mirror of https://github.com/JDAI-CV/fast-reid.git
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
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# 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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from ..losses.loss_utils import one_hot
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class Circle(nn.Module):
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def __init__(self, cfg, in_feat):
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super().__init__()
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self._num_classes = cfg.MODEL.HEADS.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(self._num_classes, in_feat))
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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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 = one_hot(targets, 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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