[Fix] mmcv.utils -> mmengine.utils (#1295)

* [Fix] mmcv.utils -> mmengine.utils

* mmcv -> mmengine

mmocr/evaluation/metrics/recog_metric.py

@@ -3,7 +3,7 @@
 from difflib import SequenceMatcher
 from typing import Dict, Optional, Sequence, Union
 
-import mmcv
+import mmengine
 from mmengine.evaluator import BaseMetric
 from rapidfuzz.distance import Levenshtein
 
@@ -45,7 +45,7 @@ def __init__(self,
         self.valid_symbol = re.compile(valid_symbol)
         if isinstance(mode, str):
             mode = [mode]
-        assert mmcv.is_seq_of(mode, str)
+        assert mmengine.is_seq_of(mode, str)
         assert set(mode).issubset(
             {'exact', 'ignore_case', 'ignore_case_symbol'})
         self.mode = set(mode)

mmocr/models/common/backbones/unet.py

@@ -3,8 +3,8 @@
 import torch.nn as nn
 import torch.utils.checkpoint as cp
 from mmcv.cnn import ConvModule, build_norm_layer
-from mmcv.utils.parrots_wrapper import _BatchNorm
 from mmengine.model import BaseModule
+from mmengine.utils.parrots_wrapper import _BatchNorm
 
 from mmocr.registry import MODELS
 

mmocr/models/textdet/postprocessors/base_postprocessor.py

@@ -2,7 +2,7 @@
 from functools import partial
 from typing import Dict, List, Optional, Sequence, Tuple, Union
 
-import mmcv
+import mmengine
 import numpy as np
 from torch import Tensor
 
@@ -138,10 +138,10 @@ def split_results(
                 is a tensor, or a list of N lists of tensors if
                 ``pred_results`` is a list of tensors.
         """
-        assert isinstance(pred_results, Tensor) or mmcv.is_seq_of(
+        assert isinstance(pred_results, Tensor) or mmengine.is_seq_of(
             pred_results, Tensor)
 
-        if mmcv.is_seq_of(pred_results, Tensor):
+        if mmengine.is_seq_of(pred_results, Tensor):
             for i in range(1, len(pred_results)):
                 assert pred_results[0].shape[0] == pred_results[i].shape[0], \
                     'The first dimension of all tensors should be the same'

mmocr/models/textrecog/postprocessors/base_textrecog_postprocessor.py

@@ -2,7 +2,7 @@
 import warnings
 from typing import Dict, Optional, Sequence, Tuple, Union
 
-import mmcv
+import mmengine
 import torch
 from mmengine.data import LabelData
 
@@ -48,7 +48,7 @@ def __init__(self,
             'end': self.dictionary.end_idx,
             'unknown': self.dictionary.unknown_idx,
         }
-        if not mmcv.is_list_of(ignore_chars, str):
+        if not mmengine.is_list_of(ignore_chars, str):
             raise TypeError('ignore_chars must be list of str')
         ignore_indexes = list()
         for ignore_char in ignore_chars:

mmocr/utils/__init__.py

@@ -1,6 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.utils import Registry, build_from_cfg
-
 from .bbox_utils import (bbox2poly, bbox_center_distance, bbox_diag_distance,
                          bezier2polygon, is_on_same_line, rescale_bboxes,
                          stitch_boxes_into_lines)
@@ -28,11 +26,10 @@
                      RecForwardResults, RecSampleList)
 
