-
Notifications
You must be signed in to change notification settings - Fork 3
/
Datasets.py
396 lines (346 loc) · 26 KB
/
Datasets.py
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
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
import os
import torch
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler, SequentialSampler
import matplotlib.pyplot as plt
import numpy as np
class ClassificationDataset:
def __init__(self, data_dir, class_labels, shape, testset_size, trainset_size, expected_files):
# Basic Dataset Info
self._class_labels = tuple(class_labels)
self._shape = tuple(shape)
self._testset_size = testset_size
self._trainset_size = trainset_size
self._data_dir = data_dir
if not isinstance(expected_files, list):
self._expected_files = [expected_files]
else:
self._expected_files = expected_files
self._download = True if any(
not os.path.isfile(os.path.join(self._data_dir, file)) for file in self._expected_files) else False
def name(self):
assert self.__class__.__name__ != 'ClassificationDataset'
return self.__class__.__name__
def num_classes(self):
return len(self._class_labels)
def class_labels(self):
return self._class_labels
def input_channels(self):
return self._shape[0]
def shape(self):
return self._shape
def max_test_size(self):
return self._testset_size
def max_train_size(self):
return self._trainset_size
def testset(self, batch_size, max_samples=None, device='cuda'):
if device.lower() == 'cuda' and torch.cuda.is_available():
num_workers, pin_memory = 32, True
else:
print('Warning: Did not find working GPU - Loading dataset on CPU')
num_workers, pin_memory = 4, False
test_dataset = self._test_importer()
if max_samples is None or max_samples >= self._testset_size:
testset_sz = self._testset_size
test_sampler = None
else:
testset_sz = max_samples
test_sampler = SequentialSampler(list(range(max_samples)))
test_gen = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, sampler=test_sampler,
num_workers=num_workers, pin_memory=pin_memory)
return test_gen, testset_sz
def trainset(self, batch_size=128, valid_size=0.0, max_samples=None, augment=True, shuffle=True, random_seed=None,
device='cuda'):
if device.lower() == 'cuda' and torch.cuda.is_available():
num_workers, pin_memory = 32, True
else:
print('Warning: Did not find working GPU - Loading dataset on CPU')
num_workers, pin_memory = 4, False
max_samples = self._trainset_size if max_samples is None else min(self._trainset_size, max_samples)
assert ((valid_size >= 0) and (valid_size <= 1)), "Valid_size should be in the range [0, 1]."
train_dataset = self._train_importer(augment)
val_dataset = self._train_importer(False) # Don't augment validation
indices = list(range(self._trainset_size))
if shuffle:
if random_seed is not None:
np.random.seed(random_seed)
np.random.shuffle(indices)
indices = indices[:max_samples] # Truncate to desired size
split = int(np.floor(valid_size * max_samples)) # Split validation
train_ids, valid_ids = indices[split:], indices[:split]
num_train = len(train_ids)
num_valid = len(valid_ids)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
sampler=SubsetRandomSampler(train_ids), num_workers=num_workers,
pin_memory=pin_memory)
valid_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size,
sampler=SubsetRandomSampler(valid_ids), num_workers=num_workers,
pin_memory=pin_memory)
return (train_loader, num_train), (valid_loader, num_valid)
def _train_importer(self, augment=True):
raise NotImplementedError
def _test_importer(self):
raise NotImplementedError
class CIFAR100(ClassificationDataset):
def __init__(self, data_dir):
super().__init__(
data_dir=data_dir,
class_labels=CIFAR100_NAMES,
shape=(3, 32, 32),
testset_size=10000,
trainset_size=50000,
expected_files=os.path.join('CIFAR100', 'cifar-100-python.tar.gz')
)
def _train_importer(self, augment=True):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if augment:
ops = [transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize]
else:
ops = [transforms.ToTensor(),
normalize]
return datasets.CIFAR100(root=os.path.join(self._data_dir, 'CIFAR100'), train=True, download=self._download,
transform=transforms.Compose(ops))
def _test_importer(self):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
ops = [transforms.ToTensor(), normalize]
return datasets.CIFAR100(root=os.path.join(self._data_dir, 'CIFAR100'), train=False, download=self._download,
transform=transforms.Compose(ops))
class ImageNet(ClassificationDataset):
def __init__(self, data_dir):
super().__init__(data_dir=data_dir,
class_labels=IMAGENET_LABEL_NAMES,
shape=(3, 224, 224),
testset_size=50000,
trainset_size=1200000,
expected_files=[os.path.join('ImageNet', 'train'), os.path.join('ImageNet', 'val')])
def _train_importer(self, augment=True):
normalize = transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
if augment:
ops = [transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize]
else:
ops = [transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize]
return datasets.ImageFolder(root=os.path.join(self._data_dir, 'train'),
transform=transforms.Compose(ops))
def _test_importer(self):
normalize = transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
ops = [transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize]
return datasets.ImageFolder(root=os.path.join(self._data_dir, 'val'),
transform=transforms.Compose(ops))
class Datasets:
_implemented = {
'CIFAR100': CIFAR100,
'ImageNet': ImageNet
}
@staticmethod
def which():
return tuple(Datasets._implemented.keys())
@staticmethod
def get(dataset_name, data_dir):
return Datasets._implemented[dataset_name](data_dir)
IMAGENET_LABEL_NAMES = ('kit_fox', 'english_setter', 'siberian_husky', 'australian_terrier',
'english_springer', 'grey_whale', 'lesser_panda', 'egyptian_cat', 'ibex',
'persian_cat', 'cougar', 'gazelle', 'porcupine', 'sea_lion', 'malamute',
'badger', 'great_dane', 'walker_hound', 'welsh_springer_spaniel', 'whippet',
'scottish_deerhound', 'killer_whale', 'mink', 'african_elephant', 'weimaraner',
'soft_coated_wheaten_terrier', 'dandie_dinmont', 'red_wolf',
'old_english_sheepdog', 'jaguar', 'otterhound', 'bloodhound', 'airedale',
'hyena', 'meerkat', 'giant_schnauzer', 'titi', 'three_toed_sloth', 'sorrel',
'black_footed_ferret', 'dalmatian', 'black_and_tan_coonhound', 'papillon',
'skunk', 'staffordshire_bullterrier', 'mexican_hairless',
'bouvier_des_flandres', 'weasel', 'miniature_poodle', 'cardigan', 'malinois',
'bighorn', 'fox_squirrel', 'colobus', 'tiger_cat', 'lhasa', 'impala', 'coyote',
'yorkshire_terrier', 'newfoundland', 'brown_bear', 'red_fox',
'norwegian_elkhound', 'rottweiler', 'hartebeest', 'saluki', 'grey_fox',
