07d BigGanTorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "496ee29e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0.dev20230120'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floating-source",
   "metadata": {},
   "source": [
    "# BigGAN Demo (Work in Progress Code)\n",
    "This is a bare bones example created based on code from the following repository:\n",
    "\n",
    "- https://github.com/ajbrock/BigGAN-PyTorch \n",
    "- weights file: https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW\n",
    "\n",
    "This repository was written by Andrew Brock (the first author of the BigGAN paper). It is a port of the architecture into PyTorch. I have manipulated various codes to load up BigGAN and do some sampling from it. \n",
    "\n",
    "This notebook is still a work in progress, but it will work for loading BigGAN and sampling from it. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "indoor-penalty",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'tench, Tinca tinca',\n",
       " 1: 'goldfish, Carassius auratus',\n",
       " 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\n",
       " 3: 'tiger shark, Galeocerdo cuvieri',\n",
       " 4: 'hammerhead, hammerhead shark',\n",
       " 5: 'electric ray, crampfish, numbfish, torpedo',\n",
       " 6: 'stingray',\n",
       " 7: 'cock',\n",
       " 8: 'hen',\n",
       " 9: 'ostrich, Struthio camelus',\n",
       " 10: 'brambling, Fringilla montifringilla',\n",
       " 11: 'goldfinch, Carduelis carduelis',\n",
       " 12: 'house finch, linnet, Carpodacus mexicanus',\n",
       " 13: 'junco, snowbird',\n",
       " 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n",
       " 15: 'robin, American robin, Turdus migratorius',\n",
       " 16: 'bulbul',\n",
       " 17: 'jay',\n",
       " 18: 'magpie',\n",
       " 19: 'chickadee',\n",
       " 20: 'water ouzel, dipper',\n",
       " 21: 'kite',\n",
       " 22: 'bald eagle, American eagle, Haliaeetus leucocephalus',\n",
       " 23: 'vulture',\n",
       " 24: 'great grey owl, great gray owl, Strix nebulosa',\n",
       " 25: 'European fire salamander, Salamandra salamandra',\n",
       " 26: 'common newt, Triturus vulgaris',\n",
       " 27: 'eft',\n",
       " 28: 'spotted salamander, Ambystoma maculatum',\n",
       " 29: 'axolotl, mud puppy, Ambystoma mexicanum',\n",
       " 30: 'bullfrog, Rana catesbeiana',\n",
       " 31: 'tree frog, tree-frog',\n",
       " 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n",
       " 33: 'loggerhead, loggerhead turtle, Caretta caretta',\n",
       " 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',\n",
       " 35: 'mud turtle',\n",
       " 36: 'terrapin',\n",
       " 37: 'box turtle, box tortoise',\n",
       " 38: 'banded gecko',\n",
       " 39: 'common iguana, iguana, Iguana iguana',\n",
       " 40: 'American chameleon, anole, Anolis carolinensis',\n",
       " 41: 'whiptail, whiptail lizard',\n",
       " 42: 'agama',\n",
       " 43: 'frilled lizard, Chlamydosaurus kingi',\n",
       " 44: 'alligator lizard',\n",
       " 45: 'Gila monster, Heloderma suspectum',\n",
       " 46: 'green lizard, Lacerta viridis',\n",
       " 47: 'African chameleon, Chamaeleo chamaeleon',\n",
       " 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',\n",
       " 49: 'African crocodile, Nile crocodile, Crocodylus niloticus',\n",
       " 50: 'American alligator, Alligator mississipiensis',\n",
       " 51: 'triceratops',\n",
       " 52: 'thunder snake, worm snake, Carphophis amoenus',\n",
       " 53: 'ringneck snake, ring-necked snake, ring snake',\n",
       " 54: 'hognose snake, puff adder, sand viper',\n",
       " 55: 'green snake, grass snake',\n",
       " 56: 'king snake, kingsnake',\n",
       " 57: 'garter snake, grass snake',\n",
       " 58: 'water snake',\n",
       " 59: 'vine snake',\n",
       " 60: 'night snake, Hypsiglena torquata',\n",
       " 61: 'boa constrictor, Constrictor constrictor',\n",
       " 62: 'rock python, rock snake, Python sebae',\n",
       " 63: 'Indian cobra, Naja naja',\n",
       " 64: 'green mamba',\n",
       " 65: 'sea snake',\n",
       " 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n",
       " 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n",
       " 68: 'sidewinder, horned rattlesnake, Crotalus cerastes',\n",
       " 69: 'trilobite',\n",
       " 70: 'harvestman, daddy longlegs, Phalangium opilio',\n",
       " 71: 'scorpion',\n",
       " 72: 'black and gold garden spider, Argiope aurantia',\n",
       " 73: 'barn spider, Araneus cavaticus',\n",
       " 74: 'garden spider, Aranea diademata',\n",
       " 75: 'black widow, Latrodectus mactans',\n",
       " 76: 'tarantula',\n",
       " 77: 'wolf spider, hunting spider',\n",
       " 78: 'tick',\n",
       " 79: 'centipede',\n",
       " 80: 'black grouse',\n",
       " 81: 'ptarmigan',\n",
       " 82: 'ruffed grouse, partridge, Bonasa umbellus',\n",
       " 83: 'prairie chicken, prairie grouse, prairie fowl',\n",
       " 84: 'peacock',\n",
       " 85: 'quail',\n",
       " 86: 'partridge',\n",
       " 87: 'African grey, African gray, Psittacus erithacus',\n",
       " 88: 'macaw',\n",
       " 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n",
       " 90: 'lorikeet',\n",
       " 91: 'coucal',\n",
       " 92: 'bee eater',\n",
       " 93: 'hornbill',\n",
       " 94: 'hummingbird',\n",
       " 95: 'jacamar',\n",
       " 96: 'toucan',\n",
       " 97: 'drake',\n",
       " 98: 'red-breasted merganser, Mergus serrator',\n",
       " 99: 'goose',\n",
       " 100: 'black swan, Cygnus atratus',\n",
       " 101: 'tusker',\n",
       " 102: 'echidna, spiny anteater, anteater',\n",
       " 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',\n",
       " 104: 'wallaby, brush kangaroo',\n",
       " 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',\n",
       " 106: 'wombat',\n",
       " 107: 'jellyfish',\n",
       " 108: 'sea anemone, anemone',\n",
       " 109: 'brain coral',\n",
       " 110: 'flatworm, platyhelminth',\n",
       " 111: 'nematode, nematode worm, roundworm',\n",
       " 112: 'conch',\n",
       " 113: 'snail',\n",
       " 114: 'slug',\n",
       " 115: 'sea slug, nudibranch',\n",
       " 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n",
       " 117: 'chambered nautilus, pearly nautilus, nautilus',\n",
       " 118: 'Dungeness crab, Cancer magister',\n",
       " 119: 'rock crab, Cancer irroratus',\n",
       " 120: 'fiddler crab',\n",
       " 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n",
       " 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',\n",
       " 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',\n",
       " 124: 'crayfish, crawfish, crawdad, crawdaddy',\n",
       " 125: 'hermit crab',\n",
