mmpretrain.datasets.oxfordiiitpet 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List

from mmengine import get_file_backend, list_from_file

from mmpretrain.registry import DATASETS
from .base_dataset import BaseDataset
from .categories import OxfordIIITPet_CATEGORIES

[文档]@DATASETS.register_module() class OxfordIIITPet(BaseDataset): """The Oxford-IIIT Pets Dataset. Support the `Oxford-IIIT Pets Dataset <>`_ Dataset. After downloading and decompression, the dataset directory structure is as follows. Oxford-IIIT_Pets dataset directory: :: Oxford-IIIT_Pets ├── images │ ├── Abyssinian_1.jpg │ ├── Abyssinian_2.jpg │ └── ... ├── annotations │ ├── trainval.txt │ ├── test.txt │ ├── list.txt │ └── ... └── .... Args: data_root (str): The root directory for Oxford-IIIT Pets dataset. split (str, optional): The dataset split, supports "trainval" and "test". Default to "trainval". Examples: >>> from mmpretrain.datasets import OxfordIIITPet >>> train_dataset = OxfordIIITPet(data_root='data/Oxford-IIIT_Pets', split='trainval') >>> train_dataset Dataset OxfordIIITPet Number of samples: 3680 Number of categories: 37 Root of dataset: data/Oxford-IIIT_Pets >>> test_dataset = OxfordIIITPet(data_root='data/Oxford-IIIT_Pets', split='test') >>> test_dataset Dataset OxfordIIITPet Number of samples: 3669 Number of categories: 37 Root of dataset: data/Oxford-IIIT_Pets """ # noqa: E501 METAINFO = {'classes': OxfordIIITPet_CATEGORIES} def __init__(self, data_root: str, split: str = 'trainval', **kwargs): splits = ['trainval', 'test'] assert split in splits, \ f"The split must be one of {splits}, but get '{split}'" self.split = split self.backend = get_file_backend(data_root, enable_singleton=True) if split == 'trainval': ann_file = self.backend.join_path('annotations', 'trainval.txt') else: ann_file = self.backend.join_path('annotations', 'test.txt') data_prefix = 'images' test_mode = split == 'test' super(OxfordIIITPet, self).__init__( ann_file=ann_file, data_root=data_root, data_prefix=data_prefix, test_mode=test_mode, **kwargs) def load_data_list(self): """Load images and ground truth labels.""" pairs = list_from_file(self.ann_file) data_list = [] for pair in pairs: img_name, class_id, _, _ = pair.split() img_name = f'{img_name}.jpg' img_path = self.backend.join_path(self.img_prefix, img_name) gt_label = int(class_id) - 1 info = dict(img_path=img_path, gt_label=gt_label) data_list.append(info) return data_list def extra_repr(self) -> List[str]: """The extra repr information of the dataset.""" body = [ f'Root of dataset: \t{self.data_root}', ] return body
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