[Fix] replace mmcv's function and modules imported with mmengine's (o…

…pen-mmlab#8594)

* use mmengine's load_state_dict and load_checkpoint

* from mmengine import dump

* from mmengine import FileClient dump list_from_file

* remove redundant registry

* update

* update

* update

* replace _load_checkpoint with CheckpointLoad.load_checkpoint

* changes according to mmcv open-mmlab#2216

* changes due to mmengine open-mmlab#447

* changes due mmengine open-mmlab#447 and mmcv open-mmlab#2217

* changes due mmengine open-mmlab#447 and mmcv open-mmlab#2217

* update

* update

* update

.dev_scripts/benchmark_inference_fps.py

@@ -3,9 +3,10 @@
 import os
 import os.path as osp
 
-import mmcv
-from mmcv import Config, DictAction
-from mmcv.runner import init_dist
+from mmengine.config import Config, DictAction
+from mmengine.dist import init_dist
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist
 from terminaltables import GithubFlavoredMarkdownTable
 
 from tools.analysis_tools.benchmark import repeat_measure_inference_speed
@@ -164,7 +165,7 @@ def results2markdown(result_dict):
                     result_dict[cfg_path] = dict(fps=0, ms_times_pre_image=0)
 
     if args.out:
-        mmcv.mkdir_or_exist(args.out)
-        mmcv.dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
+        mkdir_or_exist(args.out)
+        dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
 
     results2markdown(result_dict)

.dev_scripts/benchmark_test_image.py

@@ -6,6 +6,7 @@
 import mmcv
 from mmengine.config import Config
 from mmengine.logging import MMLogger
+from mmengine.utils import mkdir_or_exist
 
 from mmdet.apis import inference_detector, init_detector
 from mmdet.registry import VISUALIZERS
@@ -58,7 +59,7 @@ def inference_model(config_name, checkpoint, visualizer, args, logger=None):
         out_file = None
         if args.out_dir is not None:
             out_dir = args.out_dir
-            mmcv.mkdir_or_exist(out_dir)
+            mkdir_or_exist(out_dir)
 
             out_file = osp.join(
                 out_dir,

.dev_scripts/download_checkpoints.py

@@ -6,9 +6,9 @@
 import os.path as osp
 from multiprocessing import Pool
 
-import mmcv
 import torch
-from mmcv import Config
+from mmengine.config import Config
+from mmengine.utils import mkdir_or_exist
 
 
 def download(url, out_file, min_bytes=math.pow(1024, 2), progress=True):
@@ -52,7 +52,7 @@ def parse_args():
 
 if __name__ == '__main__':
     args = parse_args()
-    mmcv.mkdir_or_exist(args.out)
+    mkdir_or_exist(args.out)
 
     cfg = Config.fromfile(args.config)
 

.dev_scripts/gather_models.py

@@ -7,9 +7,11 @@
 import subprocess
 from collections import OrderedDict
 
-import mmcv
 import torch
 import yaml
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist, scandir
 
 
 def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
@@ -49,12 +51,12 @@ def process_checkpoint(in_file, out_file):
 
 
 def is_by_epoch(config):
-    cfg = mmcv.Config.fromfile('./configs/' + config)
+    cfg = Config.fromfile('./configs/' + config)
     return cfg.runner.type == 'EpochBasedRunner'
 
 
 def get_final_epoch_or_iter(config):
-    cfg = mmcv.Config.fromfile('./configs/' + config)
+    cfg = Config.fromfile('./configs/' + config)
     if cfg.runner.type == 'EpochBasedRunner':
         return cfg.runner.max_epochs
     else:
@@ -71,7 +73,7 @@ def get_best_epoch_or_iter(exp_dir):
 
 
 def get_real_epoch_or_iter(config):
-    cfg = mmcv.Config.fromfile('./configs/' + config)
+    cfg = Config.fromfile('./configs/' + config)
     if cfg.runner.type == 'EpochBasedRunner':
         epoch = cfg.runner.max_epochs
         if cfg.data.train.type == 'RepeatDataset':
@@ -142,7 +144,7 @@ def get_dataset_name(config):
         WIDERFaceDataset='WIDER Face',
         OpenImagesDataset='OpenImagesDataset',
         OpenImagesChallengeDataset='OpenImagesChallengeDataset')
-    cfg = mmcv.Config.fromfile('./configs/' + config)
+    cfg = Config.fromfile('./configs/' + config)
     return name_map[cfg.dataset_type]
 
