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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import List | |
| import mat4py | |
| from mmengine import get_file_backend | |
| from mmpretrain.registry import DATASETS | |
| from .base_dataset import BaseDataset | |
| class Flowers102(BaseDataset): | |
| """The Oxford 102 Flower Dataset. | |
| Support the `Oxford 102 Flowers Dataset <https://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset. | |
| After downloading and decompression, the dataset directory structure is as follows. | |
| Flowers102 dataset directory: :: | |
| Flowers102 | |
| ├── jpg | |
| │ ├── image_00001.jpg | |
| │ ├── image_00002.jpg | |
| │ └── ... | |
| ├── imagelabels.mat | |
| ├── setid.mat | |
| └── ... | |
| Args: | |
| data_root (str): The root directory for Oxford 102 Flowers dataset. | |
| split (str, optional): The dataset split, supports "train", | |
| "val", "trainval", and "test". Default to "trainval". | |
| Examples: | |
| >>> from mmpretrain.datasets import Flowers102 | |
| >>> train_dataset = Flowers102(data_root='data/Flowers102', split='trainval') | |
| >>> train_dataset | |
| Dataset Flowers102 | |
| Number of samples: 2040 | |
| Root of dataset: data/Flowers102 | |
| >>> test_dataset = Flowers102(data_root='data/Flowers102', split='test') | |
| >>> test_dataset | |
| Dataset Flowers102 | |
| Number of samples: 6149 | |
| Root of dataset: data/Flowers102 | |
| """ # noqa: E501 | |
| 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 | |
| ann_file = 'imagelabels.mat' | |
| data_prefix = 'jpg' | |
| train_test_split_file = 'setid.mat' | |
| test_mode = split == 'test' | |
| self.backend = get_file_backend(data_root, enable_singleton=True) | |
| self.train_test_split_file = self.backend.join_path( | |
| data_root, train_test_split_file) | |
| super(Flowers102, 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.""" | |
| label_dict = mat4py.loadmat(self.ann_file)['labels'] | |
| split_list = mat4py.loadmat(self.train_test_split_file) | |
| if self.split == 'train': | |
| split_list = split_list['trnid'] | |
| elif self.split == 'val': | |
| split_list = split_list['valid'] | |
| elif self.split == 'test': | |
| split_list = split_list['tstid'] | |
| else: | |
| train_ids = split_list['trnid'] | |
| val_ids = split_list['valid'] | |
| train_ids.extend(val_ids) | |
| split_list = train_ids | |
| data_list = [] | |
| for sample_id in split_list: | |
| img_name = 'image_%05d.jpg' % (sample_id) | |
| img_path = self.backend.join_path(self.img_prefix, img_name) | |
| gt_label = int(label_dict[sample_id - 1]) - 1 | |
| 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 | |