From b4db7175014421dd77b20c05806a9dda3fa9eb1e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dirnb=C3=B6ck=2C=20Jasmin=20Viktoria?= Date: Mon, 10 Jun 2024 08:28:17 +0200 Subject: [PATCH] initial commit --- .gitignore | 3 + README.md | 5 +- args.py | 118 + assets/imagenet1000_clsidx_to_labels.txt | 1000 ++++ assets/imagenet_class_to_id_map.json | 1 + assets/imagenet_patch.gz | Bin 0 -> 254407 bytes assets/imagenet_test_image_ids.txt | 5000 ++++++++++++++++++++ assets/resnet18_best_sparse_rs_patches.tar | Bin 0 -> 6021944 bytes calculate_score_based_mask.py | 128 + data/__init__.py | 1 + data/loader.py | 160 + evaluate_imagenet_c.py | 175 + evaluate_imagenet_patch.py | 198 + magnitude_based/__init__.py | 1 + magnitude_based/prune.py | 28 + models/__init__.py | 14 + models/resnet.py | 231 + prune_with_augmentation.py | 261 + sample_script_magnitude_based_pruning.sh | 26 + sample_script_score_based_pruning.sh | 30 + score_based/__init__.py | 2 + score_based/layers.py | 120 + score_based/model.py | 245 + utils/__init__.py | 10 + utils/apply_patch.py | 84 + utils/calibration_tools.py | 33 + utils/cutout.py | 80 + utils/eval.py | 104 + utils/loaders.py | 184 + utils/logging.py | 132 + utils/metrics_sparsity.py | 40 + utils/pixmix_utils.py | 231 + utils/schedules.py | 88 + utils/train.py | 50 + 34 files changed, 8782 insertions(+), 1 deletion(-) create mode 100644 .gitignore create mode 100644 args.py create mode 100644 assets/imagenet1000_clsidx_to_labels.txt create mode 100644 assets/imagenet_class_to_id_map.json create mode 100644 assets/imagenet_patch.gz create mode 100644 assets/imagenet_test_image_ids.txt create mode 100644 assets/resnet18_best_sparse_rs_patches.tar create mode 100644 calculate_score_based_mask.py create mode 100644 data/__init__.py create mode 100644 data/loader.py create mode 100644 evaluate_imagenet_c.py create mode 100644 evaluate_imagenet_patch.py create mode 100644 magnitude_based/__init__.py create mode 100644 magnitude_based/prune.py create mode 100644 models/__init__.py create mode 100644 models/resnet.py create mode 100644 prune_with_augmentation.py create mode 100644 sample_script_magnitude_based_pruning.sh create mode 100644 sample_script_score_based_pruning.sh create mode 100644 score_based/__init__.py create mode 100644 score_based/layers.py create mode 100644 score_based/model.py create mode 100644 utils/__init__.py create mode 100644 utils/apply_patch.py create mode 100644 utils/calibration_tools.py create mode 100644 utils/cutout.py create mode 100644 utils/eval.py create mode 100644 utils/loaders.py create mode 100644 utils/logging.py create mode 100644 utils/metrics_sparsity.py create mode 100644 utils/pixmix_utils.py create mode 100644 utils/schedules.py create mode 100644 utils/train.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d37545f --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +*__pycache__* +*.py[cod] +checkpoints/ \ No newline at end of file diff --git a/README.md b/README.md index da3df60..70c8c6b 100644 --- a/README.md +++ b/README.md @@ -23,5 +23,8 @@ Authors of this work are affiliated with Graz University of Technology, Institut Parts of this code repository is based on the following works: +* https://github.com/pralab/ImageNet-Patch * https://github.com/inspire-group/hydra -* ... +* https://github.com/allenai/hidden-networks +* https://github.com/andyzoujm/pixmix +* https://github.com/uoguelph-mlrg/Cutout diff --git a/args.py b/args.py new file mode 100644 index 0000000..3a070ca --- /dev/null +++ b/args.py @@ -0,0 +1,118 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser(description="PyTorch Training") + + parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', + help='model architecture (default: resnet50)') + parser.add_argument('--pretrained', dest='pretrained', action='store_true', + help='use pre-trained model') + parser.add_argument('--test_all', action='store_true', + help='Run all validation set (default: will run 5k test samples from RobustBench)') + parser.add_argument('-c', '--corruption', action='store_true', + help='Corruptions to be used for test set evaluations') + parser.add_argument('--augmentation', default="standard", type=str, + help='augmentation type') + parser.add_argument('--dir', default='/data/', type=str, metavar='DIR', + help='Path to dataset ') + parser.add_argument('--mixing_set', default='/data/fractals_and_fvis/', type=str, metavar='DIR', + help='Path to mixing set') + parser.add_argument('--batch_size', default=1, type=int, metavar='N', + help='Evaluation mini-batch size (default: 1)') + parser.add_argument('--path', default=None, type=str, metavar='PATH', + help='Path for trained model checkpoint to load') + parser.add_argument('--seed', default=42, type=int, metavar='N', + help='Randomization seed number (default: 42)') + parser.add_argument('--prune', action='store_true', + help='Prune given model') + parser.add_argument('--pruning_ratio', default=0, type=float, metavar='N', + help='Pruning Ratio to prune model') + parser.add_argument('--pruning_type', default='global', type=str, + help='Pruning type: "global", "local"') + parser.add_argument('--model_name', default=None, type=str, + help='Save Model filename location') + parser.add_argument('--lr', '--learning-rate', default=0.01, type=float, + metavar='LR', help='initial learning rate', dest='lr') + parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') + parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)', + dest='weight_decay') + parser.add_argument('--epochs', default=90, type=int, metavar='N', + help='number of total epochs to run') + parser.add_argument('--print-freq', default=10, type=int, + metavar='N', help='print frequency (default: 10)') + parser.add_argument('--start_epoch', default=0, type=int, metavar='N', + help='number of total epochs to run') + parser.add_argument("--optimizer", type=str, default="sgd", + choices=("sgd", "adam", "rmsprop")) + parser.add_argument("--warmup-epochs", type=int, default=0, + help="Number of warmup epochs") + parser.add_argument("--num-classes", type=int, default=1000, + help="Number of output classes in the model") + parser.add_argument("--resume", type=str, default="", + help="path to latest checkpoint (default:None)") + parser.add_argument( "--lr-schedule", type=str, default="cosine", choices=("step", "cosine"), + help="Learning rate schedule") + + # PixMix arguments + parser.add_argument('--aug-severity', default=1, type=int, + help='Severity of base augmentation operators') + parser.add_argument( '--beta', default=4, type=int, + help='Severity of mixing') + parser.add_argument('--k_mixing', default=4,type=int, + help='Mixing iterations') + parser.add_argument('--all-ops', '--all', action='store_true', default=True, + help='Turn on all augmentation operations (+brightness,contrast,color,sharpness).') + # CutOut arguments + parser.add_argument('--cutout_length', default=56, type=int, metavar='N', + help='length of hole (usually half of dim)') + + + #Score Based Pruning + parser.add_argument( + "--layer-type", type=str, choices=("dense", "subnet"), help="dense | subnet" + ) + parser.add_argument( + "--scaled-score-init", + action="store_true", + default=False, + help="Init importance scores proportaional to weights (default kaiming init)", + ) + parser.add_argument( + "--exp-mode", + type=str, + choices=("pretrain", "prune", "finetune"), + default="prune", + help="Train networks following one of these methods.", + ) + parser.add_argument( + "--freeze-bn", + action="store_true", + default=False, + help="freeze batch-norm parameters in pruning", + ) + parser.add_argument( + "--scores_init_type", + choices=("kaiming_normal", "kaiming_uniform", "xavier_uniform", "xavier_normal", "weight_magnitude"), + help="Which init to use for relevance scores", + ) + parser.add_argument( + "--init_type", + choices=("kaiming_normal", "kaiming_uniform", "signed_const"), + help="Which init to use for weight parameters: kaiming_normal | kaiming_uniform | signed_const", + ) + parser.add_argument( + "--k", + type=float, + default=1.0, + help="Fraction of weight variables kept in subnet", + ) + parser.add_argument( + "--save-dense", + action="store_true", + default=False, + help="Save dense model alongwith subnets.", + ) + + return parser.parse_args() diff --git a/assets/imagenet1000_clsidx_to_labels.txt b/assets/imagenet1000_clsidx_to_labels.txt new file mode 100644 index 0000000..2e3ae32 --- /dev/null +++ b/assets/imagenet1000_clsidx_to_labels.txt @@ -0,0 +1,1000 @@ +{0: 'tench, Tinca tinca', + 1: 'goldfish, Carassius auratus', + 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', + 3: 'tiger shark, Galeocerdo cuvieri', + 4: 'hammerhead, hammerhead shark', + 5: 'electric ray, crampfish, numbfish, torpedo', + 6: 'stingray', + 7: 'cock', + 8: 'hen', + 9: 'ostrich, Struthio camelus', + 10: 'brambling, Fringilla montifringilla', + 11: 'goldfinch, Carduelis carduelis', + 12: 'house finch, linnet, Carpodacus mexicanus', + 13: 'junco, snowbird', + 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', + 15: 'robin, American robin, Turdus migratorius', + 16: 'bulbul', + 17: 'jay', + 18: 'magpie', + 19: 'chickadee', + 20: 'water ouzel, dipper', + 21: 'kite', + 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', + 23: 'vulture', + 24: 'great grey owl, great gray owl, Strix nebulosa', + 25: 'European fire salamander, Salamandra salamandra', + 26: 'common newt, Triturus vulgaris', + 27: 'eft', + 28: 'spotted salamander, Ambystoma maculatum', + 29: 'axolotl, mud puppy, Ambystoma mexicanum', + 30: 'bullfrog, Rana catesbeiana', + 31: 'tree frog, tree-frog', + 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', + 33: 'loggerhead, loggerhead turtle, Caretta caretta', + 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', + 35: 'mud turtle', + 36: 'terrapin', + 37: 'box turtle, box tortoise', + 38: 'banded gecko', + 39: 'common iguana, iguana, Iguana iguana', + 40: 'American chameleon, anole, Anolis carolinensis', + 41: 'whiptail, whiptail lizard', + 42: 'agama', + 43: 'frilled lizard, Chlamydosaurus kingi', + 44: 'alligator lizard', + 45: 'Gila monster, Heloderma suspectum', + 46: 'green lizard, Lacerta viridis', + 47: 'African chameleon, Chamaeleo chamaeleon', + 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', + 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', + 50: 'American alligator, Alligator mississipiensis', + 51: 'triceratops', + 52: 'thunder snake, worm snake, Carphophis amoenus', + 53: 'ringneck snake, ring-necked snake, ring snake', + 54: 'hognose snake, puff adder, sand viper', + 55: 'green snake, grass snake', + 56: 'king snake, kingsnake', + 57: 'garter snake, grass snake', + 58: 'water snake', + 59: 'vine snake', + 60: 'night snake, Hypsiglena torquata', + 61: 'boa constrictor, Constrictor constrictor', + 62: 'rock python, rock snake, Python sebae', + 63: 'Indian cobra, Naja naja', + 64: 'green mamba', + 65: 'sea snake', + 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', + 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', + 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', + 69: 'trilobite', + 70: 'harvestman, daddy longlegs, Phalangium opilio', + 71: 'scorpion', + 72: 'black and gold garden spider, Argiope aurantia', + 73: 'barn spider, Araneus cavaticus', + 74: 'garden spider, Aranea diademata', + 75: 'black widow, Latrodectus mactans', + 76: 'tarantula', + 77: 'wolf spider, hunting spider', + 78: 'tick', + 79: 'centipede', + 80: 'black grouse', + 81: 'ptarmigan', + 82: 'ruffed grouse, partridge, Bonasa umbellus', + 83: 'prairie chicken, prairie grouse, prairie fowl', + 84: 'peacock', + 85: 'quail', + 86: 'partridge', + 87: 'African grey, African gray, Psittacus erithacus', + 88: 'macaw', + 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', + 90: 'lorikeet', + 91: 'coucal', + 92: 'bee eater', + 93: 'hornbill', + 94: 'hummingbird', + 95: 'jacamar', + 96: 'toucan', + 97: 'drake', + 98: 'red-breasted merganser, Mergus serrator', + 99: 'goose', + 100: 'black swan, Cygnus atratus', + 101: 'tusker', + 102: 'echidna, spiny anteater, anteater', + 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', + 104: 'wallaby, brush kangaroo', + 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', + 106: 'wombat', + 107: 'jellyfish', + 108: 'sea anemone, anemone', + 109: 'brain coral', + 110: 'flatworm, platyhelminth', + 111: 'nematode, nematode worm, roundworm', + 112: 'conch', + 113: 'snail', + 114: 'slug', + 115: 'sea slug, nudibranch', + 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', + 117: 'chambered nautilus, pearly nautilus, nautilus', + 118: 'Dungeness crab, Cancer magister', + 119: 'rock crab, Cancer irroratus', + 120: 'fiddler crab', + 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', + 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', + 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', + 124: 'crayfish, crawfish, crawdad, crawdaddy', + 125: 'hermit crab', + 126: 'isopod', + 127: 'white stork, Ciconia ciconia', + 128: 'black stork, Ciconia nigra', + 129: 'spoonbill', + 130: 'flamingo', + 131: 'little blue heron, Egretta caerulea', + 132: 'American egret, great white heron, Egretta albus', + 133: 'bittern', + 134: 'crane', + 135: 'limpkin, Aramus pictus', + 136: 'European gallinule, Porphyrio porphyrio', + 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', + 138: 'bustard', + 139: 'ruddy turnstone, Arenaria interpres', + 140: 'red-backed sandpiper, dunlin, Erolia alpina', + 141: 'redshank, Tringa totanus', + 142: 'dowitcher', + 143: 'oystercatcher, oyster catcher', + 144: 'pelican', + 145: 'king penguin, Aptenodytes patagonica', + 146: 'albatross, mollymawk', + 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', + 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', + 149: 'dugong, Dugong dugon', + 150: 'sea lion', + 151: 'Chihuahua', + 152: 'Japanese spaniel', + 153: 'Maltese dog, Maltese terrier, Maltese', + 154: 'Pekinese, Pekingese, Peke', + 155: 'Shih-Tzu', + 156: 'Blenheim spaniel', + 157: 'papillon', + 158: 'toy terrier', + 159: 'Rhodesian ridgeback', + 160: 'Afghan hound, Afghan', + 161: 'basset, basset hound', + 162: 'beagle', + 163: 'bloodhound, sleuthhound', + 164: 'bluetick', + 165: 'black-and-tan coonhound', + 166: 'Walker hound, Walker foxhound', + 167: 'English foxhound', + 168: 'redbone', + 169: 