       " 126: 'isopod',\n",
       " 127: 'white stork, Ciconia ciconia',\n",
       " 128: 'black stork, Ciconia nigra',\n",
       " 129: 'spoonbill',\n",
       " 130: 'flamingo',\n",
       " 131: 'little blue heron, Egretta caerulea',\n",
       " 132: 'American egret, great white heron, Egretta albus',\n",
       " 133: 'bittern',\n",
       " 134: 'crane',\n",
       " 135: 'limpkin, Aramus pictus',\n",
       " 136: 'European gallinule, Porphyrio porphyrio',\n",
       " 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n",
       " 138: 'bustard',\n",
       " 139: 'ruddy turnstone, Arenaria interpres',\n",
       " 140: 'red-backed sandpiper, dunlin, Erolia alpina',\n",
       " 141: 'redshank, Tringa totanus',\n",
       " 142: 'dowitcher',\n",
       " 143: 'oystercatcher, oyster catcher',\n",
       " 144: 'pelican',\n",
       " 145: 'king penguin, Aptenodytes patagonica',\n",
       " 146: 'albatross, mollymawk',\n",
       " 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',\n",
       " 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n",
       " 149: 'dugong, Dugong dugon',\n",
       " 150: 'sea lion',\n",
       " 151: 'Chihuahua',\n",
       " 152: 'Japanese spaniel',\n",
       " 153: 'Maltese dog, Maltese terrier, Maltese',\n",
       " 154: 'Pekinese, Pekingese, Peke',\n",
       " 155: 'Shih-Tzu',\n",
       " 156: 'Blenheim spaniel',\n",
       " 157: 'papillon',\n",
       " 158: 'toy terrier',\n",
       " 159: 'Rhodesian ridgeback',\n",
       " 160: 'Afghan hound, Afghan',\n",
       " 161: 'basset, basset hound',\n",
       " 162: 'beagle',\n",
       " 163: 'bloodhound, sleuthhound',\n",
       " 164: 'bluetick',\n",
       " 165: 'black-and-tan coonhound',\n",
       " 166: 'Walker hound, Walker foxhound',\n",
       " 167: 'English foxhound',\n",
       " 168: 'redbone',\n",
       " 169: 'borzoi, Russian wolfhound',\n",
       " 170: 'Irish wolfhound',\n",
       " 171: 'Italian greyhound',\n",
       " 172: 'whippet',\n",
       " 173: 'Ibizan hound, Ibizan Podenco',\n",
       " 174: 'Norwegian elkhound, elkhound',\n",
       " 175: 'otterhound, otter hound',\n",
       " 176: 'Saluki, gazelle hound',\n",
       " 177: 'Scottish deerhound, deerhound',\n",
       " 178: 'Weimaraner',\n",
       " 179: 'Staffordshire bullterrier, Staffordshire bull terrier',\n",
       " 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',\n",
       " 181: 'Bedlington terrier',\n",
       " 182: 'Border terrier',\n",
       " 183: 'Kerry blue terrier',\n",
       " 184: 'Irish terrier',\n",
       " 185: 'Norfolk terrier',\n",
       " 186: 'Norwich terrier',\n",
       " 187: 'Yorkshire terrier',\n",
       " 188: 'wire-haired fox terrier',\n",
       " 189: 'Lakeland terrier',\n",
       " 190: 'Sealyham terrier, Sealyham',\n",
       " 191: 'Airedale, Airedale terrier',\n",
       " 192: 'cairn, cairn terrier',\n",
       " 193: 'Australian terrier',\n",
       " 194: 'Dandie Dinmont, Dandie Dinmont terrier',\n",
       " 195: 'Boston bull, Boston terrier',\n",
       " 196: 'miniature schnauzer',\n",
       " 197: 'giant schnauzer',\n",
       " 198: 'standard schnauzer',\n",
       " 199: 'Scotch terrier, Scottish terrier, Scottie',\n",
       " 200: 'Tibetan terrier, chrysanthemum dog',\n",
       " 201: 'silky terrier, Sydney silky',\n",
       " 202: 'soft-coated wheaten terrier',\n",
       " 203: 'West Highland white terrier',\n",
       " 204: 'Lhasa, Lhasa apso',\n",
       " 205: 'flat-coated retriever',\n",
       " 206: 'curly-coated retriever',\n",
       " 207: 'golden retriever',\n",
       " 208: 'Labrador retriever',\n",
       " 209: 'Chesapeake Bay retriever',\n",
       " 210: 'German short-haired pointer',\n",
       " 211: 'vizsla, Hungarian pointer',\n",
       " 212: 'English setter',\n",
       " 213: 'Irish setter, red setter',\n",
       " 214: 'Gordon setter',\n",
       " 215: 'Brittany spaniel',\n",
       " 216: 'clumber, clumber spaniel',\n",
       " 217: 'English springer, English springer spaniel',\n",
       " 218: 'Welsh springer spaniel',\n",
       " 219: 'cocker spaniel, English cocker spaniel, cocker',\n",
       " 220: 'Sussex spaniel',\n",
       " 221: 'Irish water spaniel',\n",
       " 222: 'kuvasz',\n",
       " 223: 'schipperke',\n",
       " 224: 'groenendael',\n",
       " 225: 'malinois',\n",
       " 226: 'briard',\n",
       " 227: 'kelpie',\n",
       " 228: 'komondor',\n",
       " 229: 'Old English sheepdog, bobtail',\n",
       " 230: 'Shetland sheepdog, Shetland sheep dog, Shetland',\n",
       " 231: 'collie',\n",
       " 232: 'Border collie',\n",
       " 233: 'Bouvier des Flandres, Bouviers des Flandres',\n",
       " 234: 'Rottweiler',\n",
       " 235: 'German shepherd, German shepherd dog, German police dog, alsatian',\n",
       " 236: 'Doberman, Doberman pinscher',\n",
       " 237: 'miniature pinscher',\n",
       " 238: 'Greater Swiss Mountain dog',\n",
       " 239: 'Bernese mountain dog',\n",
       " 240: 'Appenzeller',\n",
       " 241: 'EntleBucher',\n",
       " 242: 'boxer',\n",
       " 243: 'bull mastiff',\n",
       " 244: 'Tibetan mastiff',\n",
       " 245: 'French bulldog',\n",
       " 246: 'Great Dane',\n",
       " 247: 'Saint Bernard, St Bernard',\n",
       " 248: 'Eskimo dog, husky',\n",
       " 249: 'malamute, malemute, Alaskan malamute',\n",
       " 250: 'Siberian husky',\n",
       " 251: 'dalmatian, coach dog, carriage dog',\n",
       " 252: 'affenpinscher, monkey pinscher, monkey dog',\n",
       " 253: 'basenji',\n",
       " 254: 'pug, pug-dog',\n",
       " 255: 'Leonberg',\n",
       " 256: 'Newfoundland, Newfoundland dog',\n",
       " 257: 'Great Pyrenees',\n",
       " 258: 'Samoyed, Samoyede',\n",
       " 259: 'Pomeranian',\n",
       " 260: 'chow, chow chow',\n",
       " 261: 'keeshond',\n",
       " 262: 'Brabancon griffon',\n",
       " 263: 'Pembroke, Pembroke Welsh corgi',\n",
       " 264: 'Cardigan, Cardigan Welsh corgi',\n",
       " 265: 'toy poodle',\n",
       " 266: 'miniature poodle',\n",
       " 267: 'standard poodle',\n",
       " 268: 'Mexican hairless',\n",
       " 269: 'timber wolf, grey wolf, gray wolf, Canis lupus',\n",
       " 270: 'white wolf, Arctic wolf, Canis lupus tundrarum',\n",
       " 271: 'red wolf, maned wolf, Canis rufus, Canis niger',\n",
       " 272: 'coyote, prairie wolf, brush wolf, Canis latrans',\n",
       " 273: 'dingo, warrigal, warragal, Canis dingo',\n",
       " 274: 'dhole, Cuon alpinus',\n",
       " 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n",
       " 276: 'hyena, hyaena',\n",
       " 277: 'red fox, Vulpes vulpes',\n",
       " 278: 'kit fox, Vulpes macrotis',\n",
       " 279: 'Arctic fox, white fox, Alopex lagopus',\n",
       " 280: 'grey fox, gray fox, Urocyon cinereoargenteus',\n",
       " 281: 'tabby, tabby cat',\n",
       " 282: 'tiger cat',\n",
       " 283: 'Persian cat',\n",
       " 284: 'Siamese cat, Siamese',\n",
       " 285: 'Egyptian cat',\n",
       " 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',\n",
       " 287: 'lynx, catamount',\n",
       " 288: 'leopard, Panthera pardus',\n",
       " 289: 'snow leopard, ounce, Panthera uncia',\n",
       " 290: 'jaguar, panther, Panthera onca, Felis onca',\n",
       " 291: 'lion, king of beasts, Panthera leo',\n",
       " 292: 'tiger, Panthera tigris',\n",
       " 293: 'cheetah, chetah, Acinonyx jubatus',\n",
       " 294: 'brown bear, bruin, Ursus arctos',\n",
       " 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',\n",
       " 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n",
       " 297: 'sloth bear, Melursus ursinus, Ursus ursinus',\n",
       " 298: 'mongoose',\n",
       " 299: 'meerkat, mierkat',\n",
       " 300: 'tiger beetle',\n",
       " 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n",
       " 302: 'ground beetle, carabid beetle',\n",
       " 303: 'long-horned beetle, longicorn, longicorn beetle',\n",
       " 304: 'leaf beetle, chrysomelid',\n",
       " 305: 'dung beetle',\n",
       " 306: 'rhinoceros beetle',\n",
       " 307: 'weevil',\n",
       " 308: 'fly',\n",
       " 309: 'bee',\n",
       " 310: 'ant, emmet, pismire',\n",
       " 311: 'grasshopper, hopper',\n",
       " 312: 'cricket',\n",
       " 313: 'walking stick, walkingstick, stick insect',\n",
       " 314: 'cockroach, roach',\n",
       " 315: 'mantis, mantid',\n",
       " 316: 'cicada, cicala',\n",
       " 317: 'leafhopper',\n",
       " 318: 'lacewing, lacewing fly',\n",
       " 319: \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n",
       " 320: 'damselfly',\n",
       " 321: 'admiral',\n",
       " 322: 'ringlet, ringlet butterfly',\n",
       " 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n",
       " 324: 'cabbage butterfly',\n",
       " 325: 'sulphur butterfly, sulfur butterfly',\n",
       " 326: 'lycaenid, lycaenid butterfly',\n",
       " 327: 'starfish, sea star',\n",
       " 328: 'sea urchin',\n",
       " 329: 'sea cucumber, holothurian',\n",
       " 330: 'wood rabbit, cottontail, cottontail rabbit',\n",
       " 331: 'hare',\n",
       " 332: 'Angora, Angora rabbit',\n",
       " 333: 'hamster',\n",
       " 334: 'porcupine, hedgehog',\n",
       " 335: 'fox squirrel, eastern fox squirrel, Sciurus niger',\n",
       " 336: 'marmot',\n",
       " 337: 'beaver',\n",
       " 338: 'guinea pig, Cavia cobaya',\n",
       " 339: 'sorrel',\n",
       " 340: 'zebra',\n",
       " 341: 'hog, pig, grunter, squealer, Sus scrofa',\n",
       " 342: 'wild boar, boar, Sus scrofa',\n",
       " 343: 'warthog',\n",
       " 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n",
       " 345: 'ox',\n",
       " 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n",
       " 347: 'bison',\n",
       " 348: 'ram, tup',\n",
       " 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',\n",
       " 350: 'ibex, Capra ibex',\n",
       " 351: 'hartebeest',\n",
       " 352: 'impala, Aepyceros melampus',\n",
       " 353: 'gazelle',\n",
       " 354: 'Arabian camel, dromedary, Camelus dromedarius',\n",
       " 355: 'llama',\n",
       " 356: 'weasel',\n",
       " 357: 'mink',\n",
       " 358: 'polecat, fitch, foulmart, foumart, Mustela putorius',\n",
       " 359: 'black-footed ferret, ferret, Mustela nigripes',\n",
       " 360: 'otter',\n",
       " 361: 'skunk, polecat, wood pussy',\n",
       " 362: 'badger',\n",
       " 363: 'armadillo',\n",
       " 364: 'three-toed sloth, ai, Bradypus tridactylus',\n",
       " 365: 'orangutan, orang, orangutang, Pongo pygmaeus',\n",
       " 366: 'gorilla, Gorilla gorilla',\n",
       " 367: 'chimpanzee, chimp, Pan troglodytes',\n",
       " 368: 'gibbon, Hylobates lar',\n",
       " 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n",
       " 370: 'guenon, guenon monkey',\n",
       " 371: 'patas, hussar monkey, Erythrocebus patas',\n",
       " 372: 'baboon',\n",
       " 373: 'macaque',\n",
       " 374: 'langur',\n",
       " 375: 'colobus, colobus monkey',\n",
       " 376: 'proboscis monkey, Nasalis larvatus',\n",
       " 377: 'marmoset',\n",
       " 378: 'capuchin, ringtail, Cebus capucinus',\n",
       " 379: 'howler monkey, howler',\n",
       " 380: 'titi, titi monkey',\n",
       " 381: 'spider monkey, Ateles geoffroyi',\n",
       " 382: 'squirrel monkey, Saimiri sciureus',\n",
       " 383: 'Madagascar cat, ring-tailed lemur, Lemur catta',\n",
       " 384: 'indri, indris, Indri indri, Indri brevicaudatus',\n",
       " 385: 'Indian elephant, Elephas maximus',\n",
       " 386: 'African elephant, Loxodonta africana',\n",
       " 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n",
       " 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
       " 389: 'barracouta, snoek',\n",
       " 390: 'eel',\n",
       " 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',\n",
       " 392: 'rock beauty, Holocanthus tricolor',\n",
       " 393: 'anemone fish',\n",
       " 394: 'sturgeon',\n",
       " 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n",
       " 396: 'lionfish',\n",
       " 397: 'puffer, pufferfish, blowfish, globefish',\n",
       " 398: 'abacus',\n",
       " 399: 'abaya',\n",
       " 400: \"academic gown, academic robe, judge's robe\",\n",
       " 401: 'accordion, piano accordion, squeeze box',\n",
       " 402: 'acoustic guitar',\n",
       " 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n",
       " 404: 'airliner',\n",
       " 405: 'airship, dirigible',\n",
       " 406: 'altar',\n",
       " 407: 'ambulance',\n",
       " 408: 'amphibian, amphibious vehicle',\n",
       " 409: 'analog clock',\n",
       " 410: 'apiary, bee house',\n",
       " 411: 'apron',\n",
       " 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',\n",
       " 413: 'assault rifle, assault gun',\n",
       " 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n",
       " 415: 'bakery, bakeshop, bakehouse',\n",
       " 416: 'balance beam, beam',\n",
       " 417: 'balloon',\n",
       " 418: 'ballpoint, ballpoint pen, ballpen, Biro',\n",
       " 419: 'Band Aid',\n",
       " 420: 'banjo',\n",
       " 421: 'bannister, banister, balustrade, balusters, handrail',\n",
       " 422: 'barbell',\n",
       " 423: 'barber chair',\n",
       " 424: 'barbershop',\n",
       " 425: 'barn',\n",
       " 426: 'barometer',\n",
       " 427: 'barrel, cask',\n",
       " 428: 'barrow, garden cart, lawn cart, wheelbarrow',\n",
       " 429: 'baseball',\n",
       " 430: 'basketball',\n",
       " 431: 'bassinet',\n",
       " 432: 'bassoon',\n",
       " 433: 'bathing cap, swimming cap',\n",
