[FIx]Replace mmcv.fileclient to mmengine.fileclient (#1737)

* fix mmcv file client

* fix set_env

* fix mmcv file load and dump

.dev_scripts/gather_models.py

@@ -23,6 +23,7 @@ import subprocess
 from os import path as osp
 
 import mmcv
+import mmengine
 import torch
 
 # build schedule look-up table to automatically find the final model
@@ -222,7 +223,7 @@ def main():
 
     models = dict(models=publish_model_infos)
     print(f'Totally gathered {len(publish_model_infos)} models')
-    mmcv.dump(models, osp.join(models_out, 'model_info.json'))
+    mmengine.dump(models, osp.join(models_out, 'model_info.json'))
 
 
 if __name__ == '__main__':

mmdet3d/apis/inference.py

@@ -4,7 +4,7 @@ from copy import deepcopy
 from os import path as osp
 from typing import Sequence, Union
 
-import mmcv
+import mmengine
 import numpy as np
 import torch
 import torch.nn as nn
@@ -187,7 +187,7 @@ def inference_multi_modality_detector(model: nn.Module,
     box_type_3d, box_mode_3d = \
         get_box_type(cfg.test_dataloader.dataset.box_type_3d)
 
-    data_list = mmcv.load(ann_file)['data_list']
+    data_list = mmengine.load(ann_file)['data_list']
     assert len(imgs) == len(data_list)
 
     data = []
@@ -269,7 +269,7 @@ def inference_mono_3d_detector(model: nn.Module,
     box_type_3d, box_mode_3d = \
         get_box_type(cfg.test_dataloader.dataset.box_type_3d)
 
-    data_list = mmcv.load(ann_file)
+    data_list = mmengine.load(ann_file)
     assert len(imgs) == len(data_list)
 
     data = []

mmdet3d/datasets/det3d_dataset.py

@@ -3,7 +3,7 @@ import copy
 from os import path as osp
 from typing import Callable, List, Optional, Union
 
-import mmcv
+import mmengine
 import numpy as np
 from mmengine.dataset import BaseDataset
 
@@ -75,7 +75,7 @@ class Det3DDataset(BaseDataset):
                  file_client_args: dict = dict(backend='disk'),
                  **kwargs):
         # init file client
-        self.file_client = mmcv.FileClient(**file_client_args)
+        self.file_client = mmengine.FileClient(**file_client_args)
         self.filter_empty_gt = filter_empty_gt
         self.load_eval_anns = load_eval_anns
         _default_modality_keys = ('use_lidar', 'use_camera')

mmdet3d/datasets/kitti2d_dataset.py

 # Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
+import mmengine
 import numpy as np
 
 from mmdet3d.datasets import Det3DDataset
@@ -87,7 +87,7 @@ class Kitti2DDataset(Det3DDataset):
         Returns:
             list[dict]: List of annotations.
         """
-        self.data_infos = mmcv.load(ann_file)
+        self.data_infos = mmengine.load(ann_file)
         self.cat2label = {
             cat_name: i
             for i, cat_name in enumerate(self.CLASSES)

mmdet3d/datasets/seg3d_dataset.py

@@ -2,7 +2,7 @@
 from os import path as osp
 from typing import Callable, Dict, List, Optional, Sequence, Union
 
-import mmcv
+import mmengine
 import numpy as np
 from mmengine.dataset import BaseDataset
 
@@ -72,7 +72,7 @@ class Seg3DDataset(BaseDataset):
                  file_client_args: dict = dict(backend='disk'),
                  **kwargs) -> None:
         # init file client
-        self.file_client = mmcv.FileClient(**file_client_args)
+        self.file_client = mmengine.FileClient(**file_client_args)
         self.modality = modality
         self.load_eval_anns = load_eval_anns
 

mmdet3d/datasets/transforms/dbsampler.py

@@ -3,7 +3,7 @@ import copy
 import os
 import warnings
 
-import mmcv
+import mmengine
 import numpy as np
 
 from mmdet3d.datasets.transforms import data_augment_utils
@@ -115,20 +115,21 @@ class DataBaseSampler(object):
         self.cat2label = {name: i for i, name in enumerate(classes)}
         self.label2cat = {i: name for i, name in enumerate(classes)}
         self.points_loader = TRANSFORMS.build(points_loader)
-        self.file_client = mmcv.FileClient(**file_client_args)
+        self.file_client = mmengine.FileClient(**file_client_args)
 
