Adrian Brodzik brodzik

## decrypt.ps1
$directoryPath = "C:\Users"

$aesKey = [System.Text.Encoding]::UTF8.GetBytes("MySecretKey12345")

$aes = [System.Security.Cryptography.Aes]::Create()
$aes.Mode = [System.Security.Cryptography.CipherMode]::CBC
$aes.Padding = [System.Security.Cryptography.PaddingMode]::PKCS7
$aes.Key = $aesKey

function Decrypt-File($filePath) {

## genbytes.sh
nfiles=10
nbytes=$((2**20))

for i in $(seq -f "%03g" 0 $(($nfiles - 1))); do
    dd if=/dev/urandom bs=$nbytes count=1 > $i.bin
done

## colab_idle.js
function ClickConnect() {
    document.querySelector("colab-toolbar-button").click()
} setInterval(ClickConnect, 600000)

## compress_mp4.sh
./ffmpeg -i input.mp4 -vcodec libx264 -crf 20 output.mp4

## get_lr.py
def get_lr(optimizer):
    for p in optimizer.param_groups:
        return p["lr"]

## count_parameters.py
def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

## seed_everything.py
import os
import random
import numpy as np
import torch


def seed_everything(seed):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    np.random.seed(seed)

## move.sh
find . -mindepth 2 -type f -exec mv --backup=numbered {} . \;

## crop_and_label.py
import cv2
import os
import glob
import argparse
from tqdm import tqdm
import pandas as pd


def click_and_crop(event, x, y, flags, param, points):
    if event == cv2.EVENT_LBUTTONUP:

## pytorch-classification-example.py
import os
import random

import cv2
import numpy as np
import pandas as pd
import torch
import torch.cuda
import torch.nn as nn
from albumentations import *
	$directoryPath = "C:\Users"

	$aesKey = [System.Text.Encoding]::UTF8.GetBytes("MySecretKey12345")

	$aes = [System.Security.Cryptography.Aes]::Create()
	$aes.Mode = [System.Security.Cryptography.CipherMode]::CBC
	$aes.Padding = [System.Security.Cryptography.PaddingMode]::PKCS7
	$aes.Key = $aesKey

	function Decrypt-File($filePath) {
	nfiles=10
	nbytes=$((2**20))

	for i in $(seq -f "%03g" 0 $(($nfiles - 1))); do
	dd if=/dev/urandom bs=$nbytes count=1 > $i.bin
	done
	function ClickConnect() {
	document.querySelector("colab-toolbar-button").click()
	} setInterval(ClickConnect, 600000)
	def get_lr(optimizer):
	for p in optimizer.param_groups:
	return p["lr"]
	def count_parameters(model):
	return sum(p.numel() for p in model.parameters() if p.requires_grad)
	import os
	import random
	import numpy as np
	import torch


	def seed_everything(seed):
	random.seed(seed)
	os.environ["PYTHONHASHSEED"] = str(seed)
	np.random.seed(seed)
	import cv2
	import os
	import glob
	import argparse
	from tqdm import tqdm
	import pandas as pd


	def click_and_crop(event, x, y, flags, param, points):
	if event == cv2.EVENT_LBUTTONUP: