Guillermo Gómez guillermogotre

## unnormalize.py
import torchvision

class UnNormalize(torchvision.transforms.Normalize):
    def __init__(self,mean,std,*args,**kwargs):
        new_mean = [-m/s for m,s in zip(mean,std)]
        new_std = [1/s for s in std]
        super().__init__(new_mean, new_std, *args, **kwargs)

# imagenet_norm = dict(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
# UnNormalize(**imagenet_norm)

## helper.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Title</title>
    <style>
        td{
            border: 1px black solid;
            width: 20px;
            height: 20px;

## Solution.java
import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;

public class Solution {

    public static void main(String[] args) {
        Scanner in = new Scanner(System.in);

## main.cpp
/**
	Autor: Guillermo G.
	DNI: XXXXXXXX-Z
	Grupo: XX
**/

/**
@file main.cpp
@brief
Universidad de Granada
	import torchvision

	class UnNormalize(torchvision.transforms.Normalize):
	def __init__(self,mean,std,args,*kwargs):
	new_mean = [-m/s for m,s in zip(mean,std)]
	new_std = [1/s for s in std]
	super().__init__(new_mean, new_std, args, *kwargs)

	# imagenet_norm = dict(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
	# UnNormalize(**imagenet_norm)
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<title>Title</title>
	<style>
	td{
	border: 1px black solid;
	width: 20px;
	height: 20px;
	import java.io.*;
	import java.util.*;
	import java.text.*;
	import java.math.*;
	import java.util.regex.*;

	public class Solution {

	public static void main(String[] args) {
	Scanner in = new Scanner(System.in);
	/**
	Autor: Guillermo G.
	DNI: XXXXXXXX-Z
	Grupo: XX
	**/

	/**
	@file main.cpp
	@brief
	Universidad de Granada