       " 434: 'bath towel',\n",
       " 435: 'bathtub, bathing tub, bath, tub',\n",
       " 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',\n",
       " 437: 'beacon, lighthouse, beacon light, pharos',\n",
       " 438: 'beaker',\n",
       " 439: 'bearskin, busby, shako',\n",
       " 440: 'beer bottle',\n",
       " 441: 'beer glass',\n",
       " 442: 'bell cote, bell cot',\n",
       " 443: 'bib',\n",
       " 444: 'bicycle-built-for-two, tandem bicycle, tandem',\n",
       " 445: 'bikini, two-piece',\n",
       " 446: 'binder, ring-binder',\n",
       " 447: 'binoculars, field glasses, opera glasses',\n",
       " 448: 'birdhouse',\n",
       " 449: 'boathouse',\n",
       " 450: 'bobsled, bobsleigh, bob',\n",
       " 451: 'bolo tie, bolo, bola tie, bola',\n",
       " 452: 'bonnet, poke bonnet',\n",
       " 453: 'bookcase',\n",
       " 454: 'bookshop, bookstore, bookstall',\n",
       " 455: 'bottlecap',\n",
       " 456: 'bow',\n",
       " 457: 'bow tie, bow-tie, bowtie',\n",
       " 458: 'brass, memorial tablet, plaque',\n",
       " 459: 'brassiere, bra, bandeau',\n",
       " 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n",
       " 461: 'breastplate, aegis, egis',\n",
       " 462: 'broom',\n",
       " 463: 'bucket, pail',\n",
       " 464: 'buckle',\n",
       " 465: 'bulletproof vest',\n",
       " 466: 'bullet train, bullet',\n",
       " 467: 'butcher shop, meat market',\n",
       " 468: 'cab, hack, taxi, taxicab',\n",
       " 469: 'caldron, cauldron',\n",
       " 470: 'candle, taper, wax light',\n",
       " 471: 'cannon',\n",
       " 472: 'canoe',\n",
       " 473: 'can opener, tin opener',\n",
       " 474: 'cardigan',\n",
       " 475: 'car mirror',\n",
       " 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n",
       " 477: \"carpenter's kit, tool kit\",\n",
       " 478: 'carton',\n",
       " 479: 'car wheel',\n",
       " 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',\n",
       " 481: 'cassette',\n",
       " 482: 'cassette player',\n",
       " 483: 'castle',\n",
       " 484: 'catamaran',\n",
       " 485: 'CD player',\n",
       " 486: 'cello, violoncello',\n",
       " 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n",
       " 488: 'chain',\n",
       " 489: 'chainlink fence',\n",
       " 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',\n",
       " 491: 'chain saw, chainsaw',\n",
       " 492: 'chest',\n",
       " 493: 'chiffonier, commode',\n",
       " 494: 'chime, bell, gong',\n",
       " 495: 'china cabinet, china closet',\n",
       " 496: 'Christmas stocking',\n",
       " 497: 'church, church building',\n",
       " 498: 'cinema, movie theater, movie theatre, movie house, picture palace',\n",
       " 499: 'cleaver, meat cleaver, chopper',\n",
       " 500: 'cliff dwelling',\n",
       " 501: 'cloak',\n",
       " 502: 'clog, geta, patten, sabot',\n",
       " 503: 'cocktail shaker',\n",
       " 504: 'coffee mug',\n",
       " 505: 'coffeepot',\n",
       " 506: 'coil, spiral, volute, whorl, helix',\n",
       " 507: 'combination lock',\n",
       " 508: 'computer keyboard, keypad',\n",
       " 509: 'confectionery, confectionary, candy store',\n",
       " 510: 'container ship, containership, container vessel',\n",
       " 511: 'convertible',\n",
       " 512: 'corkscrew, bottle screw',\n",
       " 513: 'cornet, horn, trumpet, trump',\n",
       " 514: 'cowboy boot',\n",
       " 515: 'cowboy hat, ten-gallon hat',\n",
       " 516: 'cradle',\n",
       " 517: 'crane',\n",
       " 518: 'crash helmet',\n",
       " 519: 'crate',\n",
       " 520: 'crib, cot',\n",
       " 521: 'Crock Pot',\n",
       " 522: 'croquet ball',\n",
       " 523: 'crutch',\n",
       " 524: 'cuirass',\n",
       " 525: 'dam, dike, dyke',\n",
       " 526: 'desk',\n",
       " 527: 'desktop computer',\n",
       " 528: 'dial telephone, dial phone',\n",
       " 529: 'diaper, nappy, napkin',\n",
       " 530: 'digital clock',\n",
       " 531: 'digital watch',\n",
       " 532: 'dining table, board',\n",
       " 533: 'dishrag, dishcloth',\n",
       " 534: 'dishwasher, dish washer, dishwashing machine',\n",
       " 535: 'disk brake, disc brake',\n",
       " 536: 'dock, dockage, docking facility',\n",
       " 537: 'dogsled, dog sled, dog sleigh',\n",
       " 538: 'dome',\n",
       " 539: 'doormat, welcome mat',\n",
       " 540: 'drilling platform, offshore rig',\n",
       " 541: 'drum, membranophone, tympan',\n",
       " 542: 'drumstick',\n",
       " 543: 'dumbbell',\n",
       " 544: 'Dutch oven',\n",
       " 545: 'electric fan, blower',\n",
       " 546: 'electric guitar',\n",
       " 547: 'electric locomotive',\n",
       " 548: 'entertainment center',\n",
       " 549: 'envelope',\n",
       " 550: 'espresso maker',\n",
       " 551: 'face powder',\n",
       " 552: 'feather boa, boa',\n",
       " 553: 'file, file cabinet, filing cabinet',\n",
       " 554: 'fireboat',\n",
       " 555: 'fire engine, fire truck',\n",
       " 556: 'fire screen, fireguard',\n",
       " 557: 'flagpole, flagstaff',\n",
       " 558: 'flute, transverse flute',\n",
       " 559: 'folding chair',\n",
       " 560: 'football helmet',\n",
       " 561: 'forklift',\n",
       " 562: 'fountain',\n",
       " 563: 'fountain pen',\n",
       " 564: 'four-poster',\n",
       " 565: 'freight car',\n",
       " 566: 'French horn, horn',\n",
       " 567: 'frying pan, frypan, skillet',\n",
       " 568: 'fur coat',\n",
       " 569: 'garbage truck, dustcart',\n",
       " 570: 'gasmask, respirator, gas helmet',\n",
       " 571: 'gas pump, gasoline pump, petrol pump, island dispenser',\n",
       " 572: 'goblet',\n",
       " 573: 'go-kart',\n",
       " 574: 'golf ball',\n",
       " 575: 'golfcart, golf cart',\n",
       " 576: 'gondola',\n",
       " 577: 'gong, tam-tam',\n",
       " 578: 'gown',\n",
       " 579: 'grand piano, grand',\n",
       " 580: 'greenhouse, nursery, glasshouse',\n",
       " 581: 'grille, radiator grille',\n",
       " 582: 'grocery store, grocery, food market, market',\n",
       " 583: 'guillotine',\n",
       " 584: 'hair slide',\n",
       " 585: 'hair spray',\n",
       " 586: 'half track',\n",
       " 587: 'hammer',\n",
       " 588: 'hamper',\n",
       " 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n",
       " 590: 'hand-held computer, hand-held microcomputer',\n",
       " 591: 'handkerchief, hankie, hanky, hankey',\n",
       " 592: 'hard disc, hard disk, fixed disk',\n",
       " 593: 'harmonica, mouth organ, harp, mouth harp',\n",
       " 594: 'harp',\n",
       " 595: 'harvester, reaper',\n",
       " 596: 'hatchet',\n",
       " 597: 'holster',\n",
       " 598: 'home theater, home theatre',\n",
       " 599: 'honeycomb',\n",
       " 600: 'hook, claw',\n",