'schipperke', 'pekinese', 'brabancon_griffon', 'west_highland_white_terrier',
'sealyham_terrier', 'guenon', 'mongoose', 'indri', 'tiger', 'irish_wolfhound',
'wild_boar', 'entlebucher', 'zebra', 'ram', 'french_bulldog', 'orangutan',
'basenji', 'leopard', 'bernese_mountain_dog', 'maltese_dog', 'norfolk_terrier',
'toy_terrier', 'vizsla', 'cairn', 'squirrel_monkey', 'groenendael', 'clumber',
'siamese_cat', 'chimpanzee', 'komondor', 'afghan_hound', 'japanese_spaniel',
'proboscis_monkey', 'guinea_pig', 'white_wolf', 'ice_bear', 'gorilla', 'borzoi',
'toy_poodle', 'kerry_blue_terrier', 'ox', 'scotch_terrier', 'tibetan_mastiff',
'spider_monkey', 'doberman', 'boston_bull', 'greater_swiss_mountain_dog',
'appenzeller', 'shih_tzu', 'irish_water_spaniel', 'pomeranian',
'bedlington_terrier', 'warthog', 'arabian_camel', 'siamang',
'miniature_schnauzer', 'collie', 'golden_retriever', 'irish_terrier',
'affenpinscher', 'border_collie', 'hare', 'boxer', 'silky_terrier', 'beagle',
'leonberg', 'german_short_haired_pointer', 'patas', 'dhole', 'baboon',
'macaque', 'chesapeake_bay_retriever', 'bull_mastiff', 'kuvasz', 'capuchin',
'pug', 'curly_coated_retriever', 'norwich_terrier', 'flat_coated_retriever',
'hog', 'keeshond', 'eskimo_dog', 'brittany_spaniel', 'standard_poodle',
'lakeland_terrier', 'snow_leopard', 'gordon_setter', 'dingo',
'standard_schnauzer', 'hamster', 'tibetan_terrier', 'arctic_fox',
'wire_haired_fox_terrier', 'basset', 'water_buffalo', 'american_black_bear',
'angora', 'bison', 'howler_monkey', 'hippopotamus', 'chow', 'giant_panda',
'american_staffordshire_terrier', 'shetland_sheepdog', 'great_pyrenees',
'chihuahua', 'tabby', 'marmoset', 'labrador_retriever', 'saint_bernard',
'armadillo', 'samoyed', 'bluetick', 'redbone', 'polecat', 'marmot', 'kelpie',
'gibbon', 'llama', 'miniature_pinscher', 'wood_rabbit', 'italian_greyhound',
'lion', 'cocker_spaniel', 'irish_setter', 'dugong', 'indian_elephant', 'beaver',
'sussex_spaniel', 'pembroke', 'blenheim_spaniel', 'madagascar_cat',
'rhodesian_ridgeback', 'lynx', 'african_hunting_dog', 'langur', 'ibizan_hound',
'timber_wolf', 'cheetah', 'english_foxhound', 'briard', 'sloth_bear',
'border_terrier', 'german_shepherd', 'otter', 'koala', 'tusker', 'echidna',
'wallaby', 'platypus', 'wombat', 'revolver', 'umbrella', 'schooner',
'soccer_ball', 'accordion', 'ant', 'starfish', 'chambered_nautilus',
'grand_piano', 'laptop', 'strawberry', 'airliner', 'warplane', 'airship',
'balloon', 'space_shuttle', 'fireboat', 'gondola', 'speedboat', 'lifeboat',
'canoe', 'yawl', 'catamaran', 'trimaran', 'container_ship', 'liner', 'pirate',
'aircraft_carrier', 'submarine', 'wreck', 'half_track', 'tank', 'missile',
'bobsled', 'dogsled', 'bicycle_built_for_two', 'mountain_bike', 'freight_car',
'passenger_car', 'barrow', 'shopping_cart', 'motor_scooter', 'forklift',
'electric_locomotive', 'steam_locomotive', 'amphibian', 'ambulance',
'beach_wagon', 'cab', 'convertible', 'jeep', 'limousine', 'minivan', 'model_t',
'racer', 'sports_car', 'go_kart', 'golfcart', 'moped', 'snowplow',
'fire_engine', 'garbage_truck', 'pickup', 'tow_truck', 'trailer_truck',
'moving_van', 'police_van', 'recreational_vehicle', 'streetcar', 'snowmobile',
'tractor', 'mobile_home', 'tricycle', 'unicycle', 'horse_cart', 'jinrikisha',
