changes according to mmcv open-mmlab#2216

.dev_scripts/benchmark_inference_fps.py

@@ -4,8 +4,9 @@
 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 terminaltables import GithubFlavoredMarkdownTable
 
 from tools.analysis_tools.benchmark import repeat_measure_inference_speed
@@ -165,6 +166,6 @@ def results2markdown(result_dict):
 
     if args.out:
         mmcv.mkdir_or_exist(args.out)
-        mmcv.dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
+        dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
 
     results2markdown(result_dict)

.dev_scripts/gather_models.py

@@ -10,6 +10,7 @@
 import mmcv
 import torch
 import yaml
+from mmengine.fileio import dump
 
 
 def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
@@ -328,7 +329,7 @@ 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

@@ -4,7 +4,8 @@
 import os.path as osp
 
 import mmcv
-from mmcv import Config
+from mmengine.config import Config
+from mmengine.fileio import dump, load
 
 
 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()
@@ -87,8 +88,7 @@ def parse_args():
 
     if metrics_out:
         mmcv.mkdir_or_exist(metrics_out)
-        mmcv.dump(result_dict,
-                  osp.join(metrics_out, 'batch_test_metric_info.json'))
+        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

@@ -5,6 +5,7 @@
 
 import mmcv
 from gather_models import get_final_results
+from mmengine.fileio import dump
 
 try:
     import xlrd
@@ -137,8 +138,7 @@ 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'))
+            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 +5,10 @@
 from os.path import dirname, exists, join
 
 import pytest
-from mmcv import Config, ProgressBar
-from mmcv.runner import _load_checkpoint
+# TODO
+from mmcv import ProgressBar
+from mmengine.config import Config
+from mmengine.runner import CheckpointLoader
 
 from mmdet.models import build_detector
 
@@ -120,7 +122,7 @@ 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:

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
 

mmdet/datasets/transforms/formatting.py

@@ -1,6 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
-from mmcv.parallel import DataContainer as DC
 from mmcv.transforms import to_tensor
 from mmcv.transforms.base import BaseTransform
 from mmengine.data import InstanceData, PixelData
@@ -223,45 +222,6 @@ def __repr__(self):
             f'(keys={self.keys}, order={self.order})'
 
 
-@TRANSFORMS.register_module()
-class ToDataContainer:
-    """Convert results to :obj:`mmcv.DataContainer` by given fields.
-
-    Args:
-        fields (Sequence[dict]): Each field is a dict like
-            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
-            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
-            Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
-            dict(key='gt_labels'))``.
-    """
-
-    def __init__(self,
-                 fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
-                         dict(key='gt_labels'))):
-        self.fields = fields
-
-    def __call__(self, results):
-        """Call function to convert data in results to
-        :obj:`mmcv.DataContainer`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted to \
-                :obj:`mmcv.DataContainer`.
-        """
-
-        for field in self.fields:
-            field = field.copy()
-            key = field.pop('key')
-            results[key] = DC(results[key], **field)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(fields={self.fields})'
-
-
 @TRANSFORMS.register_module()
 class WrapFieldsToLists:
     """Wrap fields of the data dictionary into lists for evaluation.

mmdet/datasets/wider_face.py

@@ -2,7 +2,7 @@
 import os.path as osp
 import xml.etree.ElementTree as ET
 
-import mmcv
+from mmengine.fileio import list_from_file
 
 from mmdet.registry import DATASETS
 from .xml_style import XMLDataset
@@ -33,7 +33,7 @@ def load_annotations(self, ann_file):
         """
 
         data_infos = []
-        img_ids = mmcv.list_from_file(ann_file)
+        img_ids = list_from_file(ann_file)
         for img_id in img_ids:
             filename = f'{img_id}.jpg'
             xml_path = osp.join(self.img_prefix, 'Annotations',

mmdet/engine/hooks/set_epoch_info_hook.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcv.parallel import is_module_wrapper
-from mmcv.runner import Hook
+from mmengine.hooks import Hook
+from mmengine.model.wrappers import is_model_wrapper
 
 from mmdet.registry import HOOKS
 
@@ -12,6 +12,6 @@ class SetEpochInfoHook(Hook):
     def before_train_epoch(self, runner):
         epoch = runner.epoch
         model = runner.model
-        if is_module_wrapper(model):
+        if is_model_wrapper(model):
             model = model.module
         model.set_epoch(epoch)