Implementing neural style transfer using PyTorch

Having discussed the internals of a neural style transfer system, we are all set to build one using PyTorch. In the form of an exercise, we will load a style and a content image. Then, we will load the pretrained VGG model. After defining which layers to compute the style and content loss on, we will trim the model so that it only retains the relevant layers. Finally, we will train the neural style transfer model to refine the generated image epoch by epoch.

Loading the content and style images

In this exercise, we will only show the important parts of the code for demonstration purposes. To access the full code, go to our GitHub repository [3]. Follow these steps:

1. First, we need to import the necessary libraries:

   from PIL import Image
   import matplotlib.pyplot as plt
   import torch
   import torch.nn as nn
   import torch.optim as optim
   import torchvision
   dvc = torch.device("cuda" if torch.cuda.is_available...