mirror of https://github.com/JDAI-CV/fast-reid.git
68 lines
1.9 KiB
Python
68 lines
1.9 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 numpy as np
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import torch
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import torch.nn.functional as F
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from torch.nn import Parameter
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from ..layers import *
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from ..losses.loss_utils import one_hot
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from ..model_utils import weights_init_kaiming
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class QAMHead(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 = NoBiasBatchNorm1d(in_feat)
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self.bnneck.apply(weights_init_kaiming)
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# classifier
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# self.adaptive_s = False
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self._s = 6.0
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self._m = 0.50
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self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
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self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
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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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# get cos(theta)
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cosine = F.linear(F.normalize(bn_feat), F.normalize(self.weight))
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# add margin
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theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7)) # for numerical stability
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# --------------------------- convert label to one-hot ---------------------------
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targets = one_hot(targets, self._num_classes)
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phi = (2 * np.pi - (theta + self._m)) ** 2
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others = (2 * np.pi - theta) ** 2
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pred_class_logits = targets * phi + (1.0 - targets) * others
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# logits re-scale
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pred_class_logits *= self._s
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return pred_class_logits, global_feat
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