import torch

import os

import config

import numpy as np

from PIL import Image

from torchvision.utils import save_image

from model import Generator

from utils import load_checkpoint, plot_examples

from torch import nn

from torch import optim

import os, random

import argparse

#create the code as easy as you can

result_path = "./Test_Results"

isExist = os.path.exists(result_path)

if not isExist:

os.makedirs(result_path)

gen = Generator(alpha = 0.4, beta = 0.1, in_channels=3).to(config.DEVICE)

opt_gen = optim.Adam(gen.parameters(), lr=config.LEARNING_RATE, betas=(0.9, 0.999))

if config.LOAD_MODEL:

load_checkpoint(

config.CHECKPOINT_GEN,

gen,

opt_gen,

config.LEARNING_RATE,

)

src = "./demo/"

#src = "./internet/"

plot_examples(src, gen, result_path)