Add PPResNet from PaddleHub conversion (#4)

* add PPResNet impl and demo

* add benchmark impl and results for PPResNet

README.md

@@ -16,8 +16,8 @@ Hardware Setup:
 -->
 
 ***Important Notes***:
-- The time data that shown on the following tables presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
-- The time data that shown on the following tables is the median of benchmark runs.
+- The time data that shown on the following table presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
+- The time data that shown on the following table is the median of 10 runs. Different metrics may be applied to some specific models.
 - View [benchmark/config](./benchmark/config) for more details on benchmarking different models.
 
 <!--
@@ -33,6 +33,7 @@ Hardware Setup:
 | [DB](./models/text_detection_db)         | 640x480 | 137.38 | 2780.78 |
 | [CRNN](./models/text_recognition_crnn)   | 100x32  | 50.21  | 234.32  |
 | [SFace](./models/face_recognition_sface) | 112x112 | 8.69 | 96.79 |
+| [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58
 
 
 ## License

benchmark/benchmark.py

@@ -75,6 +75,10 @@ class Data:
         if self._use_label:
             self._labels = self._load_label()
 
+        self._to_rgb = kwargs.pop('toRGB', False)
+        self._resize = tuple(kwargs.pop('resize', []))
+        self._center_crop = kwargs.pop('centerCrop', None)
+
     def _load_label(self):
         labels = dict.fromkeys(self._files, None)
         for filename in self._files:
@@ -83,6 +87,19 @@ class Data:
 
     def __getitem__(self, idx):
         image = cv.imread(os.path.join(self._path, self._files[idx]))
+
+        if self._to_rgb:
+            image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+        if self._resize:
+            image = cv.resize(image, self._resize)
+        if self._center_crop:
+            h, w, _ = image.shape
+            w_crop = int((w - self._center_crop) / 2.)
+            assert w_crop >= 0
+            h_crop = int((h - self._center_crop) / 2.)
+            assert h_crop >= 0
+            image = image[w_crop:w-w_crop, h_crop:h-h_crop, :]
+
         if self._use_label:
             return self._files[idx], image, self._labels[self._files[idx]]
         else:
@@ -113,7 +130,10 @@ class Metric:
         if len(args) == 1:
             for size in self._sizes:
                 img_r = cv.resize(img, size)
-                model.setInputSize(size)
+                try:
+                    model.setInputSize(size)
+                except:
+                    pass
                 # TODO: batched inference
                 # input_data = [img] * self._batch_size
                 input_data = img_r
@@ -192,11 +212,13 @@ def build_from_cfg(cfg, registery):
     obj = registery.get(obj_name)
     return obj(**cfg)
 
-def prepend_pythonpath(cfg, key1, key2):
-    pythonpath = os.environ['PYTHONPATH']
-    if cfg[key1][key2].startswith('/'):
-        return
-    cfg[key1][key2] = os.path.join(pythonpath, cfg[key1][key2])
+def prepend_pythonpath(cfg):
+    for k, v in cfg.items():
+        if isinstance(v, dict):
+            prepend_pythonpath(v)
+        else:
+            if 'path' in k.lower():
+                cfg[k] = os.path.join(os.environ['PYTHONPATH'], v)
 
 if __name__ == '__main__':
     assert args.cfg.endswith('yaml'), 'Currently support configs of yaml format only.'
@@ -204,8 +226,7 @@ if __name__ == '__main__':
         cfg = yaml.safe_load(f)
 
     # prepend PYTHONPATH to each path
-    prepend_pythonpath(cfg['Benchmark'], key1='data', key2='path')
-    prepend_pythonpath(cfg, key1='Model', key2='modelPath')
+    prepend_pythonpath(cfg)
 
     # Instantiate benchmarking
     benchmark = Benchmark(**cfg['Benchmark'])

benchmark/config/image_classification_ppresnet.yaml

+Benchmark:
+  name: "Image Classification Benchmark"
+  data:
+    path: "benchmark/data/image_classification"
+    files: ["coffee_mug.jpg", "umbrella.jpg", "wall_clock.jpg"]
+    toRGB: True
+    resize: [256, 256]
+    centerCrop: 224
+  metric:
+    warmup: 3
+    repeat: 10
+    batchSize: 1
+    reduction: 'median'
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "PPResNet"
+  modelPath: "models/image_classification_ppresnet/image_classification_ppresnet50.onnx"
+  labelPath: "models/image_classification_ppresnet/imagenet_labels.txt"
\ No newline at end of file

