99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import List
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from mmengine import get_file_backend, list_from_file
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from mmpretrain.registry import DATASETS
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from .base_dataset import BaseDataset
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from .categories import FGVCAIRCRAFT_CATEGORIES
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@DATASETS.register_module()
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class FGVCAircraft(BaseDataset):
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"""The FGVC_Aircraft Dataset.
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Support the `FGVC_Aircraft Dataset <https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/>`_ Dataset.
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After downloading and decompression, the dataset directory structure is as follows.
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FGVC_Aircraft dataset directory: ::
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fgvc-aircraft-2013b
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└── data
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├── images
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│ ├── 1.jpg
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│ ├── 2.jpg
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│ └── ...
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├── images_variant_train.txt
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├── images_variant_test.txt
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├── images_variant_trainval.txt
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├── images_variant_val.txt
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├── variants.txt
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└── ....
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Args:
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data_root (str): The root directory for FGVC_Aircraft dataset.
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split (str, optional): The dataset split, supports "train",
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"val", "trainval", and "test". Default to "trainval".
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Examples:
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>>> from mmpretrain.datasets import FGVCAircraft
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>>> train_dataset = FGVCAircraft(data_root='data/fgvc-aircraft-2013b', split='trainval')
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>>> train_dataset
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Dataset FGVCAircraft
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Number of samples: 6667
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Number of categories: 100
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Root of dataset: data/fgvc-aircraft-2013b
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>>> test_dataset = FGVCAircraft(data_root='data/fgvc-aircraft-2013b', split='test')
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>>> test_dataset
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Dataset FGVCAircraft
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Number of samples: 3333
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Number of categories: 100
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Root of dataset: data/fgvc-aircraft-2013b
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""" # noqa: E501
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METAINFO = {'classes': FGVCAIRCRAFT_CATEGORIES}
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def __init__(self, data_root: str, split: str = 'trainval', **kwargs):
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splits = ['train', 'val', 'trainval', 'test']
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assert split in splits, \
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f"The split must be one of {splits}, but get '{split}'"
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self.split = split
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self.backend = get_file_backend(data_root, enable_singleton=True)
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ann_file = self.backend.join_path('data',
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f'images_variant_{split}.txt')
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data_prefix = self.backend.join_path('data', 'images')
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test_mode = split == 'test'
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super(FGVCAircraft, self).__init__(
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ann_file=ann_file,
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data_root=data_root,
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test_mode=test_mode,
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data_prefix=data_prefix,
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**kwargs)
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def load_data_list(self):
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"""Load images and ground truth labels."""
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pairs = list_from_file(self.ann_file)
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data_list = []
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for pair in pairs:
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pair = pair.split()
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img_name = pair[0]
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class_name = ' '.join(pair[1:])
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img_name = f'{img_name}.jpg'
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img_path = self.backend.join_path(self.img_prefix, img_name)
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gt_label = self.METAINFO['classes'].index(class_name)
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info = dict(img_path=img_path, gt_label=gt_label)
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data_list.append(info)
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return data_list
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def extra_repr(self) -> List[str]:
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"""The extra repr information of the dataset."""
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body = [
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f'Root of dataset: \t{self.data_root}',
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]
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return body
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