text: import argparse
  import os
  import numpy as np
  import math
  import sys
  
  import torchvision.transforms as transforms
  from torchvision.utils import save_image
  
  from torch.utils.data import DataLoader
  from torchvision import datasets
  from torch.autograd import Variable
  
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.autograd as autograd
  import torch
  import sys
  sys.path.append("..")
  from cesm import CLDHGH
  os.makedirs("images", exist_ok=True)
  
  parser = argparse.ArgumentParser()
  parser.add_argument("--n_epochs", type=int, default=400, help="number of epochs of training")
  parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
  parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
  parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
  parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
  parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
  parser.add_argument("--latent_dim", type=int, default=4, help="dimensionality of the latent space")
  parser.add_argument("--img_size", type=int, default=16, help="size of each image dimension")
  parser.add_argument("--channels", type=int, default=1, help="number of image channels")
  parser.add_argument("--n_critic", type=int, default=5, help="number of training steps for discriminator per iter")
  parser.add_argument("--clip_value", type=float, default=0.01, help="lower and upper clip value for disc. weights")
  parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen image samples")
  parser.add_argument('--hidden_dims', nargs='*', type=int)
  parser.add_argument("--lm", type=float, default=10, help="lambda")
  parser.add_argument("--save", type=str,  help="saving path")
  opt = parser.parse_args()
  print(opt)
  
  img_shape = (opt.channels, opt.img_size, opt.img_size)
  
  cuda = True if torch.cuda.is_available() else False
  
  
  class Generator(nn.Module):
      def __init__(self):
          super(Generator, self).__init__()
  
          self.in_channels=opt.channels
          self.latent_dim = opt.latent_dim
          
          
          modules = []
          if opt.hidden_dims is None:
              hidden_dims = [32, 64, 128, 256]
          else:
              hidden_dims=opt.hidden_dims
          #print(hidden_dims)
          self.last_fm_nums=hidden_dims[-1]
          self.last_fm_size=int( opt.img_size/(2**len(hidden_dims)) )
          # Build Encoder
          in_channels=opt.channels
          for h_dim in hidden_dims:
              modules.append(
                  nn.Sequential(
                      nn.Conv2d(in_channels, out_channels=in_channels,
                                kernel_size= 3, stride= 1, padding  = 1),###added layer
                      nn.Conv2d(in_channels, out_channels=h_dim,
                                kernel_size= 3, stride= 2, padding  = 1),
                      nn.BatchNorm2d(h_dim),
                      nn.LeakyReLU())
              )
              in_channels = h_dim
  
          self.encoder = nn.Sequential(*modules)
  
          self.fc_z = nn.Linear(hidden_dims[-1]*self.last_fm_size*self.last_fm_size, self.latent_dim)
  
  
          # Build Decoder
          modules = []
  
          self.decoder_input = nn.Linear(self.latent_dim, hidden_dims[-1] * self.last_fm_size*self.last_fm_size)
  
          hidden_dims.reverse()
  
          for i in range(len(hidden_dims) - 1):
              modules.append(
                  nn.Sequential(
                      nn.ConvTranspose2d(hidden_dims[i],
                                         hidden_dims[i],
                                         kernel_size=3,
                                         stride = 1,
                                         padding=1,
                                         output_padding=0),##added layer
                      nn.ConvTranspose2d(hidden_dims[i],
                                         hidden_dims[i + 1],
                                         kernel_size=3,
                                         stride = 2,
                                         padding=1,
                                         output_padding=1),
                      nn.BatchNorm2d(hidden_dims[i + 1]),
                      nn.LeakyReLU())
              )
  
  
  
          self.decoder = nn.Sequential(*modules)
  
          self.final_layer_1 = nn.Sequential(
                              nn.ConvTranspose2d(hidden_dims[-1],
                                         hidden_dims[-1],
                                         kernel_size=3,
                                         stride = 1,
                                         padding=1,
                                         output_padding=0),##added layer
                              nn.ConvTranspose2d(hidden_dims[-1],
                                                 hidden_dims[-1],
                                                 kernel_size=3,
                                                 stride=2,
                                                 padding=1,
                                                 output_padding=1),
                              nn.BatchNorm2d(hidden_dims[-1]),
                              nn.LeakyReLU(),
                              )
          self.final_layer_2=nn.Sequential(nn.Conv2d(hidden_dims[-1], out_channels= opt.channels,
                                        kernel_size= 3, padding= 1),
                              nn.Tanh())
  
      def encode(self, input) :
          """
          Encodes the input by passing through the encoder network
          and returns the latent codes.
          :param input: (Tensor) Input tensor to encoder [N x C x H x W]
          :return: (Tensor) List of latent codes
          """
          result = self.encoder(input)
          result = torch.flatten(result, start_dim=1)
  