       " 601: 'hoopskirt, crinoline',\n",
       " 602: 'horizontal bar, high bar',\n",
       " 603: 'horse cart, horse-cart',\n",
       " 604: 'hourglass',\n",
       " 605: 'iPod',\n",
       " 606: 'iron, smoothing iron',\n",
       " 607: \"jack-o'-lantern\",\n",
       " 608: 'jean, blue jean, denim',\n",
       " 609: 'jeep, landrover',\n",
       " 610: 'jersey, T-shirt, tee shirt',\n",
       " 611: 'jigsaw puzzle',\n",
       " 612: 'jinrikisha, ricksha, rickshaw',\n",
       " 613: 'joystick',\n",
       " 614: 'kimono',\n",
       " 615: 'knee pad',\n",
       " 616: 'knot',\n",
       " 617: 'lab coat, laboratory coat',\n",
       " 618: 'ladle',\n",
       " 619: 'lampshade, lamp shade',\n",
       " 620: 'laptop, laptop computer',\n",
       " 621: 'lawn mower, mower',\n",
       " 622: 'lens cap, lens cover',\n",
       " 623: 'letter opener, paper knife, paperknife',\n",
       " 624: 'library',\n",
       " 625: 'lifeboat',\n",
       " 626: 'lighter, light, igniter, ignitor',\n",
       " 627: 'limousine, limo',\n",
       " 628: 'liner, ocean liner',\n",
       " 629: 'lipstick, lip rouge',\n",
       " 630: 'Loafer',\n",
       " 631: 'lotion',\n",
       " 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',\n",
       " 633: \"loupe, jeweler's loupe\",\n",
       " 634: 'lumbermill, sawmill',\n",
       " 635: 'magnetic compass',\n",
       " 636: 'mailbag, postbag',\n",
       " 637: 'mailbox, letter box',\n",
       " 638: 'maillot',\n",
       " 639: 'maillot, tank suit',\n",
       " 640: 'manhole cover',\n",
       " 641: 'maraca',\n",
       " 642: 'marimba, xylophone',\n",
       " 643: 'mask',\n",
       " 644: 'matchstick',\n",
       " 645: 'maypole',\n",
       " 646: 'maze, labyrinth',\n",
       " 647: 'measuring cup',\n",
       " 648: 'medicine chest, medicine cabinet',\n",
       " 649: 'megalith, megalithic structure',\n",
       " 650: 'microphone, mike',\n",
       " 651: 'microwave, microwave oven',\n",
       " 652: 'military uniform',\n",
       " 653: 'milk can',\n",
       " 654: 'minibus',\n",
       " 655: 'miniskirt, mini',\n",
       " 656: 'minivan',\n",
       " 657: 'missile',\n",
       " 658: 'mitten',\n",
       " 659: 'mixing bowl',\n",
       " 660: 'mobile home, manufactured home',\n",
       " 661: 'Model T',\n",
       " 662: 'modem',\n",
       " 663: 'monastery',\n",
       " 664: 'monitor',\n",
       " 665: 'moped',\n",
       " 666: 'mortar',\n",
       " 667: 'mortarboard',\n",
       " 668: 'mosque',\n",
       " 669: 'mosquito net',\n",
       " 670: 'motor scooter, scooter',\n",
       " 671: 'mountain bike, all-terrain bike, off-roader',\n",
       " 672: 'mountain tent',\n",
       " 673: 'mouse, computer mouse',\n",
       " 674: 'mousetrap',\n",
       " 675: 'moving van',\n",
       " 676: 'muzzle',\n",
       " 677: 'nail',\n",
       " 678: 'neck brace',\n",
       " 679: 'necklace',\n",
       " 680: 'nipple',\n",
       " 681: 'notebook, notebook computer',\n",
       " 682: 'obelisk',\n",
       " 683: 'oboe, hautboy, hautbois',\n",
       " 684: 'ocarina, sweet potato',\n",
       " 685: 'odometer, hodometer, mileometer, milometer',\n",
       " 686: 'oil filter',\n",
       " 687: 'organ, pipe organ',\n",
       " 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n",
       " 689: 'overskirt',\n",
       " 690: 'oxcart',\n",
       " 691: 'oxygen mask',\n",
       " 692: 'packet',\n",
       " 693: 'paddle, boat paddle',\n",
       " 694: 'paddlewheel, paddle wheel',\n",
       " 695: 'padlock',\n",
       " 696: 'paintbrush',\n",
       " 697: \"pajama, pyjama, pj's, jammies\",\n",
       " 698: 'palace',\n",
       " 699: 'panpipe, pandean pipe, syrinx',\n",
       " 700: 'paper towel',\n",
       " 701: 'parachute, chute',\n",
       " 702: 'parallel bars, bars',\n",
       " 703: 'park bench',\n",
       " 704: 'parking meter',\n",
       " 705: 'passenger car, coach, carriage',\n",
       " 706: 'patio, terrace',\n",
       " 707: 'pay-phone, pay-station',\n",
       " 708: 'pedestal, plinth, footstall',\n",
       " 709: 'pencil box, pencil case',\n",
       " 710: 'pencil sharpener',\n",
       " 711: 'perfume, essence',\n",
       " 712: 'Petri dish',\n",
       " 713: 'photocopier',\n",
       " 714: 'pick, plectrum, plectron',\n",
       " 715: 'pickelhaube',\n",
       " 716: 'picket fence, paling',\n",
       " 717: 'pickup, pickup truck',\n",
       " 718: 'pier',\n",
       " 719: 'piggy bank, penny bank',\n",
       " 720: 'pill bottle',\n",
       " 721: 'pillow',\n",
       " 722: 'ping-pong ball',\n",
       " 723: 'pinwheel',\n",
       " 724: 'pirate, pirate ship',\n",
       " 725: 'pitcher, ewer',\n",
       " 726: \"plane, carpenter's plane, woodworking plane\",\n",
       " 727: 'planetarium',\n",
       " 728: 'plastic bag',\n",
       " 729: 'plate rack',\n",
       " 730: 'plow, plough',\n",
       " 731: \"plunger, plumber's helper\",\n",
       " 732: 'Polaroid camera, Polaroid Land camera',\n",
       " 733: 'pole',\n",
       " 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',\n",
       " 735: 'poncho',\n",
       " 736: 'pool table, billiard table, snooker table',\n",
       " 737: 'pop bottle, soda bottle',\n",
       " 738: 'pot, flowerpot',\n",
       " 739: \"potter's wheel\",\n",
       " 740: 'power drill',\n",
       " 741: 'prayer rug, prayer mat',\n",
       " 742: 'printer',\n",
       " 743: 'prison, prison house',\n",
       " 744: 'projectile, missile',\n",
       " 745: 'projector',\n",
       " 746: 'puck, hockey puck',\n",
       " 747: 'punching bag, punch bag, punching ball, punchball',\n",
       " 748: 'purse',\n",
       " 749: 'quill, quill pen',\n",
       " 750: 'quilt, comforter, comfort, puff',\n",
       " 751: 'racer, race car, racing car',\n",
       " 752: 'racket, racquet',\n",
       " 753: 'radiator',\n",
       " 754: 'radio, wireless',\n",
       " 755: 'radio telescope, radio reflector',\n",
       " 756: 'rain barrel',\n",
       " 757: 'recreational vehicle, RV, R.V.',\n",
       " 758: 'reel',\n",
       " 759: 'reflex camera',\n",
       " 760: 'refrigerator, icebox',\n",
       " 761: 'remote control, remote',\n",
       " 762: 'restaurant, eating house, eating place, eatery',\n",
       " 763: 'revolver, six-gun, six-shooter',\n",
       " 764: 'rifle',\n",
       " 765: 'rocking chair, rocker',\n",
       " 766: 'rotisserie',\n",
       " 767: 'rubber eraser, rubber, pencil eraser',\n",
       " 768: 'rugby ball',\n",
       " 769: 'rule, ruler',\n",
       " 770: 'running shoe',\n",
       " 771: 'safe',\n",
       " 772: 'safety pin',\n",
       " 773: 'saltshaker, salt shaker',\n",
       " 774: 'sandal',\n",
       " 775: 'sarong',\n",
       " 776: 'sax, saxophone',\n",
       " 777: 'scabbard',\n",
