mmagic.datasets.basic_image_dataset
¶
Module Contents¶
Classes¶
BasicImageDataset for open source projects in OpenMMLab/MMagic. |
Attributes¶
- mmagic.datasets.basic_image_dataset.IMG_EXTENSIONS = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif',...[source]¶
- class mmagic.datasets.basic_image_dataset.BasicImageDataset(ann_file: str = '', metainfo: Optional[dict] = None, data_root: Optional[str] = None, data_prefix: dict = dict(img=''), pipeline: List[Union[dict, Callable]] = [], test_mode: bool = False, filename_tmpl: dict = dict(), search_key: Optional[str] = None, backend_args: Optional[dict] = None, img_suffix: Optional[Union[str, Tuple[str]]] = IMG_EXTENSIONS, recursive: bool = False, **kwards)[source]¶
Bases:
mmengine.dataset.BaseDataset
BasicImageDataset for open source projects in OpenMMLab/MMagic.
This dataset is designed for low-level vision tasks with image, such as super-resolution and inpainting.
The annotation file is optional.
If use annotation file, the annotation format can be shown as follows.
Case 1 (CelebA-HQ): 000001.png 000002.png Case 2 (DIV2K): 0001_s001.png (480,480,3) 0001_s002.png (480,480,3) 0001_s003.png (480,480,3) 0002_s001.png (480,480,3) 0002_s002.png (480,480,3) Case 3 (Vimeo90k): 00001/0266 (256, 448, 3) 00001/0268 (256, 448, 3)
- Parameters
ann_file (str) – Annotation file path. Defaults to ‘’.
metainfo (dict, optional) – Meta information for dataset, such as class information. Defaults to None.
data_root (str, optional) – The root directory for
data_prefix
andann_file
. Defaults to None.data_prefix (dict, optional) – Prefix for training data. Defaults to dict(img=None, ann=None).
pipeline (list, optional) – Processing pipeline. Defaults to [].
test_mode (bool, optional) –
test_mode=True
means in test phase. Defaults to False.filename_tmpl (dict) – Template for each filename. Note that the template excludes the file extension. Default: dict().
search_key (str) – The key used for searching the folder to get data_list. Default: ‘gt’.
backend_args (dict, optional) – Arguments to instantiate the prefix of uri corresponding backend. Defaults to None.
suffix (str or tuple[str], optional) – File suffix that we are interested in. Default: None.
recursive (bool) – If set to True, recursively scan the directory. Default: False.
Note
Assume the file structure as the following:
mmagic (root) ├── mmagic ├── tools ├── configs ├── data │ ├── DIV2K │ │ ├── DIV2K_train_HR │ │ │ ├── image.png │ │ ├── DIV2K_train_LR_bicubic │ │ │ ├── X2 │ │ │ ├── X3 │ │ │ ├── X4 │ │ │ │ ├── image_x4.png │ │ ├── DIV2K_valid_HR │ │ ├── DIV2K_valid_LR_bicubic │ │ │ ├── X2 │ │ │ ├── X3 │ │ │ ├── X4 │ ├── places │ │ ├── test_set │ │ ├── train_set | | ├── meta | | | ├── Places365_train.txt | | | ├── Places365_val.txt
Examples
Case 1: Loading DIV2K dataset for training a SISR model.
dataset = BasicImageDataset( ann_file='', metainfo=dict( dataset_type='div2k', task_name='sisr'), data_root='data/DIV2K', data_prefix=dict( gt='DIV2K_train_HR', img='DIV2K_train_LR_bicubic/X4'), filename_tmpl=dict(img='{}_x4', gt='{}'), pipeline=[])
Case 2: Loading places dataset for training an inpainting model.
dataset = BasicImageDataset( ann_file='meta/Places365_train.txt', metainfo=dict( dataset_type='places365', task_name='inpainting'), data_root='data/places', data_prefix=dict(gt='train_set'), pipeline=[])
- load_data_list() List[dict] [source]¶
Load data list from folder or annotation file.
- Returns
A list of annotation.
- Return type
list[dict]
- _get_path_list()[source]¶
Get list of paths from annotation file or folder of dataset.
- Returns
A list of paths.
- Return type
list[dict]