add prototype imagenet dataset (#4640)

* add prototype imagenet dataset * add missing checksums * fix mypy * add human readable categories * cleanup * sort categories ascending based on wnid * remove accidentally added file * cleanup category file generation * fix mypy

add prototype imagenet dataset (#4640)
* add prototype imagenet dataset * add missing checksums * fix mypy * add human readable categories * cleanup * sort categories ascending based on wnid * remove accidentally added file * cleanup category file generation * fix mypy
58f313bb · Philip Meier · GitHub · 588c698d · 58f313bb · 58f313bb
Unverified Commit 58f313bb authored Oct 21, 2021 by Philip Meier Committed by GitHub Oct 21, 2021
6 changed files
--- a/torchvision/prototype/datasets/_builtin/__init__.py
+++ b/torchvision/prototype/datasets/_builtin/__init__.py
@@ -2,6 +2,7 @@ from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import Cifar10, Cifar100
 from .coco import Coco
+from .imagenet import ImageNet
 from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
 from .sbd import SBD
 from .voc import VOC
--- a/torchvision/prototype/datasets/_builtin/imagenet.categories
+++ b/torchvision/prototype/datasets/_builtin/imagenet.categories
+tench,n01440764
+goldfish,n01443537
+great white shark,n01484850
+tiger shark,n01491361
+hammerhead,n01494475
+electric ray,n01496331
+stingray,n01498041
+cock,n01514668
+hen,n01514859
+ostrich,n01518878
+brambling,n01530575
+goldfinch,n01531178
+house finch,n01532829
+junco,n01534433
+indigo bunting,n01537544
+robin,n01558993
+bulbul,n01560419
+jay,n01580077
+magpie,n01582220
+chickadee,n01592084
+water ouzel,n01601694
+kite,n01608432
+bald eagle,n01614925
+vulture,n01616318
+great grey owl,n01622779
+European fire salamander,n01629819
+common newt,n01630670
+eft,n01631663
+spotted salamander,n01632458
+axolotl,n01632777
+bullfrog,n01641577
+tree frog,n01644373
+tailed frog,n01644900
+loggerhead,n01664065
+leatherback turtle,n01665541
+mud turtle,n01667114
+terrapin,n01667778
+box turtle,n01669191
+banded gecko,n01675722
+common iguana,n01677366
+American chameleon,n01682714
+whiptail,n01685808
+agama,n01687978
+frilled lizard,n01688243
+alligator lizard,n01689811
+Gila monster,n01692333
+green lizard,n01693334
+African chameleon,n01694178
+Komodo dragon,n01695060
+African crocodile,n01697457
+American alligator,n01698640
+triceratops,n01704323
+thunder snake,n01728572
+ringneck snake,n01728920
+hognose snake,n01729322
+green snake,n01729977
+king snake,n01734418
+garter snake,n01735189
+water snake,n01737021
+vine snake,n01739381
+night snake,n01740131
+boa constrictor,n01742172
+rock python,n01744401
+Indian cobra,n01748264
+green mamba,n01749939
+sea snake,n01751748
+horned viper,n01753488
+diamondback,n01755581
+sidewinder,n01756291
+trilobite,n01768244
+harvestman,n01770081
+scorpion,n01770393
+black and gold garden spider,n01773157
+barn spider,n01773549
+garden spider,n01773797
+black widow,n01774384
+tarantula,n01774750
+wolf spider,n01775062
+tick,n01776313
+centipede,n01784675
+black grouse,n01795545
+ptarmigan,n01796340
+ruffed grouse,n01797886
+prairie chicken,n01798484
+peacock,n01806143
+quail,n01806567
+partridge,n01807496
+African grey,n01817953
+macaw,n01818515
+sulphur-crested cockatoo,n01819313
+lorikeet,n01820546
+coucal,n01824575
+bee eater,n01828970
+hornbill,n01829413
+hummingbird,n01833805
+jacamar,n01843065
+toucan,n01843383
+drake,n01847000
+red-breasted merganser,n01855032
+goose,n01855672
+black swan,n01860187
+tusker,n01871265
+echidna,n01872401
+platypus,n01873310
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+koala,n01882714
+wombat,n01883070
+jellyfish,n01910747
+sea anemone,n01914609
+brain coral,n01917289
+flatworm,n01924916
+nematode,n01930112
+conch,n01943899
+snail,n01944390
