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