Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

import torch.nn as nn
import torch.optim as optim
import tqdm
from cryptography.fernet import Fernet
from torch.utils.data import DataLoader

from mlmodelwatermarking.loggers.logger import logger
from mlmodelwatermarking.verification import verify

warnings.filterwarnings('ignore')


class Trainer:
def __init__(
self,
model,
args,
trainset=None,
valset=None,
testset=None,
specialset=None):

""" Main wrapper class to watermark Pytorch models.

Args:
model (Object): model to be trained and watermarked
trainset (Object): training dataset
valset (Object): validation dataset
testset (Object): test dataset
specialset (Object, optional): trigger set for 'selected'

args (dict): args for watermarking

"""

# Non optional
self.model = model
self.trainset = trainset
self.args = args
self.valset = valset
self.testset = testset
self.specialset = specialset

self.watermark = args.watermark

if args.optimizer == 'SGD':
self.optimizer = optim.SGD(self.model.parameters(), lr=args.lr)
self.patch_args = args.trigger_patch_args

if args.gpu:
available = torch.cuda.is_available()
self.device = torch.device('cuda' if available else 'cpu')
else:
self.device = 'cpu'
if self.args.verbose is False:
logger.disable()
if self.watermark:
logger.info('Generation of the trigers')
loaders = self.generate_trigger()
(self.trainloader, self.valloader,