[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

configs/_base_/datasets/kitti-3d-3class.py

@@ -4,7 +4,7 @@ data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 db_sampler = dict(
     data_root=data_root,

configs/_base_/datasets/kitti-3d-car.py

@@ -4,7 +4,7 @@ data_root = 'data/kitti/'
 class_names = ['Car']
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 db_sampler = dict(
     data_root=data_root,

configs/_base_/datasets/kitti-mono3d.py

@@ -2,7 +2,7 @@ dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
 input_modality = dict(use_lidar=False, use_camera=True)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 file_client_args = dict(backend='disk')
 # Uncomment the following if use ceph or other file clients.

configs/_base_/datasets/lyft-3d.py

@@ -78,7 +78,7 @@ train_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_train.pkl',
         pipeline=train_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         data_prefix=data_prefix,
         test_mode=False,
@@ -94,7 +94,7 @@ test_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         data_prefix=data_prefix,
         test_mode=True,
@@ -110,7 +110,7 @@ val_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         test_mode=True,
         data_prefix=data_prefix,

configs/_base_/datasets/nus-3d.py

@@ -6,7 +6,7 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 dataset_type = 'NuScenesDataset'
 data_root = 'data/nuscenes/'
 # Input modality for nuScenes dataset, this is consistent with the submission

configs/_base_/datasets/nus-mono3d.py

@@ -4,7 +4,7 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 # Input modality for nuScenes dataset, this is consistent with the submission
 # format which requires the information in input_modality.
 input_modality = dict(use_lidar=False, use_camera=True)

configs/_base_/datasets/s3dis-seg.py

 # For S3DIS seg we usually do 13-class segmentation
 class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
                'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 dataset_type = 'S3DISSegDataset'
 data_root = 'data/s3dis/'
 input_modality = dict(use_lidar=True, use_camera=False)

configs/_base_/datasets/scannet-3d.py

@@ -3,7 +3,7 @@ dataset_type = 'ScanNetDataset'
 data_root = 'data/scannet/'
 
 metainfo = dict(
-    CLASSES=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
+    classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
              'bookshelf', 'picture', 'counter', 'desk', 'curtain',
              'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
              'garbagebin'))

configs/_base_/datasets/scannet-seg.py

@@ -3,7 +3,7 @@ class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
                'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
                'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
                'bathtub', 'otherfurniture')
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 dataset_type = 'ScanNetSegDataset'
 data_root = 'data/scannet/'
 input_modality = dict(use_lidar=True, use_camera=False)

configs/_base_/datasets/sunrgbd-3d.py

@@ -3,7 +3,7 @@ data_root = 'data/sunrgbd/'
 class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
                'night_stand', 'bookshelf', 'bathtub')
 
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 file_client_args = dict(backend='disk')
 # Uncomment the following if use ceph or other file clients.

configs/_base_/datasets/waymoD5-3d-3class.py

@@ -16,7 +16,7 @@ file_client_args = dict(backend='disk')
 #     })
 
 class_names = ['Car', 'Pedestrian', 'Cyclist']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
 input_modality = dict(use_lidar=True, use_camera=False)

configs/_base_/datasets/waymoD5-3d-car.py

@@ -11,7 +11,7 @@ file_client_args = dict(backend='disk')
 #     backend='petrel', path_mapping=dict(data='s3://waymo_data/'))
 
 class_names = ['Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
 input_modality = dict(use_lidar=True, use_camera=False)

configs/_base_/datasets/waymoD5-mono3d-3class.py

@@ -56,7 +56,7 @@ eval_pipeline = [
     dict(type='Pack3DDetInputs', keys=['img']),
 ]
 
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 train_dataloader = dict(
     batch_size=3,

configs/_base_/datasets/waymoD5-mv3d-3class.py

@@ -62,7 +62,7 @@ eval_pipeline = [
     dict(type='MultiViewWrapper', transforms=test_transforms),
     dict(type='Pack3DDetInputs', keys=['img'])
 ]
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 train_dataloader = dict(
     batch_size=2,

configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py

@@ -210,7 +210,7 @@ model = dict(
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 input_modality = dict(use_lidar=True, use_camera=False)
 db_sampler = dict(
     data_root=data_root,

configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py

@@ -81,7 +81,7 @@ model = dict(
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 input_modality = dict(use_lidar=True, use_camera=False)
 db_sampler = dict(
     data_root=data_root,

configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py

@@ -107,7 +107,7 @@ model = dict(
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 input_modality = dict(use_lidar=True, use_camera=False)
 db_sampler = dict(

configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py

@@ -99,7 +99,7 @@ model = dict(
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 input_modality = dict(use_lidar=True, use_camera=False)
 db_sampler = dict(
     data_root=data_root,

configs/centerpoint/centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py

@@ -126,7 +126,7 @@ train_dataloader = dict(
             data_root=data_root,
             ann_file='nuscenes_infos_train.pkl',
             pipeline=train_pipeline,
-            metainfo=dict(CLASSES=class_names),
+            metainfo=dict(classes=class_names),
             test_mode=False,
             data_prefix=data_prefix,
             use_valid_flag=True,
@@ -134,8 +134,8 @@ train_dataloader = dict(
             # and box_type_3d='Depth' in sunrgbd and scannet dataset.
             box_type_3d='LiDAR')))
 test_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
 val_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
 
 train_cfg = dict(val_interval=20)

configs/centerpoint/centerpoint_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py

@@ -122,8 +122,8 @@ test_pipeline = [
 train_dataloader = dict(
     dataset=dict(
         dataset=dict(
-            pipeline=train_pipeline, metainfo=dict(CLASSES=class_names))))
+            pipeline=train_pipeline, metainfo=dict(classes=class_names))))
 test_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
 val_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))