         # load data base infos
         if hasattr(self.file_client, 'get_local_path'):
             with self.file_client.get_local_path(info_path) as local_path:
                 # loading data from a file-like object needs file format
-                db_infos = mmcv.load(open(local_path, 'rb'), file_format='pkl')
+                db_infos = mmengine.load(
+                    open(local_path, 'rb'), file_format='pkl')
         else:
             warnings.warn(
                 'The used MMCV version does not have get_local_path. '
                 f'We treat the {info_path} as local paths and it '
                 'might cause errors if the path is not a local path. '
                 'Please use MMCV>= 1.3.16 if you meet errors.')
-            db_infos = mmcv.load(info_path)
+            db_infos = mmengine.load(info_path)
 
         # filter database infos
         from mmengine.logging import MMLogger

mmdet3d/datasets/transforms/loading.py

@@ -2,6 +2,7 @@
 from typing import List
 
 import mmcv
+import mmengine
 import numpy as np
 from mmcv.transforms import LoadImageFromFile
 from mmcv.transforms.base import BaseTransform
@@ -176,7 +177,7 @@ class LoadPointsFromMultiSweeps(BaseTransform):
             np.ndarray: An array containing point clouds data.
         """
         if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
+            self.file_client = mmengine.FileClient(**self.file_client_args)
         try:
             pts_bytes = self.file_client.get(pts_filename)
             points = np.frombuffer(pts_bytes, dtype=np.float32)
@@ -434,7 +435,7 @@ class LoadPointsFromFile(BaseTransform):
             np.ndarray: An array containing point clouds data.
         """
         if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
+            self.file_client = mmengine.FileClient(**self.file_client_args)
         try:
             pts_bytes = self.file_client.get(pts_filename)
             points = np.frombuffer(pts_bytes, dtype=np.float32)
@@ -690,7 +691,7 @@ class LoadAnnotations3D(LoadAnnotations):
         pts_instance_mask_path = results['pts_instance_mask_path']
 
         if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
+            self.file_client = mmengine.FileClient(**self.file_client_args)
         try:
             mask_bytes = self.file_client.get(pts_instance_mask_path)
             pts_instance_mask = np.frombuffer(mask_bytes, dtype=np.int64)
@@ -717,7 +718,7 @@ class LoadAnnotations3D(LoadAnnotations):
         pts_semantic_mask_path = results['pts_semantic_mask_path']
 
         if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
+            self.file_client = mmengine.FileClient(**self.file_client_args)
         try:
             mask_bytes = self.file_client.get(pts_semantic_mask_path)
             # add .copy() to fix read-only bug

mmdet3d/evaluation/functional/lyft_eval.py

@@ -2,6 +2,7 @@
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 from lyft_dataset_sdk.eval.detection.mAP_evaluation import (Box3D, get_ap,
                                                             get_class_names,
@@ -80,7 +81,7 @@ def load_lyft_predictions(res_path):
     Returns:
         list[dict]: List of prediction dictionaries.
     """
-    predictions = mmcv.load(res_path)
+    predictions = mmengine.load(res_path)
     predictions = predictions['results']
     all_preds = []
     for sample_token in predictions.keys():
@@ -136,7 +137,7 @@ def lyft_eval(lyft, data_root, res_path, eval_set, output_dir, logger=None):
     print_log(APs_table.table, logger=logger)
 
     res_path = osp.join(output_dir, 'lyft_metrics.json')
-    mmcv.dump(metrics, res_path)
+    mmengine.dump(metrics, res_path)
     return metrics
 
 

mmdet3d/evaluation/metrics/kitti_metric.py

@@ -4,6 +4,7 @@ from os import path as osp
 from typing import Dict, List, Optional, Sequence, Union
 
 import mmcv
+import mmengine
 import numpy as np
 import torch
 from mmcv.utils import print_log
@@ -426,7 +427,7 @@ class KittiMetric(BaseMetric):
                 out = f'{pklfile_prefix}.pkl'
             else:
                 out = pklfile_prefix
-            mmcv.dump(det_annos, out)
+            mmengine.dump(det_annos, out)
             print(f'Result is saved to {out}.')
 
