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Commit e129194a authored by Sugon_ldc's avatar Sugon_ldc
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add new model resnet50v1.5

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ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:21.03-py3
FROM ${FROM_IMAGE_NAME}
ADD requirements.txt /workspace/
WORKDIR /workspace/
RUN pip install --no-cache-dir -r requirements.txt
ADD . /workspace/rn50
WORKDIR /workspace/rn50
LICENSE 0 → 100644
View file @ e129194a
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
LOC_synset_mapping.json 0 → 100644
View file @ e129194a
["tench, Tinca tinca",
"goldfish, Carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
"tiger shark, Galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, Struthio camelus",
"brambling, Fringilla montifringilla",
"goldfinch, Carduelis carduelis",
"house finch, linnet, Carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, Passerina cyanea",
"robin, American robin, Turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, American eagle, Haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, Strix nebulosa",
"European fire salamander, Salamandra salamandra",
"common newt, Triturus vulgaris",
"eft",
"spotted salamander, Ambystoma maculatum",
"axolotl, mud puppy, Ambystoma mexicanum",
"bullfrog, Rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
"loggerhead, loggerhead turtle, Caretta caretta",
"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, Iguana iguana",
"American chameleon, anole, Anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, Chlamydosaurus kingi",
"alligator lizard",
"Gila monster, Heloderma suspectum",
"green lizard, Lacerta viridis",
"African chameleon, Chamaeleo chamaeleon",
"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
"African crocodile, Nile crocodile, Crocodylus niloticus",
"American alligator, Alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, Carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, Hypsiglena torquata",
"boa constrictor, Constrictor constrictor",
"rock python, rock snake, Python sebae",
"Indian cobra, Naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
"diamondback, diamondback rattlesnake, Crotalus adamanteus",
"sidewinder, horned rattlesnake, Crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, Phalangium opilio",
"scorpion",
"black and gold garden spider, Argiope aurantia",
"barn spider, Araneus cavaticus",
"garden spider, Aranea diademata",
"black widow, Latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, Bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"African grey, African gray, Psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, Mergus serrator",
"goose",
"black swan, Cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"Dungeness crab, Cancer magister",
"rock crab, Cancer irroratus",
"fiddler crab",
"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
"American lobster, Northern lobster, Maine lobster, Homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, Ciconia ciconia",
"black stork, Ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, Egretta caerulea",
"American egret, great white heron, Egretta albus",
"bittern",
"crane",
"limpkin, Aramus pictus",
"European gallinule, Porphyrio porphyrio",
"American coot, marsh hen, mud hen, water hen, Fulica americana",
"bustard",
"ruddy turnstone, Arenaria interpres",
"red-backed sandpiper, dunlin, Erolia alpina",
"redshank, Tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, Aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
"dugong, Dugong dugon",
"sea lion",
"Chihuahua",
"Japanese spaniel",
"Maltese dog, Maltese terrier, Maltese",
"Pekinese, Pekingese, Peke",
"Shih-Tzu",
"Blenheim spaniel",
"papillon",
"toy terrier",
"Rhodesian ridgeback",
"Afghan hound, Afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"Walker hound, Walker foxhound",
"English foxhound",
"redbone",
"borzoi, Russian wolfhound",
"Irish wolfhound",
"Italian greyhound",
"whippet",
"Ibizan hound, Ibizan Podenco",
"Norwegian elkhound, elkhound",
"otterhound, otter hound",
"Saluki, gazelle hound",
"Scottish deerhound, deerhound",
"Weimaraner",
"Staffordshire bullterrier, Staffordshire bull terrier",
"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
"Bedlington terrier",
"Border terrier",
"Kerry blue terrier",
"Irish terrier",
"Norfolk terrier",
"Norwich terrier",
"Yorkshire terrier",
"wire-haired fox terrier",
"Lakeland terrier",
"Sealyham terrier, Sealyham",
"Airedale, Airedale terrier",
"cairn, cairn terrier",
"Australian terrier",
"Dandie Dinmont, Dandie Dinmont terrier",
"Boston bull, Boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"Scotch terrier, Scottish terrier, Scottie",
"Tibetan terrier, chrysanthemum dog",
"silky terrier, Sydney silky",
"soft-coated wheaten terrier",
"West Highland white terrier",
"Lhasa, Lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"Labrador retriever",
"Chesapeake Bay retriever",
"German short-haired pointer",
"vizsla, Hungarian pointer",
"English setter",
"Irish setter, red setter",
"Gordon setter",
"Brittany spaniel",
"clumber, clumber spaniel",
"English springer, English springer spaniel",
"Welsh springer spaniel",
"cocker spaniel, English cocker spaniel, cocker",
"Sussex spaniel",
"Irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"Old English sheepdog, bobtail",
"Shetland sheepdog, Shetland sheep dog, Shetland",
"collie",
"Border collie",
"Bouvier des Flandres, Bouviers des Flandres",
"Rottweiler",
"German shepherd, German shepherd dog, German police dog, alsatian",
"Doberman, Doberman pinscher",
"miniature pinscher",
"Greater Swiss Mountain dog",
"Bernese mountain dog",
"Appenzeller",
"EntleBucher",
"boxer",
"bull mastiff",
"Tibetan mastiff",
"French bulldog",
"Great Dane",
"Saint Bernard, St Bernard",
"Eskimo dog, husky",
"malamute, malemute, Alaskan malamute",
"Siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"Leonberg",
"Newfoundland, Newfoundland dog",
"Great Pyrenees",
"Samoyed, Samoyede",
"Pomeranian",
"chow, chow chow",
"keeshond",
"Brabancon griffon",
"Pembroke, Pembroke Welsh corgi",
"Cardigan, Cardigan Welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"Mexican hairless",
"timber wolf, grey wolf, gray wolf, Canis lupus",
"white wolf, Arctic wolf, Canis lupus tundrarum",
"red wolf, maned wolf, Canis rufus, Canis niger",
"coyote, prairie wolf, brush wolf, Canis latrans",
"dingo, warrigal, warragal, Canis dingo",
"dhole, Cuon alpinus",
"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
"hyena, hyaena",
"red fox, Vulpes vulpes",
"kit fox, Vulpes macrotis",
"Arctic fox, white fox, Alopex lagopus",
"grey fox, gray fox, Urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"Persian cat",
"Siamese cat, Siamese",
"Egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
"lynx, catamount",
"leopard, Panthera pardus",
"snow leopard, ounce, Panthera uncia",
"jaguar, panther, Panthera onca, Felis onca",
"lion, king of beasts, Panthera leo",
"tiger, Panthera tigris",
"cheetah, chetah, Acinonyx jubatus",
"brown bear, bruin, Ursus arctos",
"American black bear, black bear, Ursus americanus, Euarctos americanus",
"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
"sloth bear, Melursus ursinus, Ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"Angora, Angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, Sciurus niger",
"marmot",
"beaver",
"guinea pig, Cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, Sus scrofa",
"wild boar, boar, Sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, Hippopotamus amphibius",
"ox",
"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
"ibex, Capra ibex",
"hartebeest",
"impala, Aepyceros melampus",
"gazelle",
"Arabian camel, dromedary, Camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, Mustela putorius",
"black-footed ferret, ferret, Mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, Bradypus tridactylus",
"orangutan, orang, orangutang, Pongo pygmaeus",
"gorilla, Gorilla gorilla",
"chimpanzee, chimp, Pan troglodytes",
"gibbon, Hylobates lar",
"siamang, Hylobates syndactylus, Symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, Erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, Nasalis larvatus",
"marmoset",
"capuchin, ringtail, Cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, Ateles geoffroyi",
"squirrel monkey, Saimiri sciureus",
"Madagascar cat, ring-tailed lemur, Lemur catta",
"indri, indris, Indri indri, Indri brevicaudatus",
"Indian elephant, Elephas maximus",
"African elephant, Loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
"rock beauty, Holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, Lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, Biro",
"Band Aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
"cassette",
"cassette player",
"castle",
"catamaran",
"CD player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"Christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"Crock Pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"Dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"French horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"iPod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, T-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"Loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"Model T",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, CRO",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"Petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"Polaroid camera, Polaroid Land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, RV, R.V.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, CRT screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, U-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"Windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, Virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"French loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"Granny Smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"]
README.md 0 → 100644
View file @ e129194a
# Convolutional Network for Image Classification in PyTorch
In this repository you will find implementations of various image classification models.