+slug,n01945685
+sea slug,n01950731
+chiton,n01955084
+chambered nautilus,n01968897
+Dungeness crab,n01978287
+rock crab,n01978455
+fiddler crab,n01980166
+king crab,n01981276
+American lobster,n01983481
+spiny lobster,n01984695
+crayfish,n01985128
+hermit crab,n01986214
+isopod,n01990800
+white stork,n02002556
+black stork,n02002724
+spoonbill,n02006656
+flamingo,n02007558
+little blue heron,n02009229
+American egret,n02009912
+bittern,n02011460
+crane,n02012849
+limpkin,n02013706
+European gallinule,n02017213
+American coot,n02018207
+bustard,n02018795
+ruddy turnstone,n02025239
+red-backed sandpiper,n02027492
+redshank,n02028035
+dowitcher,n02033041
+oystercatcher,n02037110
+pelican,n02051845
+king penguin,n02056570
+albatross,n02058221
+grey whale,n02066245
+killer whale,n02071294
+dugong,n02074367
+sea lion,n02077923
+Chihuahua,n02085620
+Japanese spaniel,n02085782
+Maltese dog,n02085936
+Pekinese,n02086079
+Shih-Tzu,n02086240
+Blenheim spaniel,n02086646
+papillon,n02086910
+toy terrier,n02087046
+Rhodesian ridgeback,n02087394
+Afghan hound,n02088094
+basset,n02088238
+beagle,n02088364
+bloodhound,n02088466
+bluetick,n02088632
+black-and-tan coonhound,n02089078
+Walker hound,n02089867
+English foxhound,n02089973
+redbone,n02090379
+borzoi,n02090622
+Irish wolfhound,n02090721
+Italian greyhound,n02091032
+whippet,n02091134
+Ibizan hound,n02091244
+Norwegian elkhound,n02091467
+otterhound,n02091635
+Saluki,n02091831
+Scottish deerhound,n02092002
+Weimaraner,n02092339
+Staffordshire bullterrier,n02093256
+American Staffordshire terrier,n02093428
+Bedlington terrier,n02093647
+Border terrier,n02093754
+Kerry blue terrier,n02093859
+Irish terrier,n02093991
+Norfolk terrier,n02094114
+Norwich terrier,n02094258
+Yorkshire terrier,n02094433
+wire-haired fox terrier,n02095314
+Lakeland terrier,n02095570
+Sealyham terrier,n02095889
+Airedale,n02096051
+cairn,n02096177
+Australian terrier,n02096294
+Dandie Dinmont,n02096437
+Boston bull,n02096585
+miniature schnauzer,n02097047
+giant schnauzer,n02097130
+standard schnauzer,n02097209
+Scotch terrier,n02097298
+Tibetan terrier,n02097474
+silky terrier,n02097658
+soft-coated wheaten terrier,n02098105
+West Highland white terrier,n02098286
+Lhasa,n02098413
+flat-coated retriever,n02099267
+curly-coated retriever,n02099429
+golden retriever,n02099601
+Labrador retriever,n02099712
+Chesapeake Bay retriever,n02099849
+German short-haired pointer,n02100236
+vizsla,n02100583
+English setter,n02100735
+Irish setter,n02100877
+Gordon setter,n02101006
+Brittany spaniel,n02101388
+clumber,n02101556
+English springer,n02102040
+Welsh springer spaniel,n02102177
+cocker spaniel,n02102318
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+kuvasz,n02104029
+schipperke,n02104365
+groenendael,n02105056
+malinois,n02105162
+briard,n02105251
+kelpie,n02105412
+komondor,n02105505
+Old English sheepdog,n02105641
+Shetland sheepdog,n02105855
+collie,n02106030
+Border collie,n02106166
+Bouvier des Flandres,n02106382
+Rottweiler,n02106550
+German shepherd,n02106662
+Doberman,n02107142
+miniature pinscher,n02107312
+Greater Swiss Mountain dog,n02107574
+Bernese mountain dog,n02107683
+Appenzeller,n02107908
+EntleBucher,n02108000
+boxer,n02108089
+bull mastiff,n02108422
+Tibetan mastiff,n02108551
+French bulldog,n02108915
+Great Dane,n02109047
+Saint Bernard,n02109525
+Eskimo dog,n02109961
+malamute,n02110063
+Siberian husky,n02110185
+dalmatian,n02110341
+affenpinscher,n02110627
+basenji,n02110806
+pug,n02110958
+Leonberg,n02111129
+Newfoundland,n02111277
+Great Pyrenees,n02111500
+Samoyed,n02111889
+Pomeranian,n02112018
+chow,n02112137
+keeshond,n02112350
+Brabancon griffon,n02112706
+Pembroke,n02113023
+Cardigan,n02113186
+toy poodle,n02113624
+miniature poodle,n02113712
+standard poodle,n02113799
