mmpretrain.datasets.fgvcaircraft 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 FGVCAIRCRAFT_CATEGORIES

[文档]@DATASETS.register_module() class FGVCAircraft(BaseDataset): """The FGVC_Aircraft Dataset. Support the `FGVC_Aircraft Dataset <>`_ 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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