Optimization Process - ( Style Transferring with PyTorch )

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Syntax

def run_style_transfer(cnn, normalization_mean, normalization_std,
                       content_img, style_img, input_img, num_steps=300,
                       style_weight=1000000, content_weight=1):
    """Run the style transfer."""
    print('Building the style transfer model..')
    model, style_losses, content_losses = get_style_model_and_losses(cnn,
        normalization_mean, normalization_std, style_img, content_img)
    optimizer = optim.LBFGS([input_img.requires_grad_()])
    print('Optimizing..')
    run = [0]
    while run[0] <= num_steps:
        def closure():
            # correct the values of updated input image
            input_img.data.clamp_(0, 1)

            optimizer.zero_grad()

            model(input_img)

            style_score = 0
            content_score = 0

            for sl in style_losses:
                style_score += sl.loss
            for cl in content_losses:
                content_score += cl.loss

            style_score *= style_weight
            content_score *= content_weight

            loss = style_score + content_score
            loss.backward()

            run[0] += 1
            if run[0] % 50 == 0:
                print("run {}:".format(run))
                print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                    style_score.item(), content_score.item()))
                print()

            return style_score + content_score

        optimizer.step(closure)

    # a last correction...
    input_img.data.clamp_(0, 1)

    return input_img

Example

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load pre-trained model
cnn = models.vgg19(pretrained=True).features.to(device).eval()

# Load content and style images
content_img = image_loader("content.jpg").to(device)
style_img = image_loader("style.jpg").to(device)

# Initialize output image to white noise
input_img = torch.randn(content_img.shape, device=device)

# Run style transfer
output = run_style_transfer(cnn, MEAN, STD, content_img, style_img, input_img)

Output

The output of the above code will be the final stylized image which is not shown here.

Explanation

Style transfer is the process of taking the style of one image and applying it to the content of another image. In PyTorch, we use an optimization process to create the stylized image. Our objective is to minimize the loss between the stylized image, the content image, and the style image.

One way to achieve this is to create a model that takes as input the content image, the style image, and a random initial image. We then adjust the initial image to minimize the loss using an optimization algorithm.

The optimization algorithm used in this example is the LBFGS algorithm, which is provided by the optim module of PyTorch. The closure() function is passed as an argument to the LBFGS optimizer to compute the loss and gradients during every iteration of the optimization process.

Use

The optimization process is a key component in style transfer using PyTorch. It is used to minimize the loss between the stylized image, the content image, and the style image, in order to produce a stylized image that combines the content of one image with the style of another.

Important Points

• The optimization process is used to minimize the loss between the stylized image, the content image, and the style image
• PyTorch provides different optimization algorithms to choose from
• The closure() function is used to compute the loss and gradients during every iteration of the optimization process

Summary

In conclusion, the optimization process is a key component in style transfer using PyTorch. It is used to minimize the loss between the stylized image, the content image, and the style image, in order to produce a stylized image that combines the content of one image with the style of another. PyTorch provides different optimization algorithms to choose from, and the closure() function is used to compute the loss and gradients during every iteration of the optimization process.