+Mexican hairless,n02113978
+timber wolf,n02114367
+white wolf,n02114548
+red wolf,n02114712
+coyote,n02114855
+dingo,n02115641
+dhole,n02115913
+African hunting dog,n02116738
+hyena,n02117135
+red fox,n02119022
+kit fox,n02119789
+Arctic fox,n02120079
+grey fox,n02120505
+tabby,n02123045
+tiger cat,n02123159
+Persian cat,n02123394
+Siamese cat,n02123597
+Egyptian cat,n02124075
+cougar,n02125311
+lynx,n02127052
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+snow leopard,n02128757
+jaguar,n02128925
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+cheetah,n02130308
+brown bear,n02132136
+American black bear,n02133161
+ice bear,n02134084
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+bee,n02206856
+ant,n02219486
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+cricket,n02229544
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+mantis,n02236044
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+hog,n02395406
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+hippopotamus,n02398521
+ox,n02403003
+water buffalo,n02408429
+bison,n02410509
+ram,n02412080
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+hartebeest,n02422106
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+llama,n02437616
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+mink,n02442845
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+black-footed ferret,n02443484
+otter,n02444819
+skunk,n02445715
+badger,n02447366
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+three-toed sloth,n02457408
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+chimpanzee,n02481823
+gibbon,n02483362
+siamang,n02483708
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+baboon,n02486410
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+titi,n02493509
+spider monkey,n02493793
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+Madagascar cat,n02497673
+indri,n02500267
+Indian elephant,n02504013
+African elephant,n02504458
+lesser panda,n02509815
+giant panda,n02510455
+barracouta,n02514041
+eel,n02526121
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+rock beauty,n02606052
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+panpipe,n03884397
+paper towel,n03887697
+parachute,n03888257
+parallel bars,n03888605
+park bench,n03891251
+parking meter,n03891332
+passenger car,n03895866
+patio,n03899768
+pay-phone,n03902125
+pedestal,n03903868
+pencil box,n03908618
+pencil sharpener,n03908714
+perfume,n03916031
+Petri dish,n03920288
+photocopier,n03924679
+pick,n03929660
+pickelhaube,n03929855
+picket fence,n03930313
+pickup,n03930630
+pier,n03933933
+piggy bank,n03935335
+pill bottle,n03937543
+pillow,n03938244
+ping-pong ball,n03942813
+pinwheel,n03944341
+pirate,n03947888
+pitcher,n03950228
+plane,n03954731
+planetarium,n03956157
+plastic bag,n03958227
+plate rack,n03961711
+plow,n03967562
+plunger,n03970156
+Polaroid camera,n03976467
+pole,n03976657
+police van,n03977966
+poncho,n03980874
+pool table,n03982430
+pop bottle,n03983396
+pot,n03991062
+potter's wheel,n03992509
+power drill,n03995372
+prayer rug,n03998194
+printer,n04004767
+prison,n04005630
+projectile,n04008634
+projector,n04009552
+puck,n04019541
+punching bag,n04023962
+purse,n04026417
+quill,n04033901
+quilt,n04033995
+racer,n04037443
+racket,n04039381
+radiator,n04040759
+radio,n04041544
+radio telescope,n04044716
+rain barrel,n04049303
+recreational vehicle,n04065272
+reel,n04067472
+reflex camera,n04069434
+refrigerator,n04070727
+remote control,n04074963
+restaurant,n04081281
+revolver,n04086273
+rifle,n04090263
+rocking chair,n04099969
+rotisserie,n04111531
+rubber eraser,n04116512
+rugby ball,n04118538
+rule,n04118776
+running shoe,n04120489
+safe,n04125021
+safety pin,n04127249
+saltshaker,n04131690
+sandal,n04133789
+sarong,n04136333