 
@@ -226,10 +228,10 @@ def main():
     args = parse_args()
     models_root = args.root
     models_out = args.out
-    mmcv.mkdir_or_exist(models_out)
+    mkdir_or_exist(models_out)
 
     # find all models in the root directory to be gathered
-    raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
+    raw_configs = list(scandir('./configs', '.py', recursive=True))
 
     # filter configs that is not trained in the experiments dir
     used_configs = []
@@ -261,7 +263,7 @@ def main():
         log_json_path = list(
             sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
         log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1]
-        cfg = mmcv.Config.fromfile('./configs/' + used_config)
+        cfg = Config.fromfile('./configs/' + used_config)
         results_lut = cfg.evaluation.metric
         if not isinstance(results_lut, list):
             results_lut = [results_lut]
@@ -292,7 +294,7 @@ def main():
     publish_model_infos = []
     for model in model_infos:
         model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
-        mmcv.mkdir_or_exist(model_publish_dir)
+        mkdir_or_exist(model_publish_dir)
 
         model_name = osp.split(model['config'])[-1].split('.')[0]
 
@@ -328,7 +330,7 @@ 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'))
+    dump(models, osp.join(models_out, 'model_info.json'))
 
     pwc_files = convert_model_info_to_pwc(publish_model_infos)
     for name in pwc_files:

.dev_scripts/gather_test_benchmark_metric.py

@@ -3,8 +3,9 @@
 import glob
 import os.path as osp
 
-import mmcv
-from mmcv import Config
+from mmengine.config import Config
+from mmengine.fileio import dump, load
+from mmengine.utils import mkdir_or_exist
 
 
 def parse_args():
@@ -49,7 +50,7 @@ def parse_args():
                 if len(json_list) > 0:
                     log_json_path = list(sorted(json_list))[-1]
 
-                    metric = mmcv.load(log_json_path)
+                    metric = load(log_json_path)
                     if config in metric.get('config', {}):
 
                         new_metrics = dict()
@@ -86,9 +87,8 @@ def parse_args():
                 print(f'{config} not exist dir: {metric_json_dir}')
 
     if metrics_out:
-        mmcv.mkdir_or_exist(metrics_out)
-        mmcv.dump(result_dict,
-                  osp.join(metrics_out, 'batch_test_metric_info.json'))
+        mkdir_or_exist(metrics_out)
+        dump(result_dict, osp.join(metrics_out, 'batch_test_metric_info.json'))
     if not args.not_show:
         print('===================================')
         for config_name, metrics in result_dict.items():

.dev_scripts/gather_train_benchmark_metric.py

@@ -3,8 +3,10 @@
 import glob
 import os.path as osp
 
-import mmcv
 from gather_models import get_final_results
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist
 
 try:
     import xlrd
@@ -78,7 +80,7 @@ def parse_args():
             result_path = osp.join(root_path, config_name)
             if osp.exists(result_path):
                 # 1 read config
-                cfg = mmcv.Config.fromfile(config)
+                cfg = Config.fromfile(config)
                 total_epochs = cfg.runner.max_epochs
                 final_results = cfg.evaluation.metric
                 if not isinstance(final_results, list):
@@ -136,9 +138,8 @@ def parse_args():
 
         # 4 save or print results
         if metrics_out:
-            mmcv.mkdir_or_exist(metrics_out)
-            mmcv.dump(result_dict,
-                      osp.join(metrics_out, 'model_metric_info.json'))
+            mkdir_or_exist(metrics_out)
+            dump(result_dict, osp.join(metrics_out, 'model_metric_info.json'))
         if not args.not_show:
             print('===================================')
             for config_name, metrics in result_dict.items():