Detailed information on each model can be found here:
## Table Of Contents
* [Models](#models)
* [Validation accuracy results](#validation-accuracy-results)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 80GB)](#training-performance-nvidia-dgx-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 16GB (8x V100 16GB)](#training-performance-nvidia-dgx-1-16gb-8x-v100-16gb)
* [Training performance: NVIDIA DGX-2 (16x V100 32GB)](#training-performance-nvidia-dgx-2-16x-v100-32gb)
* [Model comparison](#model-comparison)
* [Accuracy vs FLOPS](#accuracy-vs-flops)
* [Latency vs Throughput on different batch sizes](#latency-vs-throughput-on-different-batch-sizes)
## Models
The following table provides links to where you can find additional information on each model:
| **Model** | **Link**|
|:-:|:-:|
| resnet50 | [README](./resnet50v1.5/README.md) |
| resnext101-32x4d | [README](./resnext101-32x4d/README.md) |
| se-resnext101-32x4d | [README](./se-resnext101-32x4d/README.md) |
| EfficientNet | [README](./efficientnet/README.md) |
## Validation accuracy results
Our results were obtained by running the applicable
training scripts in the 20.12 PyTorch NGC container
on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the validation accuracy results of the
three classification models side-by-side.
| **Model** | **Mixed Precision Top1** | **Mixed Precision Top5** | **32 bit Top1** | **32 bit Top5** |
|:----------------------:|:------------------------:|:------------------------:|:---------------:|:---------------:|
| efficientnet-b0 | 77.63 | 93.82 | 77.31 | 93.76 |
| efficientnet-b4 | 82.98 | 96.44 | 82.92 | 96.43 |
| efficientnet-widese-b0 | 77.89 | 94.00 | 77.97 | 94.05 |
| efficientnet-widese-b4 | 83.28 | 96.45 | 83.30 | 96.47 |
| resnet50 | 78.60 | 94.19 | 78.69 | 94.16 |
| resnext101-32x4d | 80.43 | 95.06 | 80.40 | 95.04 |
| se-resnext101-32x4d | 81.00 | 95.48 | 81.09 | 95.45 |
## Training performance results
### Training performance: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the applicable
training scripts in the 21.03 PyTorch NGC container
on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
Performance numbers (in images per second)
were averaged over an entire training epoch.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the training accuracy results of
all the classification models side-by-side.
| **Model** | **Mixed Precision** | **TF32** | **Mixed Precision Speedup** |
|:----------------------:|:-------------------:|:----------:|:---------------------------:|
| efficientnet-b0 | 16652 img/s | 8193 img/s | 2.03 x |
| efficientnet-b4 | 2570 img/s | 1223 img/s | 2.1 x |
| efficientnet-widese-b0 | 16368 img/s | 8244 img/s | 1.98 x |
| efficientnet-widese-b4 | 2585 img/s | 1223 img/s | 2.11 x |
| resnet50 | 16621 img/s | 7248 img/s | 2.29 x |
| resnext101-32x4d | 7925 img/s | 3471 img/s | 2.28 x |
| se-resnext101-32x4d | 5779 img/s | 2991 img/s | 1.93 x |
### Training performance: NVIDIA DGX-1 16G (8x V100 16GB)
Our results were obtained by running the applicable
training scripts in the 21.03 PyTorch NGC container
on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
Performance numbers (in images per second)
were averaged over an entire training epoch.
The specific training script that was run is documented
in the corresponding model's README.
The following table shows the training accuracy results of all the
classification models side-by-side.
| **Model** | **Mixed Precision** | **FP32** | **Mixed Precision Speedup** |
|:----------------------:|:-------------------:|:----------:|:---------------------------:|
| efficientnet-b0 | 7789 img/s | 4672 img/s | 1.66 x |
| efficientnet-b4 | 1366 img/s | 616 img/s | 2.21 x |
| efficientnet-widese-b0 | 7875 img/s | 4592 img/s | 1.71 x |
| efficientnet-widese-b4 | 1356 img/s | 612 img/s | 2.21 x |
| resnet50 | 8322 img/s | 2855 img/s | 2.91 x |
| resnext101-32x4d | 4065 img/s | 1133 img/s | 3.58 x |
| se-resnext101-32x4d | 2971 img/s | 1004 img/s | 2.95 x |
## Model Comparison
### Accuracy vs FLOPS
![ACCvsFLOPS](./img/ACCvsFLOPS.png)
Plot describes relationship between floating point operations
needed for computing forward pass on a 224px x 224px image,
for the implemented models.
Dot size indicates number of trainable parameters.
### Latency vs Throughput on different batch sizes
![LATvsTHR](./img/LATvsTHR.png)
Plot describes relationship between
inference latency, throughput and batch size
for the implemented models.
checkpoint2model.py 0 → 100644
View file @ e129194a
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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.
import argparse
import torch
def add_parser_arguments(parser):
parser.add_argument(
"--checkpoint-path", metavar="<path>", help="checkpoint filename"
)
parser.add_argument(
"--weight-path", metavar="<path>", help="name of file in which to store weights"
)
parser.add_argument("--ema", action="store_true", default=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch ImageNet Training")
add_parser_arguments(parser)
args = parser.parse_args()
checkpoint = torch.load(args.checkpoint_path, map_location=torch.device("cpu"))
key = "state_dict" if not args.ema else "ema_state_dict"
model_state_dict = {
k[len("module.") :] if "module." in k else k: v
for k, v in checkpoint["state_dict"].items()
}
print(f"Loaded model, acc : {checkpoint['best_prec1']}")
torch.save(model_state_dict, args.weight_path)
classify.py 0 → 100644
View file @ e129194a
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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.
from PIL import Image
import argparse
import numpy as np
import json
import torch
from torch.cuda.amp import autocast
import torch.backends.cudnn as cudnn
from image_classification import models
import torchvision.transforms as transforms
from image_classification.models import (
resnet50,
resnext101_32x4d,
se_resnext101_32x4d,
efficientnet_b0,
efficientnet_b4,
efficientnet_widese_b0,
efficientnet_widese_b4,
efficientnet_quant_b0,
efficientnet_quant_b4,
)
def available_models():
models = {
m.name: m
for m in [
resnet50,
resnext101_32x4d,
se_resnext101_32x4d,
efficientnet_b0,
efficientnet_b4,
efficientnet_widese_b0,
efficientnet_widese_b4,
efficientnet_quant_b0,
efficientnet_quant_b4,
]
}
return models
def add_parser_arguments(parser):
model_names = available_models().keys()
parser.add_argument("--image-size", default="224", type=int)
parser.add_argument(
"--arch",
"-a",
metavar="ARCH",
default="resnet50",
choices=model_names,
help="model architecture: " + " | ".join(model_names) + " (default: resnet50)",
)
parser.add_argument(
"--precision", metavar="PREC", default="AMP", choices=["AMP", "FP32"]
)
parser.add_argument("--cpu", action="store_true", help="perform inference on CPU")
parser.add_argument("--image", metavar="<path>", help="path to classified image")
def load_jpeg_from_file(path, image_size, cuda=True):
img_transforms = transforms.Compose(
[
transforms.Resize(image_size + 32),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
]
)
img = img_transforms(Image.open(path))
with torch.no_grad():
# mean and std are not multiplied by 255 as they are in training script
# torch dataloader reads data into bytes whereas loading directly
# through PIL creates a tensor with floats in [0,1] range
mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
if cuda:
mean = mean.cuda()
std = std.cuda()
img = img.cuda()
img = img.float()
input = img.unsqueeze(0).sub_(mean).div_(std)
return input
def check_quant_weight_correctness(checkpoint_path, model):
state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
state_dict = {
k[len("module.") :] if k.startswith("module.") else k: v
for k, v in state_dict.items()
}
quantizers_sd_keys = {
f"{n[0]}._amax" for n in model.named_modules() if "quantizer" in n[0]
}
sd_all_keys = quantizers_sd_keys | set(model.state_dict().keys())
assert set(state_dict.keys()) == sd_all_keys, (
f"Passed quantized architecture, but following keys are missing in "
f"checkpoint: {list(sd_all_keys - set(state_dict.keys()))}"
)
def main(args, model_args):
imgnet_classes = np.array(json.load(open("./LOC_synset_mapping.json", "r")))
try:
model = available_models()[args.arch](**model_args.__dict__)
except RuntimeError as e:
print_in_box(
"Error when creating model, did you forget to run checkpoint2model script?"