         return det_annos
@@ -517,7 +518,7 @@ class KittiMetric(BaseMetric):
                 out = f'{pklfile_prefix}.pkl'
             else:
                 out = pklfile_prefix
-            mmcv.dump(det_annos, out)
+            mmengine.dump(det_annos, out)
             print(f'Result is saved to {out}.')
 
         if submission_prefix is not None:

mmdet3d/evaluation/metrics/lyft_metric.py

@@ -6,6 +6,7 @@ from os import path as osp
 from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import mmcv
+import mmengine
 import numpy as np
 import pandas as pd
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
@@ -192,7 +193,7 @@ class LyftMetric(BaseMetric):
             json_path (str): Path of the result json file.
             csv_savepath (str): Path to save the csv file.
         """
-        results = mmcv.load(json_path)['results']
+        results = mmengine.load(json_path)['results']
         sample_list_path = osp.join(self.data_root, 'sample_submission.csv')
         data = pd.read_csv(sample_list_path)
         Id_list = list(data['Id'])
@@ -269,7 +270,7 @@ class LyftMetric(BaseMetric):
         mmcv.mkdir_or_exist(jsonfile_prefix)
         res_path = osp.join(jsonfile_prefix, 'results_lyft.json')
         print('Results writes to', res_path)
-        mmcv.dump(lyft_submissions, res_path)
+        mmengine.dump(lyft_submissions, res_path)
         return res_path
 
     def lyft_evaluate(self,

mmdet3d/evaluation/metrics/nuscenes_metric.py

@@ -5,6 +5,7 @@ from os import path as osp
 from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import mmcv
+import mmengine
 import numpy as np
 import pyquaternion
 import torch
@@ -235,7 +236,7 @@ class NuScenesMetric(BaseMetric):
         nusc_eval.main(render_curves=False)
 
         # record metrics
-        metrics = mmcv.load(osp.join(output_dir, 'metrics_summary.json'))
+        metrics = mmengine.load(osp.join(output_dir, 'metrics_summary.json'))
         detail = dict()
         metric_prefix = f'{result_name}_NuScenes'
         for name in classes:
@@ -464,7 +465,7 @@ class NuScenesMetric(BaseMetric):
         mmcv.mkdir_or_exist(jsonfile_prefix)
         res_path = osp.join(jsonfile_prefix, 'results_nusc.json')
         print('Results writes to', res_path)
-        mmcv.dump(nusc_submissions, res_path)
+        mmengine.dump(nusc_submissions, res_path)
         return res_path
 
     def _format_lidar_bbox(self,
@@ -538,7 +539,7 @@ class NuScenesMetric(BaseMetric):
         mmcv.mkdir_or_exist(jsonfile_prefix)
         res_path = osp.join(jsonfile_prefix, 'results_nusc.json')
         print('Results writes to', res_path)
-        mmcv.dump(nusc_submissions, res_path)
+        mmengine.dump(nusc_submissions, res_path)
         return res_path
 
 

mmdet3d/evaluation/metrics/waymo_metric.py

@@ -4,6 +4,7 @@ from os import path as osp
 from typing import Dict, List, Optional, Union
 
 import mmcv
+import mmengine
 import numpy as np
 import torch
 from mmcv.utils import print_log
@@ -443,7 +444,7 @@ class WaymoMetric(KittiMetric):
                 out = f'{pklfile_prefix}.pkl'
             else:
                 out = pklfile_prefix
-            mmcv.dump(det_annos, out)
+            mmengine.dump(det_annos, out)
             print(f'Result is saved to {out}.')
 
         return det_annos

mmdet3d/utils/setup_env.py

@@ -68,7 +68,6 @@ def register_all_modules(init_default_scope: bool = True) -> None:
             Defaults to True.
     """  # noqa
     import mmdet3d.datasets  # noqa: F401,F403
-    import mmdet3d.engine.scheduler  # noqa: F401,F403
     import mmdet3d.evaluation.metrics  # noqa: F401,F403
     import mmdet3d.structures  # noqa: F401,F403
     import mmdet3d.visualization  # noqa: F401,F403

tools/dataset_converters/indoor_converter.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import os
 
