model.py

import torch.nn as nn

class StyleTransferModel(nn.Module):
    def __init__(self, base_model):
        super(StyleTransferModel, self).__init__()
        vgg19 = base_model
        # Freeze the parameters
        for param in vgg19.parameters():
            param.requires_grad = False

        # Split VGG19 into blocks for feature extraction
        self.block1 = vgg19[:4]  # conv1_1, relu, conv1_2, relu
        self.pool1 = vgg19[4]  # maxpool
        self.block2 = vgg19[5:9]  # conv2_1, relu, conv2_2, relu
        self.pool2 = vgg19[9]  # maxpool
        self.block3 = vgg19[10:18]  # conv3_1 to relu3_4
        self.pool3 = vgg19[18]  # maxpool
        self.block4 = vgg19[19:27]  # conv4_1 to relu4_4
        self.pool4 = vgg19[27]  # maxpool
        self.block5 = vgg19[28:36]  # conv5_1 to relu5_4

        # Define content and style layers
        self.content_layers = ["block4"]  # We'll use output of block4 for content
        self.style_layers = [
            "block1",
            "block2",
            "block3",
            "block4",
            "block5",
        ]  # All blocks for style

    def forward(self, x):
        # create a dict to save the results
        features = {}

        # Block 1
        x = self.block1(x)
        features["block1"] = x
        x = self.pool1(x)

        # Block 2
        x = self.block2(x)
        features["block2"] = x
        x = self.pool2(x)

        # Block 3
        x = self.block3(x)
        features["block3"] = x
        x = self.pool3(x)

        # Block 4
        x = self.block4(x)
        features["block4"] = x
        x = self.pool4(x)

        # Block 5
        x = self.block5(x)
        features["block5"] = x

        return features