          # Split the result into mu and var components
          # of the latent Gaussian distribution
          z = self.fc_z(result)
          return z
  
      def decode(self, z) :
          result = self.decoder_input(z)
          result = result.view(-1, self.last_fm_nums, self.last_fm_size, self.last_fm_size)
          result = self.decoder(result)
          result = self.final_layer_1(result)
          result= self.final_layer_2(result)
          
          return result
  
      def forward(self, input, **kwargs) :
          z = self.encode(input)
          return  self.decode(z)
  
  
  class Discriminator(nn.Module):
      def __init__(self):
          super(Discriminator, self).__init__()
  
          self.in_channels=opt.channels
          self.latent_dim = opt.latent_dim
          
          
          modules = []
          if opt.hidden_dims is None:
              hidden_dims = [32, 64, 128, 256]
          else:
              hidden_dims=opt.hidden_dims
          self.last_fm_nums=hidden_dims[-1]
          self.last_fm_size=int( opt.img_size/(2**len(hidden_dims)) )
          # Build Encoder
          in_channels=opt.channels
          for h_dim in hidden_dims:
              modules.append(
                  nn.Sequential(
                      nn.Conv2d(in_channels, out_channels=in_channels,
                                kernel_size= 3, stride= 1, padding  = 1),###added layer
                      nn.Conv2d(in_channels, out_channels=h_dim,
                                kernel_size= 3, stride= 2, padding  = 1),
                      nn.BatchNorm2d(h_dim),
                      nn.LeakyReLU())
              )
              in_channels = h_dim
  
          self.model = nn.Sequential(*modules)
  
          self.adv_layer = nn.Sequential( nn.Linear(hidden_dims[-1]*self.last_fm_size*self.last_fm_size, 1),nn.Sigmoid())
  
      def forward(self, img):
          out = self.model(img)
          out = out.view(out.shape[0], -1)
          validity = self.adv_layer(out)
  
          return validity
  
  
  # Loss weight for gradient penalty
  lambda_gp = 10
  
  # Initialize generator and discriminator
  generator = Generator()
  discriminator = Discriminator()
  
  if cuda:
      generator.cuda()
      discriminator.cuda()
  
  # Configure data loader
  datapath="/home/jliu447/lossycompression/multisnapshot-data-cleaned/CLDHGH"
  dataloader = torch.utils.data.DataLoader(
      dataset=CLDHGH(path=datapath,start=0,end=50,size=opt.img_size,gan_scale=True),
      batch_size=opt.batch_size,
      shuffle=True,
  )
  
  # Optimizers
  optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
  optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
  
  Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
  
  
  def compute_gradient_penalty(D, real_samples, fake_samples):
      """Calculates the gradient penalty loss for WGAN GP"""
      # Random weight term for interpolation between real and fake samples
      alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))
      # Get random interpolation between real and fake samples
      interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
      d_interpolates = D(interpolates)
      fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)
      # Get gradient w.r.t. interpolates
      gradients = autograd.grad(
          outputs=d_interpolates,
          inputs=interpolates,
          grad_outputs=fake,
          create_graph=True,
          retain_graph=True,
          only_inputs=True,
      )[0]
      gradients = gradients.view(gradients.size(0), -1)
      gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
      return gradient_penalty
  
  
  # ----------
  #  Training
  # ----------
  
  batches_done = 0
  for epoch in range(opt.n_epochs):
      for i, (imgs, _) in enumerate(dataloader):
  
          # Configure input
          real_imgs = Variable(imgs.type(Tensor))
  
          # ---------------------
          #  Train Discriminator
          # ---------------------
  
          optimizer_D.zero_grad()
  
          # Sample noise as generator input
          #z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))
  
          # Generate a batch of images
          fake_imgs = generator(real_imgs)
  
          # Real images
          real_validity = discriminator(real_imgs)
          # Fake images
          fake_validity = discriminator(fake_imgs)
          # Gradient penalty
          gradient_penalty = compute_gradient_penalty(discriminator, real_imgs.data, fake_imgs.data)
          # Adversarial loss
          d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + lambda_gp * gradient_penalty
  
          d_loss.backward()
          optimizer_D.step()
  
          optimizer_G.zero_grad()
  
          # Train the generator every n_critic steps
          if i % opt.n_critic == 0:
  
              # -----------------
              #  Train Generator
              # -----------------
  
              # Generate a batch of images
              fake_imgs = generator(real_imgs)
              # Loss measures generator's ability to fool the discriminator
              # Train on fake images
              fake_validity = discriminator(fake_imgs)
              g_loss = -torch.mean(fake_validity)+opt.lm*F.mse_loss(real_imgs,fake_imgs)
  
              g_loss.backward()
              optimizer_G.step()
  
              print(
                  "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
                  % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
              )
  
              #if batches_done % opt.sample_interval == 0:
              #    save_image(fake_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)
  
              batches_done += opt.n_critic
  
  
  torch.save(generator, opt.save)
  torch.save(generator.state_dict(),opt.save+'.params.pt')
  