text: from __future__ import division
  from torchvision import models
  from torchvision import transforms
  from PIL import Image
  import argparse
  import torch
  import torchvision
  import torch.nn as nn
  import numpy as np
  
  
  # Device configuration
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  def load_image(image_path, transform=None, max_size=None, shape=None):
      """Load an image and convert it to a torch tensor."""
      image = Image.open(image_path)
      
      if max_size:
          scale = max_size / max(image.size)
          size = np.array(image.size) * scale
          image = image.resize(size.astype(int), Image.ANTIALIAS)
      
      if shape:
          image = image.resize(shape, Image.LANCZOS)
      
      if transform:
          image = transform(image).unsqueeze(0)
      
      return image.to(device)
  
  
  class VGGNet(nn.Module):
      def __init__(self):
          """Select conv1_1 ~ conv5_1 activation maps."""
          super(VGGNet, self).__init__()
          self.select = ['0', '5', '10', '19', '28'] 
          self.vgg = models.vgg19(pretrained=True).features
          
      def forward(self, x):
          """Extract multiple convolutional feature maps."""
          features = []
          for name, layer in self.vgg._modules.items():
              x = layer(x)
              if name in self.select:
                  features.append(x)
          return features
  
  
  def main(config):
      
      # Image preprocessing
      # VGGNet was trained on ImageNet where images are normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
      # We use the same normalization statistics here.
      transform = transforms.Compose([
          transforms.ToTensor(),
          transforms.Normalize(mean=(0.485, 0.456, 0.406), 
                               std=(0.229, 0.224, 0.225))])
      
      # Load content and style images
      # Make the style image same size as the content image
      content = load_image(config.content, transform, max_size=config.max_size)
      style = load_image(config.style, transform, shape=[content.size(2), content.size(3)])
      
      # Initialize a target image with the content image
      target = content.clone().requires_grad_(True)
      
      optimizer = torch.optim.Adam([target], lr=config.lr, betas=[0.5, 0.999])
      vgg = VGGNet().to(device).eval()
      
      for step in range(config.total_step):
          
          # Extract multiple(5) conv feature vectors
          target_features = vgg(target)
          content_features = vgg(content)
          style_features = vgg(style)
  
          style_loss = 0
          content_loss = 0
          for f1, f2, f3 in zip(target_features, content_features, style_features):
              # Compute content loss with target and content images
              content_loss += torch.mean((f1 - f2)**2)
  
              # Reshape convolutional feature maps
              _, c, h, w = f1.size()
              f1 = f1.view(c, h * w)
              f3 = f3.view(c, h * w)
  
              # Compute gram matrix
              f1 = torch.mm(f1, f1.t())
              f3 = torch.mm(f3, f3.t())
  
              # Compute style loss with target and style images
              style_loss += torch.mean((f1 - f3)**2) / (c * h * w) 
          
          # Compute total loss, backprop and optimize
          loss = content_loss + config.style_weight * style_loss 
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if (step+1) % config.log_step == 0:
              print ('Step [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}' 
                     .format(step+1, config.total_step, content_loss.item(), style_loss.item()))
  
          if (step+1) % config.sample_step == 0:
              # Save the generated image
              denorm = transforms.Normalize((-2.12, -2.04, -1.80), (4.37, 4.46, 4.44))
              img = target.clone().squeeze()
              img = denorm(img).clamp_(0, 1)
              torchvision.utils.save_image(img, 'output-{}.png'.format(step+1))
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      parser.add_argument('--content', type=str, default='png/content.png')
      parser.add_argument('--style', type=str, default='png/style.png')
      parser.add_argument('--max_size', type=int, default=400)
      parser.add_argument('--total_step', type=int, default=2000)
      parser.add_argument('--log_step', type=int, default=10)
      parser.add_argument('--sample_step', type=int, default=500)
      parser.add_argument('--style_weight', type=float, default=100)
      parser.add_argument('--lr', type=float, default=0.003)
      config = parser.parse_args()
      print(config)
      main(config)