)
raise e
if args.arch in ["efficientnet-quant-b0", "efficientnet-quant-b4"]:
check_quant_weight_correctness(model_args.pretrained_from_file, model)
if not args.cpu:
model = model.cuda()
model.eval()
input = load_jpeg_from_file(args.image, args.image_size, cuda=not args.cpu)
with torch.no_grad(), autocast(enabled=args.precision == "AMP"):
output = torch.nn.functional.softmax(model(input), dim=1)
output = output.float().cpu().view(-1).numpy()
top5 = np.argsort(output)[-5:][::-1]
print(args.image)
for c, v in zip(imgnet_classes[top5], output[top5]):
print(f"{c}: {100*v:.1f}%")
def print_in_box(msg):
print("#" * (len(msg) + 10))
print(f"#### {msg} ####")
print("#" * (len(msg) + 10))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch ImageNet Classification")
add_parser_arguments(parser)
args, rest = parser.parse_known_args()
model_args, rest = available_models()[args.arch].parser().parse_known_args(rest)
assert len(rest) == 0, f"Unknown args passed: {rest}"
cudnn.benchmark = True
main(args, model_args)
configs.yml 0 → 100644
View file @ e129194a
precision:
AMP:
static_loss_scale: 128
amp: True
FP32:
amp: False
TF32:
amp: False
platform:
DGX1V-16G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX1V-32G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
T4:
workers: 8
DGX1V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX2V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGXA100:
workers: 10
prefetch: 4
gpu_affinity: socket_unique_contiguous
Z100L:
workers: 8
prefetch: 4
gpu_affinity: none
mode:
benchmark_training: &benchmark_training
print_freq: 1
epochs: 3
training_only: True
evaluate: False
save_checkpoints: False
benchmark_training_short:
<<: *benchmark_training
epochs: 1
data_backend: synthetic
prof: 100
benchmark_inference: &benchmark_inference
print_freq: 1
epochs: 1
training_only: False
evaluate: True
save_checkpoints: False
convergence:
print_freq: 20
training_only: False
evaluate: False
save_checkpoints: True
evaluate:
print_freq: 20
training_only: False
evaluate: True
epochs: 1
save_checkpoints: False
anchors:
# ResNet_like params: {{{
resnet_params: &resnet_params
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.875
warmup: 8
epochs: 250
data_backend: pytorch
num_classes: 1000
image_size: 224
interpolation: bilinear
resnet_params_896: &resnet_params_896
<<: *resnet_params
optimizer_batch_size: 896
lr: 0.896
weight_decay: 6.103515625e-05
resnet_params_1k: &resnet_params_1k
<<: *resnet_params
optimizer_batch_size: 1024
lr: 1.024
weight_decay: 6.103515625e-05
resnet_params_2k: &resnet_params_2k
<<: *resnet_params
optimizer_batch_size: 2048
lr: 2.048
weight_decay: 3.0517578125e-05
resnet_params_4k: &resnet_params_4k
<<: *resnet_params
optimizer_batch_size: 4096
lr: 4.096
weight_decay: 3.0517578125e-05
# }}}
# EfficienNet Params: {{{
efficientnet_params: &efficientnet_params
optimizer: rmsprop
rmsprop_alpha: 0.9
rmsprop_eps: 0.01
print_freq: 100
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.9
warmup: 16
epochs: 400
data_backend: pytorch
augmentation: autoaugment
num_classes: 1000
interpolation: bicubic
efficientnet_b0_params_4k: &efficientnet_b0_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.08
weight_decay: 1e-05
image_size: 224
efficientnet_b4_params_4k: &efficientnet_b4_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.16
weight_decay: 5e-06
image_size: 380
# }}}
models:
resnet50: # {{{
DGX1V: &RN50_DGX1V
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_896
batch_size: 112
DGX1V-16G:
<<: *RN50_DGX1V
DGX1V-32G:
<<: *RN50_DGX1V
DGX2V:
AMP:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
DGXA100:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
TF32:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
T4:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_2k
batch_size: 128
Z100L:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_2k
batch_size: 128
# }}}
resnext101-32x4d: # {{{
DGX1V: &RNXT_DGX1V
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *RNXT_DGX1V
DGX1V-32G:
<<: *RNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
# }}}
se-resnext101-32x4d: # {{{
DGX1V: &SERNXT_DGX1V
AMP:
<<: *resnet_params_896
arch: se-resnext101-32x4d
batch_size: 112
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *SERNXT_DGX1V
DGX1V-32G:
<<: *SERNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
# }}}
efficientnet-widese-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
# }}}
efficientnet-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
# }}}
efficientnet-quant-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
# }}}
efficientnet-widese-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
# }}}
efficientnet-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
# }}}
efficientnet-quant-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
# }}}
configs.yml.bak 0 → 100644
View file @ e129194a
precision:
AMP:
static_loss_scale: 128
amp: True
FP32:
amp: False
TF32:
amp: False
platform:
DGX1V-16G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX1V-32G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
T4:
workers: 8
DGX1V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX2V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGXA100:
workers: 10
prefetch: 4
gpu_affinity: socket_unique_contiguous
mode:
benchmark_training: &benchmark_training
print_freq: 1
epochs: 3
training_only: True
evaluate: False
save_checkpoints: False
benchmark_training_short:
<<: *benchmark_training
epochs: 1
data_backend: synthetic
prof: 100
benchmark_inference: &benchmark_inference
print_freq: 1
epochs: 1
training_only: False
evaluate: True
save_checkpoints: False
convergence:
print_freq: 20
training_only: False
evaluate: False
save_checkpoints: True
evaluate:
print_freq: 20
training_only: False
evaluate: True
epochs: 1
save_checkpoints: False
anchors:
# ResNet_like params: {{{
resnet_params: &resnet_params
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.875
warmup: 8
epochs: 250
data_backend: pytorch
num_classes: 1000
image_size: 224
interpolation: bilinear
resnet_params_896: &resnet_params_896
<<: *resnet_params
optimizer_batch_size: 896
lr: 0.896
weight_decay: 6.103515625e-05
resnet_params_1k: &resnet_params_1k
<<: *resnet_params
optimizer_batch_size: 1024
lr: 1.024
weight_decay: 6.103515625e-05
resnet_params_2k: &resnet_params_2k
<<: *resnet_params
optimizer_batch_size: 2048
lr: 2.048
weight_decay: 3.0517578125e-05
resnet_params_4k: &resnet_params_4k
<<: *resnet_params
optimizer_batch_size: 4096
lr: 4.096
weight_decay: 3.0517578125e-05
# }}}
# EfficienNet Params: {{{
efficientnet_params: &efficientnet_params
optimizer: rmsprop
rmsprop_alpha: 0.9
rmsprop_eps: 0.01
print_freq: 100
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.9
warmup: 16
epochs: 400
data_backend: pytorch
augmentation: autoaugment
num_classes: 1000
interpolation: bicubic
efficientnet_b0_params_4k: &efficientnet_b0_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.08
weight_decay: 1e-05
image_size: 224
efficientnet_b4_params_4k: &efficientnet_b4_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.16
weight_decay: 5e-06
image_size: 380
# }}}
models:
resnet50: # {{{
DGX1V: &RN50_DGX1V
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_896
batch_size: 112
DGX1V-16G:
<<: *RN50_DGX1V
DGX1V-32G:
<<: *RN50_DGX1V
DGX2V:
AMP:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
DGXA100:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
TF32:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
T4:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_2k
batch_size: 128
# }}}
resnext101-32x4d: # {{{
DGX1V: &RNXT_DGX1V
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *RNXT_DGX1V
DGX1V-32G:
<<: *RNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
# }}}
se-resnext101-32x4d: # {{{
DGX1V: &SERNXT_DGX1V
AMP:
<<: *resnet_params_896
arch: se-resnext101-32x4d
batch_size: 112
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *SERNXT_DGX1V
DGX1V-32G:
<<: *SERNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
# }}}
efficientnet-widese-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
# }}}
efficientnet-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
# }}}
efficientnet-quant-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
# }}}
efficientnet-widese-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
# }}}
efficientnet-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
# }}}
efficientnet-quant-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
# }}}
dllogger-v1.0.0 @ 0540a439
Subproject commit 0540a43971f4a8a16693a9de9de73c1072020769
dllogger/__init__.py 0 → 100644
View file @ e129194a
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# 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
#
# http://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.
from .logger import (
Backend,
Verbosity,
Logger,
default_step_format,
default_metric_format,
StdOutBackend,
JSONStreamBackend,
)
__version__ = "1.0.0"
class DLLoggerNotInitialized(Exception):
pass
class DLLLoggerAlreadyInitialized(Exception):
pass
class NotInitializedObject(object):
def __getattribute__(self, name):
raise DLLoggerNotInitialized(
"DLLogger not initialized. Initialize DLLogger with init(backends) function"
)
GLOBAL_LOGGER = NotInitializedObject()
def log(step, data, verbosity=Verbosity.DEFAULT):
GLOBAL_LOGGER.log(step, data, verbosity=verbosity)
def metadata(metric, metadata):
GLOBAL_LOGGER.metadata(metric, metadata)
def flush():
GLOBAL_LOGGER.flush()
def init(backends):
global GLOBAL_LOGGER
try:
if isinstance(GLOBAL_LOGGER, Logger):
raise DLLLoggerAlreadyInitialized()
except DLLoggerNotInitialized:
GLOBAL_LOGGER = Logger(backends)
dllogger/logger.py 0 → 100644
View file @ e129194a
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# 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
#
# http://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.
from abc import ABC, abstractmethod
from collections import defaultdict
from datetime import datetime
import json
import atexit
class Backend(ABC):
def __init__(self, verbosity):
self._verbosity = verbosity
@property
def verbosity(self):
return self._verbosity
@abstractmethod
def log(self, timestamp, elapsedtime, step, data):
pass
@abstractmethod
def metadata(self, timestamp, elapsedtime, metric, metadata):
pass
class Verbosity:
OFF = -1
DEFAULT = 0
VERBOSE = 1
class Logger:
def __init__(self, backends):
self.backends = backends
atexit.register(self.flush)
self.starttime = datetime.now()
def metadata(self, metric, metadata):
timestamp = datetime.now()
elapsedtime = (timestamp - self.starttime).total_seconds()
for b in self.backends:
b.metadata(timestamp, elapsedtime, metric, metadata)
def log(self, step, data, verbosity=1):
timestamp = datetime.now()
elapsedtime = (timestamp - self.starttime).total_seconds()
for b in self.backends:
if b.verbosity >= verbosity:
b.log(timestamp, elapsedtime, step, data)
def flush(self):
for b in self.backends:
b.flush()
def default_step_format(step):
return str(step)
def default_metric_format(metric, metadata, value):
unit = metadata["unit"] if "unit" in metadata.keys() else ""
format = "{" + metadata["format"] + "}" if "format" in metadata.keys() else "{}"
return "{} : {} {}".format(
metric, format.format(value) if value is not None else value, unit
)
def default_prefix_format(timestamp):
return "DLL {} - ".format(timestamp)
class StdOutBackend(Backend):
def __init__(
self,
verbosity,
step_format=default_step_format,
metric_format=default_metric_format,
prefix_format=default_prefix_format,
):
super().__init__(verbosity=verbosity)
self._metadata = defaultdict(dict)
self.step_format = step_format
self.metric_format = metric_format
self.prefix_format = prefix_format
def metadata(self, timestamp, elapsedtime, metric, metadata):
self._metadata[metric].update(metadata)
def log(self, timestamp, elapsedtime, step, data):
print(
"{}{} {}".format(
self.prefix_format(timestamp),
self.step_format(step),
" ".join(
[
self.metric_format(m, self._metadata[m], v)
for m, v in data.items()
]
),
)
)
def flush(self):
pass
class JSONStreamBackend(Backend):
def __init__(self, verbosity, filename, append=False):
super().__init__(verbosity=verbosity)
self._filename = filename
self.file = open(filename, "a" if append else "w")
atexit.register(self.file.close)
def metadata(self, timestamp, elapsedtime, metric, metadata):
self.file.write(
"DLLL {}\n".format(
json.dumps(
dict(
timestamp=str(timestamp.timestamp()),
elapsedtime=str(elapsedtime),
datetime=str(timestamp),
type="METADATA",
metric=metric,
metadata=metadata,
)
)
)
)
def log(self, timestamp, elapsedtime, step, data):
self.file.write(
"DLLL {}\n".format(
json.dumps(
dict(
timestamp=str(timestamp.timestamp()),
datetime=str(timestamp),
elapsedtime=str(elapsedtime),
type="LOG",
step=step,
data=data,
)
)
)
)
def flush(self):
self.file.flush()
efficientnet/README.md 0 → 100644
View file @ e129194a
# EfficientNet For PyTorch
This repository provides a script and recipe to train the EfficientNet model to
achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.