-import mmcv
+import mmengine
 import numpy as np
 
 from tools.dataset_converters.s3dis_data_utils import S3DISData, S3DISSegData
@@ -54,17 +54,17 @@ def create_indoor_info_file(data_path,
 
         infos_train = train_dataset.get_infos(
             num_workers=workers, has_label=True)
-        mmcv.dump(infos_train, train_filename, 'pkl')
+        mmengine.dump(infos_train, train_filename, 'pkl')
         print(f'{pkl_prefix} info train file is saved to {train_filename}')
 
         infos_val = val_dataset.get_infos(num_workers=workers, has_label=True)
-        mmcv.dump(infos_val, val_filename, 'pkl')
+        mmengine.dump(infos_val, val_filename, 'pkl')
         print(f'{pkl_prefix} info val file is saved to {val_filename}')
 
     if pkl_prefix == 'scannet':
         infos_test = test_dataset.get_infos(
             num_workers=workers, has_label=False)
-        mmcv.dump(infos_test, test_filename, 'pkl')
+        mmengine.dump(infos_test, test_filename, 'pkl')
         print(f'{pkl_prefix} info test file is saved to {test_filename}')
 
     # generate infos for the semantic segmentation task
@@ -100,7 +100,7 @@ def create_indoor_info_file(data_path,
             info = dataset.get_infos(num_workers=workers, has_label=True)
             filename = os.path.join(save_path,
                                     f'{pkl_prefix}_infos_{split}.pkl')
-            mmcv.dump(info, filename, 'pkl')
+            mmengine.dump(info, filename, 'pkl')
             print(f'{pkl_prefix} info {split} file is saved to {filename}')
             seg_dataset = S3DISSegData(
                 data_root=data_path,

tools/dataset_converters/kitti_converter.py

@@ -3,6 +3,7 @@ from collections import OrderedDict
 from pathlib import Path
 
 import mmcv
+import mmengine
 import numpy as np
 from nuscenes.utils.geometry_utils import view_points
 
@@ -196,7 +197,7 @@ def create_kitti_info_file(data_path,
     _calculate_num_points_in_gt(data_path, kitti_infos_train, relative_path)
     filename = save_path / f'{pkl_prefix}_infos_train.pkl'
     print(f'Kitti info train file is saved to {filename}')
-    mmcv.dump(kitti_infos_train, filename)
+    mmengine.dump(kitti_infos_train, filename)
     kitti_infos_val = get_kitti_image_info(
         data_path,
         training=True,
@@ -208,10 +209,10 @@ def create_kitti_info_file(data_path,
     _calculate_num_points_in_gt(data_path, kitti_infos_val, relative_path)
     filename = save_path / f'{pkl_prefix}_infos_val.pkl'
     print(f'Kitti info val file is saved to {filename}')
-    mmcv.dump(kitti_infos_val, filename)
+    mmengine.dump(kitti_infos_val, filename)
     filename = save_path / f'{pkl_prefix}_infos_trainval.pkl'
     print(f'Kitti info trainval file is saved to {filename}')
-    mmcv.dump(kitti_infos_train + kitti_infos_val, filename)
+    mmengine.dump(kitti_infos_train + kitti_infos_val, filename)
 
     kitti_infos_test = get_kitti_image_info(
         data_path,
@@ -224,7 +225,7 @@ def create_kitti_info_file(data_path,
         relative_path=relative_path)
     filename = save_path / f'{pkl_prefix}_infos_test.pkl'
     print(f'Kitti info test file is saved to {filename}')
-    mmcv.dump(kitti_infos_test, filename)
+    mmengine.dump(kitti_infos_test, filename)
 
