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Source code for tllib.vision.datasets.stanford_cars

"""
@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 StanfordCars(ImageList): """`The Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ \ contains 16,185 images of 196 classes of cars. \ Each category has been split roughly in a 50-50 split. \ There are 8,144 images for training and 8,041 images for testing. 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/a54e58909c1d494ca92f/?dl=1"), ("train", "train.tgz", "https://cloud.tsinghua.edu.cn/f/cec99ada79b249bfa70c/?dl=1"), ("test", "test.tgz", "https://cloud.tsinghua.edu.cn/f/9c506c093f5b46c1a19f/?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 = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196'] 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(StanfordCars, self).__init__(root, StanfordCars.CLASSES, data_list_file=data_list_file, **kwargs)

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