import os
from typing import Optional
from .imagelist import ImageList
from ._util import download as download_data, check_exits
[docs]class VisDA2017(ImageList):
"""`VisDA-2017 <http://ai.bu.edu/visda-2017/assets/attachments/VisDA_2017.pdf>`_ Dataset
Parameters:
- **root** (str): Root directory of dataset
- **task** (str): The task (domain) to create dataset. Choices include ``'T'``: training and ``'V'``: validation.
- **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, ``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/
aeroplance/
*.png
...
validation/
image_list/
train.txt
validation.txt
"""
download_list = [
("image_list", "image_list.zip", "https://cloud.tsinghua.edu.cn/f/58034129e0204ef6b840/?dl=1"),
("train", "train.tar", "http://csr.bu.edu/ftp/visda17/clf/train.tar"),
("validation", "validation.tar", "http://csr.bu.edu/ftp/visda17/clf/validation.tar")
]
image_list = {
"T": "image_list/train.txt",
"V": "image_list/validation.txt"
}
CLASSES = ['aeroplane', 'bicycle', 'bus', 'car', 'horse', 'knife',
'motorcycle', 'person', 'plant', 'skateboard', 'train', 'truck']
def __init__(self, root: str, task: str, download: Optional[bool] = False, **kwargs):
assert task in self.image_list
data_list_file = os.path.join(root, self.image_list[task])
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(VisDA2017, self).__init__(root, VisDA2017.CLASSES, data_list_file=data_list_file, **kwargs)