+sax,n04141076
+scabbard,n04141327
+scale,n04141975
+school bus,n04146614
+schooner,n04147183
+scoreboard,n04149813
+screen,n04152593
+screw,n04153751
+screwdriver,n04154565
+seat belt,n04162706
+sewing machine,n04179913
+shield,n04192698
+shoe shop,n04200800
+shoji,n04201297
+shopping basket,n04204238
+shopping cart,n04204347
+shovel,n04208210
+shower cap,n04209133
+shower curtain,n04209239
+ski,n04228054
+ski mask,n04229816
+sleeping bag,n04235860
+slide rule,n04238763
+sliding door,n04239074
+slot,n04243546
+snorkel,n04251144
+snowmobile,n04252077
+snowplow,n04252225
+soap dispenser,n04254120
+soccer ball,n04254680
+sock,n04254777
+solar dish,n04258138
+sombrero,n04259630
+soup bowl,n04263257
+space bar,n04264628
+space heater,n04265275
+space shuttle,n04266014
+spatula,n04270147
+speedboat,n04273569
+spider web,n04275548
+spindle,n04277352
+sports car,n04285008
+spotlight,n04286575
+stage,n04296562
+steam locomotive,n04310018
+steel arch bridge,n04311004
+steel drum,n04311174
+stethoscope,n04317175
+stole,n04325704
+stone wall,n04326547
+stopwatch,n04328186
+stove,n04330267
+strainer,n04332243
+streetcar,n04335435
+stretcher,n04336792
+studio couch,n04344873
+stupa,n04346328
+submarine,n04347754
+suit,n04350905
+sundial,n04355338
+sunglass,n04355933
+sunglasses,n04356056
+sunscreen,n04357314
+suspension bridge,n04366367
+swab,n04367480
+sweatshirt,n04370456
+swimming trunks,n04371430
+swing,n04371774
+switch,n04372370
+syringe,n04376876
+table lamp,n04380533
+tank,n04389033
+tape player,n04392985
+teapot,n04398044
+teddy,n04399382
+television,n04404412
+tennis ball,n04409515
+thatch,n04417672
+theater curtain,n04418357
+thimble,n04423845
+thresher,n04428191
+throne,n04429376
+tile roof,n04435653
+toaster,n04442312
+tobacco shop,n04443257
+toilet seat,n04447861
+torch,n04456115
+totem pole,n04458633
+tow truck,n04461696
+toyshop,n04462240
+tractor,n04465501
+trailer truck,n04467665
+tray,n04476259
+trench coat,n04479046
+tricycle,n04482393
+trimaran,n04483307
+tripod,n04485082
+triumphal arch,n04486054
+trolleybus,n04487081
+trombone,n04487394
+tub,n04493381
+turnstile,n04501370
+typewriter keyboard,n04505470
+umbrella,n04507155
+unicycle,n04509417
+upright,n04515003
+vacuum,n04517823
+vase,n04522168
+vault,n04523525
+velvet,n04525038
+vending machine,n04525305
+vestment,n04532106
+viaduct,n04532670
+violin,n04536866
+volleyball,n04540053
+waffle iron,n04542943
+wall clock,n04548280
+wallet,n04548362
+wardrobe,n04550184
+warplane,n04552348
+washbasin,n04553703
+washer,n04554684
+water bottle,n04557648
+water jug,n04560804
+water tower,n04562935
+whiskey jug,n04579145
+whistle,n04579432
+wig,n04584207
+window screen,n04589890
+window shade,n04590129
+Windsor tie,n04591157
+wine bottle,n04591713
+wing,n04592741
+wok,n04596742
+wooden spoon,n04597913
+wool,n04599235
+worm fence,n04604644
+wreck,n04606251
+yawl,n04612504
+yurt,n04613696
+web site,n06359193
+comic book,n06596364
+crossword puzzle,n06785654
+street sign,n06794110
+traffic light,n06874185
+book jacket,n07248320
+menu,n07565083
+plate,n07579787
+guacamole,n07583066
+consomme,n07584110
+hot pot,n07590611
+trifle,n07613480
+ice cream,n07614500
+ice lolly,n07615774
+French loaf,n07684084
+bagel,n07693725
+pretzel,n07695742
+cheeseburger,n07697313
+hotdog,n07697537
+mashed potato,n07711569
+head cabbage,n07714571
+broccoli,n07714990
+cauliflower,n07715103
+zucchini,n07716358
+spaghetti squash,n07716906
+acorn squash,n07717410
+butternut squash,n07717556
+cucumber,n07718472
+artichoke,n07718747
+bell pepper,n07720875
+cardoon,n07730033
+mushroom,n07734744
+Granny Smith,n07742313
+strawberry,n07745940
+orange,n07747607
+lemon,n07749582
+fig,n07753113
+pineapple,n07753275
+banana,n07753592
+jackfruit,n07754684
+custard apple,n07760859
+pomegranate,n07768694