'oxcart', 'bassinet', 'cradle', 'crib', 'four_poster', 'bookcase',
'china_cabinet', 'medicine_chest', 'chiffonier', 'table_lamp', 'file',
'park_bench', 'barber_chair', 'throne', 'folding_chair', 'rocking_chair',
'studio_couch', 'toilet_seat', 'desk', 'pool_table', 'dining_table',
'entertainment_center', 'wardrobe', 'granny_smith', 'orange', 'lemon', 'fig',
'pineapple', 'banana', 'jackfruit', 'custard_apple', 'pomegranate', 'acorn',
'hip', 'ear', 'rapeseed', 'corn', 'buckeye', 'organ', 'upright', 'chime',
'drum', 'gong', 'maraca', 'marimba', 'steel_drum', 'banjo', 'cello', 'violin',
'harp', 'acoustic_guitar', 'electric_guitar', 'cornet', 'french_horn',
'trombone', 'harmonica', 'ocarina', 'panpipe', 'bassoon', 'oboe', 'sax',
'flute', 'daisy', 'yellow_ladys_slipper', 'cliff', 'valley', 'alp', 'volcano',
'promontory', 'sandbar', 'coral_reef', 'lakeside', 'seashore', 'geyser',
'hatchet', 'cleaver', 'letter_opener', 'plane', 'power_drill', 'lawn_mower',
'hammer', 'corkscrew', 'can_opener', 'plunger', 'screwdriver', 'shovel', 'plow',
'chain_saw', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house_finch',
'junco', 'indigo_bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee',
'water_ouzel', 'kite', 'bald_eagle', 'vulture', 'great_grey_owl',
'black_grouse', 'ptarmigan', 'ruffed_grouse', 'prairie_chicken', 'peacock',
'quail', 'partridge', 'african_grey', 'macaw', 'sulphur_crested_cockatoo',
'lorikeet', 'coucal', 'bee_eater', 'hornbill', 'hummingbird', 'jacamar',
'toucan', 'drake', 'red_breasted_merganser', 'goose', 'black_swan',
'white_stork', 'black_stork', 'spoonbill', 'flamingo', 'american_egret',
'little_blue_heron', 'bittern', 'crane', 'limpkin', 'american_coot', 'bustard',
'ruddy_turnstone', 'red_backed_sandpiper', 'redshank', 'dowitcher',
'oystercatcher', 'european_gallinule', 'pelican', 'king_penguin', 'albatross',
'great_white_shark', 'tiger_shark', 'hammerhead', 'electric_ray', 'stingray',
'barracouta', 'coho', 'tench', 'goldfish', 'eel', 'rock_beauty', 'anemone_fish',
'lionfish', 'puffer', 'sturgeon', 'gar', 'loggerhead', 'leatherback_turtle',
'mud_turtle', 'terrapin', 'box_turtle', 'banded_gecko', 'common_iguana',
'american_chameleon', 'whiptail', 'agama', 'frilled_lizard', 'alligator_lizard',
'gila_monster', 'green_lizard', 'african_chameleon', 'komodo_dragon',
'triceratops', 'african_crocodile', 'american_alligator', 'thunder_snake',
'ringneck_snake', 'hognose_snake', 'green_snake', 'king_snake', 'garter_snake',
'water_snake', 'vine_snake', 'night_snake', 'boa_constrictor', 'rock_python',
'indian_cobra', 'green_mamba', 'sea_snake', 'horned_viper', 'diamondback',
'sidewinder', 'european_fire_salamander', 'common_newt', 'eft',
'spotted_salamander', 'axolotl', 'bullfrog', 'tree_frog', 'tailed_frog',
'whistle', 'wing', 'paintbrush', 'hand_blower', 'oxygen_mask', 'snorkel',
'loudspeaker', 'microphone', 'screen', 'mouse', 'electric_fan', 'oil_filter',
'strainer', 'space_heater', 'stove', 'guillotine', 'barometer', 'rule',
'odometer', 'scale', 'analog_clock', 'digital_clock', 'wall_clock', 'hourglass',
'sundial', 'parking_meter', 'stopwatch', 'digital_watch', 'stethoscope',
'syringe', 'magnetic_compass', 'binoculars', 'projector', 'sunglasses', 'loupe',
'radio_telescope', 'bow', 'cannon', 'assault_rifle', 'rifle', 'projectile',
