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mmpretrain.datasets.food101 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 FOOD101_CATEGORIES


[文档]@DATASETS.register_module() class Food101(BaseDataset): """The Food101 Dataset. Support the `Food101 Dataset <https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/>`_ Dataset. After downloading and decompression, the dataset directory structure is as follows. Food101 dataset directory: :: food-101 ├── images │ ├── class_x │ │ ├── xx1.jpg │ │ ├── xx2.jpg │ │ └── ... │ ├── class_y │ │ ├── yy1.jpg │ │ ├── yy2.jpg │ │ └── ... │ └── ... ├── meta │ ├── train.txt │ └── test.txt └── .... Args: data_root (str): The root directory for Food101 dataset. split (str, optional): The dataset split, supports "train" and "test". Default to "train". Examples: >>> from mmpretrain.datasets import Food101 >>> train_dataset = Food101(data_root='data/food-101', split='train') >>> train_dataset Dataset Food101 Number of samples: 75750 Number of categories: 101 Root of dataset: data/food-101 >>> test_dataset = Food101(data_root='data/food-101', split='test') >>> test_dataset Dataset Food101 Number of samples: 25250 Number of categories: 101 Root of dataset: data/food-101 """ # noqa: E501 METAINFO = {'classes': FOOD101_CATEGORIES} def __init__(self, data_root: str, split: str = 'train', **kwargs): splits = ['train', '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 == 'train': ann_file = self.backend.join_path('meta', 'train.txt') else: ann_file = self.backend.join_path('meta', 'test.txt') test_mode = split == 'test' data_prefix = 'images' super(Food101, 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: class_name, img_name = pair.split('/') img_name = f'{img_name}.jpg' img_path = self.backend.join_path(self.img_prefix, class_name, 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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