+hay,n07802026
+carbonara,n07831146
+chocolate sauce,n07836838
+dough,n07860988
+meat loaf,n07871810
+pizza,n07873807
+potpie,n07875152
+burrito,n07880968
+red wine,n07892512
+espresso,n07920052
+cup,n07930864
+eggnog,n07932039
+alp,n09193705
+bubble,n09229709
+cliff,n09246464
+coral reef,n09256479
+geyser,n09288635
+lakeside,n09332890
+promontory,n09399592
+sandbar,n09421951
+seashore,n09428293
+valley,n09468604
+volcano,n09472597
+ballplayer,n09835506
+groom,n10148035
+scuba diver,n10565667
+rapeseed,n11879895
+daisy,n11939491
+yellow lady's slipper,n12057211
+corn,n12144580
+acorn,n12267677
+hip,n12620546
+buckeye,n12768682
+coral fungus,n12985857
+agaric,n12998815
+gyromitra,n13037406
+stinkhorn,n13040303
+earthstar,n13044778
+hen-of-the-woods,n13052670
+bolete,n13054560
+ear,n13133613
+toilet tissue,n15075141
--- a/torchvision/prototype/datasets/_builtin/imagenet.py
+++ b/torchvision/prototype/datasets/_builtin/imagenet.py
+import io
+import pathlib
+import re
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torchdata.datapipes.iter import IterDataPipe, LineReader, KeyZipper, Mapper, TarArchiveReader, Filter, Shuffler
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    HttpResource,
+    OnlineResource,
+    DatasetType,
+)
+from torchvision.prototype.datasets.utils._internal import (
+    INFINITE_BUFFER_SIZE,
+    BUILTIN_DIR,
+    path_comparator,
+    Enumerator,
+    getitem,
+    read_mat,
+    FrozenMapping,
+)
+
+
+class ImageNet(Dataset):
+    @property
+    def info(self) -> DatasetInfo:
+        name = "imagenet"
+        categories, wnids = zip(*DatasetInfo.read_categories_file(BUILTIN_DIR / f"{name}.categories"))
+
+        return DatasetInfo(
+            name,
+            type=DatasetType.IMAGE,
+            categories=categories,
+            homepage="https://www.image-net.org/",
+            valid_options=dict(split=("train", "val")),
+            extra=dict(
+                wnid_to_category=FrozenMapping(zip(wnids, categories)),
+                category_to_wnid=FrozenMapping(zip(categories, wnids)),
+            ),
+        )
+
+    @property
+    def category_to_wnid(self) -> Dict[str, str]:
+        return self.info.extra.category_to_wnid
+
+    @property
+    def wnid_to_category(self) -> Dict[str, str]:
+        return self.info.extra.wnid_to_category
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        if config.split == "train":
+            images = HttpResource(
+                "ILSVRC2012_img_train.tar",
+                sha256="b08200a27a8e34218a0e58fde36b0fe8f73bc377f4acea2d91602057c3ca45bb",
+            )
+        else:  # config.split == "val"
+            images = HttpResource(
+                "ILSVRC2012_img_val.tar",
+                sha256="c7e06a6c0baccf06d8dbeb6577d71efff84673a5dbdd50633ab44f8ea0456ae0",
+            )
+
+        devkit = HttpResource(
+            "ILSVRC2012_devkit_t12.tar.gz",
+            sha256="b59243268c0d266621fd587d2018f69e906fb22875aca0e295b48cafaa927953",
+        )
+
+        return [images, devkit]
+
+    _TRAIN_IMAGE_NAME_PATTERN = re.compile(r"(?P<wnid>n\d{8})_\d+[.]JPEG")
+
+    def _collate_train_data(self, data: Tuple[str, io.IOBase]) -> Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]]:
+        path = pathlib.Path(data[0])
+        wnid = self._TRAIN_IMAGE_NAME_PATTERN.match(path.name).group("wnid")  # type: ignore[union-attr]
+        category = self.wnid_to_category[wnid]
+        label = self.categories.index(category)
+        return (label, category, wnid), data
+
+    _VAL_IMAGE_NAME_PATTERN = re.compile(r"ILSVRC2012_val_(?P<id>\d{8})[.]JPEG")
+
+    def _val_image_key(self, data: Tuple[str, Any]) -> int:
+        path = pathlib.Path(data[0])
+        return int(self._VAL_IMAGE_NAME_PATTERN.match(path.name).group("id"))  # type: ignore[union-attr]
