[Fix] replace mmcv's function and modules imported with mmengine's and sync...

[Fix] replace mmcv's function and modules imported with mmengine's and sync the latest mmengine (#1765)

* replace mmcv to mmengine

* fix

* fix comments

tools/analysis_tools/benchmark.py

@@ -5,7 +5,7 @@ import time
 import torch
 from mmcv import Config
 from mmcv.parallel import MMDataParallel
-from mmcv.runner import load_checkpoint, wrap_fp16_model
+from mmengine.runner import load_checkpoint
 
 from mmdet3d.datasets import build_dataset
 from mmdet3d.models import build_detector
@@ -56,9 +56,6 @@ def main():
     # build the model and load checkpoint
     cfg.model.train_cfg = None
     model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        wrap_fp16_model(model)
     load_checkpoint(model, args.checkpoint, map_location='cpu')
     if args.fuse_conv_bn:
         model = fuse_module(model)

tools/dataset_converters/create_gt_database.py

@@ -3,9 +3,10 @@ import pickle
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
-from mmcv import track_iter_progress
 from mmcv.ops import roi_align
+from mmengine import track_iter_progress
 from pycocotools import mask as maskUtils
 from pycocotools.coco import COCO
 
@@ -223,7 +224,7 @@ def create_groundtruth_database(dataset_class_name,
     if db_info_save_path is None:
         db_info_save_path = osp.join(data_path,
                                      f'{info_prefix}_dbinfos_train.pkl')
-    mmcv.mkdir_or_exist(database_save_path)
+    mmengine.mkdir_or_exist(database_save_path)
     all_db_infos = dict()
     if with_mask:
         coco = COCO(osp.join(data_path, mask_anno_path))
@@ -585,7 +586,7 @@ class GTDatabaseCreater:
         if self.db_info_save_path is None:
             self.db_info_save_path = osp.join(
                 self.data_path, f'{self.info_prefix}_dbinfos_train.pkl')
-        mmcv.mkdir_or_exist(self.database_save_path)
+        mmengine.mkdir_or_exist(self.database_save_path)
         if self.with_mask:
             self.coco = COCO(osp.join(self.data_path, self.mask_anno_path))
             imgIds = self.coco.getImgIds()
@@ -599,9 +600,9 @@ class GTDatabaseCreater:
             dataset.pre_pipeline(input_dict)
             return input_dict
 
-        multi_db_infos = mmcv.track_parallel_progress(
-            self.create_single, ((loop_dataset(i)
-                                  for i in range(len(dataset))), len(dataset)),
+        multi_db_infos = mmengine.track_parallel_progress(
+            self.create_single,
+            ((loop_dataset(i) for i in range(len(dataset))), len(dataset)),
             self.num_worker)
         print('Make global unique group id')
         group_counter_offset = 0

tools/dataset_converters/kitti_converter.py

@@ -109,8 +109,8 @@ class _NumPointsInGTCalculater:
         return info
 
     def calculate(self, infos):
-        ret_infos = mmcv.track_parallel_progress(self.calculate_single, infos,
-                                                 self.num_worker)
+        ret_infos = mmengine.track_parallel_progress(self.calculate_single,
+                                                     infos, self.num_worker)
         for i, ret_info in enumerate(ret_infos):
             infos[i] = ret_info
 
@@ -120,7 +120,7 @@ def _calculate_num_points_in_gt(data_path,
                                 relative_path,
                                 remove_outside=True,
                                 num_features=4):
-    for info in mmcv.track_iter_progress(infos):
+    for info in mmengine.track_iter_progress(infos):
         pc_info = info['point_cloud']
         image_info = info['image']
         calib = info['calib']
@@ -325,7 +325,7 @@ def _create_reduced_point_cloud(data_path,
     """
     kitti_infos = mmengine.load(info_path)
 
-    for info in mmcv.track_iter_progress(kitti_infos):
+    for info in mmengine.track_iter_progress(kitti_infos):
         pc_info = info['point_cloud']
         image_info = info['image']
         calib = info['calib']
@@ -428,7 +428,7 @@ def export_2d_annotation(root_path, info_path, mono3d=True):
     coco_ann_id = 0
     coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
     from os import path as osp
-    for info in mmcv.track_iter_progress(kitti_infos):
+    for info in mmengine.track_iter_progress(kitti_infos):
         coco_infos = get_2d_boxes(info, occluded=[0, 1, 2, 3], mono3d=mono3d)
         (height, width,
          _) = mmcv.imread(osp.join(root_path,

tools/dataset_converters/kitti_data_utils.py

@@ -4,7 +4,7 @@ from concurrent import futures as futures
 from os import path as osp
 from pathlib import Path
 