       " 778: 'scale, weighing machine',\n",
       " 779: 'school bus',\n",
       " 780: 'schooner',\n",
       " 781: 'scoreboard',\n",
       " 782: 'screen, CRT screen',\n",
       " 783: 'screw',\n",
       " 784: 'screwdriver',\n",
       " 785: 'seat belt, seatbelt',\n",
       " 786: 'sewing machine',\n",
       " 787: 'shield, buckler',\n",
       " 788: 'shoe shop, shoe-shop, shoe store',\n",
       " 789: 'shoji',\n",
       " 790: 'shopping basket',\n",
       " 791: 'shopping cart',\n",
       " 792: 'shovel',\n",
       " 793: 'shower cap',\n",
       " 794: 'shower curtain',\n",
       " 795: 'ski',\n",
       " 796: 'ski mask',\n",
       " 797: 'sleeping bag',\n",
       " 798: 'slide rule, slipstick',\n",
       " 799: 'sliding door',\n",
       " 800: 'slot, one-armed bandit',\n",
       " 801: 'snorkel',\n",
       " 802: 'snowmobile',\n",
       " 803: 'snowplow, snowplough',\n",
       " 804: 'soap dispenser',\n",
       " 805: 'soccer ball',\n",
       " 806: 'sock',\n",
       " 807: 'solar dish, solar collector, solar furnace',\n",
       " 808: 'sombrero',\n",
       " 809: 'soup bowl',\n",
       " 810: 'space bar',\n",
       " 811: 'space heater',\n",
       " 812: 'space shuttle',\n",
       " 813: 'spatula',\n",
       " 814: 'speedboat',\n",
       " 815: \"spider web, spider's web\",\n",
       " 816: 'spindle',\n",
       " 817: 'sports car, sport car',\n",
       " 818: 'spotlight, spot',\n",
       " 819: 'stage',\n",
       " 820: 'steam locomotive',\n",
       " 821: 'steel arch bridge',\n",
       " 822: 'steel drum',\n",
       " 823: 'stethoscope',\n",
       " 824: 'stole',\n",
       " 825: 'stone wall',\n",
       " 826: 'stopwatch, stop watch',\n",
       " 827: 'stove',\n",
       " 828: 'strainer',\n",
       " 829: 'streetcar, tram, tramcar, trolley, trolley car',\n",
       " 830: 'stretcher',\n",
       " 831: 'studio couch, day bed',\n",
       " 832: 'stupa, tope',\n",
       " 833: 'submarine, pigboat, sub, U-boat',\n",
       " 834: 'suit, suit of clothes',\n",
       " 835: 'sundial',\n",
       " 836: 'sunglass',\n",
       " 837: 'sunglasses, dark glasses, shades',\n",
       " 838: 'sunscreen, sunblock, sun blocker',\n",
       " 839: 'suspension bridge',\n",
       " 840: 'swab, swob, mop',\n",
       " 841: 'sweatshirt',\n",
       " 842: 'swimming trunks, bathing trunks',\n",
       " 843: 'swing',\n",
       " 844: 'switch, electric switch, electrical switch',\n",
       " 845: 'syringe',\n",
       " 846: 'table lamp',\n",
       " 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n",
       " 848: 'tape player',\n",
       " 849: 'teapot',\n",
       " 850: 'teddy, teddy bear',\n",
       " 851: 'television, television system',\n",
       " 852: 'tennis ball',\n",
       " 853: 'thatch, thatched roof',\n",
       " 854: 'theater curtain, theatre curtain',\n",
       " 855: 'thimble',\n",
       " 856: 'thresher, thrasher, threshing machine',\n",
       " 857: 'throne',\n",
       " 858: 'tile roof',\n",
       " 859: 'toaster',\n",
       " 860: 'tobacco shop, tobacconist shop, tobacconist',\n",
       " 861: 'toilet seat',\n",
       " 862: 'torch',\n",
       " 863: 'totem pole',\n",
       " 864: 'tow truck, tow car, wrecker',\n",
       " 865: 'toyshop',\n",
       " 866: 'tractor',\n",
       " 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',\n",
       " 868: 'tray',\n",
       " 869: 'trench coat',\n",
       " 870: 'tricycle, trike, velocipede',\n",
       " 871: 'trimaran',\n",
       " 872: 'tripod',\n",
       " 873: 'triumphal arch',\n",
       " 874: 'trolleybus, trolley coach, trackless trolley',\n",
       " 875: 'trombone',\n",
       " 876: 'tub, vat',\n",
       " 877: 'turnstile',\n",
       " 878: 'typewriter keyboard',\n",
       " 879: 'umbrella',\n",
       " 880: 'unicycle, monocycle',\n",
       " 881: 'upright, upright piano',\n",
       " 882: 'vacuum, vacuum cleaner',\n",
       " 883: 'vase',\n",
       " 884: 'vault',\n",
       " 885: 'velvet',\n",
       " 886: 'vending machine',\n",
       " 887: 'vestment',\n",
       " 888: 'viaduct',\n",
       " 889: 'violin, fiddle',\n",
       " 890: 'volleyball',\n",
       " 891: 'waffle iron',\n",
       " 892: 'wall clock',\n",
       " 893: 'wallet, billfold, notecase, pocketbook',\n",
       " 894: 'wardrobe, closet, press',\n",
       " 895: 'warplane, military plane',\n",
       " 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n",
       " 897: 'washer, automatic washer, washing machine',\n",
       " 898: 'water bottle',\n",
       " 899: 'water jug',\n",
       " 900: 'water tower',\n",
       " 901: 'whiskey jug',\n",
       " 902: 'whistle',\n",
       " 903: 'wig',\n",
       " 904: 'window screen',\n",
       " 905: 'window shade',\n",
       " 906: 'Windsor tie',\n",
       " 907: 'wine bottle',\n",
       " 908: 'wing',\n",
       " 909: 'wok',\n",
       " 910: 'wooden spoon',\n",
       " 911: 'wool, woolen, woollen',\n",
       " 912: 'worm fence, snake fence, snake-rail fence, Virginia fence',\n",
       " 913: 'wreck',\n",
       " 914: 'yawl',\n",
       " 915: 'yurt',\n",
       " 916: 'web site, website, internet site, site',\n",
       " 917: 'comic book',\n",
       " 918: 'crossword puzzle, crossword',\n",
       " 919: 'street sign',\n",
       " 920: 'traffic light, traffic signal, stoplight',\n",
       " 921: 'book jacket, dust cover, dust jacket, dust wrapper',\n",
       " 922: 'menu',\n",
       " 923: 'plate',\n",
       " 924: 'guacamole',\n",
       " 925: 'consomme',\n",
       " 926: 'hot pot, hotpot',\n",
       " 927: 'trifle',\n",
       " 928: 'ice cream, icecream',\n",
       " 929: 'ice lolly, lolly, lollipop, popsicle',\n",
       " 930: 'French loaf',\n",
       " 931: 'bagel, beigel',\n",
       " 932: 'pretzel',\n",
       " 933: 'cheeseburger',\n",
       " 934: 'hotdog, hot dog, red hot',\n",
       " 935: 'mashed potato',\n",
       " 936: 'head cabbage',\n",
       " 937: 'broccoli',\n",
       " 938: 'cauliflower',\n",
       " 939: 'zucchini, courgette',\n",
       " 940: 'spaghetti squash',\n",
       " 941: 'acorn squash',\n",
       " 942: 'butternut squash',\n",
       " 943: 'cucumber, cuke',\n",
       " 944: 'artichoke, globe artichoke',\n",
       " 945: 'bell pepper',\n",
       " 946: 'cardoon',\n",
       " 947: 'mushroom',\n",
       " 948: 'Granny Smith',\n",
       " 949: 'strawberry',\n",
       " 950: 'orange',\n",
       " 951: 'lemon',\n",
       " 952: 'fig',\n",
       " 953: 'pineapple, ananas',\n",
       " 954: 'banana',\n",
       " 955: 'jackfruit, jak, jack',\n",
       " 956: 'custard apple',\n",
       " 957: 'pomegranate',\n",
       " 958: 'hay',\n",
       " 959: 'carbonara',\n",
       " 960: 'chocolate sauce, chocolate syrup',\n",
