Created
September 18, 2021 20:28
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| def get_features(image, model, layers=None): | |
| """ Run an image forward through a model and get the features for | |
| a set of layers. Default layers are for VGGNet matching Gatys et al (2016) | |
| """ | |
| ## Complete mapping layer names of PyTorch's VGGNet to names from the paper | |
| ## Need the layers for the content and style representations of an image | |
| if layers is None: | |
| layers = {'0': 'conv1_1', | |
| '5': 'conv2_1', | |
| '10': 'conv3_1', | |
| '19': 'conv4_1', | |
| '21': 'conv4_2', ## content representation | |
| '28': 'conv5_1'} | |
| features = {} | |
| x = image | |
| # model._modules is a dictionary holding each module in the model | |
| for name, layer in model._modules.items(): | |
| x = layer(x) | |
| if name in layers: | |
| features[layers[name]] = x | |
| return features | |
| def gram_matrix(tensor): | |
| """ Calculate the Gram Matrix of a given tensor | |
| Gram Matrix: https://en.wikipedia.org/wiki/Gramian_matrix | |
| """ | |
| # get the batch_size, depth, height, and width of the Tensor | |
| _, d, h, w = tensor.size() | |
| # reshape so we're multiplying the features for each channel | |
| tensor = tensor.view(d, h * w) | |
| # calculate the gram matrix | |
| gram = torch.mm(tensor, tensor.t()) | |
| return gram | |
| # get content and style features only once before training | |
| content_features = get_features(content, vgg) | |
| style_features = get_features(style, vgg) | |
| # calculate the gram matrices for each layer of our style representation | |
| style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features} | |
| # create a third "target" image and prep it for change | |
| # it is a good idea to start of with the target as a copy of our *content* image | |
| # then iteratively change its style | |
| target = content.clone().requires_grad_(True).to(device) |
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