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