+
+    def _collate_val_data(
+        self, data: Tuple[Tuple[int, int], Tuple[str, io.IOBase]]
+    ) -> Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]]:
+        label_data, image_data = data
+        _, label = label_data
+        category = self.categories[label]
+        wnid = self.category_to_wnid[category]
+        return (label, category, wnid), image_data
+
+    def _collate_and_decode_sample(
+        self,
+        data: Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]],
+        *,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> Dict[str, Any]:
+        ann_data, image_data = data
+        label, category, wnid = ann_data
+        path, buffer = image_data
+        return dict(
+            path=path,
+            image=decoder(buffer) if decoder else buffer,
+            label=torch.tensor(label),
+            category=category,
+            wnid=wnid,
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> IterDataPipe[Dict[str, Any]]:
+        images_dp, devkit_dp = resource_dps
+
+        images_dp = TarArchiveReader(images_dp)
+
+        if config.split == "train":
+            # the train archive is a tar of tars
+            dp = TarArchiveReader(images_dp)
+            # dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
+            dp = Mapper(dp, self._collate_train_data)
+        else:
+            devkit_dp = TarArchiveReader(devkit_dp)
+            devkit_dp = Filter(devkit_dp, path_comparator("name", "ILSVRC2012_validation_ground_truth.txt"))
+            devkit_dp = LineReader(devkit_dp, return_path=False)
+            devkit_dp = Mapper(devkit_dp, int)
+            devkit_dp = Enumerator(devkit_dp, 1)
+            devkit_dp = Shuffler(devkit_dp, buffer_size=INFINITE_BUFFER_SIZE)
+
+            dp = KeyZipper(
+                devkit_dp,
+                images_dp,
+                key_fn=getitem(0),
+                ref_key_fn=self._val_image_key,
+                buffer_size=INFINITE_BUFFER_SIZE,
+            )
+            dp = Mapper(dp, self._collate_val_data)
+
+        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+
+    # Although the WordNet IDs (wnids) are unique, the corresponding categories are not. For example, both n02012849
+    # and n03126707 are labeled 'crane' while the first means the bird and the latter means the construction equipment
+    _WNID_MAP = {
+        "n03126707": "construction crane",
+        "n03710721": "tank suit",
+    }
+
+    def _generate_categories(self, root: pathlib.Path) -> List[Tuple[str, str]]:
+        resources = self.resources(self.default_config)
+        devkit_dp = resources[1].to_datapipe(root / self.name)
+        devkit_dp = TarArchiveReader(devkit_dp)
+        devkit_dp = Filter(devkit_dp, path_comparator("name", "meta.mat"))
+
+        meta = next(iter(devkit_dp))[1]
+        synsets = read_mat(meta, squeeze_me=True)["synsets"]
+        categories_and_wnids = [
+            (self._WNID_MAP.get(wnid, category.split(",", 1)[0]), wnid)
+            for _, wnid, category, _, num_children, *_ in synsets
+            # if num_children > 0, we are looking at a superclass that has no direct instance
+            if num_children == 0
+        ]
+        categories_and_wnids.sort(key=lambda category_and_wnid: category_and_wnid[1])
+
+        return categories_and_wnids
--- a/torchvision/prototype/datasets/generate_category_files.py
+++ b/torchvision/prototype/datasets/generate_category_files.py
 import argparse
+import csv
 import sys
-import unittest.mock
-import warnings
-
-with warnings.catch_warnings():
-    warnings.filterwarnings("ignore", message=r"The categories file .+? does not exist.", category=UserWarning)
-
-    from torchvision.prototype import datasets

