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Runtime error
| import torch | |
| import torch.nn as nn | |
| from criteria.lpips.networks import get_network, LinLayers | |
| from criteria.lpips.utils import get_state_dict | |
| class LPIPS(nn.Module): | |
| r"""Creates a criterion that measures | |
| Learned Perceptual Image Patch Similarity (LPIPS). | |
| Arguments: | |
| net_type (str): the network type to compare the features: | |
| 'alex' | 'squeeze' | 'vgg'. Default: 'alex'. | |
| version (str): the version of LPIPS. Default: 0.1. | |
| """ | |
| def __init__(self, net_type: str = 'alex', version: str = '0.1'): | |
| assert version in ['0.1'], 'v0.1 is only supported now' | |
| super(LPIPS, self).__init__() | |
| # pretrained network | |
| self.net = get_network(net_type).to("cuda") | |
| # linear layers | |
| self.lin = LinLayers(self.net.n_channels_list).to("cuda") | |
| self.lin.load_state_dict(get_state_dict(net_type, version)) | |
| def forward(self, x: torch.Tensor, y: torch.Tensor): | |
| feat_x, feat_y = self.net(x), self.net(y) | |
| diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)] | |
| res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)] | |
| return torch.sum(torch.cat(res, 0)) / x.shape[0] | |