main_online_imagenet_tpt.py

# Copyright (c) Alibaba Group
import argparse
import torch
import torchvision.datasets as datasets
import torch.nn.functional as F
import clip
import os
import math
import numpy as np
import copy
from PIL import Image
import torchvision.transforms as transforms
# from data.datautils import AugMixAugmenter
import numpy as np
from PIL import Image, ImageOps, ImageEnhance

from MAPLS.mapls import mapls
from MAPLS.common import lsc

from tpt.custom_clip import get_coop
from tpt.cocoop import get_cocoop
from tpt.tpt_utils import *

model_names = ['RN50', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px']
parser = argparse.ArgumentParser(description='OnZeta for ImageNet')
parser.add_argument('--data_path', default='/home/li325/space_mlai/pengxiao_space/dataset/ImageNet/', type=str,
                    help='dataset path')
parser.add_argument('-a', '--arch', metavar='ARCH', default='RN50',
                    choices=model_names,
                    help='model architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: RN50)')
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
                    help='number of data loading workers (default: 8)')
parser.add_argument('-b', '--batch-size', default=1, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256)')
parser.add_argument('--tau_t', default=0.01, type=float)
parser.add_argument('--tau_i', default=0.04, type=float)
parser.add_argument('--cw', default=0.5, type=float)
parser.add_argument('--cr', default=20, type=float)
parser.add_argument('--alpha', default=1, type=float)
parser.add_argument('--beta', default=0.8, type=float)
parser.add_argument('--repeat', default=1, type=int)

# TPT
# parser.add_argument('--cocoop',  default=False, help="use cocoop's output as prompt initialization")
parser.add_argument('--test_sets', type=str, default='I', help='test dataset (multiple datasets split by slash)')
parser.add_argument('--dataset_mode', type=str, default='test', help='which split to use: train/val/test')
parser.add_argument('--resolution', default=224, type=int, help='CLIP image resolution')
# parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
#                     help='number of data loading workers (default: 4)')
# parser.add_argument('-b', '--batch-size', default=1, type=int, metavar='N')
parser.add_argument('--lr', '--learning-rate', default=5e-3, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('-p', '--print-freq', default=200, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--gpu', default=0, type=int,
                    help='GPU id to use.')
parser.add_argument('--tpt', default=True, help='run test-time prompt tuning')
parser.add_argument('--selection_p', default=0.1, type=float, help='confidence selection percentile')
parser.add_argument('--tta_steps', default=1, type=int, help='test-time-adapt steps')
parser.add_argument('--n_ctx', default=4, type=int, help='number of tunable tokens')
parser.add_argument('--ctx_init', default=None, type=str, help='init tunable prompts')
parser.add_argument('--cocoop', default=True, help="use cocoop's output as prompt initialization")
parser.add_argument('--load',
                    default="/home/li325/space_mlai/pengxiao_space/OnZeta/tpt/pretrained_cocoop/rn50_ep50_16shots/nctx4_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50",
                    type=str, help='path to a pre-trained coop/cocoop')
parser.add_argument('--seed', type=int, default=0)


try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC

IMAGE_SIZE = 224

normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                 std=[0.26862954, 0.26130258, 0.27577711])

# AugMix Transforms
def get_preaugment():
    return transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
        ])

def augmix(image, preprocess, aug_list, severity=1):
    preaugment = get_preaugment()
    x_orig = preaugment(image)
    x_processed = preprocess(x_orig)
    if len(aug_list) == 0:
        return x_processed
    w = np.float32(np.random.dirichlet([1.0, 1.0, 1.0]))
    m = np.float32(np.random.beta(1.0, 1.0))

    mix = torch.zeros_like(x_processed)
    for i in range(3):
        x_aug = x_orig.copy()
        for _ in range(np.random.randint(1, 4)):
            x_aug = np.random.choice(aug_list)(x_aug, severity)
        mix += w[i] * preprocess(x_aug)
    mix = m * x_processed + (1 - m) * mix
    return mix

def int_parameter(level, maxval):
  """Helper function to scale `val` between 0 and maxval .

  Args:
    level: Level of the operation that will be between [0, `PARAMETER_MAX`].
    maxval: Maximum value that the operation can have. This will be scaled to
      level/PARAMETER_MAX.

  Returns:
    An int that results from scaling `maxval` according to `level`.
  """
  return int(level * maxval / 10)


def float_parameter(level, maxval):
  """Helper function to scale `val` between 0 and maxval.

