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