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from collections import OrderedDict |
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import torch |
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def normalize_activation(x, eps=1e-10): |
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norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True)) |
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return x / (norm_factor + eps) |
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def get_state_dict(net_type: str = 'alex', version: str = '0.1'): |
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url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \ |
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+ f'master/lpips/weights/v{version}/{net_type}.pth' |
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old_state_dict = torch.hub.load_state_dict_from_url( |
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url, progress=True, |
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map_location=None if torch.cuda.is_available() else torch.device('cpu') |
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) |
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new_state_dict = OrderedDict() |
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for key, val in old_state_dict.items(): |
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new_key = key |
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new_key = new_key.replace('lin', '') |
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new_key = new_key.replace('model.', '') |
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new_state_dict[new_key] = val |
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return new_state_dict |
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