  Args:
    level: Level of the operation that will be between [0, `PARAMETER_MAX`].
    maxval: Maximum value that the operation can have. This will be scaled to
      level/PARAMETER_MAX.

  Returns:
    A float that results from scaling `maxval` according to `level`.
  """
  return float(level) * maxval / 10.


def sample_level(n):
  return np.random.uniform(low=0.1, high=n)


def autocontrast(pil_img, _):
  return ImageOps.autocontrast(pil_img)


def equalize(pil_img, _):
  return ImageOps.equalize(pil_img)


def posterize(pil_img, level):
  level = int_parameter(sample_level(level), 4)
  return ImageOps.posterize(pil_img, 4 - level)


def rotate(pil_img, level):
  degrees = int_parameter(sample_level(level), 30)
  if np.random.uniform() > 0.5:
    degrees = -degrees
  return pil_img.rotate(degrees, resample=Image.BILINEAR)


def solarize(pil_img, level):
  level = int_parameter(sample_level(level), 256)
  return ImageOps.solarize(pil_img, 256 - level)


def shear_x(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, level, 0, 0, 1, 0),
                           resample=Image.BILINEAR)


def shear_y(pil_img, level):
  level = float_parameter(sample_level(level), 0.3)
  if np.random.uniform() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, level, 1, 0),
                           resample=Image.BILINEAR)


def translate_x(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, level, 0, 1, 0),
                           resample=Image.BILINEAR)


def translate_y(pil_img, level):
  level = int_parameter(sample_level(level), IMAGE_SIZE / 3)
  if np.random.random() > 0.5:
    level = -level
  return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE),
                           Image.AFFINE, (1, 0, 0, 0, 1, level),
                           resample=Image.BILINEAR)


# operation that overlaps with ImageNet-C's test set
def color(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Color(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def contrast(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Contrast(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def brightness(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Brightness(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def sharpness(pil_img, level):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Sharpness(pil_img).enhance(level)


augmentations = [
    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
    translate_x, translate_y
]

class AugMixAugmenter(object):
    def __init__(self, base_transform, preprocess, n_views=2, augmix=False,
                 severity=1):
        self.base_transform = base_transform
        self.preprocess = preprocess
        self.n_views = n_views
        if augmix:
            self.aug_list = augmentations.augmentations
        else:
            self.aug_list = []
        self.severity = severity

    def __call__(self, x):
        image = self.preprocess(self.base_transform(x))
        views = [augmix(x, self.preprocess, self.aug_list, self.severity) for _ in range(self.n_views)]
        return [image] + views

def load_model_weight(load_path, model, device, args):
    if os.path.isfile(load_path):
        print("=> loading checkpoint '{}'".format(load_path))
        checkpoint = torch.load(load_path, map_location=device)
        state_dict = checkpoint['state_dict']
        # Ignore fixed token vectors
        if "token_prefix" in state_dict:
            del state_dict["token_prefix"]

        if "token_suffix" in state_dict:
            del state_dict["token_suffix"]

        args.start_epoch = checkpoint['epoch']
        try:
            best_acc1 = checkpoint['best_acc1']
        except:
            best_acc1 = torch.tensor(0)
        if device is not 'cpu':
            # best_acc1 may be from a checkpoint from a different GPU
            best_acc1 = best_acc1.to(device)
        try:
            model.load_state_dict(state_dict)
        except:
            # TODO: implement this method for the generator class
            model.prompt_generator.load_state_dict(state_dict, strict=False)
        print("=> loaded checkpoint '{}' (epoch {})"
              .format(load_path, checkpoint['epoch']))
        del checkpoint
        torch.cuda.empty_cache()
    else:
        print("=> no checkpoint found at '{}'".format(load_path))

def main(lam):

    args = parser.parse_args()
    print(args)
    imagenet_classes = ["tench", "goldfish", "great white shark", "tiger shark", "hammerhead shark", "electric ray",
                        "stingray", "rooster", "hen", "ostrich", "brambling", "goldfinch", "house finch", "junco",
                        "indigo bunting", "American robin", "bulbul", "jay", "magpie", "chickadee", "American dipper",
                        "kite (bird of prey)", "bald eagle", "vulture", "great grey owl", "fire salamander",
                        "smooth newt", "newt", "spotted salamander", "axolotl", "American bullfrog", "tree frog",
                        "tailed frog", "loggerhead sea turtle", "leatherback sea turtle", "mud turtle", "terrapin",
                        "box turtle", "banded gecko", "green iguana", "Carolina anole",
                        "desert grassland whiptail lizard", "agama", "frilled-necked lizard", "alligator lizard",
                        "Gila monster", "European green lizard", "chameleon", "Komodo dragon", "Nile crocodile",
                        "American alligator", "triceratops", "worm snake", "ring-necked snake",
                        "eastern hog-nosed snake", "smooth green snake", "kingsnake", "garter snake", "water snake",
                        "vine snake", "night snake", "boa constrictor", "African rock python", "Indian cobra",
                        "green mamba", "sea snake", "Saharan horned viper", "eastern diamondback rattlesnake",
                        "sidewinder rattlesnake", "trilobite", "harvestman", "scorpion", "yellow garden spider",
                        "barn spider", "European garden spider", "southern black widow", "tarantula", "wolf spider",
                        "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse", "prairie grouse", "peafowl",
                        "quail", "partridge", "african grey parrot", "macaw", "sulphur-crested cockatoo", "lorikeet",
                        "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "duck",
                        "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", "red king crab", "American lobster", "spiny lobster", "crayfish", "hermit crab",
                        "isopod", "white stork", "black stork", "spoonbill", "flamingo", "little blue heron",
                        "great egret", "bittern bird", "crane bird", "limpkin", "common gallinule", "American coot",
                        "bustard", "ruddy turnstone", "dunlin", "common redshank", "dowitcher", "oystercatcher",
                        "pelican", "king penguin", "albatross", "grey whale", "killer whale", "dugong", "sea lion",
                        "Chihuahua", "Japanese Chin", "Maltese", "Pekingese", "Shih Tzu", "King Charles Spaniel",
                        "Papillon", "toy terrier", "Rhodesian Ridgeback", "Afghan Hound", "Basset Hound", "Beagle",
                        "Bloodhound", "Bluetick Coonhound", "Black and Tan Coonhound", "Treeing Walker Coonhound",
                        "English foxhound", "Redbone Coonhound", "borzoi", "Irish Wolfhound", "Italian Greyhound",
                        "Whippet", "Ibizan Hound", "Norwegian Elkhound", "Otterhound", "Saluki", "Scottish Deerhound",
                        "Weimaraner", "Staffordshire Bull Terrier", "American Staffordshire Terrier",
                        "Bedlington Terrier", "Border Terrier", "Kerry Blue Terrier", "Irish Terrier",
                        "Norfolk Terrier", "Norwich Terrier", "Yorkshire Terrier", "Wire Fox Terrier",
                        "Lakeland Terrier", "Sealyham Terrier", "Airedale Terrier", "Cairn Terrier",
                        "Australian Terrier", "Dandie Dinmont Terrier", "Boston Terrier", "Miniature Schnauzer",
                        "Giant Schnauzer", "Standard Schnauzer", "Scottish Terrier", "Tibetan Terrier",
                        "Australian Silky Terrier", "Soft-coated Wheaten Terrier", "West Highland White Terrier",
                        "Lhasa Apso", "Flat-Coated Retriever", "Curly-coated Retriever", "Golden Retriever",
                        "Labrador Retriever", "Chesapeake Bay Retriever", "German Shorthaired Pointer", "Vizsla",
                        "English Setter", "Irish Setter", "Gordon Setter", "Brittany dog", "Clumber Spaniel",
                        "English Springer Spaniel", "Welsh Springer Spaniel", "Cocker Spaniel", "Sussex Spaniel",
                        "Irish Water Spaniel", "Kuvasz", "Schipperke", "Groenendael dog", "Malinois", "Briard",
                        "Australian Kelpie", "Komondor", "Old English Sheepdog", "Shetland Sheepdog", "collie",
                        "Border Collie", "Bouvier des Flandres dog", "Rottweiler", "German Shepherd Dog", "Dobermann",
                        "Miniature Pinscher", "Greater Swiss Mountain Dog", "Bernese Mountain Dog",
                        "Appenzeller Sennenhund", "Entlebucher Sennenhund", "Boxer", "Bullmastiff", "Tibetan Mastiff",
                        "French Bulldog", "Great Dane", "St. Bernard", "husky", "Alaskan Malamute", "Siberian Husky",
                        "Dalmatian", "Affenpinscher", "Basenji", "pug", "Leonberger", "Newfoundland dog",
                        "Great Pyrenees dog", "Samoyed", "Pomeranian", "Chow Chow", "Keeshond", "brussels griffon",
                        "Pembroke Welsh Corgi", "Cardigan Welsh Corgi", "Toy Poodle", "Miniature Poodle",
                        "Standard Poodle", "Mexican hairless dog (xoloitzcuintli)", "grey wolf", "Alaskan tundra wolf",
                        "red wolf or maned wolf", "coyote", "dingo", "dhole", "African wild dog", "hyena", "red fox",
                        "kit fox", "Arctic fox", "grey fox", "tabby cat", "tiger cat", "Persian cat", "Siamese cat",
                        "Egyptian Mau", "cougar", "lynx", "leopard", "snow leopard", "jaguar", "lion", "tiger",
                        "cheetah", "brown bear", "American black bear", "polar bear", "sloth bear", "mongoose",
                        "meerkat", "tiger beetle", "ladybug", "ground beetle", "longhorn beetle", "leaf beetle",