'borzoi, Russian wolfhound', + 170: 'Irish wolfhound', + 171: 'Italian greyhound', + 172: 'whippet', + 173: 'Ibizan hound, Ibizan Podenco', + 174: 'Norwegian elkhound, elkhound', + 175: 'otterhound, otter hound', + 176: 'Saluki, gazelle hound', + 177: 'Scottish deerhound, deerhound', + 178: 'Weimaraner', + 179: 'Staffordshire bullterrier, Staffordshire bull terrier', + 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', + 181: 'Bedlington terrier', + 182: 'Border terrier', + 183: 'Kerry blue terrier', + 184: 'Irish terrier', + 185: 'Norfolk terrier', + 186: 'Norwich terrier', + 187: 'Yorkshire terrier', + 188: 'wire-haired fox terrier', + 189: 'Lakeland terrier', + 190: 'Sealyham terrier, Sealyham', + 191: 'Airedale, Airedale terrier', + 192: 'cairn, cairn terrier', + 193: 'Australian terrier', + 194: 'Dandie Dinmont, Dandie Dinmont terrier', + 195: 'Boston bull, Boston terrier', + 196: 'miniature schnauzer', + 197: 'giant schnauzer', + 198: 'standard schnauzer', + 199: 'Scotch terrier, Scottish terrier, Scottie', + 200: 'Tibetan terrier, chrysanthemum dog', + 201: 'silky terrier, Sydney silky', + 202: 'soft-coated wheaten terrier', + 203: 'West Highland white terrier', + 204: 'Lhasa, Lhasa apso', + 205: 'flat-coated retriever', + 206: 'curly-coated retriever', + 207: 'golden retriever', + 208: 'Labrador retriever', + 209: 'Chesapeake Bay retriever', + 210: 'German short-haired pointer', + 211: 'vizsla, Hungarian pointer', + 212: 'English setter', + 213: 'Irish setter, red setter', + 214: 'Gordon setter', + 215: 'Brittany spaniel', + 216: 'clumber, clumber spaniel', + 217: 'English springer, English springer spaniel', + 218: 'Welsh springer spaniel', + 219: 'cocker spaniel, English cocker spaniel, cocker', + 220: 'Sussex spaniel', + 221: 'Irish water spaniel', + 222: 'kuvasz', + 223: 'schipperke', + 224: 'groenendael', + 225: 'malinois', + 226: 'briard', + 227: 'kelpie', + 228: 'komondor', + 229: 'Old English sheepdog, bobtail', + 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', + 231: 'collie', + 232: 'Border collie', + 233: 'Bouvier des Flandres, Bouviers des Flandres', + 234: 'Rottweiler', + 235: 'German shepherd, German shepherd dog, German police dog, alsatian', + 236: 'Doberman, Doberman pinscher', + 237: 'miniature pinscher', + 238: 'Greater Swiss Mountain dog', + 239: 'Bernese mountain dog', + 240: 'Appenzeller', + 241: 'EntleBucher', + 242: 'boxer', + 243: 'bull mastiff', + 244: 'Tibetan mastiff', + 245: 'French bulldog', + 246: 'Great Dane', + 247: 'Saint Bernard, St Bernard', + 248: 'Eskimo dog, husky', + 249: 'malamute, malemute, Alaskan malamute', + 250: 'Siberian husky', + 251: 'dalmatian, coach dog, carriage dog', + 252: 'affenpinscher, monkey pinscher, monkey dog', + 253: 'basenji', + 254: 'pug, pug-dog', + 255: 'Leonberg', + 256: 'Newfoundland, Newfoundland dog', + 257: 'Great Pyrenees', + 258: 'Samoyed, Samoyede', + 259: 'Pomeranian', + 260: 'chow, chow chow', + 261: 'keeshond', + 262: 'Brabancon griffon', + 263: 'Pembroke, Pembroke Welsh corgi', + 264: 'Cardigan, Cardigan Welsh corgi', + 265: 'toy poodle', + 266: 'miniature poodle', + 267: 'standard poodle', + 268: 'Mexican hairless', + 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', + 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', + 271: 'red wolf, maned wolf, Canis rufus, Canis niger', + 272: 'coyote, prairie wolf, brush wolf, Canis latrans', + 273: 'dingo, warrigal, warragal, Canis dingo', + 274: 'dhole, Cuon alpinus', + 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', + 276: 'hyena, hyaena', + 277: 'red fox, Vulpes vulpes', + 278: 'kit fox, Vulpes macrotis', + 279: 'Arctic fox, white fox, Alopex lagopus', + 280: 'grey fox, gray fox, Urocyon cinereoargenteus', + 281: 'tabby, tabby cat', + 282: 'tiger cat', + 283: 'Persian cat', + 284: 'Siamese cat, Siamese', + 285: 'Egyptian cat', + 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', + 287: 'lynx, catamount', + 288: 'leopard, Panthera pardus', + 289: 'snow leopard, ounce, Panthera uncia', + 290: 'jaguar, panther, Panthera onca, Felis onca', + 291: 'lion, king of beasts, Panthera leo', + 292: 'tiger, Panthera tigris', + 293: 'cheetah, chetah, Acinonyx jubatus', + 294: 'brown bear, bruin, Ursus arctos', + 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', + 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', + 297: 'sloth bear, Melursus ursinus, Ursus ursinus', + 298: 'mongoose', + 299: 'meerkat, mierkat', + 300: 'tiger beetle', + 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', + 302: 'ground beetle, carabid beetle', + 303: 'long-horned beetle, longicorn, longicorn beetle', + 304: 'leaf beetle, chrysomelid', + 305: 'dung beetle', + 306: 'rhinoceros beetle', + 307: 'weevil', + 308: 'fly', + 309: 'bee', + 310: 'ant, emmet, pismire', + 311: 'grasshopper, hopper', + 312: 'cricket', + 313: 'walking stick, walkingstick, stick insect', + 314: 'cockroach, roach', + 315: 'mantis, mantid', + 316: 'cicada, cicala', + 317: 'leafhopper', + 318: 'lacewing, lacewing fly', + 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + 320: 'damselfly', + 321: 'admiral', + 322: 'ringlet, ringlet butterfly', + 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', + 324: 'cabbage butterfly', + 325: 'sulphur butterfly, sulfur butterfly', + 326: 'lycaenid, lycaenid butterfly', + 327: 'starfish, sea star', + 328: 'sea urchin', + 329: 'sea cucumber, holothurian', + 330: 'wood rabbit, cottontail, cottontail rabbit', + 331: 'hare', + 332: 'Angora, Angora rabbit', + 333: 'hamster', + 334: 'porcupine, hedgehog', + 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', + 336: 'marmot', + 337: 'beaver', + 338: 'guinea pig, Cavia cobaya', + 339: 'sorrel', + 340: 'zebra', + 341: 'hog, pig, grunter, squealer, Sus scrofa', + 342: 'wild boar, boar, Sus scrofa', + 343: 'warthog', + 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', + 345: 'ox', + 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', + 347: 'bison', + 348: 'ram, tup', + 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', + 350: 'ibex, Capra ibex', + 351: 'hartebeest', + 352: 'impala, Aepyceros melampus', + 353: 'gazelle', + 354: 'Arabian camel, dromedary, Camelus dromedarius', + 355: 'llama', + 356: 'weasel', + 357: 'mink', + 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', + 359: 'black-footed ferret, ferret, Mustela nigripes', + 360: 'otter', + 361: 'skunk, polecat, wood pussy', + 362: 'badger', + 363: 'armadillo', + 364: 'three-toed sloth, ai, Bradypus tridactylus', + 365: 'orangutan, orang, orangutang, Pongo pygmaeus', + 366: 'gorilla, Gorilla gorilla', + 367: 'chimpanzee, chimp, Pan troglodytes', + 368: 'gibbon, Hylobates lar', + 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', + 370: 'guenon, guenon monkey', + 371: 'patas, hussar monkey, Erythrocebus patas', + 372: 'baboon', + 373: 'macaque', + 374: 'langur', + 375: 'colobus, colobus monkey', + 376: 'proboscis monkey, Nasalis larvatus', + 377: 'marmoset', + 378: 'capuchin, ringtail, Cebus capucinus', + 379: 'howler monkey, howler', + 380: 'titi, titi monkey', + 381: 'spider monkey, Ateles geoffroyi', + 382: 'squirrel monkey, Saimiri sciureus', + 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', + 384: 'indri, indris, Indri indri, Indri brevicaudatus', + 385: 'Indian elephant, Elephas maximus', + 386: 'African elephant, Loxodonta africana', + 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', + 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', + 389: 'barracouta, snoek', + 390: 'eel', + 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', + 392: 'rock beauty, Holocanthus tricolor', + 393: 'anemone fish', + 394: 'sturgeon', + 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', + 396: 'lionfish', + 397: 'puffer, pufferfish, blowfish, globefish', + 398: 'abacus', + 399: 'abaya', + 400: "academic gown, academic robe, judge's robe", + 401: 'accordion, piano accordion, squeeze box', + 402: 'acoustic guitar', + 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', + 404: 'airliner', + 405: 'airship, dirigible', + 406: 'altar', + 407: 'ambulance', + 408: 'amphibian, amphibious vehicle', + 409: 'analog clock', + 410: 'apiary, bee house', + 411: 'apron', + 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', + 413: 'assault rifle, assault gun', + 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', + 415: 'bakery, bakeshop, bakehouse', + 416: 'balance beam, beam', + 417: 'balloon', + 418: 'ballpoint, ballpoint pen, ballpen, Biro', + 419: 'Band Aid', + 420: 'banjo', + 421: 'bannister, banister, balustrade, balusters, handrail', + 422: 'barbell', + 423: 'barber chair', + 424: 'barbershop', + 425: 'barn', + 426: 'barometer', + 427: 'barrel, cask', + 428: 'barrow, garden cart, lawn cart, wheelbarrow', + 429: 'baseball', + 430: 'basketball', + 431: 'bassinet', + 432: 'bassoon', + 433: 'bathing cap, swimming cap', + 434: 'bath towel', + 435: 'bathtub, bathing tub, bath, tub', + 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', + 437: 'beacon, lighthouse, beacon light, pharos', + 438: 'beaker', + 439: 'bearskin, busby, shako', + 440: 'beer bottle', + 441: 'beer glass', + 442: 'bell cote, bell cot', + 443: 'bib', + 444: 'bicycle-built-for-two, tandem bicycle, tandem', + 445: 'bikini, two-piece', + 446: 'binder, ring-binder', + 447: 'binoculars, field glasses, opera glasses', + 448: 'birdhouse', + 449: 'boathouse', + 450: 'bobsled, bobsleigh, bob', + 451: 'bolo tie, bolo, bola tie, bola', + 452: 'bonnet, poke bonnet', + 453: 'bookcase', + 454: 'bookshop, bookstore, bookstall', + 455: 'bottlecap', + 456: 'bow', + 457: 'bow tie, bow-tie, bowtie', + 458: 'brass, memorial tablet, plaque', + 459: 'brassiere, bra, bandeau', + 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', + 461: 'breastplate, aegis, egis', + 462: 'broom', + 463: 'bucket, pail', + 464: 'buckle', + 465: 'bulletproof vest', + 466: 'bullet train, bullet', + 467: 'butcher shop, meat market', + 468: 'cab, hack, taxi, taxicab', + 469: 'caldron, cauldron', + 470: 'candle, taper, wax light', + 471: 'cannon', + 472: 'canoe', + 473: 'can opener, tin opener', + 474: 'cardigan', + 475: 'car mirror', + 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', + 477: "carpenter's kit, tool kit", + 478: 'carton', + 479: 'car wheel', + 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', + 481: 'cassette', + 482: 'cassette player', + 483: 'castle', + 484: 'catamaran', + 485: 'CD player', + 486: 'cello, violoncello', + 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', + 488: 'chain', + 489: 'chainlink fence', + 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', + 491: 'chain saw, chainsaw', + 492: 'chest', + 493: 'chiffonier, commode', + 494: 'chime, bell, gong', + 495: 'china cabinet, china closet', + 496: 'Christmas stocking', + 497: 'church, church building', + 498: 'cinema, movie theater, movie theatre, movie house, picture palace', + 499: 'cleaver, meat cleaver, chopper', + 500: 'cliff dwelling', + 501: 'cloak', + 502: 'clog, geta, patten, sabot', + 503: 'cocktail shaker', + 504: 'coffee mug', + 505: 'coffeepot', + 506: 'coil, spiral, volute, whorl, helix', + 507: 'combination lock', + 508: 'computer keyboard, keypad', + 509: 'confectionery, confectionary, candy store', + 510: 'container ship, containership, container vessel', + 511: 'convertible', + 512: 'corkscrew, bottle screw', + 513: 'cornet, horn, trumpet, trump', + 514: 'cowboy boot', + 515: 'cowboy hat, ten-gallon hat', + 516: 'cradle', + 517: 'crane', + 518: 'crash helmet', + 519: 'crate', + 520: 'crib, cot', + 521: 'Crock Pot', + 522: 'croquet ball', + 523: 'crutch', + 524: 'cuirass', + 525: 'dam, dike, dyke', + 526: 'desk', + 527: 'desktop computer', + 528: 'dial telephone, dial phone', + 529: 'diaper, nappy, napkin', + 530: 'digital clock', + 531: 'digital watch', + 532: 'dining table, board', + 533: 'dishrag, dishcloth', + 534: 'dishwasher, dish washer, dishwashing machine', + 535: 'disk brake, disc brake', + 536: 'dock, dockage, docking facility', + 537: 'dogsled, dog sled, dog sleigh', + 538: 'dome', + 539: 'doormat, welcome mat', + 540: 'drilling platform, offshore rig', + 541: 'drum, membranophone, tympan', + 542: 'drumstick', + 543: 'dumbbell', + 544: 'Dutch oven', + 545: 'electric fan, blower', + 546: 'electric guitar', + 547: 'electric locomotive', + 548: 'entertainment center', + 549: 'envelope', + 550: 'espresso maker', + 551: 'face powder', + 552: 'feather boa, boa', + 553: 'file, file cabinet, filing cabinet', + 554: 'fireboat', + 555: 'fire engine, fire truck', + 556: 'fire screen, fireguard', + 557: 'flagpole, flagstaff', + 558: 'flute, transverse flute', + 559: 'folding chair', + 560: 'football helmet', + 561: 'forklift', + 562: 'fountain', + 563: 'fountain pen', + 564: 'four-poster', + 565: 'freight car', + 566: 'French horn, horn', + 567: 'frying pan, frypan, skillet', + 568: 'fur coat', + 569: 'garbage truck, dustcart', + 570: 'gasmask, respirator, gas helmet', + 571: 'gas pump, gasoline pump, petrol pump, island dispenser', + 572: 'goblet', + 573: 'go-kart', + 574: 'golf ball', + 575: 'golfcart, golf cart', + 576: 'gondola', + 577: 'gong, tam-tam', + 578: 'gown', + 579: 'grand piano, grand', + 580: 'greenhouse, nursery, glasshouse', + 581: 'grille, radiator grille', + 582: 'grocery store, grocery, food market, market', + 583: 'guillotine', + 584: 'hair slide', + 585: 'hair spray', + 586: 'half track', + 587: 'hammer', + 588: 'hamper', + 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', + 590: 'hand-held computer, hand-held microcomputer', + 591: 'handkerchief, hankie, hanky, hankey', + 592: 'hard disc, hard disk, fixed disk', + 593: 'harmonica, mouth organ, harp, mouth harp', + 594: 'harp', + 595: 'harvester, reaper', + 596: 'hatchet', + 597: 'holster', + 598: 'home theater, home theatre', + 599: 'honeycomb', + 600: 'hook, claw', + 601: 'hoopskirt, crinoline', + 602: 'horizontal bar, high bar', + 603: 'horse cart, horse-cart', + 604: 'hourglass', + 605: 'iPod', + 606: 'iron, smoothing iron', + 607: "jack-o'-lantern", + 608: 'jean, blue jean, denim', + 609: 'jeep, landrover', + 610: 'jersey, T-shirt, tee shirt', + 611: 'jigsaw puzzle', + 612: 'jinrikisha, ricksha, rickshaw', + 613: 'joystick', + 614: 'kimono', + 615: 'knee pad', + 616: 'knot', + 617: 'lab coat, laboratory coat', + 618: 'ladle', + 619: 'lampshade, lamp shade', + 620: 'laptop, laptop