'computer_keyboard', 'typewriter_keyboard', 'crane', 'lighter', 'abacus',
'cash_machine', 'slide_rule', 'desktop_computer', 'hand_held_computer',
'notebook', 'web_site', 'harvester', 'thresher', 'printer', 'slot',
'vending_machine', 'sewing_machine', 'joystick', 'switch', 'hook', 'car_wheel',
'paddlewheel', 'pinwheel', 'potters_wheel', 'gas_pump', 'carousel', 'swing',
'reel', 'radiator', 'puck', 'hard_disc', 'sunglass', 'pick', 'car_mirror',
'solar_dish', 'remote_control', 'disk_brake', 'buckle', 'hair_slide', 'knot',
'combination_lock', 'padlock', 'nail', 'safety_pin', 'screw', 'muzzle',
'seat_belt', 'ski', 'candle', 'jack_o_lantern', 'spotlight', 'torch',
'neck_brace', 'pier', 'tripod', 'maypole', 'mousetrap', 'spider_web',
'trilobite', 'harvestman', 'scorpion', 'black_and_gold_garden_spider',
'barn_spider', 'garden_spider', 'black_widow', 'tarantula', 'wolf_spider',
'tick', 'centipede', 'isopod', 'dungeness_crab', 'rock_crab', 'fiddler_crab',
'king_crab', 'american_lobster', 'spiny_lobster', 'crayfish', 'hermit_crab',
'tiger_beetle', 'ladybug', 'ground_beetle', 'long_horned_beetle', 'leaf_beetle',
'dung_beetle', 'rhinoceros_beetle', 'weevil', 'fly', 'bee', 'grasshopper',
'cricket', 'walking_stick', 'cockroach', 'mantis', 'cicada', 'leafhopper',
'lacewing', 'dragonfly', 'damselfly', 'admiral', 'ringlet', 'monarch',
'cabbage_butterfly', 'sulphur_butterfly', 'lycaenid', 'jellyfish',
'sea_anemone', 'brain_coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug',
'sea_slug', 'chiton', 'sea_urchin', 'sea_cucumber', 'iron', 'espresso_maker',
'microwave', 'dutch_oven', 'rotisserie', 'toaster', 'waffle_iron', 'vacuum',
'dishwasher', 'refrigerator', 'washer', 'crock_pot', 'frying_pan', 'wok',
'caldron', 'coffeepot', 'teapot', 'spatula', 'altar', 'triumphal_arch', 'patio',
'steel_arch_bridge', 'suspension_bridge', 'viaduct', 'barn', 'greenhouse',
'palace', 'monastery', 'library', 'apiary', 'boathouse', 'church', 'mosque',
'stupa', 'planetarium', 'restaurant', 'cinema', 'home_theater', 'lumbermill',
'coil', 'obelisk', 'totem_pole', 'castle', 'prison', 'grocery_store', 'bakery',
'barbershop', 'bookshop', 'butcher_shop', 'confectionery', 'shoe_shop',
'tobacco_shop', 'toyshop', 'fountain', 'cliff_dwelling', 'yurt', 'dock',
'brass', 'megalith', 'bannister', 'breakwater', 'dam', 'chainlink_fence',
'picket_fence', 'worm_fence', 'stone_wall', 'grille', 'sliding_door',
'turnstile', 'mountain_tent', 'scoreboard', 'honeycomb', 'plate_rack',
'pedestal', 'beacon', 'mashed_potato', 'bell_pepper', 'head_cabbage',
'broccoli', 'cauliflower', 'zucchini', 'spaghetti_squash', 'acorn_squash',
'butternut_squash', 'cucumber', 'artichoke', 'cardoon', 'mushroom',
'shower_curtain', 'jean', 'carton', 'handkerchief', 'sandal', 'ashcan', 'safe',
'plate', 'necklace', 'croquet_ball', 'fur_coat', 'thimble', 'pajama',
'running_shoe', 'cocktail_shaker', 'chest', 'manhole_cover', 'modem', 'tub',
'tray', 'balance_beam', 'bagel', 'prayer_rug', 'kimono', 'hot_pot',
'whiskey_jug', 'knee_pad', 'book_jacket', 'spindle', 'ski_mask', 'beer_bottle',
'crash_helmet', 'bottlecap', 'tile_roof', 'mask', 'maillot', 'petri_dish',
'football_helmet', 'bathing_cap', 'teddy', 'holster', 'pop_bottle',
'photocopier', 'vestment', 'crossword_puzzle', 'golf_ball', 'trifle', 'suit',
'water_tower', 'feather_boa', 'cloak', 'red_wine', 'drumstick', 'shield',