-import mmcv
+import mmengine
 import numpy as np
 from PIL import Image
 from skimage import io
@@ -280,7 +280,7 @@ def get_kitti_image_info(path,
             plane_path = get_plane_path(idx, path, training, relative_path)
             if relative_path:
                 plane_path = str(root_path / plane_path)
-            lines = mmcv.list_from_file(plane_path)
+            lines = mmengine.list_from_file(plane_path)
             info['plane'] = np.array([float(i) for i in lines[3].split()])
 
         if annotations is not None:
@@ -501,8 +501,9 @@ class WaymoInfoGatherer:
     def gather(self, image_ids):
         if not isinstance(image_ids, list):
             image_ids = list(range(image_ids))
-        image_infos = mmcv.track_parallel_progress(self.gather_single,
-                                                   image_ids, self.num_worker)
+        image_infos = mmengine.track_parallel_progress(self.gather_single,
+                                                       image_ids,
+                                                       self.num_worker)
         return list(image_infos)
 
 

tools/dataset_converters/lyft_converter.py

@@ -114,7 +114,7 @@ def _fill_trainval_infos(lyft,
     train_lyft_infos = []
     val_lyft_infos = []
 
-    for sample in mmcv.track_iter_progress(lyft.sample):
+    for sample in mmengine.track_iter_progress(lyft.sample):
         lidar_token = sample['data']['LIDAR_TOP']
         sd_rec = lyft.get('sample_data', sample['data']['LIDAR_TOP'])
         cs_record = lyft.get('calibrated_sensor',
@@ -127,7 +127,7 @@ def _fill_trainval_infos(lyft,
         lidar_path = abs_lidar_path.split(f'{os.getcwd()}/')[-1]
         # relative path
 
-        mmcv.check_file_exist(lidar_path)
+        mmengine.check_file_exist(lidar_path)
 
         info = {
             'lidar_path': lidar_path,
@@ -247,7 +247,7 @@ def export_2d_annotation(root_path, info_path, version):
     ]
     coco_ann_id = 0
     coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
-    for info in mmcv.track_iter_progress(lyft_infos):
+    for info in mmengine.track_iter_progress(lyft_infos):
         for cam in camera_types:
             cam_info = info['cams'][cam]
             coco_infos = get_2d_boxes(

tools/dataset_converters/nuimage_converter.py

@@ -160,7 +160,7 @@ def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
 
     images = []
     print('Process image meta information...')
-    for sample_info in mmcv.track_iter_progress(nuim.sample_data):
+    for sample_info in mmengine.track_iter_progress(nuim.sample_data):
         if sample_info['is_key_frame']:
             img_idx = len(images)
             images.append(
@@ -172,8 +172,8 @@ def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
                     height=sample_info['height']))
 
     seg_root = f'{out_dir}semantic_masks'
-    mmcv.mkdir_or_exist(seg_root)
-    mmcv.mkdir_or_exist(osp.join(data_root, 'calibrated'))
+    mmengine.mkdir_or_exist(seg_root)
+    mmengine.mkdir_or_exist(osp.join(data_root, 'calibrated'))
 
     global process_img_anno
 
@@ -185,11 +185,11 @@ def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
 
     print('Process img annotations...')
     if nproc > 1:
-        outputs = mmcv.track_parallel_progress(
+        outputs = mmengine.track_parallel_progress(
             process_img_anno, images, nproc=nproc)
     else:
         outputs = []
-        for img_info in mmcv.track_iter_progress(images):
+        for img_info in mmengine.track_iter_progress(images):
             outputs.append(process_img_anno(img_info))
 
     # Determine the index of object annotation
@@ -208,7 +208,7 @@ def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
     coco_format_json = dict(
         images=images, annotations=annotations, categories=categories)
 