computer', + 621: 'lawn mower, mower', + 622: 'lens cap, lens cover', + 623: 'letter opener, paper knife, paperknife', + 624: 'library', + 625: 'lifeboat', + 626: 'lighter, light, igniter, ignitor', + 627: 'limousine, limo', + 628: 'liner, ocean liner', + 629: 'lipstick, lip rouge', + 630: 'Loafer', + 631: 'lotion', + 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', + 633: "loupe, jeweler's loupe", + 634: 'lumbermill, sawmill', + 635: 'magnetic compass', + 636: 'mailbag, postbag', + 637: 'mailbox, letter box', + 638: 'maillot', + 639: 'maillot, tank suit', + 640: 'manhole cover', + 641: 'maraca', + 642: 'marimba, xylophone', + 643: 'mask', + 644: 'matchstick', + 645: 'maypole', + 646: 'maze, labyrinth', + 647: 'measuring cup', + 648: 'medicine chest, medicine cabinet', + 649: 'megalith, megalithic structure', + 650: 'microphone, mike', + 651: 'microwave, microwave oven', + 652: 'military uniform', + 653: 'milk can', + 654: 'minibus', + 655: 'miniskirt, mini', + 656: 'minivan', + 657: 'missile', + 658: 'mitten', + 659: 'mixing bowl', + 660: 'mobile home, manufactured home', + 661: 'Model T', + 662: 'modem', + 663: 'monastery', + 664: 'monitor', + 665: 'moped', + 666: 'mortar', + 667: 'mortarboard', + 668: 'mosque', + 669: 'mosquito net', + 670: 'motor scooter, scooter', + 671: 'mountain bike, all-terrain bike, off-roader', + 672: 'mountain tent', + 673: 'mouse, computer mouse', + 674: 'mousetrap', + 675: 'moving van', + 676: 'muzzle', + 677: 'nail', + 678: 'neck brace', + 679: 'necklace', + 680: 'nipple', + 681: 'notebook, notebook computer', + 682: 'obelisk', + 683: 'oboe, hautboy, hautbois', + 684: 'ocarina, sweet potato', + 685: 'odometer, hodometer, mileometer, milometer', + 686: 'oil filter', + 687: 'organ, pipe organ', + 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', + 689: 'overskirt', + 690: 'oxcart', + 691: 'oxygen mask', + 692: 'packet', + 693: 'paddle, boat paddle', + 694: 'paddlewheel, paddle wheel', + 695: 'padlock', + 696: 'paintbrush', + 697: "pajama, pyjama, pj's, jammies", + 698: 'palace', + 699: 'panpipe, pandean pipe, syrinx', + 700: 'paper towel', + 701: 'parachute, chute', + 702: 'parallel bars, bars', + 703: 'park bench', + 704: 'parking meter', + 705: 'passenger car, coach, carriage', + 706: 'patio, terrace', + 707: 'pay-phone, pay-station', + 708: 'pedestal, plinth, footstall', + 709: 'pencil box, pencil case', + 710: 'pencil sharpener', + 711: 'perfume, essence', + 712: 'Petri dish', + 713: 'photocopier', + 714: 'pick, plectrum, plectron', + 715: 'pickelhaube', + 716: 'picket fence, paling', + 717: 'pickup, pickup truck', + 718: 'pier', + 719: 'piggy bank, penny bank', + 720: 'pill bottle', + 721: 'pillow', + 722: 'ping-pong ball', + 723: 'pinwheel', + 724: 'pirate, pirate ship', + 725: 'pitcher, ewer', + 726: "plane, carpenter's plane, woodworking plane", + 727: 'planetarium', + 728: 'plastic bag', + 729: 'plate rack', + 730: 'plow, plough', + 731: "plunger, plumber's helper", + 732: 'Polaroid camera, Polaroid Land camera', + 733: 'pole', + 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', + 735: 'poncho', + 736: 'pool table, billiard table, snooker table', + 737: 'pop bottle, soda bottle', + 738: 'pot, flowerpot', + 739: "potter's wheel", + 740: 'power drill', + 741: 'prayer rug, prayer mat', + 742: 'printer', + 743: 'prison, prison house', + 744: 'projectile, missile', + 745: 'projector', + 746: 'puck, hockey puck', + 747: 'punching bag, punch bag, punching ball, punchball', + 748: 'purse', + 749: 'quill, quill pen', + 750: 'quilt, comforter, comfort, puff', + 751: 'racer, race car, racing car', + 752: 'racket, racquet', + 753: 'radiator', + 754: 'radio, wireless', + 755: 'radio telescope, radio reflector', + 756: 'rain barrel', + 757: 'recreational vehicle, RV, R.V.', + 758: 'reel', + 759: 'reflex camera', + 760: 'refrigerator, icebox', + 761: 'remote control, remote', + 762: 'restaurant, eating house, eating place, eatery', + 763: 'revolver, six-gun, six-shooter', + 764: 'rifle', + 765: 'rocking chair, rocker', + 766: 'rotisserie', + 767: 'rubber eraser, rubber, pencil eraser', + 768: 'rugby ball', + 769: 'rule, ruler', + 770: 'running shoe', + 771: 'safe', + 772: 'safety pin', + 773: 'saltshaker, salt shaker', + 774: 'sandal', + 775: 'sarong', + 776: 'sax, saxophone', + 777: 'scabbard', + 778: 'scale, weighing machine', + 779: 'school bus', + 780: 'schooner', + 781: 'scoreboard', + 782: 'screen, CRT screen', + 783: 'screw', + 784: 'screwdriver', + 785: 'seat belt, seatbelt', + 786: 'sewing machine', + 787: 'shield, buckler', + 788: 'shoe shop, shoe-shop, shoe store', + 789: 'shoji', + 790: 'shopping basket', + 791: 'shopping cart', + 792: 'shovel', + 793: 'shower cap', + 794: 'shower curtain', + 795: 'ski', + 796: 'ski mask', + 797: 'sleeping bag', + 798: 'slide rule, slipstick', + 799: 'sliding door', + 800: 'slot, one-armed bandit', + 801: 'snorkel', + 802: 'snowmobile', + 803: 'snowplow, snowplough', + 804: 'soap dispenser', + 805: 'soccer ball', + 806: 'sock', + 807: 'solar dish, solar collector, solar furnace', + 808: 'sombrero', + 809: 'soup bowl', + 810: 'space bar', + 811: 'space heater', + 812: 'space shuttle', + 813: 'spatula', + 814: 'speedboat', + 815: "spider web, spider's web", + 816: 'spindle', + 817: 'sports car, sport car', + 818: 'spotlight, spot', + 819: 'stage', + 820: 'steam locomotive', + 821: 'steel arch bridge', + 822: 'steel drum', + 823: 'stethoscope', + 824: 'stole', + 825: 'stone wall', + 826: 'stopwatch, stop watch', + 827: 'stove', + 828: 'strainer', + 829: 'streetcar, tram, tramcar, trolley, trolley car', + 830: 'stretcher', + 831: 'studio couch, day bed', + 832: 'stupa, tope', + 833: 'submarine, pigboat, sub, U-boat', + 834: 'suit, suit of clothes', + 835: 'sundial', + 836: 'sunglass', + 837: 'sunglasses, dark glasses, shades', + 838: 'sunscreen, sunblock, sun blocker', + 839: 'suspension bridge', + 840: 'swab, swob, mop', + 841: 'sweatshirt', + 842: 'swimming trunks, bathing trunks', + 843: 'swing', + 844: 'switch, electric switch, electrical switch', + 845: 'syringe', + 846: 'table lamp', + 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', + 848: 'tape player', + 849: 'teapot', + 850: 'teddy, teddy bear', + 851: 'television, television system', + 852: 'tennis ball', + 853: 'thatch, thatched roof', + 854: 'theater curtain, theatre curtain', + 855: 'thimble', + 856: 'thresher, thrasher, threshing machine', + 857: 'throne', + 858: 'tile roof', + 859: 'toaster', + 860: 'tobacco shop, tobacconist shop, tobacconist', + 861: 'toilet seat', + 862: 'torch', + 863: 'totem pole', + 864: 'tow truck, tow car, wrecker', + 865: 'toyshop', + 866: 'tractor', + 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', + 868: 'tray', + 869: 'trench coat', + 870: 'tricycle, trike, velocipede', + 871: 'trimaran', + 872: 'tripod', + 873: 'triumphal arch', + 874: 'trolleybus, trolley coach, trackless trolley', + 875: 'trombone', + 876: 'tub, vat', + 877: 'turnstile', + 878: 'typewriter keyboard', + 879: 'umbrella', + 880: 'unicycle, monocycle', + 881: 'upright, upright piano', + 882: 'vacuum, vacuum cleaner', + 883: 'vase', + 884: 'vault', + 885: 'velvet', + 886: 'vending machine', + 887: 'vestment', + 888: 'viaduct', + 889: 'violin, fiddle', + 890: 'volleyball', + 891: 'waffle iron', + 892: 'wall clock', + 893: 'wallet, billfold, notecase, pocketbook', + 894: 'wardrobe, closet, press', + 895: 'warplane, military plane', + 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', + 897: 'washer, automatic washer, washing machine', + 898: 'water bottle', + 899: 'water jug', + 900: 'water tower', + 901: 'whiskey jug', + 902: 'whistle', + 903: 'wig', + 904: 'window screen', + 905: 'window shade', + 906: 'Windsor tie', + 907: 'wine bottle', + 908: 'wing', + 909: 'wok', + 910: 'wooden spoon', + 911: 'wool, woolen, woollen', + 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', + 913: 'wreck', + 914: 'yawl', + 915: 'yurt', + 916: 'web site, website, internet site, site', + 917: 'comic book', + 918: 'crossword puzzle, crossword', + 919: 'street sign', + 920: 'traffic light, traffic signal, stoplight', + 921: 'book jacket, dust cover, dust jacket, dust wrapper', + 922: 'menu', + 923: 'plate', + 924: 'guacamole', + 925: 'consomme', + 926: 'hot pot, hotpot', + 927: 'trifle', + 928: 'ice cream, icecream', + 929: 'ice lolly, lolly, lollipop, popsicle', + 930: 'French loaf', + 931: 'bagel, beigel', + 932: 'pretzel', + 933: 'cheeseburger', + 934: 'hotdog, hot dog, red hot', + 935: 'mashed potato', + 936: 'head cabbage', + 937: 'broccoli', + 938: 'cauliflower', + 939: 'zucchini, courgette', + 940: 'spaghetti squash', + 941: 'acorn squash', + 942: 'butternut squash', + 943: 'cucumber, cuke', + 944: 'artichoke, globe artichoke', + 945: 'bell pepper', + 946: 'cardoon', + 947: 'mushroom', + 948: 'Granny Smith', + 949: 'strawberry', + 950: 'orange', + 951: 'lemon', + 952: 'fig', + 953: 'pineapple, ananas', + 954: 'banana', + 955: 'jackfruit, jak, jack', + 956: 'custard apple', + 957: 'pomegranate', + 958: 'hay', + 959: 'carbonara', + 960: 'chocolate sauce, chocolate syrup', + 961: 'dough', + 962: 'meat loaf, meatloaf', + 963: 'pizza, pizza pie', + 964: 'potpie', + 965: 'burrito', + 966: 'red wine', + 967: 'espresso', + 968: 'cup', + 969: 'eggnog', + 970: 'alp', + 971: 'bubble', + 972: 'cliff, drop, drop-off', + 973: 'coral reef', + 974: 'geyser', + 975: 'lakeside, lakeshore', + 976: 'promontory, headland, head, foreland', + 977: 'sandbar, sand bar', + 978: 'seashore, coast, seacoast, sea-coast', + 979: 'valley, vale', + 980: 'volcano', + 981: 'ballplayer, baseball player', + 982: 'groom, bridegroom', + 983: 'scuba diver', + 984: 'rapeseed', + 985: 'daisy', + 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + 987: 'corn', + 988: 'acorn', + 989: 'hip, rose hip, rosehip', + 990: 'buckeye, horse chestnut, conker', + 991: 'coral fungus', + 992: 'agaric', + 993: 'gyromitra', + 994: 'stinkhorn, carrion fungus', + 995: 'earthstar', + 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', + 997: 'bolete', + 998: 'ear, spike, capitulum', + 999: 'toilet tissue, toilet paper, bathroom tissue'} \ No newline at end of file diff --git a/assets/imagenet_class_to_id_map.json b/assets/imagenet_class_to_id_map.json new file mode 100644 index 0000000..6746aed --- /dev/null +++ b/assets/imagenet_class_to_id_map.json @@ -0,0 +1 @@ +{"n01440764": 0, "n01443537": 1, "n01484850": 2, "n01491361": 3, "n01494475": 4, "n01496331": 5, "n01498041": 6, "n01514668": 7, "n01514859": 8, "n01518878": 9, "n01530575": 10, "n01531178": 11, "n01532829": 12, "n01534433": 13, "n01537544": 14, "n01558993": 15, "n01560419": 16, "n01580077": 17, "n01582220": 18, "n01592084": 19, "n01601694": 20, 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z1PBoL5dwepmw)Ha{+IvbKl`U&{_lVCWqyD4mw)oNzkK<#Kl|fn`MY2Ki!Xouum78W z{xAN`Kl}Ut=AZrR|M6e^_y660`Jeyv<-dO4^$-7@d4m7*fBkR&tAFeFx18T!{pH`9 z@Hfx!_x`^>|MUOt_v88fyEE|zSDxsf{`LR#*Z<-F@Gt+>y1L)(@BP8bKl@u>{^osV z_22(b|It7GkN@Hy{o}v*i~r<5`Un5;AFLboBmUh#*yw-$fBwneZ1fAvU;X8u{^`H} zOVhviC;!3UwExL3S>pfx%lOa#-{0&1{`&p8zccaw`epq4NBn*5-~Q97{mJkDP5;;4 F{~tPoe}w=5 literal 0 HcmV?d00001 diff --git a/calculate_score_based_mask.py b/calculate_score_based_mask.py new file mode 100644 index 0000000..a0ef2b3 --- /dev/null +++ b/calculate_score_based_mask.py @@ -0,0 +1,128 @@ +import os +import numpy as np +import random +import copy +import models +from utils.train import train as trainer +from utils.eval import base as val +import data.loader as data_loader_aug +import torch +import torch.nn as nn +from args import parse_args +from utils.schedules import get_lr_policy, get_optimizer +from utils.logging import save_checkpoint +from score_based.model import get_layers, prepare_model, initialize_scaled_score, current_model_pruned_fraction, sanity_check_paramter_updates + +# Parts used from https://github.com/inspire-group/hydra + +def set_all_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +def main(): + args = parse_args() + set_all_seed(args.seed) + master_path = args.dir + + #Create model + cl, ll = get_layers(args.layer_type) + + model = models.__dict__[args.arch]( + cl, ll, args.init_type, num_classes=args.num_classes + ) + + prepare_model(model, args) + + # Dataloader + D = data_loader_aug.DataLoaderAugmentation(args, master_path) + train_loader, val_loader = D.get_data_loaders() + + criterion = nn.CrossEntropyLoss() + optimizer = get_optimizer(model, args) + lr_policy = get_lr_policy(args.lr_schedule)(optimizer, args) + + # Load source_net (if checkpoint provided). Only load the state_dict (required for pruning and fine-tuning) + if args.path: + if os.path.isfile(args.path): + checkpoint = torch.load(args.path) + model.load_state_dict( + checkpoint["state_dict"], strict=False + ) # allows loading dense models + else: + print("no checkpoint found") + + + if args.scaled_score_init: + initialize_scaled_score(model) + + assert not (args.path and args.resume), ( + "Incorrect setup: " + "resume => required to resume a previous experiment (loads all parameters)|| " + "source_net => required to start pruning/fine-tuning from a source model (only load state_dict)" + ) + # resume (if checkpoint provided). Continue training with preiovus settings. + if args.resume: + if os.path.isfile(args.resume): + checkpoint = torch.load(args.resume) + args.start_epoch = checkpoint["epoch"] + best_prec1 = checkpoint["best_prec1"] + model.load_state_dict(checkpoint["state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + else: + print("no checkpoint found") + + best_prec1 = 0 + + # Do not select source-net as last checkpoint as it might even be a dense model. + # Most other function won't works well with a dense layer checkpoint. + last_ckpt = copy.deepcopy(model.state_dict()) + + # Start training + for epoch in range(args.start_epoch, args.epochs + args.warmup_epochs): + # adjust learning rate + lr_policy(epoch) + + trainer( + train_loader, + model, + criterion, + optimizer, + epoch, + args + ) + + prec1, _ = val(model, val_loader, criterion, args, epoch) + + # remember best prec@1 and save checkpoint + is_best = prec1 > best_prec1 + best_prec1 = max(prec1, best_prec1) + save_checkpoint( + { + "epoch": epoch + 1, + "arch": args.arch, + "state_dict": model.state_dict(), + "best_prec1": best_prec1, + "optimizer": optimizer.state_dict(), + }, + is_best, + args, + result_dir=os.path.join(master_path, "checkpoint"), + save_dense=args.save_dense, + ) + + # Check