## Table Of Contents
* [Model overview](#model-overview)
* [Default configuration](#default-configuration)
* [Feature support matrix](#feature-support-matrix)
* [Features](#features)
* [Mixed precision training](#mixed-precision-training)
* [Enabling mixed precision](#enabling-mixed-precision)
* [Enabling TF32](#enabling-tf32)
* [Quantization](#quantization)
* [Quantization-aware training](#qat)
* [Setup](#setup)
* [Requirements](#requirements)
* [Quick Start Guide](#quick-start-guide)
* [Advanced](#advanced)
* [Scripts and sample code](#scripts-and-sample-code)
* [Command-line options](#command-line-options)
* [Dataset guidelines](#dataset-guidelines)
* [Training process](#training-process)
* [Inference process](#inference-process)
* [NGC pretrained weights](#ngc-pretrained-weights)
* [QAT process](#qat-process)
* [Performance](#performance)
* [Benchmarking](#benchmarking)
* [Training performance benchmark](#training-performance-benchmark)
* [Inference performance benchmark](#inference-performance-benchmark)
* [Results](#results)
* [Training accuracy results](#training-accuracy-results)
* [Training accuracy: NVIDIA A100 (8x A100 80GB)](#training-accuracy-nvidia-a100-8x-a100-80gb)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Example plots](#example-plots)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA A100 (8x A100 80GB)](#training-performance-nvidia-a100-8x-a100-80gb)
* [Training performance: NVIDIA DGX-1 (8x V100 16GB)](#training-performance-nvidia-dgx-1-8x-v100-16gb)
* [Training performance: NVIDIA DGX-1 (8x V100 32GB)](#training-performance-nvidia-dgx-1-8x-v100-32gb)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA A100 (1x A100 80GB)](#inference-performance-nvidia-a100-1x-a100-80gb)
* [Inference performance: NVIDIA V100 (1x V100 16GB)](#inference-performance-nvidia-v100-1x-v100-16gb)
* [QAT results](#qat-results)
* [QAT Training performance: NVIDIA DGX-1 (8x V100 32GB)](#qat-training-performance-nvidia-dgx-1-8x-v100-32gb))
* [QAT Inference accuracy](#qat-inference-accuracy)
* [Release notes](#release-notes)
* [Changelog](#changelog)
* [Known issues](#known-issues)
## Model overview
EfficientNet is an image classification model family. It was first described in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946). The scripts provided enable you to train the EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0 and, EfficientNet-WideSE-B4 models.
EfficientNet-WideSE models use Squeeze-and-Excitation layers wider than original EfficientNet models, the width of SE module is proportional to the width of Depthwise Separable Convolutions instead of block width.
WideSE models are slightly more accurate than original models.
This model is trained with mixed precision using Tensor Cores on Volta and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
We use [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) when training using Mixed Precision.
### Default configuration
The following sections highlight the default configurations for the EfficientNet models.
**Optimizer**
This model uses RMSprop with the following hyperparameters:
* Momentum (0.9)
* Learning rate (LR):
* 0.08 for 4096 batch size for B0 models
* 0.16 for 4096 batch size for B4 models
scale the learning rate.
* Learning rate schedule - we use cosine LR schedule
* We use linear warmup of the learning rate during the first 16 epochs
* Weight decay (WD):
* 1e-5 for B0 models
* 5e-6 for B4 models
* We do not apply WD on Batch Norm trainable parameters (gamma/bias)
* Label smoothing = 0.1
* [MixUp](https://arxiv.org/pdf/1710.09412.pdf) = 0.2
* We train for 400 epochs
**Optimizer for QAT**
This model uses SGD optimizer for B0 models and RMSPROP optimizer alpha=0.853 epsilon=0.00422 for B4 models. Other hyperparameters we used are:
* Momentum:
* 0.89 for B0 models
* 0.9 for B4 models
* Learning rate (LR):
* 0.0125 for 128 batch size for B0 models
* 4.09e-06 for 32 batch size for B4 models
scale the learning rate.
* Learning rate schedule:
* cosine LR schedule for B0 models
* linear LR schedule for B4 models
* Weight decay (WD):
* 4.50e-05 for B0 models
* 9.714e-04 for B4 models
* We do not apply WD on Batch Norm trainable parameters (gamma/bias)
* We train for:
*10 epochs for B0 models
*2 epochs for B4 models
**Data augmentation**
This model uses the following data augmentation:
* For training:
* Auto-augmentation
* Basic augmentation:
* Normalization
* Random resized crop to target images size (depending on model version)
* Scale from 8% to 100%
* Aspect ratio from 3/4 to 4/3
* Random horizontal flip
* For inference:
* Normalization
* Scale to target image size + 32
* Center crop to target image size
### Feature support matrix
The following features are supported by this model:
| Feature | EfficientNet
|-----------------------|--------------------------
|[DALI](https://docs.nvidia.com/deeplearning/dali/release-notes/index.html) | Yes (without autoaugmentation)
|[APEX AMP](https://nvidia.github.io/apex/amp.html) | Yes
|[QAT](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization) | Yes
#### Features
**NVIDIA DALI**
DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader
with the DALI library. For more information about DALI, refer to the [DALI product documentation](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html).
We use [NVIDIA DALI](https://github.com/NVIDIA/DALI),
which speeds up data loading when CPU becomes a bottleneck.
DALI can use CPU or GPU, and outperforms the PyTorch native dataloader.
Run training with `--data-backends dali-gpu` or `--data-backends dali-cpu` to enable DALI.
For DGXA100 and DGX1 we recommend `--data-backends dali-cpu`.
DALI currently does not support Autoaugmentation, so for best accuracy it has to be disabled.
**[APEX](https://github.com/NVIDIA/apex)**
A PyTorch extension that contains utility libraries, such as [Automatic Mixed Precision (AMP)](https://nvidia.github.io/apex/amp.html), which require minimal network code changes to leverage Tensor Cores performance. Refer to the [Enabling mixed precision](#enabling-mixed-precision) section for more details.
**[QAT](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization)**
Quantization aware training (QAT) is a method for changing precision to INT8 which speeds up the inference process at the price of a slight decrease of network accuracy. Refer to the [Quantization](#quantization) section for more details.
### Mixed precision training
Mixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:
1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.
The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.
For information about:
- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.
- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.
- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).
#### Enabling mixed precision
Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval,
while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients.
In PyTorch, loss scaling can be easily applied by using `scale_loss()` method provided by AMP. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.
For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage Tensor Cores performance.
To enable mixed precision, you can:
- Import AMP from APEX:
```python
from apex import amp
```
- Wrap model and optimizer in `amp.initialize`:
```python
model, optimizer = amp.initialize(model, optimizer, opt_level="O1", loss_scale="dynamic")
```
- Scale loss before backpropagation:
```python
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
```
#### Enabling TF32
TensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.
TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.
For more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.
### Quantization
Quantization is the process of transforming deep learning models to use parameters and computations at a lower precision. Traditionally, DNN training and inference have relied on the IEEE single-precision floating-point format, using 32 bits to represent the floating-point model weights and activation tensors.
This compute budget may be acceptable at training as most DNNs are trained in data centers or in the cloud with NVIDIA V100 or A100 GPUs that have significantly large compute capability and much larger power budgets. However, during deployment, these models are most often required to run on devices with much smaller computing resources and lower power budgets at the edge. Running a DNN inference using the full 32-bit representation is not practical for real-time analysis given the compute, memory, and power constraints of the edge.
To help reduce the compute budget, while not compromising on the structure and number of parameters in the model, you can run inference at a lower precision. Initially, quantized inferences were run at half-point precision with tensors and weights represented as 16-bit floating-point numbers. While this resulted in compute savings of about 1.2–1.5x, there was still some compute budget and memory bandwidth that could be leveraged. In lieu of this, models are now quantized to an even lower precision, with an 8-bit integer representation for weights and tensors. This results in a model that is 4x smaller in memory and about 2–4x faster in throughput.
While 8-bit quantization is appealing to save compute and memory budgets, it is a lossy process. During quantization, a small range of floating-point numbers are squeezed to a fixed number of information buckets. This results in loss of information.
