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Source code for tllib.vision.datasets.oxfordpets

"""
@author: Yifei Ji
@contact: jiyf990330@163.com
"""
import os
from typing import Optional
from .imagelist import ImageList
from ._util import download as download_data, check_exits


[docs]class OxfordIIITPets(ImageList): """`The Oxford-IIIT Pets <https://www.robots.ox.ac.uk/~vgg/data/pets/>`_ \ is a 37-category pet dataset with roughly 200 images for each class. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``test``. sample_rate (int): The sampling rates to sample random ``training`` images for each category. Choices include 100, 50, 30, 15. Default: 100. download (bool, optional): If true, downloads the dataset from the internet and puts it \ in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a \ transformed version. E.g, :class:`torchvision.transforms.RandomCrop`. target_transform (callable, optional): A function/transform that takes in the target and transforms it. .. note:: In `root`, there will exist following files after downloading. :: train/ test/ image_list/ train_100.txt train_50.txt train_30.txt train_15.txt test.txt """ download_list = [ ("image_list", "image_list.zip", "https://cloud.tsinghua.edu.cn/f/8b7fb79279174bcc8558/?dl=1"), ("train", "train.tgz", "https://cloud.tsinghua.edu.cn/f/e333a09b93a34a0ebef6/?dl=1"), ("test", "test.tgz", "https://cloud.tsinghua.edu.cn/f/ce00352d79c34ea48bf4/?dl=1"), ] image_list = { "train": "image_list/train_100.txt", "train100": "image_list/train_100.txt", "train50": "image_list/train_50.txt", "train30": "image_list/train_30.txt", "train15": "image_list/train_15.txt", "test": "image_list/test.txt", "test100": "image_list/test.txt", } CLASSES = ['Abyssinian', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'Bengal', 'Birman', 'Bombay', 'boxer', 'British_Shorthair', 'chihuahua', 'Egyptian_Mau', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'Maine_Coon', 'miniature_pinscher', 'newfoundland', 'Persian', 'pomeranian', 'pug', 'Ragdoll', 'Russian_Blue', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'Siamese', 'Sphynx', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier'] def __init__(self, root: str, split: str, sample_rate: Optional[int] = 100, download: Optional[bool] = False, **kwargs): if split == 'train': list_name = 'train' + str(sample_rate) assert list_name in self.image_list data_list_file = os.path.join(root, self.image_list[list_name]) else: data_list_file = os.path.join(root, self.image_list['test']) if download: list(map(lambda args: download_data(root, *args), self.download_list)) else: list(map(lambda file_name, _: check_exits(root, file_name), self.download_list)) super(OxfordIIITPets, self).__init__(root, OxfordIIITPets.CLASSES, data_list_file=data_list_file, **kwargs)

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