what parameters got updated in the current epoch. + sw, ss = sanity_check_paramter_updates(model, last_ckpt) + print( + f"Sanity check (exp-mode: {args.exp_mode}): Weight update - {sw}, Scores update - {ss}" + ) + + current_model_pruned_fraction( + model, os.path.join(master_path, "checkpoint"), args, verbose=True + ) + + +if __name__ == "__main__": + main() diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000..60fc4e2 --- /dev/null +++ b/data/__init__.py @@ -0,0 +1 @@ +from data.loader import * \ No newline at end of file diff --git a/data/loader.py b/data/loader.py new file mode 100644 index 0000000..f202b69 --- /dev/null +++ b/data/loader.py @@ -0,0 +1,160 @@ +import numpy as np +import torch +import torchvision.datasets as datasets +import torchvision.transforms as transforms +from torchvision.datasets import ImageFolder +import utils.cutout as cutout +import utils.pixmix_utils as pixmix_utils + +pixmix_utils.IMAGE_SIZE = 224 + +def pixmix(orig, mixing_pic, preprocess, args): + + mixings = pixmix_utils.mixings + tensorize, normalize = preprocess['tensorize'], preprocess['normalize'] + if np.random.random() < 0.5: + mixed = tensorize(augment_input(orig, args)) + else: + mixed = tensorize(orig) + + for _ in range(np.random.randint(args.k_mixing + 1)): + + if np.random.random() < 0.5: + aug_image_copy = tensorize(augment_input(orig, args)) + else: + aug_image_copy = tensorize(mixing_pic) + + mixed_op = np.random.choice(mixings) + mixed = mixed_op(mixed, aug_image_copy, args.beta) + mixed = torch.clip(mixed, 0, 1) + + return normalize(mixed) + + +def augment_input(image, args): + aug_list = pixmix_utils.augmentations_all if args.all_ops else pixmix_utils.augmentations + op = np.random.choice(aug_list) + return op(image.copy(), args.aug_severity) + + +class PixMixDataset(torch.utils.data.Dataset): + """Dataset wrapper to perform PixMix.""" + + def __init__(self, dataset, mixing_set, preprocess, args): + self.dataset = dataset + self.mixing_set = mixing_set + self.preprocess = preprocess + self.args = args + + def __getitem__(self, i): + x, y = self.dataset[i] + rnd_idx = np.random.choice(len(self.mixing_set)) + mixing_pic, _ = self.mixing_set[rnd_idx] + return pixmix(x, mixing_pic, self.preprocess, self.args), y + + def __len__(self): + return len(self.dataset) + + +class DataLoaderAugmentation: + def __init__(self, args, master_path): + self.args = args + self.master_path = master_path + + def get_data_loaders(self): + + normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + # Load the dataset for specified augmentation type + if self.args.augmentation == "pixmix": + to_tensor = transforms.ToTensor() + + train_data = ImageFolder( + self.master_path + 'imagenet/train/', + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip() + ]) + ) + + mixing_set = datasets.ImageFolder( + self.args.mixing_set, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.RandomCrop(224) + ]) + ) + + train_dataset = PixMixDataset(train_data, mixing_set, {'normalize': normalizer, 'tensorize': to_tensor}, self.args) + + if self.args.augmentation == "cutout": + train_dataset = ImageFolder( + self.master_path + 'imagenet/train/', + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalizer, + cutout.Cutout(n_holes= 1, length=self.args.cutout_length) + ]) + ) + + if self.args.augmentation == "augmix": + train_dataset = ImageFolder( + self.master_path + 'imagenet/train/', + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.AugMix(), + transforms.ToTensor(), + normalizer, + ]) + ) + + if self.args.augmentation == "autoaugment": + train_dataset = ImageFolder( + self.master_path + 'imagenet/train/', + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.AutoAugment(policy=transforms.autoaugment.AutoAugmentPolicy.IMAGENET), + transforms.ToTensor(), + normalizer, + ]) + ) + + if self.args.augmentation == "standard": + train_dataset = ImageFolder( + self.master_path + 'imagenet/train/', + transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalizer, + ]) + ) + + #validation dataset + val_loader = torch.utils.data.DataLoader( + ImageFolder( + self.master_path + 'imagenet/validation/', + transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalizer, + ]) + ), batch_size=self.args.batch_size, shuffle=False, num_workers=8, pin_memory=True) + + + def wif(id): + uint64_seed = torch.initial_seed() + ss = np.random.SeedSequence([uint64_seed]) + np.random.seed(ss.generate_state(4)) + + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=self.args.batch_size, shuffle=True, + num_workers=8, pin_memory=True, sampler=None, worker_init_fn=wif) + + + return train_loader, val_loader \ No newline at end of file diff --git a/evaluate_imagenet_c.py b/evaluate_imagenet_c.py new file mode 100644 index 0000000..fe2c0cf --- /dev/null +++ b/evaluate_imagenet_c.py @@ -0,0 +1,175 @@ +import argparse +import os +import random +import numpy +import torch +import torchvision +from torch.utils.data import DataLoader +from torchvision.datasets import ImageFolder +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize + +from utils.loaders import CustomImageFolder +from utils.metrics_sparsity import output_sparsity + +parser = argparse.ArgumentParser(description='PyTorch Model Training Codebase') +parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', + help='model architecture (default: resnet50)') +parser.add_argument('--pretrained', dest='pretrained', action='store_true', + help='use pre-trained model') +parser.add_argument('--test_all', action='store_true', + help='Run all validation set (default: will run 5k test samples from RobustBench)') +parser.add_argument('-c', '--corruption', action='store_true', + help='Corruptions to be used for test set evaluations') +parser.add_argument('--dir', default='data/', type=str, metavar='DIR', + help='Path to dataset') +parser.add_argument('--batch_size', default=1, type=int, metavar='N', + help='Evaluation mini-batch size (default: 1)') +parser.add_argument('--path', default=None, type=str, metavar='PATH', + help='Path for trained model checkpoint to load') +parser.add_argument('--seed', default=42, type=int, metavar='N', + help='Randomization seed number (default: 42)') + +CORRUPTIONS = ['gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', + 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression'] + +def compute_mce(corruption_accs): + alexnet_err = [88.6, 89.4, 92.3, 82.0, 82.6, 78.6, 79.8, 86.7, 82.7, 81.9, 56.5, 85.3, 64.6, 71.8, 60.7] + mce = 0. + for i in range(len(CORRUPTIONS)): + avg_err = 100 - numpy.mean(corruption_accs[CORRUPTIONS[i]]) + ce = 100 * avg_err / alexnet_err[i] + print(CORRUPTIONS[i], ce) + mce += ce / 15 + return mce + +def set_all_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.benchmark = False + numpy.random.seed(seed) + random.seed(seed) + +def main(): + args = parser.parse_args() + + master_path = args.dir + batch_size = args.batch_size + set_all_seed(args.seed) + pruning_ratio = args.pruning_ratio/10.0 + + # Load model + model = torchvision.models.__dict__[args.arch](pretrained=args.pretrained) + + if args.path is not None: + if 'salman' in args.path: + checkpoint = torch.load(args.path)['model'] + new_model_state = {} + for key in checkpoint.keys(): + if 'module.model.model' in key: + new_model_state[key[19:]] = checkpoint[key] + else: + if 'module.model' in key: + new_model_state[key[13:]] = checkpoint[key] + model.load_state_dict(new_model_state) + else: + checkpoint = torch.load(args.path)['state_dict'] + try: + model.load_state_dict(checkpoint) + except: + new_model_state = {} + for key in checkpoint.keys(): + if key[:7] == 'module.': + new_model_state[key[7:]] = checkpoint[key] + else: + new_model_state[key[9:]] = checkpoint[key] + model.load_state_dict(new_model_state) + + if torch.cuda.is_available(): + model = model.cuda() + + model.eval() + + print("Evaluating the model: {}".format(args.arch)) + print("Location of the model: {}".format(args.path)) + print("Was it pretrained?: {}".format(args.pretrained)) + + print("Sparsity of loaded model:") + output_sparsity(model) + + #Evaluate Model + normalizer = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + preprocess = Compose([Resize(256), CenterCrop(224), ToTensor()]) + + # Load the dataset + if args.corruption: + master_path += 'imagenet-c' + corruption_accs = {} + for corr in CORRUPTIONS: + print(corr) + for sev in [1, 2, 3, 4, 5]: #over all severities + correct_clean = 0 + n_samples = 0 + dataset = ImageFolder(os.path.join(master_path, corr, str(sev)), Compose([ToTensor()])) + data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) + + with torch.no_grad(): + for i, (x, y) in enumerate(data_loader): + if torch.cuda.is_available(): + x = x.cuda() + y = y.cuda() + + x_clean = normalizer(x) + if args.no_normalization: + x_clean = x + + pred_clean = model(x_clean) + correct_clean += (pred_clean.argmax(1) == y).sum().item() + n_samples += x.shape[0] + accuracy = 100 * (correct_clean / n_samples) + print(f"Severity: {sev}") + print(f"Accuracy: {accuracy:>0.2f}%") + if corr in corruption_accs: + corruption_accs[corr].append(accuracy) + else: + corruption_accs[corr] = [accuracy] + print(corruption_accs) + + corr_mean_accs = [numpy.mean(corruption_accs[CORRUPTIONS[i]]) for i in range(len(CORRUPTIONS))] + print('Corrupted Set Accuracies: ') + for i in range(len(CORRUPTIONS)): + print(CORRUPTIONS[i], corr_mean_accs[i]) + mean_accs = sum(corr_mean_accs) / len(CORRUPTIONS) + print('Mean: ', mean_accs) + print('mCE (normalized by AlexNet): ', compute_mce(corruption_accs)) + + else: + correct_clean = 0 + n_samples = 0 + master_path += 'imagenet/validation/' + dataset = ImageFolder(master_path, preprocess) if args.test_all else CustomImageFolder(master_path, preprocess) + data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) + + with torch.no_grad(): + for i, (x, y) in enumerate(data_loader): + if torch.cuda.is_available(): + x = x.cuda() + y = y.cuda() + + x_clean = normalizer(x) + if args.no_normalization: + x_clean = x + + pred_clean = model(x_clean) + correct_clean += (pred_clean.argmax(1) == y).sum().item() + n_samples += x.shape[0] + accuracy = 100 * (correct_clean / n_samples) + + print(f"Total number of tested samples: {n_samples}") + print(f"Clean accuracy: {accuracy:>0.2f}%") + + return + + +if __name__ == '__main__': + main() diff --git a/evaluate_imagenet_patch.py b/evaluate_imagenet_patch.py new file mode 100644 index 0000000..784ab8f --- /dev/null +++ b/evaluate_imagenet_patch.py @@ -0,0 +1,198 @@ +import argparse +import gzip +import pickle +import os +import random +import numpy as np +import torch +import torchvision +from torch.utils.data import DataLoader +from torchvision.datasets import ImageFolder +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize + +from utils.apply_patch import ApplyPatch +from utils.loaders import CustomImageFolder +from utils.metrics_sparsity import output_sparsity + + +parser = argparse.ArgumentParser(description='PyTorch Model Training Codebase') +parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', + help='model architecture (default: resnet50)') +parser.add_argument('--pretrained', dest='pretrained', action='store_true', + help='use pre-trained model') +parser.add_argument('--test_all', action='store_true', + help='Run all validation set (default: will run 5k test samples from RobustBench)') +parser.add_argument('--dir', default='data/imagenet/validation/', type=str, metavar='DIR', + help='Path to dataset') +parser.add_argument('--batch_size', default=1, type=int, metavar='N', + help='Evaluation mini-batch size (default: 1)') +parser.add_argument('--path', default=None, type=str, metavar='PATH', + help='Path for trained model checkpoint to load') +parser.add_argument('--save_path', default=None, type=str, + help='Patched image samples saving path') +parser.add_argument('--patch', default='adv', type=str, + help='Patch type: "adv", "rand", "black", "white", "bernoulli", "mean", "uniform", "srs_rn18"') +parser.add_argument('--translate_more', action='store_true', + help='play with the translation range of applied patch') +parser.add_argument('--scale_more', action='store_true', + help='play with the scale range of applied patch') +parser.add_argument('--verbose', action='store_true', + help='Print text stuff all the time') +parser.add_argument('--seed', default=42, type=int, metavar='N', + help='Randomization seed number (default: 42)') + +def set_all_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +def main(): + args = parser.parse_args() + set_all_seed(args.seed) + + # dictionary with the ImageNet label names + with open(os.path.join(os.getcwd(), "assets/imagenet1000_clsidx_to_labels.txt")) as f: + target_to_classname = eval(f.read()) + + # Load adversarial patches + with gzip.open(os.path.join(os.getcwd(), "assets/imagenet_patch.gz"), 'rb') as f: + imagenet_patch = pickle.load(f) + patches, targets, info = imagenet_patch + num_patches = patches.shape[0] + c, h, w = info['input_shape'] + + for i in range(num_patches): + if args.patch == "adv": + pass + elif args.patch == "srs_rn18": + sparse_rs_patches = torch.load(os.path.join(os.getcwd(), "assets/resnet18_best_sparse_rs_patches.tar")) + patches[i, ...] = sparse_rs_patches[i] + elif args.patch == "uniform": + patches[i, ...] = torch.as_tensor(np.random.uniform(size=(c, h, w))) + elif args.patch == "rand": + patches[i, ...] = torch.as_tensor(np.random.randn(c, h, w)) + elif args.patch == "black": + patches[i, ...] = 0 # in practice, results in a test set of 5k not 50k + elif args.patch == "white": + patches[i, ...] = 1 # in practice, results in a test set of 5k not 50k + elif args.patch == "bernoulli": + patches[i, ...] = torch.cat(c * [torch.as_tensor(np.random.binomial(n=1, p=.5, size=(1, h, w)))]) + elif args.patch == "mean": + mean_r, mean_g, mean_b = torch.mean(patches[i][:, 87:137, 87:137], dim=[1, 2]) + patches[i, 0, ...], patches[i, 1, ...], patches[i, 2, ...] = mean_r, mean_g, mean_b + else: + raise NotImplementedError + + # Choose an integer in the range 0-9 to select the patch + correct_clean = 0 + correct_adv = 0 + n_success = 0 + n_samples = 0 + n_samples_patched = 0 + + # Load the dataset + normalizer = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + preprocess = Compose([Resize(256), CenterCrop(224), ToTensor()]) + dataset = ImageFolder(args.dir, preprocess) if args.test_all else CustomImageFolder(args.dir, preprocess) + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, pin_memory=True) + print(len(dataset)) + + # Load model + model = torchvision.models.