',
         ),
         pipeline=pipeline,
-        metainfo=dict(CLASSES=['Car']),
+        metainfo=dict(classes=['Car']),
         modality=modality)
 
     input_dict = car_kitti_dataset.get_data_info(0)
@@ -105,4 +105,4 @@ def test_getitem():
     assert ann_info['gt_labels_3d'].dtype == np.int64
     # all instance have been filtered by classes
     assert len(ann_info['gt_labels_3d']) == 0
-    assert len(car_kitti_dataset.metainfo['CLASSES']) == 1
+    assert len(car_kitti_dataset.metainfo['classes']) == 1

tests/test_datasets/test_lyft_dataset.py

@@ -48,7 +48,7 @@ def test_getitem():
         ann_file,
         data_prefix=data_prefix,
         pipeline=pipeline,
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         modality=modality)
 
     lyft_dataset.prepare_data(0)
@@ -68,4 +68,4 @@ def test_getitem():
     assert 'gt_bboxes_3d' in ann_info
     assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
 
-    assert len(lyft_dataset.metainfo['CLASSES']) == 9
+    assert len(lyft_dataset.metainfo['classes']) == 9

tests/test_datasets/test_nuscenes_dataset.py

@@ -51,7 +51,7 @@ def test_getitem():
         ann_file=ann_file,
         data_prefix=data_prefix,
         pipeline=pipeline,
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         modality=modality)
 
     nus_dataset.prepare_data(0)
@@ -77,7 +77,7 @@ def test_getitem():
     assert 'gt_bboxes_3d' in ann_info
     assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
 
-    assert len(nus_dataset.metainfo['CLASSES']) == 10
+    assert len(nus_dataset.metainfo['classes']) == 10
 
     assert input_dict['token'] == 'fd8420396768425eabec9bdddf7e64b6'
     assert input_dict['timestamp'] == 1533201470.448696

tests/test_datasets/test_s3dis_dataset.py

@@ -67,7 +67,7 @@ class TestS3DISDataset(unittest.TestCase):
         s3dis_seg_dataset = S3DISSegDataset(
             data_root,
             ann_file,
-            metainfo=dict(CLASSES=classes, PALETTE=palette),
+            metainfo=dict(classes=classes, palette=palette),
             data_prefix=data_prefix,
             pipeline=pipeline,
             modality=modality,