text: import argparse
  import os
  import torch
  import torch.optim as optim
  import torch.nn as nn
  
  from AdversarialLoss import MobileImprovingAdversarialLoss,MultiSigmaAdversarialLoss, PhotoRealisticAdversarialLoss,  WassersteinAdversarialLoss
  from DeepImageDenoiser import DeepImageDenoiser , LEARNING_RATE
  from DenoiserDataset import  DenoiserDataset
  from RSGUNetGenerator import RSGUNetGenerator
  from NeuralModels import SILU, UpsampleDeConv, TransposedDeConv, PixelDeConv
  from PerceptualLoss import AdaptivePerceptualLoss, FastNeuralStylePerceptualLoss, SpectralAdaptivePerceptualLoss, WassersteinAdaptivePerceptualLoss  , SimplePerceptualLoss, SqueezeAdaptivePerceptualLoss
  from ResidualGenerator import ResidualGenerator
  from UNetGenerator import UNetGenerator
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--image_dir',         type = str,   default='./BSDS500/', help='path to dataset')
  parser.add_argument('--generator',         type = str,   default='RSGUNet', help='type of image generator')
  parser.add_argument('--criterion',         type = str,   default='MobileImproving', help='type of criterion')
  parser.add_argument('--deconv',            type = str,   default='Upsample', help='type of deconv')
  parser.add_argument('--activation',        type = str,   default='Leaky', help='type of activation')
  parser.add_argument('--optimizer',         type = str,   default='Adam', help='type of optimizer')
  parser.add_argument('--batch_size',        type = int,   default=32)
  parser.add_argument('--epochs',            type = int,   default=128)
  parser.add_argument('--resume_train',      type = bool,  default=True)
  
  args = parser.parse_args()
  
  print(torch.__version__)
  
  criterion_types =   {
                          'MobileImproving'       : MobileImprovingAdversarialLoss(),
                          'MultiSigma'            : MultiSigmaAdversarialLoss(),
                          'PhotoRealistic'        : PhotoRealisticAdversarialLoss(),
                          'Wasserstein'           : WassersteinAdversarialLoss(),
                          'FastNeuralStyle'       : FastNeuralStylePerceptualLoss(),
                          'PAN'                   : AdaptivePerceptualLoss(),
                          'SimplePerceptual'      : SimplePerceptualLoss(),
                          'SpectralPAN'           : SpectralAdaptivePerceptualLoss(),
                          'SqueezeAdaptive'       : SqueezeAdaptivePerceptualLoss(),
                          'WassersteinAdaptive'   : WassersteinAdaptivePerceptualLoss(),
                          'MSE'                   : nn.MSELoss(),
                      }
  
  generator_types = {
                          'UNet'     : UNetGenerator,
                          'RSGUNet'  : RSGUNetGenerator,
                          'Residual' : ResidualGenerator,
                      }
  
  deconv_types =      {
                          'Transposed'  : TransposedDeConv,
                          'Upsample'    : UpsampleDeConv,
                          'Pixel'       : PixelDeConv
                      }
  
  activation_types =  {
                          'ReLU' : nn.ReLU(),
                          'Leaky': nn.LeakyReLU(),
                          'PReLU': nn.PReLU(),
                          'ELU'  : nn.ELU(),
                          'SELU' : nn.SELU(),
                          'SILU' : SILU()
                      }
  
  optimizer_types =   {
                          'Adam'   : optim.Adam,
                          'RMSprop': optim.RMSprop,
                          'SGD'    : optim.SGD
                      }
  
  model = generator_types[args.generator]
  deconvLayer = (deconv_types[args.deconv] if args.deconv in deconv_types else deconv_types['upsample'])
  function = (activation_types[args.activation] if args.activation in activation_types else activation_types['Leaky'])
  generator = model(deconv=deconvLayer, activation=function)
  optimizer =(optimizer_types[args.optimizer] if args.optimizer in optimizer_types else optimizer_types['Adam'])(generator.parameters(), lr = LEARNING_RATE)
  criterion = criterion_types[args.criterion]
  
  augmentations = {'train' : True, 'val' : False}
  shufles = {'train' : True, 'val' : False}
  batch_sizes = {'train' : args.batch_size, 'val' : args.batch_size if args.batch_size < 8 else 8}
  
  image_datasets = {x: DenoiserDataset(os.path.join(args.image_dir, x),  augmentation = augmentations[x])
                      for x in ['train', 'val']}
  
  imageloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_sizes[x],
                                               shuffle=shufles[x], num_workers=4)
                  for x in ['train', 'val']}
  
  test_dataset = DenoiserDataset(args.image_dir+'/test/',  augmentation = False)
  testloader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=4)
  
  framework = DeepImageDenoiser(generator = generator, criterion = criterion, optimizer = optimizer)
  framework.approximate(dataloaders = imageloaders, num_epochs=args.epochs, resume_train=args.resume_train)
  framework.estimate(testloader)
  