'christmas_stocking', 'hoopskirt', 'menu', 'stage', 'bonnet', 'meat_loaf',
'baseball', 'face_powder', 'scabbard', 'sunscreen', 'beer_glass',
'hen_of_the_woods', 'guacamole', 'lampshade', 'wool', 'hay', 'bow_tie',
'mailbag', 'water_jug', 'bucket', 'dishrag', 'soup_bowl', 'eggnog', 'mortar',
'trench_coat', 'paddle', 'chain', 'swab', 'mixing_bowl', 'potpie',
'wine_bottle', 'shoji', 'bulletproof_vest', 'drilling_platform', 'binder',
'cardigan', 'sweatshirt', 'pot', 'birdhouse', 'hamper', 'ping_pong_ball',
'pencil_box', 'pay_phone', 'consomme', 'apron', 'punching_bag', 'backpack',
'groom', 'bearskin', 'pencil_sharpener', 'broom', 'mosquito_net', 'abaya',
'mortarboard', 'poncho', 'crutch', 'polaroid_camera', 'space_bar', 'cup',
'racket', 'traffic_light', 'quill', 'radio', 'dough', 'cuirass',
'military_uniform', 'lipstick', 'shower_cap', 'monitor', 'oscilloscope',
'mitten', 'brassiere', 'french_loaf', 'vase', 'milk_can', 'rugby_ball',
'paper_towel', 'earthstar', 'envelope', 'miniskirt', 'cowboy_hat', 'trolleybus',
'perfume', 'bathtub', 'hotdog', 'coral_fungus', 'bullet_train', 'pillow',
'toilet_tissue', 'cassette', 'carpenters_kit', 'ladle', 'stinkhorn', 'lotion',
'hair_spray', 'academic_gown', 'dome', 'crate', 'wig', 'burrito', 'pill_bottle',
'chain_mail', 'theater_curtain', 'window_shade', 'barrel', 'washbasin',
'ballpoint', 'basketball', 'bath_towel', 'cowboy_boot', 'gown', 'window_screen',
'agaric', 'cellular_telephone', 'nipple', 'barbell', 'mailbox', 'lab_coat',
'fire_screen', 'minibus', 'packet', 'maze', 'pole', 'horizontal_bar',
'sombrero', 'pickelhaube', 'rain_barrel', 'wallet', 'cassette_player',
'comic_book', 'piggy_bank', 'street_sign', 'bell_cote', 'fountain_pen',
'windsor_tie', 'volleyball', 'overskirt', 'sarong', 'purse', 'bolo_tie', 'bib',
'parachute', 'sleeping_bag', 'television', 'swimming_trunks', 'measuring_cup',
'espresso', 'pizza', 'breastplate', 'shopping_basket', 'wooden_spoon',
'saltshaker', 'chocolate_sauce', 'ballplayer', 'goblet', 'gyromitra',
'stretcher', 'water_bottle', 'dial_telephone', 'soap_dispenser', 'jersey',
'school_bus', 'jigsaw_puzzle', 'plastic_bag', 'reflex_camera', 'diaper',
'band_aid', 'ice_lolly', 'velvet', 'tennis_ball', 'gasmask', 'doormat',
'loafer', 'ice_cream', 'pretzel', 'quilt', 'maillot', 'tape_player', 'clog',
'ipod', 'bolete', 'scuba_diver', 'pitcher', 'matchstick', 'bikini', 'sock',
'cd_player', 'lens_cap', 'thatch', 'vault', 'beaker', 'bubble', 'cheeseburger',
'parallel_bars', 'flagpole', 'coffee_mug', 'rubber_eraser', 'stole',
'carbonara', 'dumbbell')
CIFAR100_NAMES = ('apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle',
'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar',
'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile',
'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',
'house', 'kangaroo', 'computer_keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard',
'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree',
'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain',
'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose',
'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel',
'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger',
'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf',
'woman', 'worm')