The minute differences which could originally be resolved using 32-bit representations are now lost because they are quantized to the same bucket in 8-bit representations. This is similar to rounding errors that one encounters when representing fractional numbers as integers. To maintain accuracy during inferences at a lower precision, it is important to try and mitigate errors arising due to this loss of information.
#### Quantization-aware training
In QAT, the quantization error is considered when training the model. The training graph is modified to simulate the lower precision behavior in the forward pass of the training process. This introduces the quantization errors as part of the training loss, which the optimizer tries to minimize during the training. Thus, QAT helps in modeling the quantization errors during training and mitigates its effects on the accuracy of the model at deployment.
However, the process of modifying the training graph to simulate lower precision behavior is intricate. To run QAT, it is necessary to insert FakeQuantization nodes for the weights of the DNN Layers and Quantize-Dequantize (QDQ) nodes to the intermediate activation tensors to compute their dynamic ranges.
For more information, see this [Quantization paper](https://arxiv.org/abs/2004.09602) and [Quantization-Aware Training](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#quantization-training) documentation.
Tutorial for `pytoch-quantization` library can be found here [`pytorch-quantization` tutorial](https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/tutorials/quant_resnet50.html).
It is important to mention that EfficientNet is NN, which is hard to quantize because the activation function all across the network is the SiLU (called also the Swish), whose negative values lie in very short range, which introduce a large quantization error. More details can be found in Appendix D of the [Quantization paper](https://arxiv.org/abs/2004.09602).
## Setup
The following section lists the requirements that you need to meet in order to start training the EfficientNet model.
### Requirements
This repository contains Dockerfile which extends the PyTorch NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)
* [PyTorch 21.03-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer
* Supported GPUs:
* [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)
* [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)
* [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)
For more information about how to get started with NGC containers, see the
following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning
DGX Documentation:
* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)
* [Running PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/running.html#running)
To set up the required environment or create your own container, as an alternative to the use of the PyTorch NGC container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
## Quick Start Guide
To train your model using mixed or TF32 precision with Tensor Cores or using FP32,
perform the following steps using the default parameters of the efficientnet model on the ImageNet dataset.
For the specifics concerning training and inference, see the [Advanced](#advanced) section.
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/Classification/
```
2. Download and pre-process the dataset.
The EfficientNet script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.
PyTorch can work directly on JPEGs, therefore, pre-processing/augmentation is not needed.
3. [Download the images](http://image-net.org/download-images).
4. Extract the training data:
```bash
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..
```
5. Extract the validation data and move the images to subfolders:
```bash
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
```
The directory in which the `train/` and `val/` directories are placed, is referred to as `<path to imagenet>` in this document.
6. Build the EfficientNet PyTorch NGC container.
```
docker build . -t nvidia_efficientnet
```
7. Start an interactive session in the NGC container to run training/inference.
```
nvidia-docker run --rm -it -v <path to imagenet>:/imagenet --ipc=host nvidia_efficientnet
```
8. Start training
To run training for a standard configuration (DGX A100/DGX-1V, AMP/TF32/FP32, 400 Epochs),
run one of the scripts in the `./efficientnet/training` directory
called `./efficientnet/training/{AMP, TF32, FP32}/{ DGX A100, DGX-1V }_efficientnet-<version>_{AMP, TF32, FP32}_{ 400 }E.sh`.
Ensure ImageNet is mounted in the `/imagenet` directory.
For example:
`bash ./efficientnet/training/AMP/DGXA100_efficientnet-b0_AMP.sh <path were to store checkpoints and logs>`
9. Start inference
You can download pre-trained weights from NGC:
```bash
wget --content-disposition -O
unzip
```
To run inference on ImageNet, run:
`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained -b <batch size> <path to imagenet>`
To run inference on JPEG image using pre-trained weights, run:
`python classify.py --arch efficientnet-<version> --pretrained --precision AMP|FP32 --image <path to JPEG image>`
## Advanced
The following sections provide greater details of the dataset, running training and inference, and the training results.
### Scripts and sample code
For a non-standard configuration, run:
* For 1 GPU
* FP32
`python ./main.py --arch efficientnet-<version> --label-smoothing 0.1 <path to imagenet>`
`python ./main.py --arch efficientnet-<version> --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>`
* For multiple GPUs
* FP32
`python ./multiproc.py --nproc_per_node 8 ./main.py --arch efficientnet-<version> --label-smoothing 0.1 <path to imagenet>`
* AMP
`python ./multiproc.py --nproc_per_node 8 ./main.py --arch efficientnet-<version> --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>`
Use `python ./main.py -h` to obtain the list of available options in the `main.py` script.
### Command-line options
To see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:
`python main.py -h`
### Dataset guidelines
To use your own dataset, divide it into directories. For example:
- Training images - `train/<class id>/<image>`
- Validation images - `val/<class id>/<image>`
If your dataset has a number of classes different than 1000, you need to pass the `--num_classes N` flag to the training script.
### Training process
All the results of the training will be stored in the directory specified with `--workspace` argument.
The script will store:
- the most recent checkpoint - `checkpoint.pth.tar` (unless `--no-checkpoints` flag is used).
- the checkpoint with the best validation accuracy - `model_best.pth.tar` (unless `--no-checkpoints` flag is used).
- the JSON log - in the file specified with the `--raport-file` flag.
Metrics gathered through training:
- `train.loss` - training loss
- `train.total_ips` - training speed measured in images/second
- `train.compute_ips` - training speed measured in images/second, not counting data loading
- `train.data_time` - time spent on waiting on data
- `train.compute_time` - time spent in forward/backward pass
To restart training from the checkpoint use the `--resume` option.
To start training from pretrained weights (for example, downloaded from NGC) use the `--pretrained-from-file` option.
The difference between `--resume` and `--pretrained-from-file` flags is that the pretrained weights contain only model weights,
and checkpoints, apart from model weights, contain optimizer state, LR scheduler state.
Checkpoints are suitable for dividing the training into parts, for example, in order
to divide the training job into shorter stages, or restart training after an infrastructure failure.
Pretrained weights can be used as a base for fine tuning the model to a different dataset,
or as a backbone to detection models.
### Inference process
Validation is done every epoch, and can be also run separately on a checkpointed model.
`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --resume <path to checkpoint> -b <batch size> <path to imagenet>`
Metrics gathered through training:
- `val.loss` - validation loss
- `val.top1` - validation top1 accuracy
- `val.top5` - validation top5 accuracy
- `val.total_ips` - inference speed measured in images/second
- `val.compute_ips` - inference speed measured in images/second, not counting data loading
- `val.data_time` - time spent on waiting on data
- `val.compute_time` - time spent on inference
To run inference on JPEG image, you have to first extract the model weights from checkpoint:
`python checkpoint2model.py --checkpoint-path <path to checkpoint> --weight-path <path where weights will be stored>`
Then, run the classification script:
`python classify.py --arch efficientnet-<version> --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
You can also run the ImageNet validation on pretrained weights:
`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained-from-file <path to pretrained weights> -b <batch size> <path to imagenet>`
#### NGC pretrained weights
Pretrained weights can be downloaded from NGC:
```bash
wget <ngc weights url>
```
URL for each model can be found in the following table:
| **Model** | **NGC weights URL** |
|:---------:|:-------------------:|
| efficientnet-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b0_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-b0_210412.pth |
| efficientnet-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b4_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-b4_210412.pth |
| efficientnet-widese-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_widese_b0_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-widese-b0_210412.pth |
| efficientnet-widese-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_widese_b4_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-widese-b4_210412.pth |
| efficientnet-quant-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b0_pyt_qat_ckpt_fp32/versions/21.03.0/files/nvidia-efficientnet-quant-b0-130421.pth |
| efficientnet-quant-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b4_pyt_qat_ckpt_fp32/versions/21.03.0/files/nvidia-efficientnet-quant-b4-130421.pth |
To run inference on ImageNet, run:
`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained -b <batch size> <path to imagenet>`
To run inference on JPEG images using pretrained weights, run:
`python classify.py --arch efficientnet-<version> --pretrained --precision AMP|FP32 --image <path to JPEG image>`
### Quantization process
EfficientNet-b0 and EfficientNet-b4 models can be quantized using the QAT process from running the `quant_main.py` script.
`python ./quant_main.py <path to imagenet> --arch efficientnet-quant-<version> --epochs <# of QAT epochs> --pretrained-from-file <path to non-quantized model weights> <any other parameters for training such as batch, momentum etc.>`
During the QAT process, evaluation is done in the same way as during standard training. `quant_main.py` works in the same way as the original `main.py` script, but with quantized models. It means that `quant_main.py` can be used to resume the QAT process with the flag `--resume`:
`python ./quant_main.py <path to imagenet> --arch efficientnet-quant-<version> --resume <path to mid-training checkpoint> ...`
or to evaluate a created checkpoint with the flag `--evaluate`:
`python ./quant_main.py --arch efficientnet-quant-<version> --evaluate --epochs 1 --resume <path to checkpoint> -b <batch size> <path to imagenet>`
It also can run on multi-GPU in an identical way as the standard `main.py` script:
`python ./multiproc.py --nproc_per_node 8 ./quant_main.py --arch efficientnet-quant-<version> ... <path to imagenet>`
There is also a possibility to transform trained models (quantized or not) into ONNX format, which is needed to convert it later into TensorRT, where quantized networks are much faster during inference. Conversion to TensorRT will be supported in the next release. The conversion to ONNX consists of two steps:
* translate checkpoint to pure weights:
`python checkpoint2model.py --checkpoint-path <path to quant checkpoint> --weight-path <path where quant weights will be stored>`
* translate pure weights to ONNX:
`python model2onnx.py --arch efficientnet-quant-<version> --pretrained-from-file <path to model quant weights> -b <batch size> --trt True`
Quantized models could also be used to classify new images using the `classify.py` flag. For example:
`python classify.py --arch efficientnet-quant-<version> --pretrained-from-file <path to quant weights> --image <path to JPEG image>`
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Benchmarking
The following section shows how to run benchmarks measuring the model performance in training and inference modes.