.dev_scripts/test_init_backbone.py

@@ -5,8 +5,9 @@
 from os.path import dirname, exists, join
 
 import pytest
-from mmcv import Config, ProgressBar
-from mmcv.runner import _load_checkpoint
+from mmengine.config import Config
+from mmengine.runner import CheckpointLoader
+from mmengine.utils import ProgressBar
 
 from mmdet.models import build_detector
 
@@ -28,7 +29,6 @@ def _get_config_directory():
 
 def _get_config_module(fname):
     """Load a configuration as a python module."""
-    from mmcv import Config
     config_dpath = _get_config_directory()
     config_fpath = join(config_dpath, fname)
     config_mod = Config.fromfile(config_fpath)
@@ -91,8 +91,8 @@ def _check_backbone(config, print_cfg=True):
     """Check out backbone whether successfully load pretrained model, by using
     `backbone.init_cfg`.
 
-    First, using `mmcv._load_checkpoint` to load the checkpoint without
-        loading models.
+    First, using `CheckpointLoader.load_checkpoint` to load the checkpoint
+        without loading models.
     Then, using `build_detector` to build models, and using
         `model.init_weights()` to initialize the parameters.
     Finally, assert weights and bias of each layer loaded from pretrained
@@ -120,7 +120,7 @@ def _check_backbone(config, print_cfg=True):
     if init_cfg is None or init_cfg.get('type') != 'Pretrained':
         init_flag = False
     if init_flag:
-        checkpoint = _load_checkpoint(init_cfg.checkpoint)
+        checkpoint = CheckpointLoader.load_checkpoint(init_cfg.checkpoint)
         if 'state_dict' in checkpoint:
             state_dict = checkpoint['state_dict']
         else:

demo/create_result_gif.py

@@ -7,6 +7,7 @@
 import matplotlib.pyplot as plt
 import mmcv
 import numpy as np
+from mmengine.utils import scandir
 
 try:
     import imageio
@@ -80,7 +81,7 @@ def create_frame_by_matplotlib(image_dir,
 
     images_list = []
     for dir_names in result_dir_names:
-        images_list.append(mmcv.scandir(osp.join(image_dir, dir_names)))
+        images_list.append(scandir(osp.join(image_dir, dir_names)))
 
     frames = []
     for paths in _generate_batch_data(zip(*images_list), nrows):

demo/video_demo.py

@@ -4,6 +4,7 @@
 import cv2
 import mmcv
 from mmcv.transforms import Compose
+from mmengine.utils import track_iter_progress
 
 from mmdet.apis import inference_detector, init_detector
 from mmdet.registry import VISUALIZERS
@@ -60,7 +61,7 @@ def main():
             args.out, fourcc, video_reader.fps,
             (video_reader.width, video_reader.height))
 
-    for frame in mmcv.track_iter_progress(video_reader):
+    for frame in track_iter_progress(video_reader):
         result = inference_detector(model, frame, test_pipeline=test_pipeline)
         visualizer.add_datasample(
             name='video',

demo/video_gpuaccel_demo.py

@@ -7,6 +7,7 @@
 import torch
 import torch.nn as nn
 from mmcv.transforms import Compose
+from mmengine.utils import track_iter_progress
 
 from mmdet.apis import init_detector
 from mmdet.registry import VISUALIZERS
@@ -113,7 +114,7 @@ def main():
 
     with torch.no_grad():
         for i, (frame_resize, frame_origin) in enumerate(
-                zip(mmcv.track_iter_progress(video_resize), video_origin)):
+                zip(track_iter_progress(video_resize), video_origin)):
             data = pack_data(frame_resize, batch_input_shape, ori_shape)
             result = model.test_step([data])[0]
 

mmdet/datasets/builder.py

@@ -49,7 +49,10 @@ def _concat_dataset(cfg, default_args=None):
 
