mirror of https://github.com/JDAI-CV/fast-reid.git
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# 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 ArcSoftmax(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.easy_margin = False
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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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nn.init.xavier_uniform_(self.weight)
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self.register_buffer('t', torch.zeros(1))
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def forward(self, features, targets):
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cosine = F.linear(F.normalize(features), F.normalize(self.weight))
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sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
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phi = cosine * self.cos_m - sine * self.sin_m # cos(theta + m)
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if self.easy_margin:
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phi = torch.where(cosine > 0, phi, cosine)
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else:
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phi = torch.where(cosine > self.threshold, phi, cosine - self.mm)
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one_hot = torch.zeros(cosine.size(), device=cosine.device)
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one_hot.scatter_(1, targets.view(-1, 1).long(), 1)
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output = (one_hot * phi) + ((1.0 - one_hot) * 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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