#### Training performance benchmark
To benchmark training, run:
* For 1 GPU
* FP32 (V100 GPUs only)
`python ./launch.py --model efficientnet-<version> --precision FP32 --mode benchmark_training --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./launch.py --model efficientnet-<version> --precision TF32 --mode benchmark_training --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./launch.py --model efficientnet-<version> --precision AMP --mode benchmark_training --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* For multiple GPUs
* FP32 (V100 GPUs only)
`python ./launch.py --model efficientnet-<version> --precision FP32 --mode benchmark_training --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-<version> --precision TF32 --mode benchmark_training --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-<version> --precision AMP --mode benchmark_training --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
Each of these scripts will run 100 iterations and save results in the `benchmark.json` file.
#### Inference performance benchmark
To benchmark inference, run:
* FP32 (V100 GPUs only)
`python ./launch.py --model efficientnet-<version> --precision FP32 --mode benchmark_inference --platform DGX1V <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* TF32 (A100 GPUs only)
`python ./launch.py --model efficientnet-<version> --precision TF32 --mode benchmark_inference --platform DGXA100 <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
* AMP
`python ./launch.py --model efficientnet-<version> --precision AMP --mode benchmark_inference --platform <DGX1V|DGXA100> <path to imagenet> --raport-file benchmark.json --epochs 1 --prof 100`
Each of these scripts will run 100 iterations and save results in the `benchmark.json` file.
### Results
Our results were obtained by running the applicable training script in the pytorch-21.03 NGC container.
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
#### Training accuracy results
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the applicable `efficientnet/training/<AMP|TF32>/*.sh` training script in the PyTorch 20.12 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.
| **Model** | **Epochs** | **GPUs** | **Top1 accuracy - TF32** | **Top1 accuracy - mixed precision** | **Time to train - TF32** | **Time to train - mixed precision** | **Time to train speedup (TF32 to mixed precision)** |
|:----------------------:|:----------:|:--------:|:------------------------:|:-----------------------------------:|:------------------------:|:-----------------------------------:|:---------------------------------------------------:|
| efficientnet-b0 | 400 | 8 | 77.16 +/- 0.07 | 77.42 +/- 0.11 | 19 | 11 | 1.727 |
| efficientnet-b4 | 400 | 8 | 82.82 +/- 0.04 | 82.85 +/- 0.09 | 126 | 66 | 1.909 |
| efficientnet-widese-b0 | 400 | 8 | 77.84 +/- 0.08 | 77.84 +/- 0.02 | 19 | 10 | 1.900 |
| efficientnet-widese-b4 | 400 | 8 | 83.13 +/- 0.11 | 83.1 +/- 0.09 | 126 | 66 | 1.909 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
Our results were obtained by running the applicable `efficientnet/training/<AMP|FP32>/*.sh` training script in the PyTorch 20.12 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.
| **Model** | **Epochs** | **GPUs** | **Top1 accuracy - FP32** | **Top1 accuracy - mixed precision** | **Time to train - FP32** | **Time to train - mixed precision** | **Time to train speedup (FP32 to mixed precision)** |
|:----------------------:|:----------:|:--------:|:------------------------:|:-----------------------------------:|:------------------------:|:-----------------------------------:|:---------------------------------------------------:|
| efficientnet-b0 | 400 | 8 | 77.02 +/- 0.04 | 77.17 +/- 0.08 | 34 | 24 | 1.417 |
| efficientnet-widese-b0 | 400 | 8 | 77.59 +/- 0.16 | 77.69 +/- 0.12 | 35 | 24 | 1.458 |
##### Example plots
The following images show an A100 run.
![ValidationLoss](./img/loss_plot.png)
![ValidationTop1](./img/top1_plot.png)
![ValidationTop5](./img/top5_plot.png)
#### Training performance results
##### Training performance: NVIDIA A100 (8x A100 80GB)
Our results were obtained by running the applicable `efficientnet/training/<AMP|TF32>/*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.
| **Model** | **GPUs** | **TF32** | **Throughput - mixed precision** | **Throughput speedup (TF32 to mixed precision)** | **TF32 Strong Scaling** | **Mixed Precision Strong Scaling** |
|:----------------------:|:--------:|:-----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|
| efficientnet-b0 | 1 | 1078 img/s | 2489 img/s | 2.3 x | 1.0 x | 1.0 x |
| efficientnet-b0 | 8 | 8193 img/s | 16652 img/s | 2.03 x | 7.59 x | 6.68 x |
| efficientnet-b0 | 16 | 16137 img/s | 29332 img/s | 1.81 x | 14.96 x | 11.78 x |
| efficientnet-b4 | 1 | 157 img/s | 331 img/s | 2.1 x | 1.0 x | 1.0 x |
| efficientnet-b4 | 8 | 1223 img/s | 2570 img/s | 2.1 x | 7.76 x | 7.75 x |
| efficientnet-b4 | 16 | 2417 img/s | 4813 img/s | 1.99 x | 15.34 x | 14.51 x |
| efficientnet-b4 | 32 | 4813 img/s | 9425 img/s | 1.95 x | 30.55 x | 28.42 x |
| efficientnet-b4 | 64 | 9146 img/s | 18900 img/s | 2.06 x | 58.05 x | 57.0 x |
| efficientnet-widese-b0 | 1 | 1078 img/s | 2512 img/s | 2.32 x | 1.0 x | 1.0 x |
| efficientnet-widese-b0 | 8 | 8244 img/s | 16368 img/s | 1.98 x | 7.64 x | 6.51 x |
| efficientnet-widese-b0 | 16 | 16062 img/s | 29798 img/s | 1.85 x | 14.89 x | 11.86 x |
| efficientnet-widese-b4 | 1 | 157 img/s | 331 img/s | 2.1 x | 1.0 x | 1.0 x |
| efficientnet-widese-b4 | 8 | 1223 img/s | 2585 img/s | 2.11 x | 7.77 x | 7.8 x |
| efficientnet-widese-b4 | 16 | 2399 img/s | 5041 img/s | 2.1 x | 15.24 x | 15.21 x |
| efficientnet-widese-b4 | 32 | 4616 img/s | 9379 img/s | 2.03 x | 29.32 x | 28.3 x |
| efficientnet-widese-b4 | 64 | 9140 img/s | 18516 img/s | 2.02 x | 58.07 x | 55.88 x |
##### Training performance: NVIDIA DGX-1 (8x V100 16GB)
Our results were obtained by running the applicable `efficientnet/training/<AMP|FP32>/*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.
| **Model** | **GPUs** | **FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** |
|:----------------------:|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|
| efficientnet-b0 | 1 | 655 img/s | 1301 img/s | 1.98 x | 1.0 x | 1.0 x |
| efficientnet-b0 | 8 | 4672 img/s | 7789 img/s | 1.66 x | 7.12 x | 5.98 x |
| efficientnet-b4 | 1 | 83 img/s | 204 img/s | 2.46 x | 1.0 x | 1.0 x |
| efficientnet-b4 | 8 | 616 img/s | 1366 img/s | 2.21 x | 7.41 x | 6.67 x |
| efficientnet-widese-b0 | 1 | 655 img/s | 1299 img/s | 1.98 x | 1.0 x | 1.0 x |
| efficientnet-widese-b0 | 8 | 4592 img/s | 7875 img/s | 1.71 x | 7.0 x | 6.05 x |
| efficientnet-widese-b4 | 1 | 83 img/s | 204 img/s | 2.45 x | 1.0 x | 1.0 x |
| efficientnet-widese-b4 | 8 | 612 img/s | 1356 img/s | 2.21 x | 7.34 x | 6.63 x |
##### Training performance: NVIDIA DGX-1 (8x V100 32GB)
Our results were obtained by running the applicable `efficientnet/training/<AMP|FP32>/*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.
| **Model** | **GPUs** | **FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** |
|:----------------------:|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|
| efficientnet-b0 | 1 | 646 img/s | 1401 img/s | 2.16 x | 1.0 x | 1.0 x |
| efficientnet-b0 | 8 | 4937 img/s | 8615 img/s | 1.74 x | 7.63 x | 6.14 x |
| efficientnet-b4 | 1 | 36 img/s | 89 img/s | 2.44 x | 1.0 x | 1.0 x |
| efficientnet-b4 | 8 | 641 img/s | 1565 img/s | 2.44 x | 17.6 x | 17.57 x |
| efficientnet-widese-b0 | 1 | 281 img/s | 603 img/s | 2.14 x | 1.0 x | 1.0 x |
| efficientnet-widese-b0 | 8 | 4924 img/s | 8870 img/s | 1.8 x | 17.49 x | 14.7 x |
| efficientnet-widese-b4 | 1 | 36 img/s | 89 img/s | 2.45 x | 1.0 x | 1.0 x |
| efficientnet-widese-b4 | 8 | 639 img/s | 1556 img/s | 2.43 x | 17.61 x | 17.44 x |
#### Inference performance results
##### Inference performance: NVIDIA A100 (1x A100 80GB)
Our results were obtained by running the applicable `efficientnet/inference/<AMP|FP32>/*.sh` inference script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.
