# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import Parameter
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class Circle(nn.Module):
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def __init__(self, cfg, in_feat, num_classes):
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super().__init__()
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self.in_feat = in_feat
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self._num_classes = num_classes
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self._s = cfg.MODEL.HEADS.SCALE
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self._m = cfg.MODEL.HEADS.MARGIN
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self.weight = Parameter(torch.Tensor(num_classes, in_feat))
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def forward(self, features, targets):
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sim_mat = F.linear(F.normalize(features), F.normalize(self.weight))
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alpha_p = F.relu(-sim_mat.detach() + 1 + self._m)
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alpha_n = F.relu(sim_mat.detach() + self._m)
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delta_p = 1 - self._m
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delta_n = self._m
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s_p = self._s * alpha_p * (sim_mat - delta_p)
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s_n = self._s * alpha_n * (sim_mat - delta_n)
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targets = F.one_hot(targets, num_classes=self._num_classes)
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pred_class_logits = targets * s_p + (1.0 - targets) * s_n
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return pred_class_logits
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def extra_repr(self):
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return 'in_features={}, num_classes={}, scale={}, margin={}'.format(
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self.in_feat, self._num_classes, self._s, self._m
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)
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