 __all__ = [
-    'Registry', 'build_from_cfg', 'collect_env', 'is_3dlist', 'is_type_list',
-    'is_none_or_type', 'equal_len', 'is_2dlist', 'valid_boundary',
-    'list_to_file', 'list_from_file', 'is_on_same_line',
-    'stitch_boxes_into_lines', 'StringStripper', 'bezier2polygon',
-    'sort_points', 'dump_ocr_data', 'recog_anno_to_imginfo',
+    'collect_env', 'is_3dlist', 'is_type_list', 'is_none_or_type', 'equal_len',
+    'is_2dlist', 'valid_boundary', 'list_to_file', 'list_from_file',
+    'is_on_same_line', 'stitch_boxes_into_lines', 'StringStripper',
+    'bezier2polygon', 'sort_points', 'dump_ocr_data', 'recog_anno_to_imginfo',
     'rescale_polygons', 'rescale_polygon', 'rescale_bboxes', 'bbox2poly',
     'crop_polygon', 'is_poly_inside_rect', 'poly2bbox', 'poly_intersection',
     'poly_iou', 'poly_make_valid', 'poly_union', 'poly2shapely',

mmocr/utils/collect_env.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.utils import collect_env as collect_base_env
-from mmcv.utils import get_git_hash
+from mmengine.utils import collect_env as collect_base_env
+from mmengine.utils import get_git_hash
 
 import mmocr
 

mmocr/utils/fileio.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import os
 
-import mmcv
+import mmengine
 
 
 def list_to_file(filename, lines):
@@ -11,7 +11,7 @@ def list_to_file(filename, lines):
         filename (str): The output filename. It will be created/overwritten.
         lines (list(str)): Data to be written.
     """
-    mmcv.mkdir_or_exist(os.path.dirname(filename))
+    mmengine.mkdir_or_exist(os.path.dirname(filename))
     with open(filename, 'w', encoding='utf-8') as fw:
         for line in lines:
             fw.write(f'{line}\n')

mmocr/utils/img_utils.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import cv2
 import numpy as np
-from mmcv.utils import is_seq_of
+from mmengine.utils import is_seq_of
 from shapely.geometry import LineString, Point
 
 from .bbox_utils import bbox_jitter

tools/analysis_tools/browse_dataset.py

@@ -2,7 +2,7 @@
 import argparse
 import os.path as osp
 
-import mmcv
+import mmengine
 from mmengine import Config, DictAction
 
 from mmocr.registry import DATASETS, VISUALIZERS
@@ -50,7 +50,7 @@ def main():
     dataset = DATASETS.build(cfg.train_dataloader.dataset)
     visualizer = VISUALIZERS.build(cfg.visualizer)
 
-    progress_bar = mmcv.ProgressBar(len(dataset))
+    progress_bar = mmengine.ProgressBar(len(dataset))
     for item in dataset:
         img = item['inputs'].permute(1, 2, 0).numpy()
         data_sample = item['data_sample'].numpy()

tools/dataset_converters/common/curvedsyntext_converter.py

@@ -3,7 +3,6 @@
 import os.path as osp
 from functools import partial
 
-import mmcv
 import mmengine
 import numpy as np
 
@@ -84,14 +83,14 @@ def convert_annotations(data,
         start_img_id=start_img_id,
         start_ann_id=start_ann_id)
     if nproc > 1:
-        data['annotations'] = mmcv.track_parallel_progress(
+        data['annotations'] = mmengine.track_parallel_progress(
             modify_annotation_with_params, data['annotations'], nproc=nproc)
-        data['images'] = mmcv.track_parallel_progress(
+        data['images'] = mmengine.track_parallel_progress(
             modify_image_info_with_params, data['images'], nproc=nproc)
     else:
-        data['annotations'] = mmcv.track_progress(
+        data['annotations'] = mmengine.track_progress(
             modify_annotation_with_params, data['annotations'])
-        data['images'] = mmcv.track_progress(
+        data['images'] = mmengine.track_progress(
             modify_image_info_with_params,
             data['images'],
         )
@@ -103,7 +102,7 @@ def main():
     args = parse_args()
     root_path = args.root_path
     out_dir = args.out_dir if args.out_dir else root_path
-    mmcv.mkdir_or_exist(out_dir)
+    mmengine.mkdir_or_exist(out_dir)
 
     anns = mmengine.load(osp.join(root_path, 'train1.json'))
     data1 = convert_annotations(anns, 'syntext_word_eng', args.num_sample,