                        "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant", "grasshopper",
                        "cricket insect", "stick insect", "cockroach", "praying mantis", "cicada", "leafhopper",
                        "lacewing", "dragonfly", "damselfly", "red admiral butterfly", "ringlet butterfly",
                        "monarch butterfly", "small white butterfly", "sulphur butterfly", "gossamer-winged butterfly",
                        "starfish", "sea urchin", "sea cucumber", "cottontail rabbit", "hare", "Angora rabbit",
                        "hamster", "porcupine", "fox squirrel", "marmot", "beaver", "guinea pig", "common sorrel horse",
                        "zebra", "pig", "wild boar", "warthog", "hippopotamus", "ox", "water buffalo", "bison",
                        "ram (adult male sheep)", "bighorn sheep", "Alpine ibex", "hartebeest", "impala (antelope)",
                        "gazelle", "arabian camel", "llama", "weasel", "mink", "European polecat",
                        "black-footed ferret", "otter", "skunk", "badger", "armadillo", "three-toed sloth", "orangutan",
                        "gorilla", "chimpanzee", "gibbon", "siamang", "guenon", "patas monkey", "baboon", "macaque",
                        "langur", "black-and-white colobus", "proboscis monkey", "marmoset", "white-headed capuchin",
                        "howler monkey", "titi monkey", "Geoffroy's spider monkey", "common squirrel monkey",
                        "ring-tailed lemur", "indri", "Asian elephant", "African bush elephant", "red panda",
                        "giant panda", "snoek fish", "eel", "silver salmon", "rock beauty fish", "clownfish",
                        "sturgeon", "gar fish", "lionfish", "pufferfish", "abacus", "abaya", "academic gown",
                        "accordion", "acoustic guitar", "aircraft carrier", "airliner", "airship", "altar", "ambulance",
                        "amphibious vehicle", "analog clock", "apiary", "apron", "trash can", "assault rifle",
                        "backpack", "bakery", "balance beam", "balloon", "ballpoint pen", "Band-Aid", "banjo",
                        "baluster / handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel",
                        "wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "swimming cap", "bath towel",
                        "bathtub", "station wagon", "lighthouse", "beaker", "military hat (bearskin or shako)",
                        "beer bottle", "beer glass", "bell tower", "baby bib", "tandem bicycle", "bikini",
                        "ring binder", "binoculars", "birdhouse", "boathouse", "bobsleigh", "bolo tie", "poke bonnet",
                        "bookcase", "bookstore", "bottle cap", "hunting bow", "bow tie", "brass memorial plaque", "bra",
                        "breakwater", "breastplate", "broom", "bucket", "buckle", "bulletproof vest",
                        "high-speed train", "butcher shop", "taxicab", "cauldron", "candle", "cannon", "canoe",
                        "can opener", "cardigan", "car mirror", "carousel", "tool kit", "cardboard box / carton",
                        "car wheel", "automated teller machine", "cassette", "cassette player", "castle", "catamaran",
                        "CD player", "cello", "mobile phone", "chain", "chain-link fence", "chain mail", "chainsaw",
                        "storage chest", "chiffonier", "bell or wind chime", "china cabinet", "Christmas stocking",
                        "church", "movie theater", "cleaver", "cliff dwelling", "cloak", "clogs", "cocktail shaker",
                        "coffee mug", "coffeemaker", "spiral or coil", "combination lock", "computer keyboard",
                        "candy store", "container ship", "convertible", "corkscrew", "cornet", "cowboy boot",
                        "cowboy hat", "cradle", "construction crane", "crash helmet", "crate", "infant bed",
                        "Crock Pot", "croquet ball", "crutch", "cuirass", "dam", "desk", "desktop computer",
                        "rotary dial telephone", "diaper", "digital clock", "digital watch", "dining table",
                        "dishcloth", "dishwasher", "disc brake", "dock", "dog sled", "dome", "doormat", "drilling rig",
                        "drum", "drumstick", "dumbbell", "Dutch oven", "electric fan", "electric guitar",
                        "electric locomotive", "entertainment center", "envelope", "espresso machine", "face powder",
                        "feather boa", "filing cabinet", "fireboat", "fire truck", "fire screen", "flagpole", "flute",
                        "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster bed",
                        "freight car", "French horn", "frying pan", "fur coat", "garbage truck",
                        "gas mask or respirator", "gas pump", "goblet", "go-kart", "golf ball", "golf cart", "gondola",
                        "gong", "gown", "grand piano", "greenhouse", "radiator grille", "grocery store", "guillotine",
                        "hair clip", "hair spray", "half-track", "hammer", "hamper", "hair dryer", "hand-held computer",
                        "handkerchief", "hard disk drive", "harmonica", "harp", "combine harvester", "hatchet",
                        "holster", "home theater", "honeycomb", "hook", "hoop skirt", "gymnastic horizontal bar",
                        "horse-drawn vehicle", "hourglass", "iPod", "clothes iron", "carved pumpkin", "jeans", "jeep",
                        "T-shirt", "jigsaw puzzle", "rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat",
                        "ladle", "lampshade", "laptop computer", "lawn mower", "lens cap", "letter opener", "library",
                        "lifeboat", "lighter", "limousine", "ocean liner", "lipstick", "slip-on shoe", "lotion",
                        "music speaker", "loupe magnifying glass", "sawmill", "magnetic compass", "messenger bag",
                        "mailbox", "tights", "one-piece bathing suit", "manhole cover", "maraca", "marimba", "mask",