 
 def create_waymo_info_file(data_path,
@@ -288,19 +289,19 @@ def create_waymo_info_file(data_path,
     num_points_in_gt_calculater.calculate(waymo_infos_train)
     filename = save_path / f'{pkl_prefix}_infos_train.pkl'
     print(f'Waymo info train file is saved to {filename}')
-    mmcv.dump(waymo_infos_train, filename)
+    mmengine.dump(waymo_infos_train, filename)
     waymo_infos_val = waymo_infos_gatherer_trainval.gather(val_img_ids)
     num_points_in_gt_calculater.calculate(waymo_infos_val)
     filename = save_path / f'{pkl_prefix}_infos_val.pkl'
     print(f'Waymo info val file is saved to {filename}')
-    mmcv.dump(waymo_infos_val, filename)
+    mmengine.dump(waymo_infos_val, filename)
     filename = save_path / f'{pkl_prefix}_infos_trainval.pkl'
     print(f'Waymo info trainval file is saved to {filename}')
-    mmcv.dump(waymo_infos_train + waymo_infos_val, filename)
+    mmengine.dump(waymo_infos_train + waymo_infos_val, filename)
     waymo_infos_test = waymo_infos_gatherer_test.gather(test_img_ids)
     filename = save_path / f'{pkl_prefix}_infos_test.pkl'
     print(f'Waymo info test file is saved to {filename}')
-    mmcv.dump(waymo_infos_test, filename)
+    mmengine.dump(waymo_infos_test, filename)
 
 
 def _create_reduced_point_cloud(data_path,
@@ -322,7 +323,7 @@ def _create_reduced_point_cloud(data_path,
         front_camera_id (int, optional): The referenced/front camera ID.
             Default: 2.
     """
-    kitti_infos = mmcv.load(info_path)
+    kitti_infos = mmengine.load(info_path)
 
     for info in mmcv.track_iter_progress(kitti_infos):
         pc_info = info['point_cloud']
@@ -419,7 +420,7 @@ def export_2d_annotation(root_path, info_path, mono3d=True):
             Default: True.
     """
     # get bbox annotations for camera
-    kitti_infos = mmcv.load(info_path)
+    kitti_infos = mmengine.load(info_path)
     cat2Ids = [
         dict(id=kitti_categories.index(cat_name), name=cat_name)
         for cat_name in kitti_categories
@@ -454,7 +455,7 @@ def export_2d_annotation(root_path, info_path, mono3d=True):
         json_prefix = f'{info_path[:-4]}_mono3d'
     else:
         json_prefix = f'{info_path[:-4]}'
-    mmcv.dump(coco_2d_dict, f'{json_prefix}.coco.json')
+    mmengine.dump(coco_2d_dict, f'{json_prefix}.coco.json')
 
 
 def get_2d_boxes(info, occluded, mono3d=True):

tools/dataset_converters/lyft_converter.py

@@ -4,6 +4,7 @@ from logging import warning
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
 from pyquaternion import Quaternion
@@ -77,18 +78,18 @@ def create_lyft_infos(root_path,
         data = dict(infos=train_lyft_infos, metadata=metadata)
         info_name = f'{info_prefix}_infos_test'
         info_path = osp.join(root_path, f'{info_name}.pkl')
-        mmcv.dump(data, info_path)
+        mmengine.dump(data, info_path)
     else:
         print(f'train sample: {len(train_lyft_infos)}, \
                 val sample: {len(val_lyft_infos)}')
         data = dict(infos=train_lyft_infos, metadata=metadata)
         train_info_name = f'{info_prefix}_infos_train'
         info_path = osp.join(root_path, f'{train_info_name}.pkl')
-        mmcv.dump(data, info_path)
+        mmengine.dump(data, info_path)
         data['infos'] = val_lyft_infos
         val_info_name = f'{info_prefix}_infos_val'
         info_val_path = osp.join(root_path, f'{val_info_name}.pkl')
-        mmcv.dump(data, info_val_path)
+        mmengine.dump(data, info_val_path)
 
 
 def _fill_trainval_infos(lyft,
@@ -234,7 +235,7 @@ def export_2d_annotation(root_path, info_path, version):
         'CAM_BACK_LEFT',
         'CAM_BACK_RIGHT',
     ]
-    lyft_infos = mmcv.load(info_path)['infos']
+    lyft_infos = mmengine.load(info_path)['infos']
     lyft = Lyft(
         data_path=osp.join(root_path, version),
         json_path=osp.join(root_path, version, version),
@@ -268,4 +269,4 @@ def export_2d_annotation(root_path, info_path, version):
                 coco_info['id'] = coco_ann_id
                 coco_2d_dict['annotations'].append(coco_info)
                 coco_ann_id += 1
-    mmcv.dump(coco_2d_dict, f'{info_path[:-4]}.coco.json')
+    mmengine.dump(coco_2d_dict, f'{info_path[:-4]}.coco.json')

