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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 CALTECH101_CATEGORIES | |
| class Caltech101(BaseDataset): | |
| """The Caltech101 Dataset. | |
| Support the `Caltech101 <https://data.caltech.edu/records/mzrjq-6wc02>`_ Dataset. | |
| After downloading and decompression, the dataset directory structure is as follows. | |
| Caltech101 dataset directory: :: | |
| caltech-101 | |
| ├── 101_ObjectCategories | |
| │ ├── class_x | |
| │ │ ├── xx1.jpg | |
| │ │ ├── xx2.jpg | |
| │ │ └── ... | |
| │ ├── class_y | |
| │ │ ├── yy1.jpg | |
| │ │ ├── yy2.jpg | |
| │ │ └── ... | |
| │ └── ... | |
| ├── Annotations | |
| │ ├── class_x | |
| │ │ ├── xx1.mat | |
| │ │ └── ... | |
| │ └── ... | |
| ├── meta | |
| │ ├── train.txt | |
| │ └── test.txt | |
| └── .... | |
| Please note that since there is no official splitting for training and | |
| test set, you can use the train.txt and text.txt provided by us or | |
| create your own annotation files. Here is the download | |
| `link <https://download.openmmlab.com/mmpretrain/datasets/caltech_meta.zip>`_ | |
| for the annotations. | |
| Args: | |
| data_root (str): The root directory for the Caltech101 dataset. | |
| split (str, optional): The dataset split, supports "train" and "test". | |
| Default to "train". | |
| Examples: | |
| >>> from mmpretrain.datasets import Caltech101 | |
| >>> train_dataset = Caltech101(data_root='data/caltech-101', split='train') | |
| >>> train_dataset | |
| Dataset Caltech101 | |
| Number of samples: 3060 | |
| Number of categories: 102 | |
| Root of dataset: data/caltech-101 | |
| >>> test_dataset = Caltech101(data_root='data/caltech-101', split='test') | |
| >>> test_dataset | |
| Dataset Caltech101 | |
| Number of samples: 6728 | |
| Number of categories: 102 | |
| Root of dataset: data/caltech-101 | |
| """ # noqa: E501 | |
| METAINFO = {'classes': CALTECH101_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') | |
| data_prefix = '101_ObjectCategories' | |
| test_mode = split == 'test' | |
| super(Caltech101, self).__init__( | |
| ann_file=ann_file, | |
| data_root=data_root, | |
| data_prefix=data_prefix, | |
| test_mode=test_mode, | |
| **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: | |
| path, gt_label = pair.split() | |
| img_path = self.backend.join_path(self.img_prefix, path) | |
| info = dict(img_path=img_path, gt_label=int(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 | |