mirror of
https://github.com/JDAI-CV/fast-reid.git
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71 lines
2.0 KiB
Python
71 lines
2.0 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 .build import REID_HEADS_REGISTRY
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from .linear_head import LinearHead
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from ..model_utils import weights_init_kaiming
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from ...layers import bn_no_bias, Flatten
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@REID_HEADS_REGISTRY.register()
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class CircleHead(nn.Module):
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def __init__(self, cfg, in_feat, pool_layer=nn.AdaptiveAvgPool2d(1)):
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super().__init__()
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self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
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self.pool_layer = nn.Sequential(
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pool_layer,
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Flatten()
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)
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# bnneck
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self.bnneck = bn_no_bias(in_feat)
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self.bnneck.apply(weights_init_kaiming)
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# classifier
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self._s = 256.0
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self._m = 0.25
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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=None):
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global_feat = self.pool_layer(features)
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bn_feat = self.bnneck(global_feat)
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if not self.training:
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return bn_feat
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cos_sim = F.linear(F.normalize(bn_feat), F.normalize(self.weight))
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alpha_p = F.relu(-cos_sim + 1 + self._m)
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alpha_n = F.relu(cos_sim + self._m)
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margin_p = 1 - self._m
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margin_n = self._m
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sp = alpha_p * (cos_sim - margin_p)
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sn = alpha_n * (cos_sim - margin_n)
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one_hot = torch.zeros(cos_sim.size()).to(targets.device)
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one_hot.scatter_(1, targets.view(-1, 1).long(), 1)
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pred_class_logits = one_hot * sp + ((1.0 - one_hot) * sn)
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pred_class_logits *= self._s
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return pred_class_logits, global_feat
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@classmethod
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def losses(cls, cfg, pred_class_logits, global_feat, gt_classes):
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return LinearHead.losses(cfg, pred_class_logits, global_feat, gt_classes)
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