benchmark/download_data.py

@@ -173,6 +173,10 @@ data_downloaders = dict(
         url='https://drive.google.com/u/0/uc?id=1lTQdZUau7ujHBqp0P6M1kccnnJgO-dRj&export=download',
         sha='a40cf095ceb77159ddd2a5902f3b4329696dd866',
         filename='text.zip'),
+    image_classification=Downloader(name='image_classification',
+        url='https://drive.google.com/u/0/uc?id=1qcsrX3CIAGTooB-9fLKYwcvoCuMgjzGU&export=download',
+        sha='987546f567f9f11d150eea78951024b55b015401',
+        filename='image_classification.zip'),
 )
 
 if __name__ == '__main__':

models/__init__.py

@@ -2,6 +2,7 @@ from .face_detection_yunet.yunet import YuNet
 from .text_detection_db.db import DB
 from .text_recognition_crnn.crnn import CRNN
 from .face_recognition_sface.sface import SFace
+from .image_classification_ppresnet.ppresnet import PPResNet
 
 class Registery:
     def __init__(self, name):
@@ -18,4 +19,5 @@ MODELS = Registery('Models')
 MODELS.register(YuNet)
 MODELS.register(DB)
 MODELS.register(CRNN)
-MODELS.register(SFace)
\ No newline at end of file
+MODELS.register(SFace)
+MODELS.register(PPResNet)

models/image_classification_ppresnet/LICENSE

+Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved
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\ No newline at end of file

models/image_classification_ppresnet/README.md

+# ResNet
+
+Deep Residual Learning for Image Recognition
+
+This model is ported from [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) using [this script from OpenCV](https://github.com/opencv/opencv/blob/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_resnet50.py).
+
+## Demo
+
+Run the following command to try the demo:
+```shell
+python demo.py --input /path/to/image
+```
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+## Reference
+
+- https://arxiv.org/abs/1512.03385
+- https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle
+- https://github.com/PaddlePaddle/PaddleHub
\ No newline at end of file

models/image_classification_ppresnet/demo.py

+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from ppresnet import PPResNet
+
+def str2bool(v):
+    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
+        return True
+    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
+        return False
+    else:
+        raise NotImplementedError
+
+parser = argparse.ArgumentParser(description='Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385, https://github.com/PaddlePaddle/PaddleHub)')
+parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
+parser.add_argument('--model', '-m', type=str, default='image_classification_ppresnet50.onnx', help='Path to the model.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    # Instantiate ResNet
+    model = PPResNet(modelPath=args.model)
+
+    # Read image and get a 224x224 crop from a 256x256 resized
+    image = cv.imread(args.input)
+    image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+    image = cv.resize(image, dsize=(256, 256))
+    image = image[16:240, 16:240, :]
+
+    # Inference
+    result = model.infer(image)
+
+    # Print result
+    print('label: {}'.format(result))
\ No newline at end of file

models/image_classification_ppresnet/imagenet_labels.txt

+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenters kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potters wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow ladys slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue

models/image_classification_ppresnet/ppresnet.py

+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+
+import numpy as np
+import cv2 as cv
+
+class PPResNet:
+    def __init__(self, modelPath, labelPath):
+        self._modelPath = modelPath
+        self._model = cv.dnn.readNet(self._modelPath)
+        self._labelPath = labelPath
+
+        self._inputNames = ''
+        self._outputNames = ['save_infer_model/scale_0.tmp_0']
+        self._inputSize = [224, 224]
+        self._mean = np.array([0.485, 0.456, 0.406])[np.newaxis, np.newaxis, :]
+        self._std = np.array([0.229, 0.224, 0.225])[np.newaxis, np.newaxis, :]
+
+        # load labels
+        self._labels = self._load_labels()
+
+    def _load_labels(self):
+        labels = []
+        with open(self._labelPath, 'r') as f:
+            for line in f:
+                labels.append(line.strip())
+        return labels
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackend(self, backend_id):
+        self._model.setPreferableBackend(backend_id)
+
+    def setTarget(self, target_id):
+        self._model.setPreferableTarget(target_id)
+
+    def _preprocess(self, image):
+        image = image.astype(np.float32, copy=False) / 255.0
+        image -= self._mean
+        image /= self._std
+        return cv.dnn.blobFromImage(image)
+
+    def infer(self, image):
+        assert image.shape[0] == self._inputSize[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self._inputSize[1])
+        assert image.shape[1] == self._inputSize[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self._inputSize[0])
+
+        # Preprocess
+        inputBlob = self._preprocess(image)
+
+        # Forward
+        self._model.setInput(inputBlob, self._inputNames)
+        outputBlob = self._model.forward(self._outputNames)
+
+        # Postprocess
+        results = self._postprocess(outputBlob)
+
+        return results
+
+    def _postprocess(self, outputBlob):
+        class_id = np.argmax(outputBlob[0])
+        return self._labels[class_id]
\ No newline at end of file