Source code for common.vision.datasets.domainnet
"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import os
from typing import Optional
from .imagelist import ImageList
from ._util import download as download_data, check_exits
[docs]class DomainNet(ImageList):
"""`DomainNet <http://ai.bu.edu/M3SDA/#dataset>`_ (cleaned version, recommended)
See `Moment Matching for Multi-Source Domain Adaptation <https://arxiv.org/abs/1812.01754>`_ for details.
Args:
root (str): Root directory of dataset
task (str): The task (domain) to create dataset. Choices include ``'c'``:clipart, \
``'i'``: infograph, ``'p'``: painting, ``'q'``: quickdraw, ``'r'``: real, ``'s'``: sketch
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.
.. note:: In `root`, there will exist following files after downloading.
::
clipart/
infograph/
painting/
quickdraw/
real/
sketch/
image_list/
clipart.txt
...
"""
download_list = [
("image_list", "image_list.zip", "https://cloud.tsinghua.edu.cn/f/90ecb35bbd374e5e8c41/?dl=1"),
("clipart", "clipart.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/clipart.zip"),
("infograph", "infograph.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/infograph.zip"),
("painting", "painting.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/painting.zip"),
("quickdraw", "quickdraw.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/quickdraw.zip"),
("real", "real.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/real.zip"),
("sketch", "sketch.zip", "http://csr.bu.edu/ftp/visda/2019/multi-source/sketch.zip"),
]
image_list = {
"c": "clipart",
"i": "infograph",
"p": "painting",
"q": "quickdraw",
"r": "real",
"s": "sketch",
}
CLASSES = ['aircraft_carrier', 'airplane', 'alarm_clock', 'ambulance', 'angel', 'animal_migration', 'ant', 'anvil',
'apple', 'arm', 'asparagus', 'axe', 'backpack', 'banana', 'bandage', 'barn', 'baseball', 'baseball_bat',
'basket', 'basketball', 'bat', 'bathtub', 'beach', 'bear', 'beard', 'bed', 'bee', 'belt', 'bench',
'bicycle', 'binoculars', 'bird', 'birthday_cake', 'blackberry', 'blueberry', 'book', 'boomerang',
'bottlecap', 'bowtie', 'bracelet', 'brain', 'bread', 'bridge', 'broccoli', 'broom', 'bucket',
'bulldozer', 'bus', 'bush', 'butterfly', 'cactus', 'cake', 'calculator', 'calendar', 'camel', 'camera',
'camouflage', 'campfire', 'candle', 'cannon', 'canoe', 'car', 'carrot', 'castle', 'cat', 'ceiling_fan',
'cello', 'cell_phone', 'chair', 'chandelier', 'church', 'circle', 'clarinet', 'clock', 'cloud',
'coffee_cup', 'compass', 'computer', 'cookie', 'cooler', 'couch', 'cow', 'crab', 'crayon', 'crocodile',
'crown', 'cruise_ship', 'cup', 'diamond', 'dishwasher', 'diving_board', 'dog', 'dolphin', 'donut',
'door', 'dragon', 'dresser', 'drill', 'drums', 'duck', 'dumbbell', 'ear', 'elbow', 'elephant',
'envelope', 'eraser', 'eye', 'eyeglasses', 'face', 'fan', 'feather', 'fence', 'finger', 'fire_hydrant',
'fireplace', 'firetruck', 'fish', 'flamingo', 'flashlight', 'flip_flops', 'floor_lamp', 'flower',
'flying_saucer', 'foot', 'fork', 'frog', 'frying_pan', 'garden', 'garden_hose', 'giraffe', 'goatee',
'golf_club', 'grapes', 'grass', 'guitar', 'hamburger', 'hammer', 'hand', 'harp', 'hat', 'headphones',
'hedgehog', 'helicopter', 'helmet', 'hexagon', 'hockey_puck', 'hockey_stick', 'horse', 'hospital',
'hot_air_balloon', 'hot_dog', 'hot_tub', 'hourglass', 'house', 'house_plant', 'hurricane', 'ice_cream',
'jacket', 'jail', 'kangaroo', 'key', 'keyboard', 'knee', 'knife', 'ladder', 'lantern', 'laptop', 'leaf',
'leg', 'light_bulb', 'lighter', 'lighthouse', 'lightning', 'line', 'lion', 'lipstick', 'lobster',
'lollipop', 'mailbox', 'map', 'marker', 'matches', 'megaphone', 'mermaid', 'microphone', 'microwave',
'monkey', 'moon', 'mosquito', 'motorbike', 'mountain', 'mouse', 'moustache', 'mouth', 'mug', 'mushroom',
'nail', 'necklace', 'nose', 'ocean', 'octagon', 'octopus', 'onion', 'oven', 'owl', 'paintbrush',
'paint_can', 'palm_tree', 'panda', 'pants', 'paper_clip', 'parachute', 'parrot', 'passport', 'peanut',
'pear', 'peas', 'pencil', 'penguin', 'piano', 'pickup_truck', 'picture_frame', 'pig', 'pillow',
'pineapple', 'pizza', 'pliers', 'police_car', 'pond', 'pool', 'popsicle', 'postcard', 'potato',
'power_outlet', 'purse', 'rabbit', 'raccoon', 'radio', 'rain', 'rainbow', 'rake', 'remote_control',
'rhinoceros', 'rifle', 'river', 'roller_coaster', 'rollerskates', 'sailboat', 'sandwich', 'saw',
'saxophone', 'school_bus', 'scissors', 'scorpion', 'screwdriver', 'sea_turtle', 'see_saw', 'shark',
'sheep', 'shoe', 'shorts', 'shovel', 'sink', 'skateboard', 'skull', 'skyscraper', 'sleeping_bag',
'smiley_face', 'snail', 'snake', 'snorkel', 'snowflake', 'snowman', 'soccer_ball', 'sock', 'speedboat',
'spider', 'spoon', 'spreadsheet', 'square', 'squiggle', 'squirrel', 'stairs', 'star', 'steak', 'stereo',
'stethoscope', 'stitches', 'stop_sign', 'stove', 'strawberry', 'streetlight', 'string_bean', 'submarine',
'suitcase', 'sun', 'swan', 'sweater', 'swing_set', 'sword', 'syringe', 'table', 'teapot', 'teddy-bear',
'telephone', 'television', 'tennis_racquet', 'tent', 'The_Eiffel_Tower', 'The_Great_Wall_of_China',
'The_Mona_Lisa', 'tiger', 'toaster', 'toe', 'toilet', 'tooth', 'toothbrush', 'toothpaste', 'tornado',
'tractor', 'traffic_light', 'train', 'tree', 'triangle', 'trombone', 'truck', 'trumpet', 't-shirt',
'umbrella', 'underwear', 'van', 'vase', 'violin', 'washing_machine', 'watermelon', 'waterslide',
'whale', 'wheel', 'windmill', 'wine_bottle', 'wine_glass', 'wristwatch', 'yoga', 'zebra', 'zigzag']
def __init__(self, root: str, task: str, split: Optional[str] = 'train', download: Optional[float] = False, **kwargs):
assert task in self.image_list
assert split in ['train', 'test']
data_list_file = os.path.join(root, "image_list", "{}_{}.txt".format(self.image_list[task], split))
print("loading {}".format(data_list_file))
if download:
list(map(lambda args: download_data(root, *args), self.download_list))
else:
list(map(lambda args: check_exits(root, args[0]), self.download_list))
super(DomainNet, self).__init__(root, DomainNet.CLASSES, data_list_file=data_list_file, **kwargs)
