Created
January 2, 2020 09:32
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| class ExclusiveLinear(nn.Module): | |
| def __init__(self, feat_dim=512, num_class=10572, norm_data=True, radius=20): | |
| super(ExclusiveLinear, self).__init__() | |
| self.num_class = num_class | |
| self.feat_dim = feat_dim | |
| self.norm_data = norm_data | |
| self.radius = float(radius) | |
| self.weight = nn.Parameter(torch.randn(self.num_class, self.feat_dim)) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| stdv = 1. / math.sqrt(self.weight.size(1)) | |
| self.weight.data.uniform_(-stdv, stdv) | |
| def forward(self, x): | |
| weight_norm = torch.nn.functional.normalize(self.weight, p=2, dim=1) | |
| cos = torch.mm(weight_norm, weight_norm.t()) | |
| cos.clamp(-1, 1) | |
| cos1 = cos.detach() | |
| cos1.scatter_(1, torch.arange(self.num_class).view(-1, 1).long().cuda(), -100) | |
| _, indices = torch.max(cos1, dim=0) | |
| mask = torch.zeros((self.num_class, self.num_class)).cuda() | |
| mask.scatter_(1, indices.view(-1, 1).long(), 1) | |
| exclusive_loss = torch.dot(cos.view(cos.numel()), mask.view(mask.numel())) / self.num_class | |
| if self.norm_data: | |
| x = torch.nn.functional.normalize(x, p=2, dim=1) | |
| x = x * self.radius | |
| return torch.nn.functional.linear(x, weight_norm), exclusive_loss |
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