+from torchvision.prototype import datasets
 from torchvision.prototype.datasets._api import find
 from torchvision.prototype.datasets.utils._internal import BUILTIN_DIR

@@ -16,21 +11,18 @@ def main(*names, force=False):
    root = datasets.home()

    for name in names:
-        file = BUILTIN_DIR / f"{name}.categories"
-        if file.exists() and not force:
+        path = BUILTIN_DIR / f"{name}.categories"
+        if path.exists() and not force:
            continue

        dataset = find(name)
        try:
-            with unittest.mock.patch(
-                "torchvision.prototype.datasets.utils._dataset.DatasetInfo._read_categories_file", return_value=[]
-            ):
-                categories = dataset._generate_categories(root)
+            categories = dataset._generate_categories(root)
        except NotImplementedError:
            continue

-        with open(file, "w") as fh:
-            fh.write("\n".join(categories) + "\n")
+        with open(path, "w", newline="") as file:
+            csv.writer(file).writerows(categories)


 def parse_args(argv=None):

--- a/torchvision/prototype/datasets/utils/_dataset.py
+++ b/torchvision/prototype/datasets/utils/_dataset.py
 import abc
+import csv
 import enum
 import io
-import os
 import pathlib
-import textwrap
-import warnings
-from collections.abc import Mapping
 from typing import (
    Any,
    Callable,
@@ -14,8 +11,6 @@ from typing import (
    Optional,
    Sequence,
    Union,
-    NoReturn,
-    Iterable,
    Tuple,
 )

@@ -26,76 +21,17 @@ from torchvision.prototype.datasets.utils._internal import (
    sequence_to_str,
 )

+from ._internal import FrozenBunch, make_repr
 from ._resource import OnlineResource


-def make_repr(name: str, items: Iterable[Tuple[str, Any]]):
-    def to_str(sep: str) -> str:
-        return sep.join([f"{key}={value}" for key, value in items])
-
-    prefix = f"{name}("
-    postfix = ")"
-    body = to_str(", ")
-
-    line_length = int(os.environ.get("COLUMNS", 80))
-    body_too_long = (len(prefix) + len(body) + len(postfix)) > line_length
-    multiline_body = len(str(body).splitlines()) > 1
-    if not (body_too_long or multiline_body):
-        return prefix + body + postfix
-
-    body = textwrap.indent(to_str(",\n"), " " * 2)
-    return f"{prefix}\n{body}\n{postfix}"
-
-
 class DatasetType(enum.Enum):
    RAW = enum.auto()
    IMAGE = enum.auto()


-class DatasetConfig(Mapping):
-    def __init__(self, *args, **kwargs):
-        data = dict(*args, **kwargs)
-        self.__dict__["__data__"] = data
-        self.__dict__["__final_hash__"] = hash(tuple(data.items()))
-
-    def __getitem__(self, name: str) -> Any:
-        return self.__dict__["__data__"][name]
-
-    def __iter__(self):
-        return iter(self.__dict__["__data__"].keys())
-
-    def __len__(self):
-        return len(self.__dict__["__data__"])
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError as error:
-            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") from error
-
-    def __setitem__(self, key: Any, value: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __setattr__(self, key: Any, value: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __delitem__(self, key: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __delattr__(self, item: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __hash__(self) -> int:
-        return self.__dict__["__final_hash__"]
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, DatasetConfig):
-            return NotImplemented
-
-        return hash(self) == hash(other)
-
-    def __repr__(self) -> str:
-        return make_repr(type(self).__name__, self.items())
+class DatasetConfig(FrozenBunch):
+    pass


 class DatasetInfo:
@@ -109,6 +45,7 @@ class DatasetInfo:
        homepage: Optional[str] = None,
        license: Optional[str] = None,
        valid_options: Optional[Dict[str, Sequence]] = None,
+        extra: Optional[Dict[str, Any]] = None,
    ) -> None:
        self.name = name.lower()
        self.type = DatasetType[type.upper()] if isinstance(type, str) else type
@@ -118,7 +55,8 @@ class DatasetInfo:
        elif isinstance(categories, int):
            categories = [str(label) for label in range(categories)]
        elif isinstance(categories, (str, pathlib.Path)):
-            categories = self._read_categories_file(pathlib.Path(categories).expanduser().resolve())
+            path = pathlib.Path(categories).expanduser().resolve()
+            categories, *_ = zip(*self.read_categories_file(path))
        self.categories = tuple(categories)

        self.citation = citation
@@ -137,16 +75,12 @@ class DatasetInfo:
            )
        self._valid_options: Dict[str, Sequence] = valid_options

-    @staticmethod
-    def _read_categories_file(path: pathlib.Path) -> List[str]:
-        if not path.exists() or not path.is_file():
-            warnings.warn(
-                f"The categories file {path} does not exist. Continuing without loaded categories.", UserWarning
-            )
-            return []
+        self.extra = FrozenBunch(extra or dict())