 # TODO: Need to refactor later
 def build_dataset(cfg, default_args=None):
-    from .dataset_wrappers import ClassBalancedDataset, MultiImageMixDataset
+    from mmengine.dataset import ClassBalancedDataset
+
+    from .dataset_wrappers import MultiImageMixDataset
+
     if cfg['type'] == 'ClassBalancedDataset':
         dataset = ClassBalancedDataset(
             build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])

mmdet/datasets/transforms/__init__.py

@@ -3,8 +3,7 @@
 from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
                          Contrast, Equalize, Invert, Posterize, Sharpness,
                          Solarize, SolarizeAdd)
-from .formatting import (ImageToTensor, PackDetInputs, ToDataContainer,
-                         ToTensor, Transpose)
+from .formatting import ImageToTensor, PackDetInputs, ToTensor, Transpose
 from .geometric import (GeomTransform, Rotate, ShearX, ShearY, TranslateX,
                         TranslateY)
 from .instaboost import InstaBoost
@@ -19,16 +18,16 @@
 from .wrappers import MultiBranch, RandomOrder
 
 __all__ = [
-    'PackDetInputs', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
-    'Transpose', 'LoadImageFromNDArray', 'LoadAnnotations',
-    'LoadPanopticAnnotations', 'LoadMultiChannelImageFromFiles',
-    'LoadProposals', 'Resize', 'RandomFlip', 'RandomCrop', 'Normalize',
-    'SegRescale', 'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion',
-    'Albu', 'InstaBoost', 'RandomCenterCropPad', 'AutoAugment', 'CutOut',
-    'ShearX', 'ShearY', 'Rotate', 'Color', 'Equalize', 'Brightness',
-    'Contrast', 'TranslateX', 'TranslateY', 'RandomShift', 'Mosaic', 'MixUp',
-    'RandomAffine', 'YOLOXHSVRandomAug', 'CopyPaste', 'FilterAnnotations',
-    'Pad', 'GeomTransform', 'ColorTransform', 'RandAugment', 'Sharpness',
-    'Solarize', 'SolarizeAdd', 'Posterize', 'AutoContrast', 'Invert',
-    'MultiBranch', 'RandomErasing', 'LoadEmptyAnnotations', 'RandomOrder'
+    'PackDetInputs', 'ToTensor', 'ImageToTensor', 'Transpose',
+    'LoadImageFromNDArray', 'LoadAnnotations', 'LoadPanopticAnnotations',
+    'LoadMultiChannelImageFromFiles', 'LoadProposals', 'Resize', 'RandomFlip',
+    'RandomCrop', 'Normalize', 'SegRescale', 'MinIoURandomCrop', 'Expand',
+    'PhotoMetricDistortion', 'Albu', 'InstaBoost', 'RandomCenterCropPad',
+    'AutoAugment', 'CutOut', 'ShearX', 'ShearY', 'Rotate', 'Color', 'Equalize',
+    'Brightness', 'Contrast', 'TranslateX', 'TranslateY', 'RandomShift',
+    'Mosaic', 'MixUp', 'RandomAffine', 'YOLOXHSVRandomAug', 'CopyPaste',
+    'FilterAnnotations', 'Pad', 'GeomTransform', 'ColorTransform',
+    'RandAugment', 'Sharpness', 'Solarize', 'SolarizeAdd', 'Posterize',
+    'AutoContrast', 'Invert', 'MultiBranch', 'RandomErasing',
+    'LoadEmptyAnnotations', 'RandomOrder'
 ]

mmdet/datasets/transforms/augment_wrappers.py

@@ -2,9 +2,9 @@
 from typing import List, Optional, Union
 
 import numpy as np
-from mmcv import ConfigDict
 from mmcv.transforms import RandomChoice
 from mmcv.transforms.utils import cache_randomness
+from mmengine.config import ConfigDict
 
 from mmdet.registry import TRANSFORMS