Creating a DCGAN with PyTorch

import os
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import utils

import errno
import os
import shutil
import sys
def to_np(var):
    """Exports torch.Tensor to Numpy array.
    """
    return var.detach().cpu().numpy()
def create_folder(folder_path):
    """Create a folder if it does not exist.
    """
    try:
        os.makedirs(folder_path)
    except OSError as _e:
        if _e.errno != errno.EEXIST:
            raise
def clear_folder(folder_path):
    """Clear all contents recursively if the folder exists.
    Create the folder if it has been accidently deleted.
    """
    create_folder(folder_path)
    for the_file in os.listdir(folder_path):
        _file_path = os.path.join(folder_path, the_file)
        try:
            if os.path.isfile(_file_path):
                os.unlink(_file_path)
            elif os.path.isdir(_file_path):
                shutil.rmtree(_file_path)
        except OSError as _e:
            print(_e)
class StdOut(object):
    """Redirect stdout to file, and print to console as well.
    """
    def __init__(self, output_file):
        self.terminal = sys.stdout
        self.log = open(output_file, "a")
    def write(self, message):
        self.terminal.write(message)
        self.terminal.flush()
        self.log.write(message)
        self.log.flush()
    def flush(self):
        self.terminal.flush()
        self.log.flush()

CUDA = True
DATA_PATH = '~/Data/mnist'
OUT_PATH = 'output'
LOG_FILE = os.path.join(OUT_PATH, 'log.txt')
BATCH_SIZE = 128
IMAGE_CHANNEL = 1
Z_DIM = 100
G_HIDDEN = 64
X_DIM = 64
D_HIDDEN = 64
EPOCH_NUM = 25
REAL_LABEL = 1
FAKE_LABEL = 0
lr = 2e-4
seed = 1