###### TF32 Inference Latency
| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| efficientnet-b0 | 1 | 130 img/s | 9.33 ms | 7.95 ms | 9.0 ms |
| efficientnet-b0 | 2 | 262 img/s | 9.39 ms | 8.51 ms | 9.5 ms |
| efficientnet-b0 | 4 | 503 img/s | 9.68 ms | 9.53 ms | 10.78 ms |
| efficientnet-b0 | 8 | 1004 img/s | 9.85 ms | 9.89 ms | 11.49 ms |
| efficientnet-b0 | 16 | 1880 img/s | 10.27 ms | 10.34 ms | 11.19 ms |
| efficientnet-b0 | 32 | 3401 img/s | 11.46 ms | 12.51 ms | 14.39 ms |
| efficientnet-b0 | 64 | 4656 img/s | 19.58 ms | 14.52 ms | 16.63 ms |
| efficientnet-b0 | 128 | 5001 img/s | 31.03 ms | 25.72 ms | 28.34 ms |
| efficientnet-b0 | 256 | 5154 img/s | 60.71 ms | 49.44 ms | 54.99 ms |
| efficientnet-b4 | 1 | 69 img/s | 16.22 ms | 14.87 ms | 15.34 ms |
| efficientnet-b4 | 2 | 133 img/s | 16.84 ms | 16.49 ms | 17.72 ms |
| efficientnet-b4 | 4 | 259 img/s | 17.33 ms | 16.39 ms | 19.67 ms |
| efficientnet-b4 | 8 | 491 img/s | 18.22 ms | 18.09 ms | 19.51 ms |
| efficientnet-b4 | 16 | 606 img/s | 28.28 ms | 26.55 ms | 26.84 ms |
| efficientnet-b4 | 32 | 651 img/s | 51.08 ms | 49.39 ms | 49.61 ms |
| efficientnet-b4 | 64 | 684 img/s | 96.23 ms | 93.54 ms | 93.78 ms |
| efficientnet-b4 | 128 | 700 img/s | 195.22 ms | 182.17 ms | 182.42 ms |
| efficientnet-b4 | 256 | 702 img/s | 380.01 ms | 361.81 ms | 371.64 ms |
| efficientnet-widese-b0 | 1 | 130 img/s | 9.49 ms | 8.76 ms | 9.68 ms |
| efficientnet-widese-b0 | 2 | 265 img/s | 9.25 ms | 8.51 ms | 9.75 ms |
| efficientnet-widese-b0 | 4 | 520 img/s | 9.42 ms | 8.67 ms | 9.97 ms |
| efficientnet-widese-b0 | 8 | 996 img/s | 12.27 ms | 9.69 ms | 11.31 ms |
| efficientnet-widese-b0 | 16 | 1916 img/s | 10.2 ms | 10.29 ms | 11.3 ms |
| efficientnet-widese-b0 | 32 | 3293 img/s | 11.71 ms | 13.0 ms | 14.57 ms |
| efficientnet-widese-b0 | 64 | 4639 img/s | 16.21 ms | 14.61 ms | 16.29 ms |
| efficientnet-widese-b0 | 128 | 4997 img/s | 30.81 ms | 25.76 ms | 26.02 ms |
| efficientnet-widese-b0 | 256 | 5166 img/s | 73.68 ms | 49.39 ms | 55.74 ms |
| efficientnet-widese-b4 | 1 | 68 img/s | 16.41 ms | 15.14 ms | 16.59 ms |
| efficientnet-widese-b4 | 2 | 135 img/s | 16.65 ms | 15.52 ms | 17.93 ms |
| efficientnet-widese-b4 | 4 | 251 img/s | 17.74 ms | 17.29 ms | 20.47 ms |
| efficientnet-widese-b4 | 8 | 501 img/s | 17.75 ms | 17.12 ms | 18.01 ms |
| efficientnet-widese-b4 | 16 | 590 img/s | 28.94 ms | 27.29 ms | 27.81 ms |
| efficientnet-widese-b4 | 32 | 651 img/s | 50.96 ms | 49.34 ms | 49.55 ms |
| efficientnet-widese-b4 | 64 | 683 img/s | 99.28 ms | 93.65 ms | 93.88 ms |
| efficientnet-widese-b4 | 128 | 700 img/s | 189.81 ms | 182.3 ms | 182.58 ms |
| efficientnet-widese-b4 | 256 | 702 img/s | 379.36 ms | 361.84 ms | 366.05 ms |
###### Mixed Precision Inference Latency
| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| efficientnet-b0 | 1 | 105 img/s | 11.21 ms | 9.9 ms | 12.55 ms |
| efficientnet-b0 | 2 | 214 img/s | 11.01 ms | 10.06 ms | 11.89 ms |
| efficientnet-b0 | 4 | 412 img/s | 11.45 ms | 11.73 ms | 13.0 ms |
| efficientnet-b0 | 8 | 803 img/s | 11.78 ms | 11.59 ms | 14.2 ms |
| efficientnet-b0 | 16 | 1584 img/s | 11.89 ms | 11.9 ms | 13.63 ms |
| efficientnet-b0 | 32 | 2915 img/s | 13.03 ms | 14.79 ms | 17.35 ms |
| efficientnet-b0 | 64 | 6315 img/s | 12.71 ms | 13.59 ms | 15.27 ms |
| efficientnet-b0 | 128 | 9311 img/s | 18.78 ms | 15.34 ms | 17.99 ms |
| efficientnet-b0 | 256 | 10239 img/s | 39.05 ms | 24.97 ms | 29.24 ms |
| efficientnet-b4 | 1 | 53 img/s | 20.45 ms | 19.06 ms | 20.36 ms |
| efficientnet-b4 | 2 | 109 img/s | 20.01 ms | 19.74 ms | 21.5 ms |
| efficientnet-b4 | 4 | 212 img/s | 20.6 ms | 19.88 ms | 22.37 ms |
| efficientnet-b4 | 8 | 416 img/s | 21.02 ms | 21.46 ms | 24.82 ms |
| efficientnet-b4 | 16 | 816 img/s | 21.53 ms | 22.91 ms | 26.06 ms |
| efficientnet-b4 | 32 | 1208 img/s | 28.4 ms | 26.77 ms | 28.3 ms |
| efficientnet-b4 | 64 | 1332 img/s | 50.55 ms | 48.23 ms | 48.49 ms |
| efficientnet-b4 | 128 | 1418 img/s | 95.84 ms | 90.12 ms | 95.76 ms |
| efficientnet-b4 | 256 | 1442 img/s | 191.48 ms | 176.19 ms | 189.04 ms |
| efficientnet-widese-b0 | 1 | 104 img/s | 11.28 ms | 10.0 ms | 12.72 ms |
| efficientnet-widese-b0 | 2 | 206 img/s | 11.41 ms | 10.65 ms | 12.72 ms |
| efficientnet-widese-b0 | 4 | 426 img/s | 11.15 ms | 10.23 ms | 11.03 ms |
| efficientnet-widese-b0 | 8 | 794 img/s | 11.9 ms | 12.68 ms | 14.17 ms |
| efficientnet-widese-b0 | 16 | 1536 img/s | 12.32 ms | 13.22 ms | 14.57 ms |
| efficientnet-widese-b0 | 32 | 2876 img/s | 14.12 ms | 14.45 ms | 16.23 ms |
| efficientnet-widese-b0 | 64 | 6183 img/s | 13.02 ms | 14.19 ms | 16.68 ms |
| efficientnet-widese-b0 | 128 | 9310 img/s | 20.06 ms | 15.24 ms | 17.84 ms |
| efficientnet-widese-b0 | 256 | 10193 img/s | 36.07 ms | 25.13 ms | 34.22 ms |
| efficientnet-widese-b4 | 1 | 53 img/s | 20.24 ms | 19.05 ms | 19.91 ms |
| efficientnet-widese-b4 | 2 | 109 img/s | 20.98 ms | 19.24 ms | 22.58 ms |
| efficientnet-widese-b4 | 4 | 213 img/s | 20.48 ms | 20.48 ms | 23.64 ms |
| efficientnet-widese-b4 | 8 | 425 img/s | 20.57 ms | 20.26 ms | 22.44 ms |
| efficientnet-widese-b4 | 16 | 800 img/s | 21.93 ms | 23.15 ms | 26.51 ms |
| efficientnet-widese-b4 | 32 | 1201 img/s | 28.51 ms | 26.89 ms | 28.13 ms |
| efficientnet-widese-b4 | 64 | 1322 img/s | 50.96 ms | 48.58 ms | 48.77 ms |
| efficientnet-widese-b4 | 128 | 1417 img/s | 96.45 ms | 90.17 ms | 90.43 ms |
| efficientnet-widese-b4 | 256 | 1439 img/s | 190.06 ms | 176.59 ms | 188.51 ms |
##### Inference performance: NVIDIA V100 (1x V100 16GB)
Our results were obtained by running the applicable `efficientnet/inference/<AMP|FP32>/*.sh` inference script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.