__dict__[args.arch](pretrained=args.pretrained) + if args.path is not None: + if 'salman' in args.path: + checkpoint = torch.load(args.path)['model'] + new_model_state = {} + for key in checkpoint.keys(): + if 'module.model.model' in key: + new_model_state[key[19:]] = checkpoint[key] + else: + if 'module.model' in key: + new_model_state[key[13:]] = checkpoint[key] + model.load_state_dict(new_model_state) + else: + checkpoint = torch.load(args.path)['state_dict'] + try: + model.load_state_dict(checkpoint) + except: + new_model_state = {} + for key in checkpoint.keys(): + if key[:7] == 'module.': + new_model_state[key[7:]] = checkpoint[key] + else: + new_model_state[key[9:]] = checkpoint[key] + model.load_state_dict(new_model_state) + + if torch.cuda.is_available(): + model = model.cuda() + + model.eval() + + print("Sparsity of loaded model:") + output_sparsity(model) + + # Apply Patches and evaluate + plot_samples = torch.empty((num_patches, 5, c, h, w)) + with torch.no_grad(): + for i, (x, y) in enumerate(data_loader): + if torch.cuda.is_available(): + x = x.cuda() + y = y.cuda() + + x_clean = normalizer(x) + pred_clean = model(x_clean) + correct_clean += (pred_clean.argmax(1) == y).sum().item() + n_samples += x.shape[0] + + for patch_id in range(num_patches): + patch = patches[patch_id].clone() + target = targets[patch_id].clone() + + if torch.cuda.is_available(): + patch, target = patch.cuda(), target.cuda() + + if patch_id == 0: + # original + apply_patch = ApplyPatch(patch, patch_size=info['patch_size'], + translation_range=(.2, .2), + rotation_range=(-45, 45), + scale_range=(0.7, 1)) + if args.scale_more: + apply_patch = ApplyPatch(patch, patch_size=info['patch_size'], + translation_range=(.4, .4), + rotation_range=(-60, 60), + scale_range=(0.5, 1)) + if args.translate_more: + apply_patch = ApplyPatch(patch, patch_size=info['patch_size'], + translation_range=(.4, .4), + rotation_range=(-60, 60), + scale_range=(0.7, 1)) + else: + apply_patch.set_patch(patch) + + patch_normalizer = Compose([apply_patch, normalizer]) + x_adv = patch_normalizer(x) + pred_adv = model(x_adv) + correct_adv += (pred_adv.argmax(1) == y).sum().item() + n_success += (pred_adv.argmax(1) == target).sum().item() + n_samples_patched += x.shape[0] + + + clean_accuracy = 100*(correct_clean / n_samples) + robust_accuracy = 100*(correct_adv / n_samples_patched) + success_rate = 100*(n_success / n_samples_patched) + + print("Evaluated the model: {}".format(args.arch)) + print("Location of the model: {}".format(args.path)) + print("Was it pretrained?: {}".format(args.pretrained)) + print("Embedded attacker patch type: {}".format(args.patch)) + print("Total number of tested samples: {}".format(n_samples)) + print("Total number of tested samples with patches: {}".format(n_samples_patched)) + print("Clean accuracy: {:>0.2f}".format(clean_accuracy)) + print("Robust accuracy: {:>0.2f}".format(robust_accuracy)) + print("Target success rate: {:>0.2f}".format(success_rate)) + +if __name__ == '__main__': + main() diff --git a/magnitude_based/__init__.py b/magnitude_based/__init__.py new file mode 100644 index 0000000..bd7c6d6 --- /dev/null +++ b/magnitude_based/__init__.py @@ -0,0 +1 @@ +from magnitude_based.prune import * \ No newline at end of file diff --git a/magnitude_based/prune.py b/magnitude_based/prune.py new file mode 100644 index 0000000..c07a7db --- /dev/null +++ b/magnitude_based/prune.py @@ -0,0 +1,28 @@ + +import numpy as np +import torch +import torch.nn.utils.prune as prune + + +def create_mask_global_lwm(model, pruning_ratio): + parameters_to_prune = [] + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear): + parameters_to_prune.append( (module, 'weight') ) + + prune.global_unstructured(parameters_to_prune, pruning_method = prune.L1Unstructured, amount=pruning_ratio) #0.8 + + return model, parameters_to_prune + +def create_mask_local_lwm(model, pruning_ratio): + parameters_to_prune = [] + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Conv2d): + prune.l1_unstructured(module, name='weight', amount=pruning_ratio) + parameters_to_prune.append( (module, 'weight') ) + + elif isinstance(module, torch.nn.Linear): + prune.l1_unstructured(module, name='weight', amount=pruning_ratio) + parameters_to_prune.append( (module, 'weight') ) + + return model, parameters_to_prune diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..1a2a31b --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,14 @@ + +from models.resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, WideResNet50_2, WideResNet50_4, WideResNet101_2 + + +__all__ = [ + "ResNet18", + "ResNet34", + "ResNet50", + "ResNet101", + "ResNet152" + "WideResNet50_2", + "WideResNet50_4", + "WideResNet101_2" +] \ No newline at end of file diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000..7769b8a --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,231 @@ +import torch +import torch.nn as nn + +def conv3x3(in_planes, out_planes, conv_layer, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return conv_layer(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, conv_layer, stride=1): + """1x1 convolution""" + return conv_layer(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False, dilation=1) + + +class BasicBlock(nn.Module): + expansion = 1 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, conv_layer, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, conv_layer, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes, conv_layer) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + __constants__ = ['downsample'] + + def __init__(self, inplanes, planes, conv_layer, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width, conv_layer) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, conv_layer, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion, conv_layer) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, conv_layer, linear_layer, block, layers, width_per_group=64, num_classes=1000, zero_init_residual=False, + groups=1, replace_stride_with_dilation=None, + norm_layer=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + self.conv_layer = conv_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + + self.conv1 = conv_layer(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = linear_layer(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, self.conv_layer, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, self.conv_layer, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, self.conv_layer, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + + def forward(self, x): + return self._forward_impl(x) + + +# NOTE: Only supporting default (kaiming_init) initializaition. +def ResNet18(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, BasicBlock, [2, 2, 2, 2], **kwargs) + +def ResNet34(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, BasicBlock, [3, 4, 6, 3], **kwargs) + +def ResNet50(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 4, 6, 3], **kwargs) + +def ResNet101(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 4, 23, 3], **kwargs) + +def ResNet152(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 8, 36, 3], **kwargs) + +def WideResNet50_2(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 4, 6, 3], width_per_group=64*2, **kwargs) + +def WideResNet50_4(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 4, 6, 3], width_per_group=64*4, **kwargs) + +def WideResNet101_2(conv_layer, linear_layer, init_type, **kwargs): + assert init_type == "kaiming_normal", "only supporting default init for Resnets" + return ResNet(conv_layer, linear_layer, Bottleneck, [3, 4, 23, 3], width_per_group=64*2, **kwargs) \ No newline at end of file diff --git a/prune_with_augmentation.py b/prune_with_augmentation.py new file mode 100644 index 0000000..1c255a9 --- /dev/null +++ b/prune_with_augmentation.py @@ -0,0 +1,261 @@ +import argparse +import os +import random +import time +import numpy as np +import torch +import torch.nn as nn +import torchvision +from torch.utils.data import DataLoader +from torchvision.datasets import ImageFolder +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +import torch.nn.functional as F +from args import parse_args +import torch.nn.utils.prune as prune +from utils.loaders import CustomImageFolder +from utils.logging import AverageMeter, ProgressMeter, save_checkpoint, save_model +from utils.eval import accuracy +from utils.train import train as trainer +import data.loader as data_loader_aug +from utils.metrics_sparsity import output_sparsity +from utils.calibration_tools import * +from magnitude_based.prune import create_mask_global_lwm, create_mask_local_lwm + + +def set_all_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.benchmark = False + np.random.seed(seed) + random.seed(seed) + +def validate(val_loader, model, criterion, args): + batch_time = AverageMeter('Time', ':6.3f') + losses = AverageMeter('Loss', ':.4e') + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + progress = ProgressMeter( + len(val_loader), + [batch_time, losses, top1, top5], + prefix='Test: ') + + # switch to evaluate mode + model.eval() + + to_np = lambda x: x.data.to('cpu').numpy() + + confidence = [] + correct = [] + + num_correct = 0 + + with torch.no_grad(): + end = time.time() + for i, (images, target) in enumerate(val_loader): + images = images.cuda() + target = target.cuda() + + # compute output + output = model(images) + + loss = criterion(output, target) + + # accuracy + pred = output.data.max(1)[1] + num_correct += pred.eq(target.data).sum().item() + + confidence.extend(to_np(F.softmax(output, dim=1).max(1)[0]).squeeze().tolist()) + pred = output.data.max(1)[1] + correct.extend(pred.eq(target).to('cpu').numpy().squeeze().tolist()) + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i) + + # TODO: this should also be done with the ProgressMeter + print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' + .format(top1=top1, top5=top5)) + + print('RMS {:.3f}\n'.format(100 * calib_err(np.array(confidence.copy()), np.array(correct.copy()), p='2'))) + + return losses.avg, top1.avg, top5.avg + + +def train_model (model, criterion, optimizer, args, master_path, parameters_to_prune): + model.train() + best_acc1 = 0 + + # Get augmented input + D = data_loader_aug.DataLoaderAugmentation(args, master_path) + train_loader, val_loader = D.get_data_loaders() + + def cosine_annealing(step, total_steps, lr_max, lr_min): + return lr_min + (lr_max - lr_min) * 0.5 * ( + 1 + np.cos(step / total_steps * np.pi)) + + scheduler = torch.optim.lr_scheduler.LambdaLR( + optimizer, + lr_lambda=lambda step: cosine_annealing( + step, + args.epochs * len(train_loader), + 1, + 1e-6 / (args.lr * args.batch_size / 256.))) + + if args.start_epoch != 0: + scheduler.step(args.start_epoch * len(train_loader)) + + model_dir = "results/checkpoints_pruned/" + args.augmentation + "/" + + for epoch in range(args.start_epoch, args.epochs): + print('Starting epoch %d / %d' % (epoch + 1, args.epochs)) + + # train for one epoch + train_losses_avg, train_top1_avg, train_top5_avg = trainer(train_loader, model, criterion, optimizer, epoch, args) + scheduler.step() + + + print("Evaluating on validation set") + val_losses_avg, val_top1_avg, val_top5_avg = validate(val_loader, model, criterion, args) + + logname = args.model_name + "_training_log.csv" + + # Save results in log file + with open(os.path.join(model_dir, logname), 'a') as f: + f.write('%03d,%0.5f,%0.5f,%0.5f,%0.5f,%0.5f,%0.5f\n' % ( + (epoch + 1), + train_losses_avg, train_top1_avg, train_top5_avg, + val_losses_avg, val_top1_avg, val_top5_avg + )) + + # remember best acc@1 and save checkpoint + is_best = val_top1_avg > best_acc1 + best_acc1 = max(val_top1_avg, best_acc1) + + save_checkpoint({ + 'epoch': epoch + 1, + 'arch': args.arch, + 'state_dict': model.state_dict(), + 'best_acc1': best_acc1, + 'optimizer' : optimizer.state_dict(), + 'parameters_to_prune:' : parameters_to_prune + }, is_best, args, model_dir, args.model_name) + + + +def test(model, args, master_path): + model.eval() + + normalizer = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + preprocess = Compose([Resize(256), CenterCrop(224), ToTensor()]) + + correct_clean = 0 + n_samples = 0 + master_path += 'imagenet/validation/' + dataset = ImageFolder(master_path, preprocess) if args.test_all else CustomImageFolder(master_path, preprocess) + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, pin_memory=True) + + with torch.no_grad(): + for i, (x, y) in enumerate(data_loader): + if torch.cuda.is_available(): + x = x.cuda() + y = y.cuda() + x_clean = normalizer(x) + pred_clean = model(x_clean) + correct_clean += (pred_clean.argmax(1) == y).sum().item() + n_samples += x.shape[0] + accuracy = 100 * (correct_clean / n_samples) + + print(f"Total number of tested samples: {n_samples}") + print(f"Clean accuracy: {accuracy:>0.2f}%") + + +def main(): + args = parse_args() + master_path = args.dir + set_all_seed(args.seed) + print("Pruning_Ratio: " + str(args.pruning_ratio)) + + # Load model + model = torchvision.models.