diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/README.md b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/README.md deleted file mode 100644 index 34bee6bfb0f15a38290b6c8bc737345fb11a931f..0000000000000000000000000000000000000000 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/README.md +++ /dev/null @@ -1,159 +0,0 @@ -```shell -$ hub install mobilenet_v2_imagenet_ssld==1.0.0 -``` - -

-
MobileNet 系列的网络结构 -

- -模型的详情可参考[论文](https://arxiv.org/pdf/1801.04381.pdf) - -## 命令行预测 - -``` -hub run mobilenet_v2_imagenet_ssld --input_path "/PATH/TO/IMAGE" -``` - -## API - -```python -def get_expected_image_width() -``` - -返回预处理的图片宽度，也就是224。 - -```python -def get_expected_image_height() -``` - -返回预处理的图片高度，也就是224。 - -```python -def get_pretrained_images_mean() -``` - -返回预处理的图片均值，也就是 \[0.485, 0.456, 0.406\]。 - -```python -def get_pretrained_images_std() -``` - -返回预处理的图片标准差，也就是 \[0.229, 0.224, 0.225\]。 - - -```python -def context(trainable=True, pretrained=True) -``` - -**参数** - -* trainable (bool): 计算图的参数是否为可训练的； -* pretrained (bool): 是否加载默认的预训练模型。 - -**返回** - -* inputs (dict): 计算图的输入，key 为 'image', value 为图片的张量； -* outputs (dict): 计算图的输出，key 为 'classification' 和 'feature_map'，其相应的值为： - * classification (paddle.fluid.framework.Variable): 分类结果，也就是全连接层的输出； - * feature\_map (paddle.fluid.framework.Variable): 特征匹配，全连接层前面的那个张量。 -* context\_prog(fluid.Program): 计算图，用于迁移学习。 - -```python -def classification(images=None, - paths=None, - batch_size=1, - use_gpu=False, - top_k=1): -``` - -**参数** - -* images (list\[numpy.ndarray\]): 图片数据，每一个图片数据的shape 均为 \[H, W, C\]，颜色空间为 BGR； -* paths (list\[str\]): 图片的路径； -* batch\_size (int): batch 的大小； -* use\_gpu (bool): 是否使用 GPU 来预测； -* top\_k (int): 返回预测结果的前 k 个。 - -**返回** - -res (list\[dict\]): 分类结果，列表的每一个元素均为字典，其中 key 为识别动物的类别，value为置信度。 - -```python -def save_inference_model(dirname, - model_filename=None, - params_filename=None, - combined=True) -``` - -将模型保存到指定路径。 - -**参数** - -* dirname: 存在模型的目录名称 -* model_filename: 模型文件名称，默认为\_\_model\_\_ -* params_filename: 参数文件名称，默认为\_\_params\_\_(仅当`combined`为True时生效) -* combined: 是否将参数保存到统一的一个文件中 - -## 代码示例 - -```python -import paddlehub as hub -import cv2 - -classifier = hub.Module(name="mobilenet_v2_imagenet_ssld") - -result = classifier.classification(images=[cv2.imread('/PATH/TO/IMAGE')]) -# or -# result = classifier.classification(paths=['/PATH/TO/IMAGE']) -``` - -## 服务部署 - -PaddleHub Serving可以部署一个在线动物识别服务。 - -## 第一步：启动PaddleHub Serving - -运行启动命令： -```shell -$ hub serving start -m mobilenet_v2_imagenet_ssld -``` - -这样就完成了一个在线动物识别服务化API的部署，默认端口号为8866。 - -**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 - -## 第二步：发送预测请求 - -配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 - -```python -import requests -import json -import cv2 -import base64 - - -def cv2_to_base64(image): - data = cv2.imencode('.jpg', image)[1] - return base64.b64encode(data.tostring()).decode('utf8') - - -# 发送HTTP请求 -data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} -headers = {"Content-type": "application/json"} -url = "http://127.0.0.1:8866/predict/mobilenet_v2_imagenet_ssld" -r = requests.post(url=url, headers=headers, data=json.dumps(data)) - -# 打印预测结果 -print(r.json()["results"]) -``` - -### 查看代码 - -[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) - -### 依赖 - -paddlepaddle >= 1.6.2 - -paddlehub >= 1.6.0 diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/__init__.py b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/data_feed.py b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/data_feed.py deleted file mode 100644 index d5ffb5efe9fdfbd143b949892aa44d851e907b41..0000000000000000000000000000000000000000 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/data_feed.py +++ /dev/null @@ -1,84 +0,0 @@ -# coding=utf-8 -import os -import time -from collections import OrderedDict - -import cv2 -import numpy as np -from PIL import Image - -__all__ = ['reader'] - -DATA_DIM = 224 -img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)) -img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)) - - -def resize_short(img, target_size): - percent = float(target_size) / min(img.size[0], img.size[1]) - resized_width = int(round(img.size[0] * percent)) - resized_height = int(round(img.size[1] * percent)) - img = img.resize((resized_width, resized_height), Image.LANCZOS) - return img - - -def crop_image(img, target_size, center): - width, height = img.size - size = target_size - if center == True: - w_start = (width - size) / 2 - h_start = (height - size) / 2 - else: - w_start = np.random.randint(0, width - size + 1) - h_start = np.random.randint(0, height - size + 1) - w_end = w_start + size - h_end = h_start + size - img = img.crop((w_start, h_start, w_end, h_end)) - return img - - -def process_image(img): - img = resize_short(img, target_size=256) - img = crop_image(img, target_size=DATA_DIM, center=True) - if img.mode != 'RGB': - img = img.convert('RGB') - img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255 - img -= img_mean - img /= img_std - return img - - -def reader(images=None, paths=None): - """ - Preprocess to yield image. - - Args: - images (list[numpy.ndarray]): images data, shape of each is [H, W, C]. - paths (list[str]): paths to images. - - Yield: - each (collections.OrderedDict): info of original image, preprocessed image. - """ - component = list() - if paths: - for im_path in paths: - each = OrderedDict() - assert os.path.isfile( - im_path), "The {} isn't a valid file path.".format(im_path) - each['org_im_path'] = im_path - each['org_im'] = Image.open(im_path) - each['org_im_width'], each['org_im_height'] = each['org_im'].size - component.append(each) - if images is not None: - assert type(images), "images is a list." - for im in images: - each = OrderedDict() - each['org_im'] = Image.fromarray(im[:, :, ::-1]) - each['org_im_path'] = 'ndarray_time={}'.format( - round(time.time(), 6) * 1e6) - each['org_im_width'], each['org_im_height'] = each['org_im'].size - component.append(each) - - for element in component: - element['image'] = process_image(element['org_im']) - yield element diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/label_list.txt b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/label_list.txt deleted file mode 100644 index a509c007481d301e524e7b3c97561132dbfcc765..0000000000000000000000000000000000000000 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/label_list.txt +++ /dev/null @@ -1,1000 +0,0 @@ -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 diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/mobilenet_v2.py b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/mobilenet_v2.py deleted file mode 100644 index 5284ef2b36ab8209bd44aebdd3a879ecd39de7df..0000000000000000000000000000000000000000 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/mobilenet_v2.py +++ /dev/null @@ -1,231 +0,0 @@ -# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. -# -# 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 __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import paddle.fluid as fluid -from paddle.fluid.initializer import MSRA -from paddle.fluid.param_attr import ParamAttr - -__all__ = [ - 'MobileNetV2_x0_25', 'MobileNetV2_x0_5', 'MobileNetV2_x0_75', - 'MobileNetV2_x1_0', 'MobileNetV2_x1_5', 'MobileNetV2_x2_0', 'MobileNetV2' -] - - -class MobileNetV2(): - def __init__(self, scale=1.0): - self.scale = scale - - def net(self, input, class_dim=1000): - scale = self.scale - bottleneck_params_list = [ - (1, 16, 1, 1), - (6, 24, 2, 2), - (6, 32, 3, 2), - (6, 64, 4, 2), - (6, 96, 3, 1), - (6, 160, 3, 2), - (6, 320, 1, 1), - ] - - #conv1 - input = self.conv_bn_layer( - input, - num_filters=int(32 * scale), - filter_size=3, - stride=2, - padding=1, - if_act=True, - name='conv1_1') - - # bottleneck sequences - i = 1 - in_c = int(32 * scale) - for layer_setting in bottleneck_params_list: - t, c, n, s = layer_setting - i += 1 - input = self.invresi_blocks( - input=input, - in_c=in_c, - t=t, - c=int(c * scale), - n=n, - s=s, - name='conv' + str(i)) - in_c = int(c * scale) - #last_conv - input = self.conv_bn_layer( - input=input, - num_filters=int(1280 * scale) if scale > 1.0 else 1280, - filter_size=1, - stride=1, - padding=0, - if_act=True, - name='conv9') - - input = fluid.layers.pool2d( - input=input, pool_type='avg', global_pooling=True) - - output = fluid.layers.fc( - input=input, - size=class_dim, - param_attr=ParamAttr(name='fc10_weights'), - bias_attr=ParamAttr(name='fc10_offset')) - return output, input - - def conv_bn_layer(self, - input, - filter_size, - num_filters, - stride, - padding, - channels=None, - num_groups=1, - if_act=True, - name=None, - use_cudnn=True): - conv = fluid.layers.conv2d( - input=input, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=padding, - groups=num_groups, - act=None, - use_cudnn=use_cudnn, - param_attr=ParamAttr(name=name + '_weights'), - bias_attr=False) - bn_name = name + '_bn' - bn = fluid.layers.batch_norm( - input=conv, - param_attr=ParamAttr(name=bn_name + "_scale"), - bias_attr=ParamAttr(name=bn_name + "_offset"), - moving_mean_name=bn_name + '_mean', - moving_variance_name=bn_name + '_variance') - if if_act: - return fluid.layers.relu6(bn) - else: - return bn - - def shortcut(self, input, data_residual): - return fluid.layers.elementwise_add(input, data_residual) - - def inverted_residual_unit(self, - input, - num_in_filter, - num_filters, - ifshortcut, - stride, - filter_size, - padding, - expansion_factor, - name=None): - num_expfilter = int(round(num_in_filter * expansion_factor)) - - channel_expand = self.conv_bn_layer( - input=input, - num_filters=num_expfilter, - filter_size=1, - stride=1, - padding=0, - num_groups=1, - if_act=True, - name=name + '_expand') - - bottleneck_conv = self.conv_bn_layer( - input=channel_expand, - num_filters=num_expfilter, - filter_size=filter_size, - stride=stride, - padding=padding, - num_groups=num_expfilter, - if_act=True, - name=name + '_dwise', - use_cudnn=False) - - linear_out = self.conv_bn_layer( - input=bottleneck_conv, - num_filters=num_filters, - filter_size=1, - stride=1, - padding=0, - num_groups=1, - if_act=False, - name=name + '_linear') - if ifshortcut: - out = self.shortcut(input=input, data_residual=linear_out) - return out - else: - return linear_out - - def invresi_blocks(self, input, in_c, t, c, n, s, name=None): - first_block = self.inverted_residual_unit( - input=input, - num_in_filter=in_c, - num_filters=c, - ifshortcut=False, - stride=s, - filter_size=3, - padding=1, - expansion_factor=t, - name=name + '_1') - - last_residual_block = first_block - last_c = c - - for i in range(1, n): - last_residual_block = self.inverted_residual_unit( - input=last_residual_block, - num_in_filter=last_c, - num_filters=c, - ifshortcut=True, - stride=1, - filter_size=3, - padding=1, - expansion_factor=t, - name=name + '_' + str(i + 1)) - return last_residual_block - - -def MobileNetV2_x0_25(): - model = MobileNetV2(scale=0.25) - return model - - -def MobileNetV2_x0_5(): - model = MobileNetV2(scale=0.5) - return model - - -def MobileNetV2_x0_75(): - model = MobileNetV2(scale=0.75) - return model - - -def MobileNetV2_x1_0(): - model = MobileNetV2(scale=1.0) - return model - - -def MobileNetV2_x1_5(): - model = MobileNetV2(scale=1.5) - return model - - -def MobileNetV2_x2_0(): - model = MobileNetV2(scale=2.0) - return model diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/module.py b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/module.py index a2bacc749572129cbb5c8e1a4c3257b812df416b..1dfba4cfe624ea216dccb918036301a08d9c466a 100644 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/module.py +++ b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/module.py @@ -1,278 +1,209 @@ -# coding=utf-8 -from __future__ import absolute_import -from __future__ import division - -import ast -import argparse +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# 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. import os -import numpy as np -import paddle.fluid as fluid -import paddlehub as hub -from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor -from paddlehub.module.module import moduleinfo, runnable, serving -from paddlehub.common.paddle_helper import add_vars_prefix - -from mobilenet_v2_imagenet_ssld.processor import postprocess, base64_to_cv2 -from mobilenet_v2_imagenet_ssld.data_feed import reader -from mobilenet_v2_imagenet_ssld.mobilenet_v2 import MobileNetV2 - - -@moduleinfo( - name="mobilenet_v2_imagenet_ssld", - type="CV/image_classification", - author="paddlepaddle", - author_email="paddle-dev@baidu.com", - summary= - "Mobilenet_V2 is a image classfication model, this module is trained with ImageNet-2012 dataset.", - version="1.0.0") -class MobileNetV2ImageNetSSLD(hub.Module): - def _initialize(self): - self.default_pretrained_model_path = os.path.join( - self.directory, "model") - label_file = os.path.join(self.directory, "label_list.txt") - with open(label_file, 'r', encoding='utf-8') as file: - self.label_list = file.read().split("\n")[:-1] - self._set_config() - - def get_expected_image_width(self): - return 224 - - def get_expected_image_height(self): - return 224 - - def get_pretrained_images_mean(self): - im_mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3) - return im_mean - - def get_pretrained_images_std(self): - im_std = np.array([0.229, 0.224, 0.225]).reshape(1, 3) - return im_std - - def _set_config(self): - """ - predictor config setting - """ - cpu_config = AnalysisConfig(self.default_pretrained_model_path) - cpu_config.disable_glog_info() - cpu_config.disable_gpu() - self.cpu_predictor = create_paddle_predictor(cpu_config) - - try: - _places = os.environ["CUDA_VISIBLE_DEVICES"] - int(_places[0]) - use_gpu = True - except: - use_gpu = False - if use_gpu: - gpu_config = AnalysisConfig(self.default_pretrained_model_path) - gpu_config.disable_glog_info() - gpu_config.enable_use_gpu( - memory_pool_init_size_mb=1000, device_id=0) - self.gpu_predictor = create_paddle_predictor(gpu_config) - - def context(self, trainable=True, pretrained=True): - """context for transfer learning. - - Args: - trainable (bool): Set parameters in program to be trainable. - pretrained (bool) : Whether to load pretrained model. - - Returns: - inputs (dict): key is 'image', corresponding vaule is image tensor. - outputs (dict): key is : - 'classification', corresponding value is the result of classification. - 'feature_map', corresponding value is the result of the layer before the fully connected layer. - context_prog (fluid.Program): program for transfer learning. - """ - context_prog = fluid.Program() - startup_prog = fluid.Program() - with fluid.program_guard(context_prog, startup_prog): - with fluid.unique_name.guard(): - image = fluid.layers.data( - name="image", shape=[3, 224, 224], dtype="float32") - mobile_net = MobileNetV2() - output, feature_map = mobile_net.net( - input=image, class_dim=len(self.label_list)) - - name_prefix = '@HUB_{}@'.format(self.name) - inputs = {'image': name_prefix + image.name} - outputs = { - 'classification': name_prefix + output.name, - 'feature_map': name_prefix + feature_map.name - } - add_vars_prefix(context_prog, name_prefix) - add_vars_prefix(startup_prog, name_prefix) - global_vars = context_prog.global_block().vars - inputs = { - key: global_vars[value] - for key, value in inputs.items() - } - outputs = { - key: global_vars[value] - for key, value in outputs.items() - } - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - # pretrained - if pretrained: - - def _if_exist(var): - b = os.path.exists( - os.path.join(self.default_pretrained_model_path, - var.name)) - return b - - fluid.io.load_vars( - exe, - self.default_pretrained_model_path, - context_prog, - predicate=_if_exist) - else: - exe.run(startup_prog) - # trainable - for param in context_prog.global_block().iter_parameters(): - param.trainable = trainable - return inputs, outputs, context_prog - - def save_inference_model(self, - dirname, - model_filename=None, - params_filename=None, - combined=True): - if combined: - model_filename = "__model__" if not model_filename else model_filename - params_filename = "__params__" if not params_filename else params_filename - place = fluid.CPUPlace() - exe = fluid.Executor(place) - - program, feeded_var_names, target_vars = fluid.io.load_inference_model( - dirname=self.default_pretrained_model_path, executor=exe) - - fluid.io.save_inference_model( - dirname=dirname, - main_program=program, - executor=exe, - feeded_var_names=feeded_var_names, - target_vars=target_vars, - model_filename=model_filename, - params_filename=params_filename) - - def classification(self, - images=None, - paths=None, - batch_size=1, - use_gpu=False, - top_k=1): - """ - API for image classification. - - Args: - images (numpy.ndarray): data of images, shape of each is [H, W, C], color space must be BGR. - paths (list[str]): The paths of images. - batch_size (int): batch size. - use_gpu (bool): Whether to use gpu. - top_k (int): Return top k results. - - Returns: - res (list[dict]): The classfication results. - """ - if use_gpu: - try: - _places = os.environ["CUDA_VISIBLE_DEVICES"] - int(_places[0]) - except: - raise RuntimeError( - "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id." - ) - - all_data = list() - for yield_data in reader(images, paths): - all_data.append(yield_data) - - total_num = len(all_data) - loop_num = int(np.ceil(total_num / batch_size)) - - res = list() - for iter_id in range(loop_num): - batch_data = list() - handle_id = iter_id * batch_size - for image_id in range(batch_size): - try: - batch_data.append(all_data[handle_id + image_id]) - except: - pass - # feed batch image - batch_image = np.array([data['image'] for data in batch_data]) - batch_image = PaddleTensor(batch_image.copy()) - predictor_output = self.gpu_predictor.run([ - batch_image - ]) if use_gpu else self.cpu_predictor.run([batch_image]) - out = postprocess( - data_out=predictor_output[0].as_ndarray(), - label_list=self.label_list, - top_k=top_k) - res += out - return res - - @serving - def serving_method(self, images, **kwargs): - """ - Run as a service. - """ - images_decode = [base64_to_cv2(image) for image in images] - results = self.classification(images=images_decode, **kwargs) - return results - - @runnable - def run_cmd(self, argvs): - """ - Run as a command. - """ - self.parser = argparse.ArgumentParser( - description="Run the {} module.".format(self.name), - prog='hub run {}'.format(self.name), - usage='%(prog)s', - add_help=True) - self.arg_input_group = self.parser.add_argument_group( - title="Input options", description="Input data. Required") - self.arg_config_group = self.parser.add_argument_group( - title="Config options", - description= - "Run configuration for controlling module behavior, not required.") - self.add_module_config_arg() - self.add_module_input_arg() - args = self.parser.parse_args(argvs) - results = self.classification( - paths=[args.input_path], - batch_size=args.batch_size, - use_gpu=args.use_gpu) - return results - - def add_module_config_arg(self): - """ - Add the command config options. - """ - self.arg_config_group.add_argument( - '--use_gpu', - type=ast.literal_eval, - default=False, - help="whether use GPU or not.") - self.arg_config_group.add_argument( - '--batch_size', - type=ast.literal_eval, - default=1, - help="batch size.") - self.arg_config_group.add_argument( - '--top_k', - type=ast.literal_eval, - default=1, - help="Return top k results.") - - def add_module_input_arg(self): - """ - Add the command input options. - """ - self.arg_input_group.add_argument( - '--input_path', type=str, help="path to image.") +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F +from paddle.nn import Conv2d, BatchNorm, Linear, Dropout +from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d +from paddlehub.module.module import moduleinfo +from paddlehub.module.cv_module import ImageClassifierModule + + +class ConvBNLayer(nn.Layer): + """Basic conv bn layer.""" + def __init__(self, + num_channels: int, + filter_size: int, + num_filters: int, + stride: int, + padding: int, + num_groups: int = 1, + name: str = None): + super(ConvBNLayer, self).__init__() + + self._conv = Conv2d(in_channels=num_channels, + out_channels=num_filters, + kernel_size=filter_size, + stride=stride, + padding=padding, + groups=num_groups, + weight_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + + self._batch_norm = BatchNorm(num_filters, + param_attr=ParamAttr(name=name + "_bn_scale"), + bias_attr=ParamAttr(name=name + "_bn_offset"), + moving_mean_name=name + "_bn_mean", + moving_variance_name=name + "_bn_variance") + + def forward(self, inputs: paddle.Tensor, if_act: bool = True): + y = self._conv(inputs) + y = self._batch_norm(y) + if if_act: + y = F.relu6(y) + return y + + +class InvertedResidualUnit(nn.Layer): + """Inverted Residual unit.""" + def __init__(self, num_channels: int, num_in_filter: int, num_filters: int, stride: int, filter_size: int, + padding: int, expansion_factor: int, name: str): + super(InvertedResidualUnit, self).__init__() + + num_expfilter = int(round(num_in_filter * expansion_factor)) + self._expand_conv = ConvBNLayer(num_channels=num_channels, + num_filters=num_expfilter, + filter_size=1, + stride=1, + padding=0, + num_groups=1, + name=name + "_expand") + + self._bottleneck_conv = ConvBNLayer(num_channels=num_expfilter, + num_filters=num_expfilter, + filter_size=filter_size, + stride=stride, + padding=padding, + num_groups=num_expfilter, + name=name + "_dwise") + + self._linear_conv = ConvBNLayer(num_channels=num_expfilter, + num_filters=num_filters, + filter_size=1, + stride=1, + padding=0, + num_groups=1, + name=name + "_linear") + + def forward(self, inputs: paddle.Tensor, ifshortcut: bool): + y = self._expand_conv(inputs, if_act=True) + y = self._bottleneck_conv(y, if_act=True) + y = self._linear_conv(y, if_act=False) + if ifshortcut: + y = paddle.elementwise_add(inputs, y) + return y + + +class InversiBlocks(nn.Layer): + """Inverted residual block composed by inverted residual unit.""" + def __init__(self, in_c: int, t: int, c: int, n: int, s: int, name: str): + super(InversiBlocks, self).__init__() + + self._first_block = InvertedResidualUnit(num_channels=in_c, + num_in_filter=in_c, + num_filters=c, + stride=s, + filter_size=3, + padding=1, + expansion_factor=t, + name=name + "_1") + + self._block_list = [] + for i in range(1, n): + block = self.add_sublayer(name + "_" + str(i + 1), + sublayer=InvertedResidualUnit(num_channels=c, + num_in_filter=c, + num_filters=c, + stride=1, + filter_size=3, + padding=1, + expansion_factor=t, + name=name + "_" + str(i + 1))) + self._block_list.append(block) + + def forward(self, inputs: paddle.Tensor): + y = self._first_block(inputs, ifshortcut=False) + for block in self._block_list: + y = block(y, ifshortcut=True) + return y + + +@moduleinfo(name="mobilenet_v2_imagenet_ssld", + type="cv/classification", + author="paddlepaddle", + author_email="", + summary="mobilenet_v2_imagenet_ssld is a classification model, " + "this module is trained with Imagenet dataset.", + version="1.0.0", + meta=ImageClassifierModule) +class MobileNet(nn.Layer): + """MobileNetV2""" + def __init__(self, class_dim: int = 1000, load_checkpoint: str = None): + super(MobileNet, self).