-    mmcv.mkdir_or_exist(out_dir)
+    mmengine.mkdir_or_exist(out_dir)
     out_file = osp.join(out_dir, f'{extra_tag}_{version}.json')
     print(f'Annotation dumped to {out_file}')
     mmengine.dump(coco_format_json, out_file)

tools/dataset_converters/nuscenes_converter.py

@@ -131,7 +131,7 @@ def get_available_scenes(nusc):
                 # path from lyftdataset is absolute path
                 lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1]
                 # relative path
-            if not mmcv.is_filepath(lidar_path):
+            if not mmengine.is_filepath(lidar_path):
                 scene_not_exist = True
                 break
             else:
@@ -165,7 +165,7 @@ def _fill_trainval_infos(nusc,
     train_nusc_infos = []
     val_nusc_infos = []
 
-    for sample in mmcv.track_iter_progress(nusc.sample):
+    for sample in mmengine.track_iter_progress(nusc.sample):
         lidar_token = sample['data']['LIDAR_TOP']
         sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
         cs_record = nusc.get('calibrated_sensor',
@@ -173,7 +173,7 @@ def _fill_trainval_infos(nusc,
         pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
         lidar_path, boxes, _ = nusc.get_sample_data(lidar_token)
 
-        mmcv.check_file_exist(lidar_path)
+        mmengine.check_file_exist(lidar_path)
 
         info = {
             'lidar_path': lidar_path,
@@ -363,7 +363,7 @@ def export_2d_annotation(root_path, info_path, version, mono3d=True):
     ]
     coco_ann_id = 0
     coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
-    for info in mmcv.track_iter_progress(nusc_infos):
+    for info in mmengine.track_iter_progress(nusc_infos):
         for cam in camera_types:
             cam_info = info['cams'][cam]
             coco_infos = get_2d_boxes(

tools/dataset_converters/s3dis_data_utils.py

@@ -3,7 +3,6 @@ import os
 from concurrent import futures as futures
 from os import path as osp
 
-import mmcv
 import mmengine
 import numpy as np
 
@@ -82,9 +81,9 @@ class S3DISData(object):
             pts_instance_mask = np.load(pts_instance_mask_path).astype(np.int)
             pts_semantic_mask = np.load(pts_semantic_mask_path).astype(np.int)
 
-            mmcv.mkdir_or_exist(osp.join(self.root_dir, 'points'))
-            mmcv.mkdir_or_exist(osp.join(self.root_dir, 'instance_mask'))
-            mmcv.mkdir_or_exist(osp.join(self.root_dir, 'semantic_mask'))
+            mmengine.mkdir_or_exist(osp.join(self.root_dir, 'points'))
+            mmengine.mkdir_or_exist(osp.join(self.root_dir, 'instance_mask'))
+            mmengine.mkdir_or_exist(osp.join(self.root_dir, 'semantic_mask'))
 
             points.tofile(
                 osp.join(self.root_dir, 'points',
@@ -194,7 +193,7 @@ class S3DISSegData(object):
     def get_seg_infos(self):
         scene_idxs, label_weight = self.get_scene_idxs_and_label_weight()
         save_folder = osp.join(self.data_root, 'seg_info')
-        mmcv.mkdir_or_exist(save_folder)
+        mmengine.mkdir_or_exist(save_folder)
         np.save(
             osp.join(save_folder, f'{self.split}_resampled_scene_idxs.npy'),
             scene_idxs)

tools/dataset_converters/scannet_data_utils.py

@@ -3,7 +3,6 @@ import os
 from concurrent import futures as futures
 from os import path as osp
 
-import mmcv
 import mmengine
 import numpy as np
 
@@ -39,8 +38,8 @@ class ScanNetData(object):
         assert split in ['train', 'val', 'test']
         split_file = osp.join(self.root_dir, 'meta_data',
                               f'scannetv2_{split}.txt')
-        mmcv.check_file_exist(split_file)
-        self.sample_id_list = mmcv.list_from_file(split_file)
+        mmengine.check_file_exist(split_file)
+        self.sample_id_list = mmengine.list_from_file(split_file)
         self.test_mode = (split == 'test')
 
     def __len__(self):
@@ -49,19 +48,19 @@ class ScanNetData(object):
     def get_aligned_box_label(self, idx):
         box_file = osp.join(self.root_dir, 'scannet_instance_data',
                             f'{idx}_aligned_bbox.npy')
-        mmcv.check_file_exist(box_file)
+        mmengine.check_file_exist(box_file)
         return np.load(box_file)
 
