[Fix] Update new config type (#2655)

* update new config

* fix bug

mmdet3d/configs/centerpoint/centerpoint_pillar02_second_secfpn_8xb4_cyclic_20e_nus_3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine.config import read_base
+
+with read_base():
     from .._base_.datasets.nus_3d import *
     from .._base_.models.centerpoint_pillar02_second_secfpn_nus import *
     from .._base_.schedules.cyclic_20e import *
@@ -29,9 +31,9 @@ class_names = [
     'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
     'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
 ]
-data_prefix.merge(
+data_prefix.update(
     dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP'))
-model.merge(
+model.update(
     dict(
         data_preprocessor=dict(
             voxel_layer=dict(point_cloud_range=point_cloud_range)),
@@ -167,13 +169,13 @@ train_dataloader.merge(
                 # and box_type_3d='Depth' in sunrgbd and scannet dataset.
                 box_type_3d='LiDAR',
                 backend_args=backend_args))))
-test_dataloader.merge(
+test_dataloader.update(
     dict(
         dataset=dict(
             pipeline=test_pipeline, metainfo=dict(classes=class_names))))
-val_dataloader.merge(
+val_dataloader.update(
     dict(
         dataset=dict(
             pipeline=test_pipeline, metainfo=dict(classes=class_names))))
 
-train_cfg.merge(dict(val_interval=20))
+train_cfg.update(dict(val_interval=20))

mmdet3d/configs/centerpoint/centerpoint_voxel01_second_secfpn_8xb4_cyclic_20e_nus_3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine import read_base
+
+with read_base():
     from .._base_.datasets.nus_3d import *
     from .._base_.models.centerpoint_voxel01_second_secfpn_nus import *
     from .._base_.schedules.cyclic_20e import *
@@ -29,9 +31,9 @@ class_names = [
     'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
     'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
 ]
-data_prefix.merge(
+data_prefix.update(
     dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP'))
-model.merge(
+model.update(
     dict(
         data_preprocessor=dict(
             voxel_layer=dict(point_cloud_range=point_cloud_range)),
@@ -167,13 +169,13 @@ train_dataloader.merge(
                 # and box_type_3d='Depth' in sunrgbd and scannet dataset.
                 box_type_3d='LiDAR',
                 backend_args=backend_args))))
-test_dataloader.merge(
+test_dataloader.update(
     dict(
         dataset=dict(
             pipeline=test_pipeline, metainfo=dict(classes=class_names))))
-val_dataloader.merge(
+val_dataloader.update(
     dict(
         dataset=dict(
             pipeline=test_pipeline, metainfo=dict(classes=class_names))))
 
-train_cfg.merge(dict(val_interval=20))
+train_cfg.update(dict(val_interval=20))

mmdet3d/configs/minkunet/minkunet34_w32_torchsparse_8xb2_laser_polar_mix_3x_semantickitti.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine import read_base
+
+with read_base():
     from .._base_.datasets.semantickitti import *
     from .._base_.models.minkunet import *
     from .._base_.schedules.schedule_3x import *
@@ -15,7 +17,7 @@ from mmdet3d.datasets.transforms.loading import (LoadAnnotations3D,
 from mmdet3d.datasets.transforms.transforms_3d import (GlobalRotScaleTrans,
                                                        LaserMix, PolarMix)
 
-model.merge(
+model.update(
     dict(
         data_preprocessor=dict(max_voxels=None),
         backbone=dict(encoder_blocks=[2, 3, 4, 6])))
@@ -92,6 +94,6 @@ train_pipeline = [
     dict(type=Pack3DDetInputs, keys=['points', 'pts_semantic_mask'])
 ]
 
-train_dataloader.merge(dict(dataset=dict(pipeline=train_pipeline)))
+train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
 
-default_hooks.merge(dict(checkpoint=dict(type=CheckpointHook, interval=1)))
+default_hooks.update(dict(checkpoint=dict(type=CheckpointHook, interval=1)))

mmdet3d/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2_80e_kitti_3d_3class.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine import read_base
+
+with read_base():
     from .._base_.schedules.cosine import *
     from .._base_.default_runtime import *
 
@@ -293,7 +295,7 @@ test_dataloader = dict(
         box_type_3d='LiDAR',
         backend_args=backend_args))
 
-optim_wrapper.merge(
+optim_wrapper.update(
     dict(
         optimizer=dict(weight_decay=0.01),
         clip_grad=dict(max_norm=35, norm_type=2),

mmdet3d/configs/pgd/pgd_r101_caffe_fpn_head_gn_4xb3_4x_kitti_mono3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine import read_base
+
+with read_base():
     from .._base_.datasets.kitti_mono3d import *
     from .._base_.models.pgd import *
     from .._base_.schedules.mmdet_schedule_1x import *
@@ -19,7 +21,7 @@ from mmdet3d.models.losses.uncertain_smooth_l1_loss import \
 from mmdet3d.models.task_modules.coders.pgd_bbox_coder import PGDBBoxCoder
 
 # model settings
-model.merge(
+model.update(
     dict(
         data_preprocessor=dict(
             type=Det3DDataPreprocessor,
@@ -121,13 +123,13 @@ test_pipeline = [
     dict(type=Pack3DDetInputs, keys=['img'])
 ]
 
-train_dataloader.merge(
+train_dataloader.update(
     dict(batch_size=3, num_workers=3, dataset=dict(pipeline=train_pipeline)))
-test_dataloader.merge(dict(dataset=dict(pipeline=test_pipeline)))
-val_dataloader.merge(dict(dataset=dict(pipeline=test_pipeline)))
+test_dataloader.update(dict(dataset=dict(pipeline=test_pipeline)))
+val_dataloader.update(dict(dataset=dict(pipeline=test_pipeline)))
 
 # optimizer
-optim_wrapper.merge(
+optim_wrapper.update(
     dict(
         optimizer=dict(lr=0.001),
         paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
@@ -146,5 +148,5 @@ param_scheduler = [
         gamma=0.1)
 ]
 
-train_cfg.merge(dict(max_epochs=48, val_interval=2))
-auto_scale_lr.merge(dict(base_batch_size=12))
+train_cfg.update(dict(max_epochs=48, val_interval=2))
+auto_scale_lr.update(dict(base_batch_size=12))

mmdet3d/configs/votenet/votenet_8xb8_scannet_3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
-if '_base_':
+from mmengine import read_base
+
+with read_base():
     from .._base_.datasets.scannet_3d import *
     from .._base_.models.votenet import *
     from .._base_.schedules.schedule_3x import *
@@ -11,7 +13,7 @@ from mmdet3d.models.task_modules.coders.partial_bin_based_bbox_coder import \
     PartialBinBasedBBoxCoder
 
 # model settings
-model.merge(
+model.update(
     dict(
         bbox_head=dict(
             num_classes=18,
@@ -39,9 +41,9 @@ model.merge(
                             [1.1511526, 1.0546296, 0.49706793],
                             [0.47535285, 0.49249494, 0.5802117]]))))
 
-default_hooks.merge(dict(logger=dict(type=LoggerHook, interval=30)))
+default_hooks.update(dict(logger=dict(type=LoggerHook, interval=30)))
 # Default setting for scaling LR automatically
 #   - `enable` means enable scaling LR automatically
 #       or not by default.
 #   - `base_batch_size` = (8 GPUs) x (8 samples per GPU).
-auto_scale_lr.merge(dict(enable=False, base_batch_size=64))
+auto_scale_lr.update(dict(enable=False, base_batch_size=64))