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https://github.com/JDAI-CV/fast-reid.git
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50 lines
1.6 KiB
Python
50 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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from .build import REID_HEADS_REGISTRY
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from ..model_utils import weights_init_kaiming
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from ...layers import *
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@REID_HEADS_REGISTRY.register()
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class BNneckHead(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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self.bnneck = NoBiasBatchNorm1d(in_feat)
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self.bnneck.apply(weights_init_kaiming)
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# identity classification layer
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if cfg.MODEL.HEADS.CLS_LAYER == 'linear':
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self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
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elif cfg.MODEL.HEADS.CLS_LAYER == 'arcface':
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self.classifier = Arcface(cfg, in_feat)
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elif cfg.MODEL.HEADS.CLS_LAYER == 'circle':
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self.classifier = Circle(cfg, in_feat)
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else:
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self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
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def forward(self, features, targets=None):
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"""
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See :class:`ReIDHeads.forward`.
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"""
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global_feat = self.pool_layer(features)
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bn_feat = self.bnneck(global_feat)
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# evaluation
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if not self.training:
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return bn_feat
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# training
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try:
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pred_class_logits = self.classifier(bn_feat)
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except TypeError:
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pred_class_logits = self.classifier(bn_feat, targets)
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return pred_class_logits, bn_feat, targets
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