Source code for common.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/d95c188cc49c404aba70/?dl=1"),
("train", "train.tgz", "https://cloud.tsinghua.edu.cn/f/d5ab63c391a949509db0/?dl=1"),
("test", "test.tgz", "https://cloud.tsinghua.edu.cn/f/04e6fd5222a84d0a8ff5/?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)
