Applying Style Transfer with PyTorch: From Monet Paintings to Real Photos

Style transfer is an exciting topic in the field of computer vision and deep learning. It involves applying the style of one image onto another, making the resulting image a blend of the content of one and the style of another. With PyTorch, a powerful deep learning library, style transfer tasks can be efficiently performed. In this article, we'll walk through how to apply style transfer using a pre-trained VGG19 model with PyTorch, taking inspiration from Monet's paintings to transform real photos.

Setting Up the Environment

Before diving into the code, we need to ensure that we have the necessary tools and libraries installed. You'll need Python, PyTorch, and additional libraries such as Pillow for image processing.

pip install torch torchvision pillow

Once you have the environment ready, you can move on to loading our model and image data.

Loading and Preprocessing Images

We'll start by loading the content and style images. The content image is the real photo you want to transform, and the style image is the Monet painting whose style you wish to apply. Here's how we can load these images:

from PIL import Image
import torchvision.transforms as transforms

# Define the image loader
loader = transforms.Compose([
    transforms.Resize((512, 512)),  # scale imported image
    transforms.ToTensor()])  # transform it into a torch tensor

# Definitions to load content and style images
def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image

content_img = image_loader("path_to_your_content_image.jpg")
style_img = image_loader("path_to_your_style_image.jpg")

Note: The images are loaded as Tensors required by PyTorch models.

Building the Model for Style Transfer

We'll use the VGG19 model for our task. VGG19 is a convolutional neural network that is pre-trained on millions of images from the ImageNet database, which makes it effective for recognizing the different layers or features in our images.

import torch
import torch.nn as nn
import torchvision.models as models

# Load the VGG19 model
cnn = models.vgg19(pretrained=True).features.eval()

Disable gradient computation for the model since we do not need to compute gradients with respect to the model parameters.

for param in cnn.parameters():
    param.requires_grad = False

Defining Loss Functions

The key to style transfer is defining loss functions that help merge the content of one image with the style of another. We define two loss functions: the content loss and the style loss. Content loss ensures that the content in the generated image is similar to the input content image, while style loss ensures that the generated image mimics the texture and colors of the style image.

class ContentLoss(nn.Module):
    def __init__(self, target):
        super(ContentLoss, self).__init__()
        self.target = target.detach()

    def forward(self, input):
        self.loss = nn.functional.mse_loss(input, self.target)
        return input

Similarly, you will need a style loss function which can be implemented using Gram matrices.

Optimizing the Image

With the loss functions defined, the next step in style transfer is to optimize the content image so that it transforms to reflect the style of the style image. This is usually done by minimizing the total loss, which is the sum of the content and style losses.

input_img = content_img.clone()

# Add the input image as an optimizable parameter
optimizer = torch.optim.LBFGS([input_img.requires_grad_()])

# Function to perform stepwise update
run = [0]
while run[0] < num_steps:
    def closure():
        input_img.data.clamp_(0, 1)
        optimizer.zero_grad()
        out = cnn(input_img)
        style_score, content_score = calculate_style_content_loss()
        loss = style_score + content_score
        loss.backward()
        run[0] += 1
        return loss
    optimizer.step(closure)

# Convert the generated image to a suitable format for display
output = transforms.ToPILImage()(input_img.squeeze(0))

That's it! You now have a tool that allows you to transform your photos with the artistic flair of Monet using PyTorch. By fine-tuning the parameters and experimenting with different paintings, the possibilities for creative outputs are endless.