       " 961: 'dough',\n",
       " 962: 'meat loaf, meatloaf',\n",
       " 963: 'pizza, pizza pie',\n",
       " 964: 'potpie',\n",
       " 965: 'burrito',\n",
       " 966: 'red wine',\n",
       " 967: 'espresso',\n",
       " 968: 'cup',\n",
       " 969: 'eggnog',\n",
       " 970: 'alp',\n",
       " 971: 'bubble',\n",
       " 972: 'cliff, drop, drop-off',\n",
       " 973: 'coral reef',\n",
       " 974: 'geyser',\n",
       " 975: 'lakeside, lakeshore',\n",
       " 976: 'promontory, headland, head, foreland',\n",
       " 977: 'sandbar, sand bar',\n",
       " 978: 'seashore, coast, seacoast, sea-coast',\n",
       " 979: 'valley, vale',\n",
       " 980: 'volcano',\n",
       " 981: 'ballplayer, baseball player',\n",
       " 982: 'groom, bridegroom',\n",
       " 983: 'scuba diver',\n",
       " 984: 'rapeseed',\n",
       " 985: 'daisy',\n",
       " 986: \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n",
       " 987: 'corn',\n",
       " 988: 'acorn',\n",
       " 989: 'hip, rose hip, rosehip',\n",
       " 990: 'buckeye, horse chestnut, conker',\n",
       " 991: 'coral fungus',\n",
       " 992: 'agaric',\n",
       " 993: 'gyromitra',\n",
       " 994: 'stinkhorn, carrion fungus',\n",
       " 995: 'earthstar',\n",
       " 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',\n",
       " 997: 'bolete',\n",
       " 998: 'ear, spike, capitulum',\n",
       " 999: 'toilet tissue, toilet paper, bathroom tissue'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from ipywidgets import widgets  # make this interactive!\n",
    "\n",
    "import torch\n",
    "\n",
    "# You can look at this for additional parameters used to train this model\n",
    "# state_dict = torch.load('138k/state_dict.pth', map_location=torch.device('cpu'))\n",
    "# state_dict\n",
    "\n",
    "# import the image net class names in proper order\n",
    "#. this is imported as a dictionary \n",
    "# this is a separate file \n",
    "from imagenet_utils import class_names\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "representative-solution",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding attention layer in G at resolution 64\n",
      "Param count for Gs initialized parameters: 70433795\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (activation): ReLU()\n",
       "  (shared): Embedding(1000, 128)\n",
       "  (linear): SNLinear(in_features=20, out_features=24576, bias=True)\n",
       "  (blocks): ModuleList(\n",
       "    (0): ModuleList(\n",
       "      (0): GBlock(\n",
       "        (activation): ReLU()\n",
       "        (conv1): SNConv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): SNConv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv_sc): SNConv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (bn1): ccbn(\n",
       "          out: 1536, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "        )\n",
       "        (bn2): ccbn(\n",
       "          out: 1536, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): ModuleList(\n",
       "      (0): GBlock(\n",
       "        (activation): ReLU()\n",
       "        (conv1): SNConv2d(1536, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): SNConv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv_sc): SNConv2d(1536, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (bn1): ccbn(\n",
       "          out: 1536, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=1536, bias=False)\n",
       "        )\n",
       "        (bn2): ccbn(\n",
       "          out: 768, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=768, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=768, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (2): ModuleList(\n",
       "      (0): GBlock(\n",
       "        (activation): ReLU()\n",
       "        (conv1): SNConv2d(768, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): SNConv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv_sc): SNConv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (bn1): ccbn(\n",
       "          out: 768, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=768, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=768, bias=False)\n",
       "        )\n",
       "        (bn2): ccbn(\n",
       "          out: 384, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=384, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=384, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (3): ModuleList(\n",
       "      (0): GBlock(\n",
       "        (activation): ReLU()\n",
       "        (conv1): SNConv2d(384, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): SNConv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv_sc): SNConv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (bn1): ccbn(\n",
       "          out: 384, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=384, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=384, bias=False)\n",
       "        )\n",
       "        (bn2): ccbn(\n",
       "          out: 192, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=192, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=192, bias=False)\n",
       "        )\n",
       "      )\n",
       "      (1): Attention(\n",
       "        (theta): SNConv2d(192, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (phi): SNConv2d(192, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (g): SNConv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (o): SNConv2d(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (4): ModuleList(\n",
       "      (0): GBlock(\n",
       "        (activation): ReLU()\n",
       "        (conv1): SNConv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): SNConv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv_sc): SNConv2d(192, 96, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (bn1): ccbn(\n",
       "          out: 192, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=192, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=192, bias=False)\n",
       "        )\n",
       "        (bn2): ccbn(\n",
       "          out: 96, in: 148, cross_replica=False\n",
       "          (gain): SNLinear(in_features=148, out_features=96, bias=False)\n",
       "          (bias): SNLinear(in_features=148, out_features=96, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (output_layer): Sequential(\n",
       "    (0): bn()\n",
       "    (1): ReLU()\n",
       "    (2): SNConv2d(96, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from BigGAN import Generator\n",
    "\n",
    "# setup Generator like it was trained\n",
    "#. these are parameters from a pre-trained model (see state_vect above)\n",
    "G = Generator(G_ch=96, \n",
    "        dim_z=120, \n",
    "        resolution=128,\n",
    "        G_attn='64', \n",