                        "matchstick", "maypole", "maze", "measuring cup", "medicine cabinet", "megalith", "microphone",
                        "microwave oven", "military uniform", "milk can", "minibus", "miniskirt", "minivan", "missile",
                        "mitten", "mixing bowl", "mobile home", "ford model t", "modem", "monastery", "monitor",
                        "moped", "mortar and pestle", "graduation cap", "mosque", "mosquito net", "vespa",
                        "mountain bike", "tent", "computer mouse", "mousetrap", "moving van", "muzzle", "metal nail",
                        "neck brace", "necklace", "baby pacifier", "notebook computer", "obelisk", "oboe", "ocarina",
                        "odometer", "oil filter", "pipe organ", "oscilloscope", "overskirt", "bullock cart",
                        "oxygen mask", "product packet / packaging", "paddle", "paddle wheel", "padlock", "paintbrush",
                        "pajamas", "palace", "pan flute", "paper towel", "parachute", "parallel bars", "park bench",
                        "parking meter", "railroad car", "patio", "payphone", "pedestal", "pencil case",
                        "pencil sharpener", "perfume", "Petri dish", "photocopier", "plectrum", "Pickelhaube",
                        "picket fence", "pickup truck", "pier", "piggy bank", "pill bottle", "pillow", "ping-pong ball",
                        "pinwheel", "pirate ship", "drink pitcher", "block plane", "planetarium", "plastic bag",
                        "plate rack", "farm plow", "plunger", "Polaroid camera", "pole", "police van", "poncho",
                        "pool table", "soda bottle", "plant pot", "potter's wheel", "power drill", "prayer rug",
                        "printer", "prison", "missile", "projector", "hockey puck", "punching bag", "purse", "quill",
                        "quilt", "race car", "racket", "radiator", "radio", "radio telescope", "rain barrel",
                        "recreational vehicle", "fishing casting reel", "reflex camera", "refrigerator",
                        "remote control", "restaurant", "revolver", "rifle", "rocking chair", "rotisserie", "eraser",
                        "rugby ball", "ruler measuring stick", "sneaker", "safe", "safety pin", "salt shaker", "sandal",
                        "sarong", "saxophone", "scabbard", "weighing scale", "school bus", "schooner", "scoreboard",
                        "CRT monitor", "screw", "screwdriver", "seat belt", "sewing machine", "shield", "shoe store",
                        "shoji screen / room divider", "shopping basket", "shopping cart", "shovel", "shower cap",
                        "shower curtain", "ski", "balaclava ski mask", "sleeping bag", "slide rule", "sliding door",
                        "slot machine", "snorkel", "snowmobile", "snowplow", "soap dispenser", "soccer ball", "sock",
                        "solar thermal collector", "sombrero", "soup bowl", "keyboard space bar", "space heater",
                        "space shuttle", "spatula", "motorboat", "spider web", "spindle", "sports car", "spotlight",
                        "stage", "steam locomotive", "through arch bridge", "steel drum", "stethoscope", "scarf",
                        "stone wall", "stopwatch", "stove", "strainer", "tram", "stretcher", "couch", "stupa",
                        "submarine", "suit", "sundial", "sunglasses", "sunglasses", "sunscreen", "suspension bridge",
                        "mop", "sweatshirt", "swim trunks / shorts", "swing", "electrical switch", "syringe",
                        "table lamp", "tank", "tape player", "teapot", "teddy bear", "television", "tennis ball",
                        "thatched roof", "front curtain", "thimble", "threshing machine", "throne", "tile roof",
                        "toaster", "tobacco shop", "toilet seat", "torch", "totem pole", "tow truck", "toy store",
                        "tractor", "semi-trailer truck", "tray", "trench coat", "tricycle", "trimaran", "tripod",
                        "triumphal arch", "trolleybus", "trombone", "hot tub", "turnstile", "typewriter keyboard",
                        "umbrella", "unicycle", "upright piano", "vacuum cleaner", "vase", "vaulted or arched ceiling",
                        "velvet fabric", "vending machine", "vestment", "viaduct", "violin", "volleyball",
                        "waffle iron", "wall clock", "wallet", "wardrobe", "military aircraft", "sink",
                        "washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle",
                        "hair wig", "window screen", "window shade", "Windsor tie", "wine bottle", "airplane wing",
                        "wok", "wooden spoon", "wool", "split-rail fence", "shipwreck", "sailboat", "yurt", "website",
                        "comic book", "crossword", "traffic or street sign", "traffic light", "dust jacket", "menu",
                        "plate", "guacamole", "consomme", "hot pot", "trifle", "ice cream", "popsicle", "baguette",
                        "bagel", "pretzel", "cheeseburger", "hot dog", "mashed potatoes", "cabbage", "broccoli",
                        "cauliflower", "zucchini", "spaghetti squash", "acorn squash", "butternut squash", "cucumber",
                        "artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith apple", "strawberry", "orange",
                        "lemon", "fig", "pineapple", "banana", "jackfruit", "cherimoya (custard apple)", "pomegranate",
                        "hay", "carbonara", "chocolate syrup", "dough", "meatloaf", "pizza", "pot pie", "burrito",
                        "red wine", "espresso", "tea cup", "eggnog", "mountain", "bubble", "cliff", "coral reef",
                        "geyser", "lakeshore", "promontory", "sandbar", "beach", "valley", "volcano", "baseball player",
                        "bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper", "corn", "acorn",
                        "rose hip", "horse chestnut seed", "coral fungus", "agaric", "gyromitra", "stinkhorn mushroom",
                        "earth star fungus", "hen of the woods mushroom", "bolete", "corn cob", "toilet paper"]