tools/dataset_converters/common/labelme_converter.py

@@ -33,7 +33,7 @@ def parse_labelme_json(json_file,
         src_img = mmcv.imread(img_full_path)
         img_basename = osp.splitext(img_file)[0]
         sub_dir = osp.join(out_dir, 'crops', img_basename)
-        mmcv.mkdir_or_exist(sub_dir)
+        mmengine.mkdir_or_exist(sub_dir)
 
     det_line_json_list = []
     recog_crop_line_str_list = []
@@ -143,7 +143,7 @@ def process(json_dir,
             nproc=1,
             recog_format='jsonl',
             warp=False):
-    mmcv.mkdir_or_exist(out_dir)
+    mmengine.mkdir_or_exist(out_dir)
 
     json_file_list = glob.glob(osp.join(json_dir, '*.json'))
 
@@ -156,10 +156,10 @@ def process(json_dir,
         warp_flag=warp)
 
     if nproc <= 1:
-        total_results = mmcv.track_progress(parse_labelme_json_func,
-                                            json_file_list)
+        total_results = mmengine.track_progress(parse_labelme_json_func,
+                                                json_file_list)
     else:
-        total_results = mmcv.track_parallel_progress(
+        total_results = mmengine.track_parallel_progress(
             parse_labelme_json_func,
             json_file_list,
             keep_order=True,
@@ -174,7 +174,7 @@ def process(json_dir,
             total_recog_crop_line_str.extend(res[1])
             total_recog_warp_line_str.extend(res[2])
 
-    mmcv.mkdir_or_exist(out_dir)
+    mmengine.mkdir_or_exist(out_dir)
     det_out_file = osp.join(out_dir, 'instances_training.txt')
     list_to_file(det_out_file, total_det_line_json_list)
 

tools/dataset_converters/kie/closeset_to_openset.py

@@ -3,7 +3,7 @@
 import json
 from functools import partial
 
-import mmcv
+import mmengine
 
 from mmocr.utils import list_from_file, list_to_file
 
@@ -89,7 +89,7 @@ def process(closeset_file, openset_file, merge_bg_others=False, n_proc=10):
 
     convert_func = partial(convert, merge_bg_others=merge_bg_others)
 
-    openset_lines = mmcv.track_parallel_progress(
+    openset_lines = mmengine.track_parallel_progress(
         convert_func, closeset_lines, nproc=n_proc)
 
     list_to_file(openset_file, openset_lines)

tools/dataset_converters/textdet/bid_converter.py

@@ -4,6 +4,7 @@
 import os.path as osp
 
 import mmcv
+import mmengine
 
 from mmocr.utils import dump_ocr_data
 
@@ -50,10 +51,10 @@ def collect_annotations(files, nproc=1):
     assert isinstance(nproc, int)
 
     if nproc > 1:
-        images = mmcv.track_parallel_progress(
+        images = mmengine.track_parallel_progress(
             load_img_info, files, nproc=nproc)
     else:
-        images = mmcv.track_progress(load_img_info, files)
+        images = mmengine.track_progress(load_img_info, files)
 
     return images
 
@@ -164,7 +165,7 @@ def parse_args():
 def main():
     args = parse_args()
     root_path = args.root_path
-    with mmcv.Timer(print_tmpl='It takes {}s to convert BID annotation'):
+    with mmengine.Timer(print_tmpl='It takes {}s to convert BID annotation'):
         files = collect_files(
             osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
         image_infos = collect_annotations(files, nproc=args.nproc)

tools/dataset_converters/textdet/ctw1500_converter.py

@@ -6,6 +6,7 @@
 from functools import partial
 
 import mmcv
+import mmengine
 import numpy as np
 from shapely.geometry import Polygon
 