    "        n_classes=1000,\n",
    "        num_G_SVs=1, \n",
    "        num_G_SV_itrs=1,\n",
    "        G_shared=True, \n",
    "        shared_dim=128, \n",
    "        hier=True,\n",
    "        mybn=False, \n",
    "        G_param='SN', \n",
    "        norm_style='bn',\n",
    "             )\n",
    "\n",
    "# setup the weights and set to evaluation mode\n",
    "torch_device = 'cpu' # mps is for an M1 mac, but could also be 'cpu' or 'cuda' for other systems\n",
    "G.load_state_dict(\n",
    "      torch.load('models/138k/G.pth', map_location=torch.device(torch_device)),\n",
    "      strict=True)\n",
    "\n",
    "G.eval()# set to eval mode for all that batch norm stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intended-accent",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Sample From BigGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "sufficient-register",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.5 s, sys: 1.66 s, total: 13.2 s\n",
      "Wall time: 3.21 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# setup sampling parameters\n",
    "num_images = 16\n",
    "preferred_class = 706 #31 # tree frog\n",
    "# 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
    "# 400: \"academic gown, academic robe, judge's robe\",\n",
    "# 422: 'barbell',\n",
    "# 706: 'patio, terrace',\n",
    "\n",
    "# sample from latent space\n",
    "z = torch.randn(num_images, 120, device=torch_device, requires_grad=False)\n",
    "z = z/2 # clamp the sampling\n",
    "z = z.to(torch_device,torch.float32) \n",
    "\n",
    "# sample from class (will treat as if one hot encoded, but integer input)\n",
    "y = torch.zeros(num_images, device=torch_device, requires_grad=False)\n",
    "if preferred_class is None:\n",
    "    y = y.random_(0,999) # now make some random classes\n",
    "\n",
    "else:\n",
    "    y = y+preferred_class # just choose one class\n",
    "    \n",
    "y = y.to(torch_device, torch.int64)    \n",
    "\n",
    "# print(z.numpy().shape)\n",
    "# print(y.numpy())\n",
    "\n",
    "# run samples through the generator\n",
    "with torch.no_grad():\n",
    "    G_z = G(z, G.shared(y))\n",
    "    \n",
    "images = G_z.detach().numpy()\n",
    "images = (images-images.min())/(images.max()-images.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "hydraulic-tamil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d76d6ffcd3b047af8fea0cd06db5fec3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=7, description='idx', max=15), Output()), _dom_classes=('widget-interact…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plt_generator(idx)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plt_generator(idx):\n",
    "    im = images[idx]\n",
    "    class_name = class_names[y.numpy()[idx]]\n",
    "    im = np.swapaxes(im,0,2) # get channels placed right\n",
    "    im = np.swapaxes(im,0,1) # transpose\n",
    "    plt.imshow(im)\n",
    "    plt.title(class_name)\n",
    "\n",
    "widgets.interact(plt_generator,idx=(0,num_images-1,1),__manual=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "antique-sarah",
   "metadata": {},
   "source": [
    "# Interpolating functions\n",
    "Now let's interpolate between two random examples. The $z$ interpolation is quite easy to understand: \n",
    "$$z_{i}=z_1\\cdot w + z_2\\cdot(1-w)$$ \n",
    "where $w$ is swept from 0 to 1.\n",
    "\n",
    "We do the same with the classes, except we interpolate the shared embedding (rather than the one hot encoding). \n",
    "\n",
    "$$ \\hat{y}_{i}=\\hat{y}_1\\cdot w + \\hat{y}_2\\cdot(1-w)  $$ \n",
    "\n",
    "where $\\hat{y}=G_{shared}(y_{OHE})$. And the shared embedding is exposed in the PyTorch BigGAN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "linear-trademark",
   "metadata": {},
   "outputs": [],
   "source": [
    "# do some interpolations? Sure.\n",
    "num_interp = 16\n",
    "class_A = preferred_class # same as above\n",
    "class_B = 934 # student choice!!!\n",
    "# 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n",
    "# 934: 'hotdog, hot dog, red hot',\n",
    "\n",
    "# sample from latent space\n",
    "# generate two latent vectors\n",
    "x0 = torch.randn(1, 1, 120, device='cpu', requires_grad=False)\n",
    "x1 = torch.randn(1, 1, 120, device='cpu', requires_grad=False)\n",
    "# get vector of linearly spaced weights 0 to 1\n",
    "lerp = torch.linspace(0, 1.0, num_interp + 2, device='cpu').to(torch.float32)\n",
    "\n",
    "# now interpolate the two vectors (they are broadcast together along new dimension)\n",
    "# this broadcast repeats x0 and x1 to get linear sum\n",
    "z = ((x0 * (1 - lerp.view(1, -1, 1))) + (x1 * lerp.view(1, -1, 1)))\n",
    "z = z.view(-1, 120) # now reshape to be same latent space size\n",
    "z = z.to('cpu',torch.float32) \n",
    "\n",
    "# sample from class (act as if one hot encoded, but integer)\n",
    "# just grab two examples that we want from each class\n",
    "y = torch.zeros(2, requires_grad=False)\n",
    "y[0] = y[0]+class_A\n",
    "y[1] = y[1]+class_B\n",
    "y = y.to('cpu',torch.int64) \n",
    "y_shared = G.shared(y)\n",
    "# now interpolate the classes based on their embedding\n",
    "# this is the same interpolation as above, where broadcasting creates new vectors\n",
    "y_shared = ((y_shared[0] * (1 - lerp.view(1, -1, 1))) + (y_shared[1] * lerp.view(1, -1, 1)))\n",
    "y_shared = y_shared.view(-1, 128) # 128 is size of embedding in BigGAN\n",
    "y_shared = y_shared.to('cpu',torch.float32)\n",
    "\n",
    "# run samples through the generator\n",
    "with torch.no_grad():\n",
    "    G_z = G(z, y_shared)\n",
    "    \n",
    "images = G_z.detach().numpy()\n",
    "images = (images-images.min())/(images.max()-images.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "incredible-cooler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4eb949d81c154d608671e6ad0ec3105d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=8, description='idx', max=17), Output()), _dom_classes=('widget-interact…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plt_generator(idx)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plt_generator(idx):\n",
    "    im = images[idx]\n",
    "    c1 = class_names[class_A]\n",
    "    c2 = class_names[class_B]\n",
    "    im = np.swapaxes(im,0,2) # get channels placed right\n",
    "    im = np.swapaxes(im,0,1) # transpose\n",
    "    plt.imshow(im)\n",
    "    plt.title(f'{c1} $\\leftrightarrow$ {c2}')\n",
    "\n",
    "widgets.interact(plt_generator,idx=(0,len(images)-1,1),__manual=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "extraordinary-simulation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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