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Source code for tllib.vision.datasets.dtd

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import os
from .imagelist import ImageList
from ._util import download as download_data, check_exits


[docs]class DTD(ImageList): """ `The Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html>`_ is an \ evolving collection of textural images in the wild, annotated with a series of human-centric attributes, \ inspired by the perceptual properties of textures. \ The task consists in classifying images of textural patterns (47 classes, with 120 training images each). \ Some of the textures are banded, bubbly, meshed, lined, or porous. \ The image size ranges between 300x300 and 640x640 pixels. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``test``. 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. """ CLASSES = ['banded', 'blotchy', 'braided', 'bubbly', 'bumpy', 'chequered', 'cobwebbed', 'cracked', 'crosshatched', 'crystalline', 'dotted', 'fibrous', 'flecked', 'freckled', 'frilly', 'gauzy', 'grid', 'grooved', 'honeycombed', 'interlaced', 'knitted', 'lacelike', 'lined', 'marbled', 'matted', 'meshed', 'paisley', 'perforated', 'pitted', 'pleated', 'polka-dotted', 'porous', 'potholed', 'scaly', 'smeared', 'spiralled', 'sprinkled', 'stained', 'stratified', 'striped', 'studded', 'swirly', 'veined', 'waffled', 'woven', 'wrinkled', 'zigzagged'] def __init__(self, root, split, download=False, **kwargs): if download: download_data(root, "dtd", "dtd.tar", "https://cloud.tsinghua.edu.cn/f/499e9eb56b0947fa933a/?dl=1") else: check_exits(root, "dtd") root = os.path.join(root, "dtd") super(DTD, self).__init__(root, DTD.CLASSES, os.path.join(root, "image_list", "{}.txt".format(split)), **kwargs)

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