Feature Extraction - ( Style Transferring with PyTorch )

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Syntax

class ContentLoss(nn.Module)
class GramMatrix(nn.Module)
class StyleLoss(nn.Module)
class Normalization(nn.Module)

Example

class StyleTransferModel(nn.Module):
    def __init__(self, cnn, normalization_mean, normalization_std, style_layers, content_layers):
        super(StyleTransferModel, self).__init__()
        self.normalization = Normalization(normalization_mean, normalization_std).to(device)
        self.content_losses = []
        self.style_losses = []
        self.model = nn.Sequential(self.normalization)
        i = 0  
        for layer in cnn.children():
            if isinstance(layer, nn.Conv2d):
                i += 1
                name = f'conv_{i}'
            elif isinstance(layer, nn.ReLU):
                name = f'relu_{i}'
                layer = nn.ReLU(inplace=False)
            elif isinstance(layer, nn.MaxPool2d):
                name = f'pool_{i}'
            elif isinstance(layer, nn.BatchNorm2d):
                name = f'bn_{i}'
            else:
                raise RuntimeError('Unrecognized layer')
            self.model.add_module(name, layer)
            if name in content_layers:
                target = self.model(content_img).detach()
                content_loss = ContentLoss(target)
                self.model.add_module(f'content_loss_{i}', content_loss)
                self.content_losses.append(content_loss)
            if name in style_layers:
                target_feature = self.model(style_img).detach()
                style_loss = StyleLoss(target_feature)
                self.model.add_module(f'style_loss_{i}', style_loss)
                self.style_losses.append(style_loss)

    def forward(self, x):
        self.model(x)
        return self

    def get_style_losses(self):
        return self.style_losses

    def get_style_scores(self, input_img):
        self.forward(input_img)
        style_scores = [sl.get_score() for sl in self.style_losses]
        return style_scores

    def get_content_loss(self):
        return self.content_losses[0].get_score()

Output

The output of the above code will be a StyleTransferModel object.

Explanation

In style transfer, feature extraction is a crucial part of the process. This involves extracting features from the input image and the style image, and using these to calculate the style and content losses.

ContentLoss, StyleLoss, GramMatrix, and Normalization are custom PyTorch modules used in the feature extraction process. ContentLoss measures the difference between the feature maps of the input image and the content image. StyleLoss measures the difference between the style images and the input image, and the GramMatrix is used to calculate style representation of the feature maps. Normalization is used to normalize the input image.

The StyleTransferModel is a complete model for performing style transfer, and it uses the custom modules described above for feature extraction.

Use

The StyleTransferModel class can be used to perform style transfer on any set of input and style images.

Important Points

• Feature extraction is a crucial part of style transfer
• Custom PyTorch modules such as ContentLoss, StyleLoss, GramMatrix, and Normalization are used to extract features from the input and style images
• StyleTransferModel is a complete model for performing style transfer that uses these custom modules for feature extraction

Summary

In conclusion, feature extraction is a crucial part of style transfer and involves extracting features from the input and the style image. Custom PyTorch modules such as ContentLoss, StyleLoss, GramMatrix, and Normalization are used for feature extraction. The StyleTransferModel is a complete model for performing style transfer that uses these custom modules for feature extraction. It can be used to perform style transfer on any set of input and style images.