@@ -72,10 +73,10 @@ def collect_annotations(files, split, nproc=1):
 
     load_img_info_with_split = partial(load_img_info, split=split)
     if nproc > 1:
-        images = mmcv.track_parallel_progress(
+        images = mmengine.track_parallel_progress(
             load_img_info_with_split, files, nproc=nproc)
     else:
-        images = mmcv.track_progress(load_img_info_with_split, files)
+        images = mmengine.track_progress(load_img_info_with_split, files)
 
     return images
 
@@ -208,7 +209,7 @@ def main():
     args = parse_args()
     root_path = args.root_path
     out_dir = args.out_dir if args.out_dir else root_path
-    mmcv.mkdir_or_exist(out_dir)
+    mmengine.mkdir_or_exist(out_dir)
 
     img_dir = osp.join(root_path, 'imgs')
     gt_dir = osp.join(root_path, 'annotations')
@@ -220,7 +221,8 @@ def main():
 
     for split, json_name in set_name.items():
         print(f'Converting {split} into {json_name}')
-        with mmcv.Timer(print_tmpl='It takes {}s to convert icdar annotation'):
+        with mmengine.Timer(
+                print_tmpl='It takes {}s to convert icdar annotation'):
             files = collect_files(
                 osp.join(img_dir, split), osp.join(gt_dir, split), split)
             image_infos = collect_annotations(files, split, nproc=args.nproc)

tools/dataset_converters/textdet/detext_converter.py

@@ -4,6 +4,7 @@
 import os.path as osp
 
 import mmcv
+import mmengine
 import numpy as np
 
 from mmocr.utils import dump_ocr_data
@@ -50,10 +51,10 @@ def collect_annotations(files, nproc=1):
     assert isinstance(nproc, int)
 
     if nproc > 1:
-        images = mmcv.track_parallel_progress(
+        images = mmengine.track_parallel_progress(
             load_img_info, files, nproc=nproc)
     else:
-        images = mmcv.track_progress(load_img_info, files)
+        images = mmengine.track_progress(load_img_info, files)
 
     return images
 
@@ -146,7 +147,7 @@ def main():
 
     for split in ['training', 'val']:
         print(f'Processing {split} set...')
-        with mmcv.Timer(
+        with mmengine.Timer(
                 print_tmpl='It takes {}s to convert DeText annotation'):
             files = collect_files(
                 osp.join(root_path, 'imgs', split),

tools/dataset_converters/textdet/funsd_converter.py

@@ -51,10 +51,10 @@ def collect_annotations(files, nproc=1):
     assert isinstance(nproc, int)
 
     if nproc > 1:
-        images = mmcv.track_parallel_progress(
+        images = mmengine.track_parallel_progress(
             load_img_info, files, nproc=nproc)
     else:
-        images = mmcv.track_progress(load_img_info, files)
+        images = mmengine.track_progress(load_img_info, files)
 
     return images
 
@@ -144,7 +144,8 @@ def main():
 
     for split in ['training', 'test']:
         print(f'Processing {split} set...')
-        with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
+        with mmengine.Timer(
+                print_tmpl='It takes {}s to convert FUNSD annotation'):
             files = collect_files(
                 osp.join(root_path, 'imgs'),
                 osp.join(root_path, 'annotations', split))

tools/dataset_converters/textdet/ic11_converter.py

@@ -4,6 +4,7 @@
 import os.path as osp
 
 import mmcv
+import mmengine
 from PIL import Image
 
 from mmocr.utils import dump_ocr_data
@@ -68,10 +69,10 @@ def collect_annotations(files, nproc=1):
     assert isinstance(nproc, int)
 
     if nproc > 1:
-        images = mmcv.track_parallel_progress(
+        images = mmengine.track_parallel_progress(
             load_img_info, files, nproc=nproc)
     else:
-        images = mmcv.track_progress(load_img_info, files)
+        images = mmengine.track_progress(load_img_info, files)
 
     return images
 
@@ -158,7 +159,7 @@ def main():
 
     for split in ['training', 'test']:
         print(f'Processing {split} set...')
-        with mmcv.Timer(print_tmpl='It takes {}s to convert annotation'):
+        with mmengine.Timer(print_tmpl='It takes {}s to convert annotation'):
             files = collect_files(
                 osp.join(root_path, 'imgs', split),
                 osp.join(root_path, 'annotations', split))