__init__() + + self.class_dim = class_dim + + bottleneck_params_list = [(1, 16, 1, 1), (6, 24, 2, 2), (6, 32, 3, 2), (6, 64, 4, 2), (6, 96, 3, 1), + (6, 160, 3, 2), (6, 320, 1, 1)] + + self.conv1 = ConvBNLayer(num_channels=3, + num_filters=int(32), + filter_size=3, + stride=2, + padding=1, + name="conv1_1") + + self.block_list = [] + i = 1 + in_c = int(32) + for layer_setting in bottleneck_params_list: + t, c, n, s = layer_setting + i += 1 + block = self.add_sublayer("conv" + str(i), + sublayer=InversiBlocks(in_c=in_c, t=t, c=int(c), n=n, s=s, name="conv" + str(i))) + self.block_list.append(block) + in_c = int(c) + + self.out_c = 1280 + self.conv9 = ConvBNLayer(num_channels=in_c, + num_filters=self.out_c, + filter_size=1, + stride=1, + padding=0, + name="conv9") + + self.pool2d_avg = AdaptiveAvgPool2d(1) + + self.out = Linear(self.out_c, + class_dim, + weight_attr=ParamAttr(name="fc10_weights"), + bias_attr=ParamAttr(name="fc10_offset")) + + if load_checkpoint is not None: + model_dict = paddle.load(load_checkpoint)[0] + self.set_dict(model_dict) + print("load custom checkpoint success") + + else: + checkpoint = os.path.join(self.directory, 'MobileNetV2_ssld_pretrained.pdparams') + if not os.path.exists(checkpoint): + os.system( + 'wget https://bj.bcebos.com/paddlehub/model/image/object_detection/yolov3_70000.pdparams -O ' + + checkpoint) + model_dict = paddle.load(checkpoint)[0] + self.set_dict(model_dict) + print("load pretrained checkpoint success") + + def forward(self, inputs: paddle.Tensor): + y = self.conv1(inputs, if_act=True) + for block in self.block_list: + y = block(y) + y = self.conv9(y, if_act=True) + y = self.pool2d_avg(y) + y = paddle.reshape(y, shape=[-1, self.out_c]) + y = self.out(y) + return y diff --git a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/processor.py b/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/processor.py deleted file mode 100644 index fa8cbb502312e6ef80697ab63b767d4077b3847b..0000000000000000000000000000000000000000 --- a/hub_module/modules/image/classification/mobilenet_v2_imagenet_ssld/processor.py +++ /dev/null @@ -1,55 +0,0 @@ -# coding=utf-8 -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import base64 -import cv2 -import os - -import numpy as np - - -def base64_to_cv2(b64str): - data = base64.b64decode(b64str.encode('utf8')) - data = np.fromstring(data, np.uint8) - data = cv2.imdecode(data, cv2.IMREAD_COLOR) - return data - - -def softmax(x): - orig_shape = x.shape - if len(x.shape) > 1: - tmp = np.max(x, axis=1) - x -= tmp.reshape((x.shape[0], 1)) - x = np.exp(x) - tmp = np.sum(x, axis=1) - x /= tmp.reshape((x.shape[0], 1)) - else: - tmp = np.max(x) - x -= tmp - x = np.exp(x) - tmp = np.sum(x) - x /= tmp - return x - - -def postprocess(data_out, label_list, top_k): - """ - Postprocess output of network, one image at a time. - - Args: - data_out (numpy.ndarray): output data of network. - label_list (list): list of label. - top_k (int): Return top k results. - """ - output = [] - for result in data_out: - result_i = softmax(result) - output_i = {} - indexs = np.argsort(result_i)[::-1][0:top_k] - for index in indexs: - label = label_list[index].split(',')[0] - output_i[label] = float(result_i[index]) - output.append(output_i) - return output