tools/dataset_converters/nuimage_converter.py

@@ -4,6 +4,7 @@ import base64
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 from nuimages import NuImages
 from nuimages.utils.utils import mask_decode, name_to_index_mapping
@@ -210,7 +211,7 @@ def export_nuim_to_coco(nuim, data_root, out_dir, extra_tag, version, nproc):
     mmcv.mkdir_or_exist(out_dir)
     out_file = osp.join(out_dir, f'{extra_tag}_{version}.json')
     print(f'Annotation dumped to {out_file}')
-    mmcv.dump(coco_format_json, out_file)
+    mmengine.dump(coco_format_json, out_file)
 
 
 def main():

tools/dataset_converters/nuscenes_converter.py

@@ -5,6 +5,7 @@ from os import path as osp
 from typing import List, Tuple, Union
 
 import mmcv
+import mmengine
 import numpy as np
 from nuscenes.nuscenes import NuScenes
 from nuscenes.utils.geometry_utils import view_points
@@ -87,18 +88,18 @@ def create_nuscenes_infos(root_path,
         data = dict(infos=train_nusc_infos, metadata=metadata)
         info_path = osp.join(root_path,
                              '{}_infos_test.pkl'.format(info_prefix))
-        mmcv.dump(data, info_path)
+        mmengine.dump(data, info_path)
     else:
         print('train sample: {}, val sample: {}'.format(
             len(train_nusc_infos), len(val_nusc_infos)))
         data = dict(infos=train_nusc_infos, metadata=metadata)
         info_path = osp.join(root_path,
                              '{}_infos_train.pkl'.format(info_prefix))
-        mmcv.dump(data, info_path)
+        mmengine.dump(data, info_path)
         data['infos'] = val_nusc_infos
         info_val_path = osp.join(root_path,
                                  '{}_infos_val.pkl'.format(info_prefix))
-        mmcv.dump(data, info_val_path)
+        mmengine.dump(data, info_val_path)
 
 
 def get_available_scenes(nusc):
@@ -353,7 +354,7 @@ def export_2d_annotation(root_path, info_path, version, mono3d=True):
         'CAM_BACK_LEFT',
         'CAM_BACK_RIGHT',
     ]
-    nusc_infos = mmcv.load(info_path)['infos']
+    nusc_infos = mmengine.load(info_path)['infos']
     nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
     # info_2d_list = []
     cat2Ids = [
@@ -396,7 +397,7 @@ def export_2d_annotation(root_path, info_path, version, mono3d=True):
         json_prefix = f'{info_path[:-4]}_mono3d'
     else:
         json_prefix = f'{info_path[:-4]}'
-    mmcv.dump(coco_2d_dict, f'{json_prefix}.coco.json')
+    mmengine.dump(coco_2d_dict, f'{json_prefix}.coco.json')
 
 
 def get_2d_boxes(nusc,

tools/dataset_converters/s3dis_data_utils.py

@@ -4,6 +4,7 @@ from concurrent import futures as futures
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 
 
@@ -171,7 +172,7 @@ class S3DISSegData(object):
                  num_points=4096,
                  label_weight_func=None):
         self.data_root = data_root
-        self.data_infos = mmcv.load(ann_file)
+        self.data_infos = mmengine.load(ann_file)
         self.split = split
         self.num_points = num_points
 

tools/dataset_converters/scannet_data_utils.py

@@ -4,6 +4,7 @@ from concurrent import futures as futures
 from os import path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 
 
@@ -218,7 +219,7 @@ class ScanNetSegData(object):
                  num_points=8192,
                  label_weight_func=None):
         self.data_root = data_root
-        self.data_infos = mmcv.load(ann_file)
+        self.data_infos = mmengine.load(ann_file)
         self.split = split
         assert split in ['train', 'val', 'test']
         self.num_points = num_points