__dict__[args.arch](pretrained=args.pretrained) + + if args.path is not None: + checkpoint = torch.load(args.path)['state_dict'] + try: + model.load_state_dict(checkpoint) + except: + new_model_state = {} + for key in checkpoint.keys(): + if key[:7] == 'module.': + new_model_state[key[7:]] = checkpoint[key] + else: + new_model_state[key[9:]] = checkpoint[key] + model.load_state_dict(new_model_state) + + if torch.cuda.is_available(): + model = model.cuda() + + model.eval() + print("Evaluating the model: {}".format(args.arch)) + print("Location of the model: {}".format(args.path)) + print("Was it pretrained?: {}".format(args.pretrained)) + + # Measure sparsity before pruning + output_sparsity(model) + + # Test model before pruning + test(model, args, master_path) + + # Apply pruning + parameters_to_prune = 0 + if args.prune: + if args.pruning_type == "global": + model, parameters_to_prune = create_mask_global_lwm(model, args.pruning_ratio) + elif args.pruning_type == "local": + model, parameters_to_prune = create_mask_local_lwm(model, args.pruning_ratio) + else: + raise NotImplementedError + + print("Sparsity AFTER pruning model:") + output_sparsity(model) + + # Test without retraining model + test(model, args, master_path) + + # Retrain remaining model parameters + criterion = nn.CrossEntropyLoss().cuda() + optimizer = torch.optim.SGD(model.parameters(), args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay, nesterov=True) + + train_model(model, criterion, optimizer, args, master_path, parameters_to_prune) + + # Prune model parameters based on mask + if args.prune: + for param in parameters_to_prune: + prune.remove(param[0], param[1]) + + output_sparsity(model) + + # Test Pruned Model + test(model, args, master_path) + + # Measure sparsity after pruning and retraining + output_sparsity(model) + + model_dir = "results/checkpoints_pruned/" + args.augmentation + "/" + save_model(model, model_dir, args.model_name) + + return + + +if __name__ == '__main__': + main() diff --git a/sample_script_magnitude_based_pruning.sh b/sample_script_magnitude_based_pruning.sh new file mode 100644 index 0000000..1ff2fcf --- /dev/null +++ b/sample_script_magnitude_based_pruning.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +model_name="NAME_OF_PRUNED_MODEL" +path="/MODEL_PATH/" + $model_name +arch="wide_resnet50_2" +pruning_type="global" +pruning_ratio=0.7 +augmentation="pixmix" + +# LWM pruning and finetuning +python -u prune_with_augmentation.py --augmentation $augmentation --pretrained --arch $arch --model_name $model_name --prune --pruning_type $pruning_type --pruning_ratio $pruning_ratio --lr 0.01 --batch_size 256 --epochs 20 + +# Evaluate on ImageNet +python -u evaluate_imagenet_c.py --arch $arch --path $path + +# Evaluate on ImageNet-C +python -u evaluate_imagenet_c.py --arch $arch --path $path --corruption + +#Evalate on ImageNet-Patch +for ((seed=0;seed<=19;seed++)); do + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "bernoulli" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "srs_rn18" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "adv" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "black" --seed $seed + +done \ No newline at end of file diff --git a/sample_script_score_based_pruning.sh b/sample_script_score_based_pruning.sh new file mode 100644 index 0000000..7a58bc5 --- /dev/null +++ b/sample_script_score_based_pruning.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +model_name="NAME_OF_PRUNED_MODEL" +path="/MODEL_PATH/" + $model_name +arch="WideResNet50_2" +pruning_type="local" +k=0.1 +augmentation="pixmix" + + +# Calculate Score Based importance mask +python -u calculate_score_based_mask.py --arch $arch --model_name $model_name --k $k --save-dense --scaled-score-init --path $path --epochs 20 --augmentation $augmentation --exp-mode "prune" --layer-type "subnet" --init_type "kaiming_normal" --pruning_type $pruning_type + +# Retrain based on local computed importance score +python -u prune_with_augmentation.py --augmentation $augmentation --pretrained --arch $arch --model_name $model_name --prune --pruning_type $pruning_type --pruning_ratio $pruning_ratio --all-ops --lr 0.01 --batch_size 256 --epochs 20 + +# Evaluate on ImageNet +python -u evaluate_imagenet_c.py --arch $arch --path $path + +# Evaluate on ImageNet-C +python -u evaluate_imagenet_c.py --arch $arch --path $path --corruption + +#Evalate on ImageNet-Patch +for ((seed=0;seed<=19;seed++)); do + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "bernoulli" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "srs_rn18" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "adv" --seed $seed + python -u evaluate_imagenet_patch.py --arch $arch --path $path --patch "black" --seed $seed + +done \ No newline at end of file diff --git a/score_based/__init__.py b/score_based/__init__.py new file mode 100644 index 0000000..f1073ca --- /dev/null +++ b/score_based/__init__.py @@ -0,0 +1,2 @@ +from score_based.layers import * +from score_based.model import * \ No newline at end of file diff --git a/score_based/layers.py b/score_based/layers.py new file mode 100644 index 0000000..208d35f --- /dev/null +++ b/score_based/layers.py @@ -0,0 +1,120 @@ +import torch +import torch.nn as nn +import torch.autograd as autograd +from torch.nn.parameter import Parameter +import torch.nn.functional as F +import math + +# https://github.com/allenai/hidden-networks +class GetSubnet(autograd.Function): + @staticmethod + def forward(ctx, scores, k): + # Get the subnetwork by sorting the scores and using the top k% + out = scores.clone() + _, idx = scores.flatten().sort() + j = int((1 - k) * scores.numel()) + + # flat_out and out access the same memory. + flat_out = out.flatten() + flat_out[idx[:j]] = 0 + flat_out[idx[j:]] = 1 + + return out + + @staticmethod + def backward(ctx, g): + # send the gradient g straight-through on the backward pass. + return g, None + +def percentile(t, q): + k = 1 + round(.01 * float(q) * (t.numel() - 1)) + return t.view(-1).kthvalue(k).values.item() + +class GetSubnetFaster(torch.autograd.Function): + @staticmethod + def forward(ctx, scores, sparsity): + zeros = torch.zeros(scores.shape) + ones = torch.ones(scores.shape) + k_val = percentile(scores, sparsity*100) + return torch.where(scores < k_val, zeros.to(scores.device), ones.to(scores.device)) + + @staticmethod + def backward(ctx, g): + return g, None, None, None + + +class SubnetConv(nn.Conv2d): + # self.k is the % of weights remaining, a real number in [0,1] + # self.popup_scores is a Parameter which has the same shape as self.weight + # Gradients to self.weight, self.bias have been turned off by default. + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + ): + super(SubnetConv, self).__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + ) + self.popup_scores = Parameter(torch.Tensor(self.weight.shape)) + nn.init.kaiming_uniform_(self.popup_scores, a=math.sqrt(5)) + + self.weight.requires_grad = False + if self.bias is not None: + self.bias.requires_grad = False + self.w = 0 + + def set_prune_rate(self, k): + self.k = k + + def forward(self, x): + # Get the subnetwork by sorting the scores. + adj = GetSubnet.apply(self.popup_scores.abs(), self.k) + + # Use only the subnetwork in the forward pass. + self.w = self.weight * adj + x = F.conv2d( + x, self.w, self.bias, self.stride, self.padding, self.dilation, self.groups + ) + return x + + +class SubnetLinear(nn.Linear): + # self.k is the % of weights remaining, a real number in [0,1] + # self.popup_scores is a Parameter which has the same shape as self.weight + # Gradients to self.weight, self.bias have been turned off. + + def __init__(self, in_features, out_features, bias=True): + super(SubnetLinear, self).__init__(in_features, out_features, bias=True) + self.popup_scores = Parameter(torch.Tensor(self.weight.shape)) + nn.init.kaiming_uniform_(self.popup_scores, a=math.sqrt(5)) + self.weight.requires_grad = False + self.bias.requires_grad = False + self.w = 0 + + def set_prune_rate(self, k): + self.k = k + + def forward(self, x): + # Get the subnetwork by sorting the scores + adj = GetSubnet.apply(self.popup_scores.abs(), self.k) + + # Use only the subnetwork in the forward pass. + self.w = self.weight * adj + x = F.linear(x, self.w, self.bias) + + return x + diff --git a/score_based/model.py b/score_based/model.py new file mode 100644 index 0000000..4a739b2 --- /dev/null +++ b/score_based/model.py @@ -0,0 +1,245 @@ +import torch +import torch.nn as nn +import os +import math +import numpy as np + +from score_based.layers import SubnetConv, SubnetLinear + +# Hydra from https://github.com/inspire-group/hydra + +# Some utils are borrowed from https://github.com/allenai/hidden-networks +def freeze_vars(model, var_name, freeze_bn=False): + """ + freeze vars. If freeze_bn then only freeze batch_norm params. + """ + + assert var_name in ["weight", "bias", "popup_scores"] + for i, v in model.named_modules(): + if hasattr(v, var_name): + if not isinstance(v, (nn.BatchNorm2d, nn.BatchNorm2d)) or freeze_bn: + if getattr(v, var_name) is not None: + getattr(v, var_name).requires_grad = False + + +def unfreeze_vars(model, var_name): + assert var_name in ["weight", "bias", "popup_scores"] + for i, v in model.named_modules(): + if hasattr(v, var_name): + if getattr(v, var_name) is not None: + getattr(v, var_name).requires_grad = True + + +def set_prune_rate_model(model, prune_rate): + for _, v in model.named_modules(): + if hasattr(v, "set_prune_rate"): + v.set_prune_rate(prune_rate) + +def set_pruning_type_model(model, pruning_type): + for _, v in model.named_modules(): + if hasattr(v, "set_pruning_type"): + v.set_pruning_type(pruning_type) + + +def get_layers(layer_type): + """ + Returns: (conv_layer, linear_layer) + """ + if layer_type == "dense": + return nn.Conv2d, nn.Linear + elif layer_type == "subnet": + return SubnetConv, SubnetLinear + else: + raise ValueError("Incorrect layer type") + + +def show_gradients(model): + for i, v in model.named_parameters(): + print(f"variable = {i}, Gradient requires_grad = {v.requires_grad}") + + +def snip_init(model, criterion, optimizer, train_loader, device, args): + print("Using SNIP initialization") + assert args.exp_mode == "pretrain" + optimizer.zero_grad() + # init the score with kaiming normal init + for m in model.modules(): + if hasattr(m, "popup_scores"): + nn.init.kaiming_normal_(m.popup_scores, mode="fan_in") + + set_prune_rate_model(model, 1.0) + unfreeze_vars(model, "popup_scores") + + # take a forward pass and get gradients + for _, data in enumerate(train_loader): + images, target = data[0].to(device), data[1].to(device) + + output = model(images) + loss = criterion(output, target) + + loss.backward() + break + + # update scores with their respective connection sensitivty + for m in model.modules(): + if hasattr(m, "popup_scores"): + m.popup_scores.data = m.popup_scores.grad.data.abs() + + # update k back to args.k. + set_prune_rate_model(model, args.k) + freeze_vars(model, "popup_scores") + + +def initialize_scores(model, init_type): + print(f"Initialization relevance score with {init_type} initialization") + for m in model.modules(): + if hasattr(m, "popup_scores"): + if init_type == "kaiming_uniform": + nn.init.kaiming_uniform_(m.popup_scores) + elif init_type == "kaiming_normal": + nn.init.kaiming_normal_(m.popup_scores) + elif init_type == "xavier_uniform": + nn.init.xavier_uniform_( + m.popup_scores, gain=nn.init.calculate_gain("relu") + ) + elif init_type == "xavier_normal": + nn.init.xavier_normal_( + m.popup_scores, gain=nn.init.calculate_gain("relu") + ) + +def initialize_scaled_score(model): + print( + "Initialization relevance score proportional to weight magnitudes (OVERWRITING SOURCE NET SCORES)" + ) + for m in model.modules(): + if hasattr(m, "popup_scores"): + n = nn.init._calculate_correct_fan(m.popup_scores, "fan_in") + # Close to kaiming unifrom init + m.popup_scores.data = ( + math.sqrt(6 / n) * m.weight.data / torch.max(torch.abs(m.weight.data)) + ) + + +def scale_rand_init(model, k): + print( + f"Initializating random weight with scaling by 1/sqrt({k}) | Only applied to CONV & FC layers" + ) + for m in model.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + m.weight.data = 1 / math.sqrt(k) * m.weight.data + + +def prepare_model(model, args): + """ + 1. Set model pruning rate + 2. Set gradients base of training mode. + """ + + set_prune_rate_model(model, args.k) + set_pruning_type_model(model, args.pruning_type) + + if args.exp_mode == "pretrain": + print(f"#################### Pre-training network ####################") + print(f"===>> gradient for importance_scores: None | training weights only") + freeze_vars(model, "popup_scores", args.freeze_bn) + unfreeze_vars(model, "weight") + unfreeze_vars(model, "bias") + + elif args.exp_mode == "prune": + print(f"#################### Pruning network ####################") + print(f"===>> gradient for weights: None | training importance scores only") + + unfreeze_vars(model, "popup_scores") + freeze_vars(model, "weight", args.freeze_bn) + freeze_vars(model, "bias", args.freeze_bn) + + elif args.exp_mode == "finetune": + print(f"#################### Fine-tuning network ####################") + print( + f"===>> gradient for importance_scores: None | fine-tuning important weigths only" + ) + freeze_vars(model, "popup_scores", args.freeze_bn) + unfreeze_vars(model, "weight") + unfreeze_vars(model, "bias") + + else: + assert False, f"{args.exp_mode} mode is not supported" + + initialize_scores(model, args.scores_init_type) + + +def subnet_to_dense(subnet_dict, p): + """ + Convert a subnet state dict (with subnet layers) to dense i.e., which can be directly + loaded in network with dense layers. + """ + dense = {} + + # load dense variables + for (k, v) in subnet_dict.items(): + if "popup_scores" not in k: + dense[k] = v + + # update dense variables + for (k, v) in subnet_dict.items(): + if "popup_scores" in k: + s = torch.abs(subnet_dict[k]) + + out = s.clone() + _, idx = s.flatten().sort() + j = int((1 - p) * s.numel()) + + flat_out = out.flatten() + flat_out[idx[:j]] = 0 + flat_out[idx[j:]] = 1 + dense[k.replace("popup_scores", "weight")] = ( + subnet_dict[k.replace("popup_scores", "weight")] * out + ) + return dense + +def dense_to_subnet(model, state_dict): + """ + Load a dict with dense-layer in a model trained with subnet layers. + """ + model.load_state_dict(state_dict, strict=False) + + +def current_model_pruned_fraction(model, result_dir, args, verbose=True): + """ + Find pruning raio per layer. Return average of them. + Result_dict should correspond to the checkpoint of model. + """ + + # load the dense models + path = os.path.join(result_dir, "checkpoint_dense_" + args.pruning_type + ".pth.tar") + + pl = [] + + if os.path.exists(path): + state_dict = torch.load(path, map_location="cpu")["state_dict"] + for i, v in model.named_modules(): + if isinstance(v, (nn.Conv2d, nn.Linear)): + if i + ".weight" in state_dict.keys(): + d = state_dict[i + ".weight"].data.cpu().numpy() + p = 100 * np.sum(d == 0) / np.size(d) + pl.append(p) + if verbose: + print(i, v, p) + return np.mean(pl) + + +def sanity_check_paramter_updates(model, last_ckpt): + """ + Check whether weigths/popup_scores gets updated or not compared to last ckpt. + ONLY does it for 1 layer (to avoid computational overhead) + """ + for i, v in model.named_modules(): + if hasattr(v, "weight") and hasattr(v, "popup_scores"): + if getattr(v, "weight") is not None: + w1 = getattr(v, "weight").data.cpu() + w2 = last_ckpt[i + ".weight"].data.cpu() + if getattr(v, "popup_scores") is not None: + s1 = getattr(v, "popup_scores").data.cpu() + s2 = last_ckpt[i + ".popup_scores"].data.cpu() + return not torch.allclose(w1, w2), not torch.allclose(s1, s2) + diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..68fe35b --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,10 @@ +from utils.logging import * +from utils.train import * +from utils.eval import * +from utils.apply_patch import * +from utils.metrics_sparsity import * +from utils.loaders import * +from utils.calibration_tools import * +from utils.cutout import * +from utils.pixmix_utils import * +from utils.schedules import * \ No newline at end of file diff --git a/utils/apply_patch.py b/utils/apply_patch.py new file mode 100644 index 0000000..7db78d1 --- /dev/null +++ b/utils/apply_patch.py @@ -0,0 +1,84 @@ +import torch +import torchvision +from torch import Tensor +from torchvision.transforms import functional as F + +# ImageNet-Patch from https://github.com/pralab/ImageNet-Patch + +class ApplyPatch(torch.nn.Module): + def __init__(self, patch, translation_range=(.2, .2), rotation_range=45, + scale_range=(0.5, 1), patch_size=50): + super().