     def get_unaligned_box_label(self, idx):
         box_file = osp.join(self.root_dir, 'scannet_instance_data',
                             f'{idx}_unaligned_bbox.npy')
-        mmcv.check_file_exist(box_file)
+        mmengine.check_file_exist(box_file)
         return np.load(box_file)
 
     def get_axis_align_matrix(self, idx):
         matrix_file = osp.join(self.root_dir, 'scannet_instance_data',
                                f'{idx}_axis_align_matrix.npy')
-        mmcv.check_file_exist(matrix_file)
+        mmengine.check_file_exist(matrix_file)
         return np.load(matrix_file)
 
     def get_images(self, idx):
@@ -83,7 +82,7 @@ class ScanNetData(object):
     def get_intrinsics(self, idx):
         matrix_file = osp.join(self.root_dir, 'posed_images', idx,
                                'intrinsic.txt')
-        mmcv.check_file_exist(matrix_file)
+        mmengine.check_file_exist(matrix_file)
         return np.loadtxt(matrix_file)
 
     def get_infos(self, num_workers=4, has_label=True, sample_id_list=None):
@@ -111,7 +110,7 @@ class ScanNetData(object):
             pts_filename = osp.join(self.root_dir, 'scannet_instance_data',
                                     f'{sample_idx}_vert.npy')
             points = np.load(pts_filename)
-            mmcv.mkdir_or_exist(osp.join(self.root_dir, 'points'))
+            mmengine.mkdir_or_exist(osp.join(self.root_dir, 'points'))
             points.tofile(
                 osp.join(self.root_dir, 'points', f'{sample_idx}.bin'))
             info['pts_path'] = osp.join('points', f'{sample_idx}.bin')
@@ -143,8 +142,10 @@ class ScanNetData(object):
                 pts_semantic_mask = np.load(pts_semantic_mask_path).astype(
                     np.int64)
 
-                mmcv.mkdir_or_exist(osp.join(self.root_dir, 'instance_mask'))
-                mmcv.mkdir_or_exist(osp.join(self.root_dir, 'semantic_mask'))
+                mmengine.mkdir_or_exist(
+                    osp.join(self.root_dir, 'instance_mask'))
+                mmengine.mkdir_or_exist(
+                    osp.join(self.root_dir, 'semantic_mask'))
 
                 pts_instance_mask.tofile(
                     osp.join(self.root_dir, 'instance_mask',
@@ -246,7 +247,7 @@ class ScanNetSegData(object):
             return
         scene_idxs, label_weight = self.get_scene_idxs_and_label_weight()
         save_folder = osp.join(self.data_root, 'seg_info')
-        mmcv.mkdir_or_exist(save_folder)
+        mmengine.mkdir_or_exist(save_folder)
         np.save(
             osp.join(save_folder, f'{self.split}_resampled_scene_idxs.npy'),
             scene_idxs)

tools/dataset_converters/sunrgbd_data_utils.py

@@ -3,6 +3,7 @@ from concurrent import futures as futures
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 from scipy import io as sio
 
@@ -85,8 +86,8 @@ class SUNRGBDData(object):
         }
         assert split in ['train', 'val', 'test']
         split_file = osp.join(self.split_dir, f'{split}_data_idx.txt')
-        mmcv.check_file_exist(split_file)
-        self.sample_id_list = map(int, mmcv.list_from_file(split_file))
+        mmengine.check_file_exist(split_file)
+        self.sample_id_list = map(int, mmengine.list_from_file(split_file))
         self.image_dir = osp.join(self.split_dir, 'image')
         self.calib_dir = osp.join(self.split_dir, 'calib')
         self.depth_dir = osp.join(self.split_dir, 'depth')
@@ -157,7 +158,7 @@ class SUNRGBDData(object):
             pc_info = {'num_features': 6, 'lidar_idx': sample_idx}
             info['point_cloud'] = pc_info
 