    # cifar100_classes = [name for name in os.listdir(args.data_path)
    #                 if os.path.isdir(os.path.join(args.data_path, name))]

    imagenet_7_templates = [
        'a photo of a {}.',
    ]

    # imagenet_7_templates = [
    #     'itap of a {}.',
    #     'a origami {}.',
    #     'a bad photo of the {}.',
    #     'a photo of the large {}.',
    #     'a {} in a video game.',
    #     'art of the {}.',
    #     'a photo of the small {}.',
    # ]


    print('load pre-trained model')
    model, preprocess = clip.load(args.arch)
    model = model.cuda()
    model.eval()

    print('load data')
    valdir = os.path.join(args.data_path, 'val')
    # valdir = os.path.join(args.data_path, '')
    base_transform = transforms.Compose([
        transforms.Resize(args.resolution, interpolation=BICUBIC),
        transforms.CenterCrop(args.resolution)])
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        normalize])
    data_transform = AugMixAugmenter(base_transform, preprocess, n_views=args.batch_size - 1,
                                     augmix=False)
    val_set = datasets.ImageFolder(valdir, transform=data_transform)
    # val_set = datasets.ImageFolder(valdir, transform=preprocess)
    loader = torch.utils.data.DataLoader(val_set, batch_size=args.batch_size, num_workers=args.workers)
    with torch.no_grad():
        image_feat = []
        image_label = []
        image_list = []
        for i, (images, target) in enumerate(loader):
            images = images[0].cuda()
            target = target.cuda()
            image_features = model.encode_image(images)
            image_feat.append(F.normalize(image_features, dim=1))
            image_label.append(target)
            # image_list.append(images)
    image_feat = torch.cat(image_feat, dim=0)
    image_label = torch.cat(image_label, dim=0)
    n = len(image_label)
    image_feat = image_feat.float()