__init__() + self.patch_size = patch_size + self.translation_range = translation_range + self.rotation_range = rotation_range + self.scale_range = scale_range + + self._transforms = None + self._patch = None + self._input_shape = None + self._mask = None + + self.set_transforms(translation_range, rotation_range, scale_range) + self.set_patch(patch) + + @property + def mask(self): + return self._mask + + @property + def transforms(self): + return self._transforms + + def set_patch(self, patch): + self._patch = patch + self._input_shape = self._patch.shape + self._mask = self._generate_mask() + + def _generate_mask(self): + mask = torch.ones_like(self._patch) + upp_l_x = self._input_shape[2] // 2 - self.patch_size // 2 + upp_l_y = self._input_shape[1] // 2 - self.patch_size // 2 + bott_r_x = self._input_shape[2] // 2 + self.patch_size // 2 + bott_r_y = self._input_shape[1] // 2 + self.patch_size // 2 + mask[:, :upp_l_x, :] = 0 + mask[:, :, :upp_l_y] = 0 + mask[:, bott_r_x:, :] = 0 + mask[:, :, bott_r_y:] = 0 + + return mask + + def set_transforms(self, translation_range, rotation_range, + scale_range): + self._transforms = MyRandomAffine( + rotation_range, translation_range, scale_range) + + def forward(self, img): + patch, mask = self.transforms(self._patch, self._mask) + inv_mask = torch.zeros_like(mask) + inv_mask[mask == 0] = 1 + return img * inv_mask + patch * mask + + +class MyRandomAffine(torchvision.transforms.RandomAffine): + def forward(self, img, mask): + fill = self.fill + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + try: + fill = [float(fill)] * F.get_image_num_channels(img) + except: + fill = [float(fill)] * F._get_image_num_channels(img) + else: + fill = [float(f) for f in fill] + try: + img_size = F.get_image_size(img) + except: + img_size = F._get_image_size(img) + ret = self.get_params(self.degrees, self.translate, self.scale, + self.shear, img_size) + transf_img = F.affine(img, *ret, interpolation=self.interpolation, + fill=fill) + transf_mask = F.affine(mask, *ret, interpolation=self.interpolation, + fill=fill) + return transf_img, transf_mask diff --git a/utils/calibration_tools.py b/utils/calibration_tools.py new file mode 100644 index 0000000..f3981ea --- /dev/null +++ b/utils/calibration_tools.py @@ -0,0 +1,33 @@ +import numpy as np + +def calib_err(confidence, correct, p='2', beta=100): + # beta is target bin size + idxs = np.argsort(confidence) + confidence = confidence[idxs] + correct = correct[idxs] + bins = [[i * beta, (i + 1) * beta] for i in range(len(confidence) // beta)] + bins[-1] = [bins[-1][0], len(confidence)] + + cerr = 0 + total_examples = len(confidence) + for i in range(len(bins) - 1): + bin_confidence = confidence[bins[i][0]:bins[i][1]] + bin_correct = correct[bins[i][0]:bins[i][1]] + num_examples_in_bin = len(bin_confidence) + + if num_examples_in_bin > 0: + difference = np.abs(np.nanmean(bin_confidence) - np.nanmean(bin_correct)) + + if p == '2': + cerr += num_examples_in_bin / total_examples * np.square(difference) + elif p == '1': + cerr += num_examples_in_bin / total_examples * difference + elif p == 'infty' or p == 'infinity' or p == 'max': + cerr = np.maximum(cerr, difference) + else: + assert False, "p must be '1', '2', or 'infty'" + + if p == '2': + cerr = np.sqrt(cerr) + + return cerr diff --git a/utils/cutout.py b/utils/cutout.py new file mode 100644 index 0000000..6c2b63d --- /dev/null +++ b/utils/cutout.py @@ -0,0 +1,80 @@ +import torch +import numpy as np +from PIL import Image, ImageEnhance, ImageOps + +# CutOut from: https://github.com/uoguelph-mlrg/Cutout +class Cutout(object): + """Randomly mask out one or more patches from an image. + + Args: + n_holes (int): Number of patches to cut out of each image. + length (int): The length (in pixels) of each square patch. + """ + def __init__(self, n_holes, length): + self.n_holes = n_holes + self.length = length + + def __call__(self, img): + """ + Args: + img (Tensor): Tensor image of size (C, H, W). + Returns: + Tensor: Image with n_holes of dimension length x length cut out of it. + """ + h = img.size(1) + w = img.size(2) + + mask = np.ones((h, w), np.float32) + + for n in range(self.n_holes): + y = np.random.randint(h) + x = np.random.randint(w) + + y1 = np.clip(y - self.length // 2, 0, h) + y2 = np.clip(y + self.length // 2, 0, h) + x1 = np.clip(x - self.length // 2, 0, w) + x2 = np.clip(x + self.length // 2, 0, w) + + mask[y1: y2, x1: x2] = 0. + + mask = torch.from_numpy(mask) + mask = mask.expand_as(img) + img = img * mask + + return img + +class Cutout_PIL: + """Randomly mask out one or more patches from an image. + + Args: + length (int): The length (in pixels) of each square patch. + """ + def __init__(self, length=16, n_holes=1): + self.n_holes = n_holes + self.length = length + + def __call__(self, img): + """ + Args: + img (Tensor): Tensor image of size (H, W, C). + Returns: + Tensor: Image with length x length cut out of it. + """ + img = np.array(img) + h = img.shape[0] + w = img.shape[1] + + for n in range(self.n_holes): + y = np.random.randint(h) + x = np.random.randint(w) + + y1 = np.clip(y - self.length // 2, 0, h) + y2 = np.clip(y + self.length // 2, 0, h) + x1 = np.clip(x - self.length // 2, 0, w) + x2 = np.clip(x + self.length // 2, 0, w) + + img[y1: y2, x1: x2, :] = 0. + + img = Image.fromarray(img) + + return img \ No newline at end of file diff --git a/utils/eval.py b/utils/eval.py new file mode 100644 index 0000000..8aebae1 --- /dev/null +++ b/utils/eval.py @@ -0,0 +1,104 @@ +import torch +import torch.nn as nn +import torchvision +from torch.autograd import Variable +import torch.nn.functional as F +from utils.logging import AverageMeter, ProgressMeter +from scipy.stats import norm +import numpy as np +import time + + +CORRUPTIONS = ['gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', + 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression'] + + +def get_output_for_batch(model, img, temp=1): + """ + model(x) is expected to return logits (instead of softmax probas) + """ + with torch.no_grad(): + out = nn.Softmax(dim=-1)(model(img) / temp) + p, index = torch.max(out, dim=-1) + return p.data.cpu().numpy(), index.data.cpu().numpy() + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def base(model, val_loader, criterion, args, epoch=0): + """ + Evaluating on unmodified validation set inputs. + """ + + print("we are here in evaluating base") + + batch_time = AverageMeter("Time", ":6.3f") + losses = AverageMeter("Loss", ":.4f") + top1 = AverageMeter("Acc_1", ":6.2f") + top5 = AverageMeter("Acc_5", ":6.2f") + progress = ProgressMeter( + len(val_loader), [batch_time, losses, top1, top5], prefix="Test: " + ) + + model.eval() + + with torch.no_grad(): + end = time.time() + for i, data in enumerate(val_loader): + images, target = data[0].cuda(), data[1].cuda() + + output = model(images) + loss = criterion(output, target) + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + batch_time.update(time.time() - end) + end = time.time() + + if (i + 1) % args.print_freq == 0: + progress.display(i) + + if writer: + progress.write_to_tensorboard( + writer, "test", epoch * len(val_loader) + i + ) + + # write a sample of test images to tensorboard (helpful for debugging) + if i == 0 and writer: + writer.add_image( + "test-images", + torchvision.utils.make_grid(images[0 : len(images) // 4]), + ) + progress.display(i) + + return top1.avg, top5.avg + + +def compute_mce(corruption_accs): + alexnet_err = [88.6, 89.4, 92.3, 82.0, 82.6, 78.6, 79.8, 86.7, 82.7, 81.9, 56.5, 85.3, 64.6, 71.8, 60.7] + mce = 0. + for i in range(len(CORRUPTIONS)): + avg_err = 100 - np.mean(corruption_accs[CORRUPTIONS[i]]) + ce = 100 * avg_err / alexnet_err[i] + print(CORRUPTIONS[i], ce) + mce += ce / 15 + return mce \ No newline at end of file diff --git a/utils/loaders.py b/utils/loaders.py new file mode 100644 index 0000000..9ccd08b --- /dev/null +++ b/utils/loaders.py @@ -0,0 +1,184 @@ +""" +This file is based on the code from https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py. +""" +import pkg_resources + +from torchvision.datasets.vision import VisionDataset + +import torch +import torch.utils.data as data +import torchvision.transforms as transforms + +from PIL import Image + +import os +import os.path +import sys + + +def make_custom_dataset(root, path_imgs, class_to_idx): + with open(pkg_resources.resource_filename(__name__, path_imgs), 'r') as f: + fnames = f.readlines() + images = [(os.path.join(root, c.split('\n')[0]), class_to_idx[c.split('/')[0]]) for c in fnames] + return images + + +class CustomDatasetFolder(VisionDataset): + """A generic data loader where the samples are arranged in this way: :: + root/class_x/xxx.ext + root/class_x/xxy.ext + root/class_x/xxz.ext + root/class_y/123.ext + root/class_y/nsdf3.ext + root/class_y/asd932_.ext + Args: + root (string): Root directory path. + loader (callable): A function to load a sample given its path. + extensions (tuple[string]): A list of allowed extensions. + both extensions and is_valid_file should not be passed. + transform (callable, optional): A function/transform that takes in + a sample and returns a transformed version. + E.g, ``transforms.RandomCrop`` for images. + target_transform (callable, optional): A function/transform that takes + in the target and transforms it. + is_valid_file (callable, optional): A function that takes path of an Image file + and check if the file is a valid_file (used to check of corrupt files) + both extensions and is_valid_file should not be passed. + Attributes: + classes (list): List of the class names. + class_to_idx (dict): Dict with items (class_name, class_index). + samples (list): List of (sample path, class_index) tuples + targets (list): The class_index value for each image in the dataset + """ + + def __init__(self, + root, + loader, + extensions=None, + transform=None, + target_transform=None, + is_valid_file=None): + super(CustomDatasetFolder, self).__init__(root) + self.transform = transform + self.target_transform = target_transform + classes, class_to_idx = self._find_classes(self.root) + samples = make_custom_dataset(self.root, '../assets/imagenet_test_image_ids.txt', class_to_idx) + if len(samples) == 0: + raise (RuntimeError("Found 0 files in subfolders of: " + + self.root + "\n" + "Supported extensions are: " + + ",".join(extensions))) + + self.loader = loader + self.extensions = extensions + + self.classes = classes + self.class_to_idx = class_to_idx + self.samples = samples + self.targets = [s[1] for s in samples] + + def _find_classes(self, dir): + """ + Finds the class folders in a dataset. + Args: + dir (string): Root directory path. + Returns: + tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary. + Ensures: + No class is a subdirectory of another. + """ + if sys.version_info >= (3, 5): + # Faster and available in Python 3.5 and above + classes = [d.name for d in os.scandir(dir) if d.is_dir()] + else: + classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] + classes.sort() + class_to_idx = {classes[i]: i for i in range(len(classes))} + return classes, class_to_idx + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + tuple: (sample, target) where target is class_index of the target class. + """ + path, target = self.samples[index] + sample = self.loader(path) + if self.transform is not None: + sample = self.transform(sample) + if self.target_transform is not None: + target = self.target_transform(target) + # return sample, target, path + return sample, target + + def __len__(self): + return len(self.samples) + + +IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', + '.tiff', '.webp') + + +def pil_loader(path): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, 'rb') as f: + img = Image.open(f) + return img.convert('RGB') + + +def accimage_loader(path): + import accimage + try: + return accimage.Image(path) + except IOError: + # Potentially a decoding problem, fall back to PIL.Image + return pil_loader(path) + + +def default_loader(path): + from torchvision import get_image_backend + if get_image_backend() == 'accimage': + return accimage_loader(path) + else: + return pil_loader(path) + + +class CustomImageFolder(CustomDatasetFolder): + """A generic data loader where the images are arranged in this way: :: + root/dog/xxx.png + root/dog/xxy.png + root/dog/xxz.png + root/cat/123.png + root/cat/nsdf3.png + root/cat/asd932_.png + Args: + root (string): Root directory path. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + loader (callable, optional): A function to load an image given its path. + is_valid_file (callable, optional): A function that takes path of an Image file + and check if the file is a valid_file (used to check of corrupt files) + Attributes: + classes (list): List of the class names. + class_to_idx (dict): Dict with items (class_name, class_index). + imgs (list): List of (image path, class_index) tuples + """ + + def __init__(self, + root, + transform=None, + target_transform=None, + loader=default_loader, + is_valid_file=None): + super(CustomImageFolder, + self).