mmpretrain.datasets.flowers102 源代码

# 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

[文档]@DATASETS.register_module() class Flowers102(BaseDataset): """The Oxford 102 Flower Dataset. Support the `Oxford 102 Flowers Dataset <>`_ 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
Read the Docs v: stable
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.