-            mmcv.mkdir_or_exist(osp.join(self.root_dir, 'points'))
+            mmengine.mkdir_or_exist(osp.join(self.root_dir, 'points'))
             pc_upright_depth_subsampled.tofile(
                 osp.join(self.root_dir, 'points', f'{sample_idx:06d}.bin'))
 

tools/dataset_converters/update_infos_to_v2.py

@@ -12,7 +12,6 @@ import copy
 import time
 from os import path as osp
 
-import mmcv
 import mmengine
 import numpy as np
 from nuscenes.nuscenes import NuScenes
@@ -276,7 +275,7 @@ def update_nuscenes_infos(pkl_path, out_dir):
     print('Start updating:')
     converted_list = []
     for i, ori_info_dict in enumerate(
-            mmcv.track_iter_progress(data_list['infos'])):
+            mmengine.track_iter_progress(data_list['infos'])):
         temp_data_info = get_empty_standard_data_info(
             camera_types=camera_types)
         temp_data_info['sample_idx'] = i
@@ -385,7 +384,7 @@ def update_kitti_infos(pkl_path, out_dir):
     data_list = mmengine.load(pkl_path)
     print('Start updating:')
     converted_list = []
-    for ori_info_dict in mmcv.track_iter_progress(data_list):
+    for ori_info_dict in mmengine.track_iter_progress(data_list):
         temp_data_info = get_empty_standard_data_info()
 
         if 'plane' in ori_info_dict:
@@ -509,7 +508,7 @@ def update_s3dis_infos(pkl_path, out_dir):
     data_list = mmengine.load(pkl_path)
     print('Start updating:')
     converted_list = []
-    for i, ori_info_dict in enumerate(mmcv.track_iter_progress(data_list)):
+    for i, ori_info_dict in enumerate(mmengine.track_iter_progress(data_list)):
         temp_data_info = get_empty_standard_data_info()
         temp_data_info['sample_id'] = i
         temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
@@ -575,7 +574,7 @@ def update_scannet_infos(pkl_path, out_dir):
     data_list = mmengine.load(pkl_path)
     print('Start updating:')
     converted_list = []
-    for ori_info_dict in mmcv.track_iter_progress(data_list):
+    for ori_info_dict in mmengine.track_iter_progress(data_list):
         temp_data_info = get_empty_standard_data_info()
         temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
             'point_cloud']['num_features']
@@ -638,7 +637,7 @@ def update_sunrgbd_infos(pkl_path, out_dir):
     data_list = mmengine.load(pkl_path)
     print('Start updating:')
     converted_list = []
-    for ori_info_dict in mmcv.track_iter_progress(data_list):
+    for ori_info_dict in mmengine.track_iter_progress(data_list):
         temp_data_info = get_empty_standard_data_info()
         temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
             'point_cloud']['num_features']
@@ -712,7 +711,7 @@ def update_lyft_infos(pkl_path, out_dir):
     print('Start updating:')
     converted_list = []
     for i, ori_info_dict in enumerate(
-            mmcv.track_iter_progress(data_list['infos'])):
+            mmengine.track_iter_progress(data_list['infos'])):
         temp_data_info = get_empty_standard_data_info()
         temp_data_info['sample_idx'] = i
         temp_data_info['token'] = ori_info_dict['token']
@@ -821,7 +820,7 @@ def update_waymo_infos(pkl_path, out_dir):
     data_list = mmengine.load(pkl_path)
     print('Start updating:')
     converted_list = []
-    for ori_info_dict in mmcv.track_iter_progress(data_list):
+    for ori_info_dict in mmengine.track_iter_progress(data_list):
         temp_data_info = get_empty_standard_data_info(camera_types)
 
         if 'plane' in ori_info_dict:

tools/dataset_converters/waymo_converter.py

@@ -14,6 +14,7 @@ from glob import glob
 from os.path import join
 
 import mmcv
+import mmengine
 import numpy as np
 import tensorflow as tf
 from waymo_open_dataset.utils import range_image_utils, transform_utils
@@ -94,8 +95,8 @@ class Waymo2KITTI(object):
     def convert(self):
         """Convert action."""
         print('Start converting ...')
-        mmcv.track_parallel_progress(self.convert_one, range(len(self)),
-                                     self.workers)
+        mmengine.track_parallel_progress(self.convert_one, range(len(self)),
+                                         self.workers)
         print('\nFinished ...')
 
     def convert_one(self, file_idx):
@@ -409,10 +410,10 @@ class Waymo2KITTI(object):
             ]
             dir_list2 = [self.image_save_dir]
         for d in dir_list1:
-            mmcv.mkdir_or_exist(d)
+            mmengine.mkdir_or_exist(d)
         for d in dir_list2:
             for i in range(5):
-                mmcv.mkdir_or_exist(f'{d}{str(i)}')
+                mmengine.mkdir_or_exist(f'{d}{str(i)}')
 
     def convert_range_image_to_point_cloud(self,
                                            frame,

tools/deployment/mmdet3d2torchserve.py

@@ -3,7 +3,7 @@ from argparse import ArgumentParser, Namespace
 from pathlib import Path
 from tempfile import TemporaryDirectory
 
