Style Transferring in PyTorch
In this topic, we will implement an artificial system based on Deep Neural Network, which will create artistic images of high perceptual quality. This system will use neural representation to separate, recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.
Neural style transfer is a way to generate images in the style of another image. The neural-style algorithm takes a content-image (a style image) as input and returns the content image as if it printed using the artistic style of the style image.
How does the neural style transfer algorithm work?
When we implement this algorithm, we define two distances; one for the content(Dc) and another for the style(Ds). Dc measures how different the content is between two images and Ds measures how different the style is between two images. We take the third image as an input and transform it in order to both minimize its content-distance with the content-image and its style-distance with the style-image.
Required Libraries
Initialization of VGG-19 model
VGG-19 model Is similar to VGG-16 model. The VGG model was introduced by Simonyan and Zisserman. VGG-19 is trained on more than a million images from the ImageNet database. This model has 19 layers deep neural network, which can classify images into 1000 objects categories.
In our initialization process, we will only import the features of the model.
When we run this code, downloading will be started, and our model features will be downloaded successfully.
Add the model to our device
When our model features are downloaded and imported, we have to add it on device either CUDA or CPU. The torch.device is the method by which we will do this process.
When we run this, it will give us expected output as:
