# 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 layers import *
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from modeling.losses import *
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from utils.weight_init import weights_init_kaiming, weights_init_classifier
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from .build import REID_HEADS_REGISTRY
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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, num_classes, pool_layer):
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super().__init__()
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self.neck_feat = cfg.MODEL.HEADS.NECK_FEAT
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self.pool_layer = pool_layer
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self.bnneck = get_norm(cfg.MODEL.HEADS.NORM, in_feat, cfg.MODEL.HEADS.NORM_SPLIT, bias_freeze=True)
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self.bnneck.apply(weights_init_kaiming)
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# identity classification layer
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cls_type = cfg.MODEL.HEADS.CLS_LAYER
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if cls_type == 'linear': self.classifier = nn.Linear(in_feat, num_classes, bias=False)
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elif cls_type == 'arcface': self.classifier = Arcface(cfg, in_feat, num_classes)
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elif cls_type == 'circle': self.classifier = Circle(cfg, in_feat, num_classes)
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else:
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raise KeyError(f"{cls_type} is invalid, please choose from "
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f"'linear', 'arcface' and 'circle'.")
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self.classifier.apply(weights_init_classifier)
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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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bn_feat = bn_feat[..., 0, 0]
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# Evaluation
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if not self.training: return bn_feat
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# Training
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try:
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cls_outputs = self.classifier(bn_feat)
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pred_class_logits = cls_outputs.detach()
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except TypeError:
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cls_outputs = self.classifier(bn_feat, targets)
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pred_class_logits = F.linear(F.normalize(bn_feat.detach()), F.normalize(self.classifier.weight.detach()))
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# Log prediction accuracy
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CrossEntropyLoss.log_accuracy(pred_class_logits, targets)
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if self.neck_feat == "before":
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feat = global_feat[..., 0, 0]
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elif self.neck_feat == "after":
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feat = bn_feat
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else:
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raise KeyError("MODEL.HEADS.NECK_FEAT value is invalid, must choose from ('after' & 'before')")
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return cls_outputs, feat
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