mirror of https://github.com/JDAI-CV/fast-reid.git
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from torch import 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 AMSoftmax(nn.Module):
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r"""Implement of large margin cosine distance:
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Args:
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in_feat: size of each input sample
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num_classes: size of each output sample
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"""
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def __init__(self, cfg, in_feat, num_classes):
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super().__init__()
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self.in_features = 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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nn.init.xavier_uniform_(self.weight)
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def forward(self, features, targets):
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# --------------------------- cos(theta) & phi(theta) ---------------------------
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cosine = F.linear(F.normalize(features), F.normalize(self.weight))
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phi = cosine - self.m
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# --------------------------- convert label to one-hot ---------------------------
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targets = F.one_hot(targets, num_classes=self._num_classes)
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output = (targets * phi) + ((1.0 - targets) * cosine)
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output *= self.s
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return output
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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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