-        with open(path, "r") as file:
-            return [line.strip() for line in file]
+    @staticmethod
+    def read_categories_file(path: pathlib.Path) -> List[List[str]]:
+        with open(path, "r", newline="") as file:
+            return [row for row in csv.reader(file)]

    @property
    def default_config(self) -> DatasetConfig:
@@ -231,5 +165,5 @@ class Dataset(abc.ABC):
        resource_dps = [resource.to_datapipe(root) for resource in self.resources(config)]
        return self._make_datapipe(resource_dps, config=config, decoder=decoder)

-    def _generate_categories(self, root: pathlib.Path) -> Sequence[str]:
+    def _generate_categories(self, root: pathlib.Path) -> Sequence[Union[str, Sequence[str]]]:
        raise NotImplementedError
--- a/torchvision/prototype/datasets/utils/_internal.py
+++ b/torchvision/prototype/datasets/utils/_internal.py
 import collections.abc
+import csv
 import difflib
 import enum
 import gzip
 import io
 import lzma
+import os
 import os.path
 import pathlib
-from typing import Collection, Sequence, Callable, Union, Any, Tuple, TypeVar, Iterator, Dict, Optional
+import textwrap
+from collections.abc import Mapping
+from typing import (
+    Collection,
+    Sequence,
+    Callable,
+    Union,
+    Any,
+    Tuple,
+    TypeVar,
+    Iterator,
+    Dict,
+    Optional,
+    NoReturn,
+    Iterable,
+)

 import numpy as np
 import PIL.Image
@@ -18,6 +35,10 @@ __all__ = [
    "BUILTIN_DIR",
    "sequence_to_str",
    "add_suggestion",
+    "make_repr",
+    "FrozenMapping",
+    "FrozenBunch",
+    "create_categories_file",
    "read_mat",
    "image_buffer_from_array",
    "SequenceIterator",
@@ -62,6 +83,82 @@ def add_suggestion(
    return f"{msg.strip()} {hint}"


+def make_repr(name: str, items: Iterable[Tuple[str, Any]]):
+    def to_str(sep: str) -> str:
+        return sep.join([f"{key}={value}" for key, value in items])
+
+    prefix = f"{name}("
+    postfix = ")"
+    body = to_str(", ")
+
+    line_length = int(os.environ.get("COLUMNS", 80))
+    body_too_long = (len(prefix) + len(body) + len(postfix)) > line_length
+    multiline_body = len(str(body).splitlines()) > 1
+    if not (body_too_long or multiline_body):
+        return prefix + body + postfix
+
+    body = textwrap.indent(to_str(",\n"), " " * 2)
+    return f"{prefix}\n{body}\n{postfix}"
+
+
+class FrozenMapping(Mapping):
+    def __init__(self, *args, **kwargs):
+        data = dict(*args, **kwargs)
+        self.__dict__["__data__"] = data
+        self.__dict__["__final_hash__"] = hash(tuple(data.items()))
+
+    def __getitem__(self, name: str) -> Any:
+        return self.__dict__["__data__"][name]
+
+    def __iter__(self):
+        return iter(self.__dict__["__data__"].keys())
+
+    def __len__(self):
+        return len(self.__dict__["__data__"])
+
+    def __setitem__(self, key: Any, value: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __delitem__(self, key: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __hash__(self) -> int:
+        return self.__dict__["__final_hash__"]
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, FrozenMapping):
+            return NotImplemented
+
+        return hash(self) == hash(other)
+
+    def __repr__(self):
+        return repr(self.__dict__["__data__"])
+
+
+class FrozenBunch(FrozenMapping):
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError as error:
+            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") from error
+
+    def __setattr__(self, key: Any, value: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __delattr__(self, item: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __repr__(self) -> str:
+        return make_repr(type(self).__name__, self.items())
+
+
+def create_categories_file(
+    root: Union[str, pathlib.Path], name: str, categories: Sequence[Union[str, Sequence[str]]], **fmtparams: Any
+) -> None:
+    with open(pathlib.Path(root) / f"{name}.categories", "w", newline="") as file:
+        csv.writer(file, **fmtparams).writerows(categories)
+
+
 def read_mat(buffer: io.IOBase, **kwargs: Any) -> Any:
    try:
        import scipy.io as sio