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# 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
@DATASETS.register_module()
class OxfordIIITPet(BaseDataset):
"""The Oxford-IIIT Pets Dataset.
Support the `Oxford-IIIT Pets Dataset <https://www.robots.ox.ac.uk/~vgg/data/pets/>`_ 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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