###### FP32 Inference Latency
| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| efficientnet-b0 | 1 | 83 img/s | 13.15 ms | 13.23 ms | 14.11 ms |
| efficientnet-b0 | 2 | 167 img/s | 13.17 ms | 13.46 ms | 14.39 ms |
| efficientnet-b0 | 4 | 332 img/s | 13.25 ms | 13.29 ms | 14.85 ms |
| efficientnet-b0 | 8 | 657 img/s | 13.42 ms | 13.86 ms | 15.77 ms |
| efficientnet-b0 | 16 | 1289 img/s | 13.78 ms | 15.02 ms | 16.99 ms |
| efficientnet-b0 | 32 | 2140 img/s | 16.46 ms | 18.92 ms | 22.2 ms |
| efficientnet-b0 | 64 | 2743 img/s | 25.14 ms | 23.44 ms | 23.79 ms |
| efficientnet-b0 | 128 | 2908 img/s | 48.03 ms | 43.98 ms | 45.36 ms |
| efficientnet-b0 | 256 | 2968 img/s | 94.86 ms | 85.62 ms | 91.01 ms |
| efficientnet-b4 | 1 | 45 img/s | 23.31 ms | 23.3 ms | 24.9 ms |
| efficientnet-b4 | 2 | 87 img/s | 24.07 ms | 23.81 ms | 25.14 ms |
| efficientnet-b4 | 4 | 160 img/s | 26.29 ms | 26.78 ms | 30.85 ms |
| efficientnet-b4 | 8 | 316 img/s | 26.65 ms | 26.44 ms | 28.61 ms |
| efficientnet-b4 | 16 | 341 img/s | 48.18 ms | 46.9 ms | 47.13 ms |
| efficientnet-b4 | 32 | 365 img/s | 89.07 ms | 87.83 ms | 88.02 ms |
| efficientnet-b4 | 64 | 374 img/s | 173.2 ms | 171.61 ms | 172.27 ms |
| efficientnet-b4 | 128 | 376 img/s | 346.32 ms | 339.74 ms | 340.37 ms |
| efficientnet-widese-b0 | 1 | 82 img/s | 13.37 ms | 12.95 ms | 13.89 ms |
| efficientnet-widese-b0 | 2 | 168 img/s | 13.11 ms | 12.45 ms | 13.94 ms |
| efficientnet-widese-b0 | 4 | 346 img/s | 12.73 ms | 12.22 ms | 12.95 ms |
| efficientnet-widese-b0 | 8 | 674 img/s | 13.07 ms | 12.75 ms | 14.93 ms |
| efficientnet-widese-b0 | 16 | 1235 img/s | 14.3 ms | 15.05 ms | 16.53 ms |
| efficientnet-widese-b0 | 32 | 2194 img/s | 15.99 ms | 17.37 ms | 19.01 ms |
| efficientnet-widese-b0 | 64 | 2747 img/s | 25.05 ms | 23.38 ms | 23.71 ms |
| efficientnet-widese-b0 | 128 | 2906 img/s | 48.05 ms | 44.0 ms | 44.59 ms |
| efficientnet-widese-b0 | 256 | 2962 img/s | 95.14 ms | 85.86 ms | 86.25 ms |
| efficientnet-widese-b4 | 1 | 43 img/s | 24.28 ms | 25.24 ms | 27.36 ms |
| efficientnet-widese-b4 | 2 | 87 img/s | 24.04 ms | 24.38 ms | 26.01 ms |
| efficientnet-widese-b4 | 4 | 169 img/s | 24.96 ms | 25.8 ms | 27.14 ms |
| efficientnet-widese-b4 | 8 | 307 img/s | 27.39 ms | 28.4 ms | 30.7 ms |
| efficientnet-widese-b4 | 16 | 342 img/s | 48.05 ms | 46.74 ms | 46.9 ms |
| efficientnet-widese-b4 | 32 | 363 img/s | 89.44 ms | 88.23 ms | 88.39 ms |
| efficientnet-widese-b4 | 64 | 373 img/s | 173.47 ms | 172.01 ms | 172.36 ms |
| efficientnet-widese-b4 | 128 | 376 img/s | 347.18 ms | 340.09 ms | 340.45 ms |
###### Mixed Precision Inference Latency
| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |
|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|
| efficientnet-b0 | 1 | 62 img/s | 17.19 ms | 18.01 ms | 18.63 ms |
| efficientnet-b0 | 2 | 119 img/s | 17.96 ms | 18.3 ms | 19.95 ms |
| efficientnet-b0 | 4 | 238 img/s | 17.9 ms | 17.8 ms | 19.13 ms |
| efficientnet-b0 | 8 | 495 img/s | 17.38 ms | 18.34 ms | 19.29 ms |
| efficientnet-b0 | 16 | 945 img/s | 18.23 ms | 19.42 ms | 21.58 ms |
| efficientnet-b0 | 32 | 1784 img/s | 19.29 ms | 20.71 ms | 22.51 ms |
| efficientnet-b0 | 64 | 3480 img/s | 20.34 ms | 22.22 ms | 24.62 ms |
| efficientnet-b0 | 128 | 5759 img/s | 26.11 ms | 22.61 ms | 24.06 ms |
| efficientnet-b0 | 256 | 6176 img/s | 49.36 ms | 41.18 ms | 43.5 ms |
| efficientnet-b4 | 1 | 34 img/s | 30.28 ms | 30.2 ms | 32.24 ms |
| efficientnet-b4 | 2 | 69 img/s | 30.12 ms | 30.02 ms | 31.92 ms |
| efficientnet-b4 | 4 | 129 img/s | 32.08 ms | 33.29 ms | 34.74 ms |
| efficientnet-b4 | 8 | 242 img/s | 34.43 ms | 37.34 ms | 41.08 ms |
| efficientnet-b4 | 16 | 488 img/s | 34.12 ms | 36.13 ms | 39.39 ms |
| efficientnet-b4 | 32 | 738 img/s | 44.67 ms | 44.85 ms | 47.86 ms |
| efficientnet-b4 | 64 | 809 img/s | 80.93 ms | 79.19 ms | 79.42 ms |
| efficientnet-b4 | 128 | 843 img/s | 156.42 ms | 152.17 ms | 152.76 ms |
| efficientnet-b4 | 256 | 847 img/s | 311.03 ms | 301.44 ms | 302.48 ms |
| efficientnet-widese-b0 | 1 | 64 img/s | 16.71 ms | 17.59 ms | 19.23 ms |
| efficientnet-widese-b0 | 2 | 129 img/s | 16.63 ms | 16.1 ms | 17.34 ms |
| efficientnet-widese-b0 | 4 | 238 img/s | 17.92 ms | 17.52 ms | 18.82 ms |
| efficientnet-widese-b0 | 8 | 445 img/s | 19.24 ms | 19.53 ms | 20.4 ms |
| efficientnet-widese-b0 | 16 | 936 img/s | 18.64 ms | 19.55 ms | 21.1 ms |
| efficientnet-widese-b0 | 32 | 1818 img/s | 18.97 ms | 20.62 ms | 23.06 ms |
| efficientnet-widese-b0 | 64 | 3572 img/s | 19.81 ms | 21.14 ms | 23.29 ms |
| efficientnet-widese-b0 | 128 | 5748 img/s | 26.18 ms | 23.72 ms | 26.1 ms |
| efficientnet-widese-b0 | 256 | 6187 img/s | 49.11 ms | 41.11 ms | 41.59 ms |
| efficientnet-widese-b4 | 1 | 32 img/s | 32.1 ms | 31.6 ms | 34.69 ms |
| efficientnet-widese-b4 | 2 | 68 img/s | 30.4 ms | 30.9 ms | 32.67 ms |
| efficientnet-widese-b4 | 4 | 123 img/s | 33.81 ms | 39.0 ms | 40.76 ms |
| efficientnet-widese-b4 | 8 | 257 img/s | 32.34 ms | 33.39 ms | 34.93 ms |
| efficientnet-widese-b4 | 16 | 497 img/s | 33.51 ms | 34.92 ms | 37.24 ms |
| efficientnet-widese-b4 | 32 | 739 img/s | 44.63 ms | 43.62 ms | 46.39 ms |
| efficientnet-widese-b4 | 64 | 808 img/s | 81.08 ms | 79.43 ms | 79.59 ms |
| efficientnet-widese-b4 | 128 | 840 img/s | 157.11 ms | 152.87 ms | 153.26 ms |
| efficientnet-widese-b4 | 256 | 846 img/s | 310.73 ms | 301.68 ms | 302.9 ms |
#### Quantization results
##### QAT Training performance: NVIDIA DGX-1 (8x V100 32GB)
| **Model** | **GPUs** | **Calibration** | **QAT model** | **FP32** | **QAT ratio** |
|:---------------------:|:---------|:---------------:|:---------------:|:----------:|:-------------:|
| efficientnet-quant-b0 | 8 | 14.71 img/s | 2644.62 img/s | 3798 img/s | 0.696 x |
| efficientnet-quant-b4 | 8 | 1.85 img/s | 310.41 img/s | 666 img/s | 0.466 x |
###### Quant Inference accuracy
The best checkpoints generated during training were used as a base for the QAT.
| **Model** | **QAT Epochs** | **QAT Top1** | **Gap between FP32 Top1 and QAT Top1** |
|:---------------------:|:--------------:|:------------:|:--------------------------------------:|
| efficientnet-quant-b0 | 10 | 77.12 | 0.51 |
| efficientnet-quant-b4 | 2 | 82.54 | 0.44 |
## Release notes
### Changelog
1. April 2020
* Initial release
### Known issues
There are no known issues with this model.
efficientnet/inference/AMP/DGXA100_efficientnet-b0_AMP.sh 0 → 100644
View file @ e129194a
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json
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