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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 FGVCAIRCRAFT_CATEGORIES
@DATASETS.register_module()
class FGVCAircraft(BaseDataset):
"""The FGVC_Aircraft Dataset.
Support the `FGVC_Aircraft Dataset <https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/>`_ Dataset.
After downloading and decompression, the dataset directory structure is as follows.
FGVC_Aircraft dataset directory: ::
fgvc-aircraft-2013b
└── data
├── images
│ ├── 1.jpg
│ ├── 2.jpg
│ └── ...
├── images_variant_train.txt
├── images_variant_test.txt
├── images_variant_trainval.txt
├── images_variant_val.txt
├── variants.txt
└── ....
Args:
data_root (str): The root directory for FGVC_Aircraft dataset.
split (str, optional): The dataset split, supports "train",
"val", "trainval", and "test". Default to "trainval".
Examples:
>>> from mmpretrain.datasets import FGVCAircraft
>>> train_dataset = FGVCAircraft(data_root='data/fgvc-aircraft-2013b', split='trainval')
>>> train_dataset
Dataset FGVCAircraft
Number of samples: 6667
Number of categories: 100
Root of dataset: data/fgvc-aircraft-2013b
>>> test_dataset = FGVCAircraft(data_root='data/fgvc-aircraft-2013b', split='test')
>>> test_dataset
Dataset FGVCAircraft
Number of samples: 3333
Number of categories: 100
Root of dataset: data/fgvc-aircraft-2013b
""" # noqa: E501
METAINFO = {'classes': FGVCAIRCRAFT_CATEGORIES}
def __init__(self, data_root: str, split: str = 'trainval', **kwargs):
splits = ['train', 'val', '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)
ann_file = self.backend.join_path('data',
f'images_variant_{split}.txt')
data_prefix = self.backend.join_path('data', 'images')
test_mode = split == 'test'
super(FGVCAircraft, self).__init__(
ann_file=ann_file,
data_root=data_root,
test_mode=test_mode,
data_prefix=data_prefix,
**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:
pair = pair.split()
img_name = pair[0]
class_name = ' '.join(pair[1:])
img_name = f'{img_name}.jpg'
img_path = self.backend.join_path(self.img_prefix, img_name)
gt_label = self.METAINFO['classes'].index(class_name)
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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