-import mmcv
+import mmengine
 
 try:
     from model_archiver.model_packaging import package_model
@@ -43,9 +43,9 @@ def mmdet3d2torchserve(
             file under `output_folder` it will be overwritten.
             Default: False.
     """
-    mmcv.mkdir_or_exist(output_folder)
+    mmengine.mkdir_or_exist(output_folder)
 
-    config = mmcv.Config.fromfile(config_file)
+    config = mmengine.Config.fromfile(config_file)
 
     with TemporaryDirectory() as tmpdir:
         config.dump(f'{tmpdir}/config.py')

tools/misc/browse_dataset.py

@@ -2,8 +2,8 @@
 import argparse
 from os import path as osp
 
-import mmcv
-from mmcv import Config, DictAction, mkdir_or_exist
+import mmengine
+from mmengine import Config, DictAction, mkdir_or_exist
 
 from mmdet3d.datasets import build_dataset
 from mmdet3d.registry import VISUALIZERS
@@ -113,7 +113,7 @@ def main():
     visualizer = VISUALIZERS.build(cfg.visualizer)
     visualizer.dataset_meta = dataset.metainfo
 
-    progress_bar = mmcv.ProgressBar(len(dataset))
+    progress_bar = mmengine.ProgressBar(len(dataset))
 
     for item in dataset:
         # the 3D Boxes in input could be in any of three coordinates

tools/misc/fuse_conv_bn.py

@@ -2,7 +2,7 @@
 import argparse
 
 import torch
-from mmcv.runner import save_checkpoint
+from mmengine.runner import save_checkpoint
 from torch import nn as nn
 
 from mmdet3d.apis import init_model

tools/model_converters/convert_h3dnet_checkpoints.py

@@ -4,7 +4,7 @@ import tempfile
 
 import torch
 from mmcv import Config
-from mmcv.runner import load_state_dict
+from mmengine.runner import load_state_dict
 
 from mmdet3d.models import build_detector
 

tools/model_converters/convert_votenet_checkpoints.py

@@ -3,8 +3,8 @@ import argparse
 import tempfile
 
 import torch
-from mmcv import Config
-from mmcv.runner import load_state_dict
+from mmengine import Config
+from mmengine.runner import load_state_dict
 
 from mmdet3d.models import build_detector
 

tools/update_data_coords.py

@@ -2,7 +2,6 @@ import argparse
 import time
 from os import path as osp
 
-import mmcv
 import mmengine
 import numpy as np
 
@@ -21,7 +20,7 @@ def update_sunrgbd_infos(root_dir, out_dir, pkl_files):
         print(f'Reading from input file: {in_path}.')
         a = mmengine.load(in_path)
         print('Start updating:')
-        for item in mmcv.track_iter_progress(a):
+        for item in mmengine.track_iter_progress(a):
             if 'rotation_y' in item['annos']:
                 item['annos']['rotation_y'] = -item['annos']['rotation_y']
                 item['annos']['gt_boxes_upright_depth'][:, -1:] = \
@@ -46,7 +45,7 @@ def update_outdoor_dbinfos(root_dir, out_dir, pkl_files):
         print('Start updating:')
         for k in a.keys():
             print(f'Updating samples of class {k}:')
-            for item in mmcv.track_iter_progress(a[k]):
+            for item in mmengine.track_iter_progress(a[k]):
                 boxes = item['box3d_lidar'].copy()
                 # swap l, w (or dx, dy)
                 item['box3d_lidar'][3] = boxes[4]
@@ -74,7 +73,7 @@ def update_nuscenes_or_lyft_infos(root_dir, out_dir, pkl_files):
         print(f'Reading from input file: {in_path}.')
         a = mmengine.load(in_path)
         print('Start updating:')
-        for item in mmcv.track_iter_progress(a['infos']):
+        for item in mmengine.track_iter_progress(a['infos']):
             boxes = item['gt_boxes'].copy()
             # swap l, w (or dx, dy)
             item['gt_boxes'][:, 3] = boxes[:, 4]