__init__(root, + loader, + IMG_EXTENSIONS if is_valid_file is None else None, + transform=transform, + target_transform=target_transform, + is_valid_file=is_valid_file) + + self.imgs = self.samples diff --git a/utils/logging.py b/utils/logging.py new file mode 100644 index 0000000..d3dbf70 --- /dev/null +++ b/utils/logging.py @@ -0,0 +1,132 @@ +import torch +import shutil +import os +from enum import Enum +from distutils.dir_util import copy_tree +from score_based.model import subnet_to_dense + +def save_checkpoint(state, is_best, args, result_dir, filename="checkpoint.pth.tar", save_dense=False): + if not os.path.exists(result_dir): + os.mkdir(result_dir) + + torch.save(state, os.path.join(result_dir, filename)) + if is_best: + shutil.copyfile( + os.path.join(result_dir, filename), + os.path.join(result_dir, filename), + ) + + if save_dense: + state["state_dict"] = subnet_to_dense(state["state_dict"], args.k) + torch.save( + subnet_to_dense(state, args.k), + os.path.join(result_dir, "checkpoint_dense.pth.tar"), + ) + + if is_best: + shutil.copyfile( + os.path.join(result_dir, filename), + os.path.join(result_dir, filename), + ) + + +def load_checkpoint(path, device): + if device is None: + return torch.load(path) + else: + return torch.load(path, map_location=device) + + +def save_model(model, model_dir, model_filename): + if not os.path.exists(model_dir): + os.makedirs(model_dir) + + model_filepath = os.path.join(model_dir, model_filename) + torch.save({'state_dict': model.state_dict()}, model_filepath) + + +def create_subdirs(sub_dir): + os.mkdir(sub_dir) + os.mkdir(os.path.join(sub_dir, "checkpoint")) + + +def write_to_file(file, data, option): + with open(file, option) as f: + f.write(data) + + +def clone_results_to_latest_subdir(src, dst): + if not os.path.exists(dst): + os.mkdir(dst) + copy_tree(src, dst) + + +class Summary(Enum): + NONE = 0 + AVERAGE = 1 + SUM = 2 + COUNT = 3 + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE): + self.name = name + self.fmt = fmt + self.summary_type = summary_type + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) + + def summary(self): + fmtstr = '' + if self.summary_type is Summary.NONE: + fmtstr = '' + elif self.summary_type is Summary.AVERAGE: + fmtstr = '{name} {avg:.3f}' + elif self.summary_type is Summary.SUM: + fmtstr = '{name} {sum:.3f}' + elif self.summary_type is Summary.COUNT: + fmtstr = '{name} {count:.3f}' + else: + raise ValueError('invalid summary type %r' % self.summary_type) + + return fmtstr.format(**self.__dict__) + + +class ProgressMeter(object): + def __init__(self, num_batches, meters, prefix=""): + self.batch_fmtstr = self._get_batch_fmtstr(num_batches) + self.meters = meters + self.prefix = prefix + + def display(self, batch): + entries = [self.prefix + self.batch_fmtstr.format(batch)] + entries += [str(meter) for meter in self.meters] + print('\t'.join(entries)) + + def display_summary(self): + entries = [" *"] + entries += [meter.summary() for meter in self.meters] + print(' '.join(entries)) + + def _get_batch_fmtstr(self, num_batches): + num_digits = len(str(num_batches // 1)) + fmt = '{:' + str(num_digits) + 'd}' + return '[' + fmt + '/' + fmt.format(num_batches) + ']' + diff --git a/utils/metrics_sparsity.py b/utils/metrics_sparsity.py new file mode 100644 index 0000000..b3291e9 --- /dev/null +++ b/utils/metrics_sparsity.py @@ -0,0 +1,40 @@ +import torch +import numpy as np +from matplotlib import pyplot as plt +from matplotlib import style + +def count_zero_weights(model): + zeros = 0 + non_zeros = 0 + for name, param in model.named_parameters(): + if param is not None: + zeros += param.numel() - param.nonzero().size(0) + non_zeros += param.nonzero().size(0) + return zeros, non_zeros + +def output_sparsity(model): + pytorch_total_params = sum(p.numel() for p in model.parameters()) + pytorch_total_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("Number of Parameters: {}".format(pytorch_total_params)) + print("Number of trainable Parameters: {}".format(pytorch_total_trainable_params)) + print("Number of Parameters Set to Zero: {}".format(count_zero_weights(model)[0])) + print("Number of Parameters Set: {}".format(count_zero_weights(model)[1])) + measure_global_sparsity(model) + +def measure_sparsity_per_layer(model): + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear): + print( + "Sparsity in " + name + ": {:.2f}%".format(100. * float(torch.sum(module.weight == 0)) + / float(module.weight.nelement()) + )) + +def measure_global_sparsity(model): + num_zero = 0 + num_elem = 0 + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear): + num_zero += (torch.sum(module.weight == 0)) + num_elem += module.weight.nelement() + + print("Global sparsity: {:.2f}%".format(100. * float(num_zero) / float(num_elem))) \ No newline at end of file diff --git a/utils/pixmix_utils.py b/utils/pixmix_utils.py new file mode 100644 index 0000000..c93eb83 --- /dev/null +++ b/utils/pixmix_utils.py @@ -0,0 +1,231 @@ +# Copyright 2019 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# PixMix from https://github.com/andyzoujm/pixmix + +"""Base augmentations operators.""" + +import numpy as np +import torch +from PIL import Image, ImageOps, ImageEnhance + +# ImageNet code should change this value +IMAGE_SIZE = 224 + +######################################################### +#################### AUGMENTATIONS ###################### +######################################################### + + +def int_parameter(level, maxval): + """Helper function to scale `val` between 0 and maxval . + + Args: + level: Level of the operation that will be between [0, `PARAMETER_MAX`]. + maxval: Maximum value that the operation can have. This will be scaled to + level/PARAMETER_MAX. + + Returns: + An int that results from scaling `maxval` according to `level`. + """ + return int(level * maxval / 10) + + +def float_parameter(level, maxval): + """Helper function to scale `val` between 0 and maxval. + + Args: + level: Level of the operation that will be between [0, `PARAMETER_MAX`]. + maxval: Maximum value that the operation can have. This will be scaled to + level/PARAMETER_MAX. + + Returns: + A float that results from scaling `maxval` according to `level`. + """ + return float(level) * maxval / 10. + + +def sample_level(n): + return np.random.uniform(low=0.1, high=n) + + +def autocontrast(pil_img, _): + # print("autocontrast") + return ImageOps.autocontrast(pil_img) + + +def equalize(pil_img, _): + # print("equalize") + return ImageOps.equalize(pil_img) + + +def posterize(pil_img, level): + # print("posterize") + level = int_parameter(sample_level(level), 4) + return ImageOps.posterize(pil_img, 4 - level) + + +def rotate(pil_img, level): + # print("rotate") + degrees = int_parameter(sample_level(level), 30) + if np.random.uniform() > 0.5: + degrees = -degrees + return pil_img.rotate(degrees, resample=Image.BILINEAR) + + +def solarize(pil_img, level): + # print("solarize") + level = int_parameter(sample_level(level), 256) + return ImageOps.solarize(pil_img, 256 - level) + + +def shear_x(pil_img, level): + # print("shear_x") + level = float_parameter(sample_level(level), 0.3) + if np.random.uniform() > 0.5: + level = -level + return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), + Image.AFFINE, (1, level, 0, 0, 1, 0), + resample=Image.BILINEAR) + + +def shear_y(pil_img, level): + # print("shear_y") + level = float_parameter(sample_level(level), 0.3) + if np.random.uniform() > 0.5: + level = -level + return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), + Image.AFFINE, (1, 0, 0, level, 1, 0), + resample=Image.BILINEAR) + + +def translate_x(pil_img, level): + # print("translate_x") + level = int_parameter(sample_level(level), IMAGE_SIZE / 3) + if np.random.random() > 0.5: + level = -level + return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), + Image.AFFINE, (1, 0, level, 0, 1, 0), + resample=Image.BILINEAR) + + +def translate_y(pil_img, level): + # print("translate_y") + level = int_parameter(sample_level(level), IMAGE_SIZE / 3) + if np.random.random() > 0.5: + level = -level + return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), + Image.AFFINE, (1, 0, 0, 0, 1, level), + resample=Image.BILINEAR) + + +# operation that overlaps with ImageNet-C's test set +def color(pil_img, level): + # print("color") + level = float_parameter(sample_level(level), 1.8) + 0.1 + return ImageEnhance.Color(pil_img).enhance(level) + + +# operation that overlaps with ImageNet-C's test set +def contrast(pil_img, level): + # print("contrast") + level = float_parameter(sample_level(level), 1.8) + 0.1 + return ImageEnhance.Contrast(pil_img).enhance(level) + + +# operation that overlaps with ImageNet-C's test set +def brightness(pil_img, level): + # print("brightness") + level = float_parameter(sample_level(level), 1.8) + 0.1 + return ImageEnhance.Brightness(pil_img).enhance(level) + + +# operation that overlaps with ImageNet-C's test set +def sharpness(pil_img, level): + # print("sharpness") + level = float_parameter(sample_level(level), 1.8) + 0.1 + return ImageEnhance.Sharpness(pil_img).enhance(level) + + +augmentations = [ + autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y, + translate_x, translate_y +] + +augmentations_all = [ + autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y, + translate_x, translate_y, color, contrast, brightness, sharpness +] + +######################################################### +######################## MIXINGS ######################## +######################################################### + +def get_ab(beta): + if np.random.random() < 0.5: + a = np.float32(np.random.beta(beta, 1)) + b = np.float32(np.random.beta(1, beta)) + else: + a = 1 + np.float32(np.random.beta(1, beta)) + b = -np.float32(np.random.beta(1, beta)) + return a, b + +def add(img1, img2, beta): + a,b = get_ab(beta) + img1, img2 = img1 * 2 - 1, img2 * 2 - 1 + out = a * img1 + b * img2 + return (out + 1) / 2 + +def multiply(img1, img2, beta): + a,b = get_ab(beta) + img1, img2 = img1 * 2, img2 * 2 + out = (img1 ** a) * (img2.clip(1e-37) ** b) + return out / 2 + +mixings = [add, multiply] + + +######################################## +##### EXTRA MIXIMGS (EXPREIMENTAL) ##### +######################################## + +def invert(img): + return 1 - img + +def screen(img1, img2, beta): + img1, img2 = invert(img1), invert(img2) + out = multiply(img1, img2, beta) + return invert(out) + +def overlay(img1, img2, beta): + case1 = multiply(img1, img2, beta) + case2 = screen(img1, img2, beta) + if np.random.random() < 0.5: + cond = img1 < 0.5 + else: + cond = img1 > 0.5 + return torch.where(cond, case1, case2) + +def darken_or_lighten(img1, img2, beta): + if np.random.random() < 0.5: + cond = img1 < img2 + else: + cond = img1 > img2 + return torch.where(cond, img1, img2) + +def swap_channel(img1, img2, beta): + channel = np.random.randint(3) + img1[channel] = img2[channel] + return img1 diff --git a/utils/schedules.py b/utils/schedules.py new file mode 100644 index 0000000..d94fa6a --- /dev/null +++ b/utils/schedules.py @@ -0,0 +1,88 @@ +import torch +import torch.nn as nn +import sys + +import numpy as np + + +def get_lr_policy(lr_schedule): + """Implement a new schduler directly in this file. + Args should contain a single choice for learning rate scheduler.""" + + d = { + "constant": constant_schedule, + "cosine": cosine_schedule, + "step": step_schedule, + } + return d[lr_schedule] + + +def get_optimizer(model, args): + if args.optimizer == "sgd": + optim = torch.optim.SGD( + model.parameters(), + lr=args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay, + ) + elif args.optimizer == "adam": + optim = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay,) + elif args.optimizer == "rmsprop": + optim = torch.optim.RMSprop( + model.parameters(), + lr=args.lr, + momentum=args.momentum, + weight_decay=args.weight_decay, + ) + else: + print(f"{args.optimizer} is not supported.") + sys.exit(0) + return optim + + +def new_lr(optimizer, lr): + for param_group in optimizer.param_groups: + param_group["lr"] = lr + + +def constant_schedule(optimizer, args): + def set_lr(epoch, lr=args.lr, epochs=args.epochs): + if epoch < args.warmup_epochs: + lr = args.warmup_lr + + new_lr(optimizer, lr) + + return set_lr + + +def cosine_schedule(optimizer, args): + def set_lr(epoch, lr=args.lr, epochs=args.epochs): + if epoch < args.warmup_epochs: + a = args.warmup_lr + else: + epoch = epoch - args.warmup_epochs + a = lr * 0.5 * (1 + np.cos((epoch - 1) / epochs * np.pi)) + + new_lr(optimizer, a) + + return set_lr + + +def step_schedule(optimizer, args): + def set_lr(epoch, lr=args.lr, epochs=args.epochs): + if epoch < args.warmup_epochs: + a = args.warmup_lr + else: + epoch = epoch - args.warmup_epochs + + a = lr + if epoch >= 0.75 * epochs: + a = lr * 0.1 + if epoch >= 0.9 * epochs: + a = lr * 0.01 + if epoch >= epochs: + a = lr * 0.001 + + new_lr(optimizer, a) + + return set_lr diff --git a/utils/train.py b/utils/train.py new file mode 100644 index 0000000..5423ae6 --- /dev/null +++ b/utils/train.py @@ -0,0 +1,50 @@ +import time +from utils.logging import AverageMeter, ProgressMeter +from utils.eval import accuracy + +def train(train_loader, model, criterion, optimizer, epoch, args): + batch_time = AverageMeter('Time', ':6.3f') + data_time = AverageMeter('Data', ':6.3f') + losses = AverageMeter('Loss', ':.4e') + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + progress = ProgressMeter( + len(train_loader), + [batch_time, data_time, losses, top1, top5], + prefix="Epoch: [{}]".format(epoch)) + + # switch to train mode + model.train() + + end = time.time() + for i, (images, target) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + + bx = images.cuda() + by = target.cuda() + + logits = model(bx) + loss = criterion(logits, by) + output, target = logits, by + + # measure accuracy and record loss + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + losses.update(loss.item(), images.size(0)) + top1.update(acc1[0], images.size(0)) + top5.update(acc5[0], images.size(0)) + + # compute gradient and do SGD step + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + progress.display(i) + + return losses.avg, top1.avg, top5.avg +

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