# 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 math
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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 Arcface(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.cos_m = math.cos(self._m)
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self.sin_m = math.sin(self._m)
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self.threshold = math.cos(math.pi - self._m)
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self.mm = math.sin(math.pi - self._m) * self._m
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self.weight = Parameter(torch.Tensor(num_classes, in_feat))
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self.register_buffer('t', torch.zeros(1))
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def forward(self, features, targets):
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# get cos(theta)
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cos_theta = F.linear(F.normalize(features), F.normalize(self.weight))
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cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
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target_logit = cos_theta[torch.arange(0, features.size(0)), targets].view(-1, 1)
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sin_theta = torch.sqrt(1.0 - torch.pow(target_logit, 2))
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cos_theta_m = target_logit * self.cos_m - sin_theta * self.sin_m # cos(target+margin)
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mask = cos_theta > cos_theta_m
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final_target_logit = torch.where(target_logit > self.threshold, cos_theta_m, target_logit - self.mm)
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hard_example = cos_theta[mask]
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with torch.no_grad():
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self.t = target_logit.mean() * 0.01 + (1 - 0.01) * self.t
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cos_theta[mask] = hard_example * (self.t + hard_example)
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cos_theta.scatter_(1, targets.view(-1, 1).long(), final_target_logit)
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pred_class_logits = cos_theta * self._s
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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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