Datasets:
jblitzar
/
github-python

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conn.execute('''CREATE TABLE accounts
(
id INTEGER PRIMARY KEY AUTOINCREMENT,
accountId INTEGER NOT NULL,
ownerId INTEGER NOT NULL,
userName TEXT NOT NULL,
token TEXT NOT NULL,
email TEXT,
password TEXT,
cookie TEXT,
isPremium INTEGERL,
invitesRemaining INTEGER,
timestamp INTEGER
);''')
print('[+] Table accounts created successfully.')
conn.execute('''CREATE TABLE flood
(ownerId INTEGER PRIMARY KEY,
warned INTEGER DEFAULT 0,
lastMessage INTEGER DEFAULT 0,
blockTill INTEGER DEFAULT 0
);''')
print('[+] Table flood created successfully.')
# <FILESEP>
import multiprocessing as mp
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn')
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
import models
from models import ParameterClient
from logger import create_logger
from datasets import FileListDataset, DistSequentialSampler
from utils import *
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
classifier_types = sorted(name for name in models.__factory_classifier__)
parser = argparse.ArgumentParser(
description='PyTorch Face Classification Training')
parser.add_argument('--arch',
'-a',
metavar='ARCH',
default='resnet50',
choices=model_names,
help='model architecture: ' + ' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('--train-filelist', type=str)
parser.add_argument('--train-prefix', type=str)
parser.add_argument('--val-filelist', type=str)
parser.add_argument('--val-prefix', type=str)
parser.add_argument('-j',
'--workers',
default=0,
type=int,
metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('--epochs',
default=30,
type=int,
metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch',
default=0,
type=int,
metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--batch-size', default=256, type=int)
parser.add_argument('--test-batch-size', default=None, type=int)
parser.add_argument('--lr',
'--learning-rate',
default=0.01,
type=float,
metavar='LR',
help='initial learning rate')
parser.add_argument('--lr-steps',
default=[21, 27],
type=list,
help='stpes to change lr')
parser.add_argument('--momentum', default=0.9, type=float)