    print('obtain text proxy')

    text_classifier = zeroshot_classifier(clip, model, imagenet_classes, imagenet_7_templates)
    text_classifier = text_classifier.float()
    logits_t = image_feat @ text_classifier
    acc1, acc5 = accuracy(logits_t, image_label, topk=(1, 5))
    top1 = (acc1 / n) * 100
    print(f'accuracy with text proxy: {top1:.2f}')

    print('online zero-shot transfer: repeat {} times'.format(args.repeat))
    num_class = len(torch.unique(image_label))
    acc_onzeta = torch.zeros(args.repeat).cuda()
    acc_onlab = torch.zeros(args.repeat).cuda()
    acc_ls = torch.zeros(args.repeat).cuda()

    #  ------------------------------------------------ TPT ------------------------------------------------
    if args.cocoop:
        model = get_cocoop(args.arch, args.test_sets, 'cpu', args.n_ctx)  # model can be coop or cocoop
        assert args.load is not None
        load_model_weight(args.load, model, 'cpu', args) # to load to cuda: device="cuda:{}".format(args.gpu)
        model_state = copy.deepcopy(model.state_dict())
    else:
        model = get_coop(args.arch, args.test_sets, args.gpu, args.n_ctx, args.ctx_init)
        if args.load is not None:
            print("Use pre-trained soft prompt (CoOp) as initialization")
            pretrained_ctx = torch.load(args.load)['state_dict']['ctx']
            # a = pretrained_ctx.size()[0]
            assert pretrained_ctx.size()[0] == args.n_ctx
            with torch.no_grad():
                model.prompt_learner.ctx.copy_(pretrained_ctx)
                model.prompt_learner.ctx_init_state = pretrained_ctx
        model_state = None
    for name, param in model.named_parameters():
        if not args.cocoop:
            if "prompt_learner" not in name:
                param.requires_grad_(False)
        else:
            if "text_encoder" not in name:
                param.requires_grad_(False)
    model = model.cuda(args.gpu)
    if args.cocoop:
        optimizer = None
        optim_state = None
    else:
        trainable_param = model.prompt_learner.parameters()
        optimizer = torch.optim.AdamW(trainable_param, args.lr)
        optim_state = copy.deepcopy(optimizer.state_dict())

    # setup automatic mixed-precision (Amp) loss scaling
    scaler = torch.cuda.amp.GradScaler(init_scale=1000)

    base_transform = transforms.Compose([
        transforms.Resize(args.resolution, interpolation=BICUBIC),
        transforms.CenterCrop(args.resolution)])
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        normalize])
    data_transform = AugMixAugmenter(base_transform, preprocess, n_views=args.batch_size - 1,
                                     augmix=False)
    batchsize = 1

    classnames_all = imagenet_classes
    classnames = []
    classnames = classnames_all

    if args.cocoop:
        model.prompt_generator.reset_classnames(classnames, args.arch)
        model = model.cpu()
        model_state = model.state_dict()
        model = model.cuda(args.gpu)
    else:
        model.reset_classnames(classnames, args.arch)
    # reset model and switch to evaluate mode
    model.eval()
    if not args.cocoop: # no need to reset cocoop because it's fixed
        with torch.no_grad():
            model.reset()
    import time
    end = time.time()

    # if args.tpt:
    #     images = torch.cat(image_list, dim=0)

    # results = test_time_adapt_eval(val_loader, model, model_state, optimizer, optim_state, scaler, args)

    #  ------------------------------------------------ TPT ------------------------------------------------

    for iter in range(args.repeat):
        idx = torch.randperm(n).cuda()
        combo_label = torch.zeros(n, num_class).cuda()
        text_label = torch.zeros(n, num_class).cuda()
        w = text_classifier.clone()  #
        rho = torch.zeros(num_class).cuda()
        # for i in range(n):
        for i, data in enumerate(loader):
            img, label = data
            device = torch.device('cuda:0')  # 确保模型在目标GPU上
            model = model.to(device).half()  # 将模型移到 GPU 并转换为 float16
            img = img[0].to(device).half()
            lr = args.cw / math.sqrt(i + 1)
            rlr = args.cr / math.sqrt(i + 1)
            beta = args.beta * math.sqrt((i + 1) / n)
            x = image_feat[idx[i], :]
            #  ------------------------------------------------ TPT ------------------------------------------------
            # FIXME we aim to tune the text_classifier
            # 之前的text_classifier就不要了，因为有新的
            # reset the tunable prompt to its initial state
            if not args.cocoop:  # no need to reset cocoop because it's fixed
                if args.tta_steps > 0:
                    with torch.no_grad():
                        model.reset()
                optimizer.load_state_dict(optim_state)
                test_time_tuning(model, x, optimizer, scaler, args)
            else:
                with torch.no_grad():
                    with torch.cuda.amp.autocast():
                        image_feature, pgen_ctx = model.gen_ctx(img, args.tpt)
                optimizer = None
                pgen_ctx = pgen_ctx.to(dtype=torch.float16)
                image_feature = image_feature.to(dtype=torch.float16)
                pgen_ctx = test_time_tuning(model, (image_feature, pgen_ctx), optimizer, scaler, args)

            #  ------------------------------------------------ TPT ------------------------------------------------
            with torch.no_grad():
                with torch.cuda.amp.autocast():
                    if args.cocoop:
                        tlabel = model((image_feature, pgen_ctx))
                    else:
                        tlabel = model(img)
            # tlabel = F.softmax(x @ text_classifier / args.tau_t, dim=0)
            tlabel = tlabel.squeeze(0)
            # print('tlabel.shape: ', tlabel.shape)
            tlabel = tlabel * torch.exp(rho)
            tlabel /= torch.sum(tlabel)
            rho -= rlr * (tlabel - args.alpha / num_class)
            rho[rho < 0] = 0
            text_label[i, :] = tlabel
            # print("pgen_ctx.shape: ",pgen_ctx.shape)
            # exit()
            vision_label = F.softmax(x @ w / args.tau_i, dim=0)
            combo_label[i, :] = beta * vision_label + (1 - beta) * tlabel
            # print("vision_label.shape: ", vision_label.shape)
            # print("tlabel.shape: ", tlabel.shape)
            grad = torch.outer(x, vision_label - tlabel)
            w -= (lr / args.tau_i) * grad
            w = F.normalize(w, dim=0)
        acc1, acc5 = accuracy(text_label, image_label[idx], topk=(1, 5))
        acc_onlab[iter] = (acc1 / n) * 100
        acc1, acc5 = accuracy(combo_label, image_label[idx], topk=(1, 5))

        # MAPLS - EM Algorithm
        pz = np.full(len(imagenet_classes), 1.0 / len(imagenet_classes))
        qy = mapls(combo_label, pz = pz, qy_mode = "soft", max_iter = 100, lam = lam)

        w = np.array(qy) / np.array(pz)
        if combo_label.is_cuda:
            combo_label_cpu = combo_label.cpu()
        qy_probs = lsc(combo_label_cpu, 1.0/w)
        qy_probs = torch.from_numpy(qy_probs)
        acc1_ls, acc5_ls = accuracy(qy_probs, image_label[idx], topk=(1, 5))

        acc_onzeta[iter] = (acc1 / n) * 100
        acc_ls[iter] = (acc1_ls / n) * 100
    print('mean acc of onlab is: {:.2f}'.format(torch.mean(acc_onlab)))
    print('mean acc of onzeta is: {:.2f}'.format(torch.mean(acc_onzeta)))
    print('mean acc of MAPLS is: {:.2f}'.format(torch.mean(acc_ls)))


def zeroshot_classifier(clip, model, classnames, templates):
    with torch.no_grad():
        zeroshot_weights = []
        for classname in classnames:
            texts = [template.format(classname) for template in templates]
            texts = clip.tokenize(texts).cuda()  # FIXME is here needed to be modified?
            class_embeddings = model.encode_text(texts)
            class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
            class_embedding = class_embeddings.mean(dim=0)
            class_embedding /= class_embedding.norm()
            zeroshot_weights.append(class_embedding)
        zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
    return zeroshot_weights


def accuracy(output, target, topk=(1,)):
    pred = output.topk(max(topk), 1, True, True)[1].t()
    pred, target = pred.cpu(), target.cpu()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    return [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk]


if __name__ == '__main__':
    # main()

    lams = [0.6]
    for lam in lams:
        main(lam)