mirror of https://github.com/JDAI-CV/fast-reid.git
44 lines
1.1 KiB
Python
44 lines
1.1 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 torch
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import torch.nn.functional as F
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from torch import nn
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from fastreid.modeling.heads import EmbeddingHead
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from fastreid.modeling.heads.build import REID_HEADS_REGISTRY
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from fastreid.layers.weight_init import weights_init_kaiming
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@REID_HEADS_REGISTRY.register()
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class AttrHead(EmbeddingHead):
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def __init__(self, cfg):
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super().__init__(cfg)
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num_classes = cfg.MODEL.HEADS.NUM_CLASSES
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self.bnneck = nn.BatchNorm1d(num_classes)
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self.bnneck.apply(weights_init_kaiming)
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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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pool_feat = self.pool_layer(features)
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neck_feat = self.bottleneck(pool_feat)
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neck_feat = neck_feat.view(neck_feat.size(0), -1)
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logits = F.linear(neck_feat, self.weight)
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logits = self.bnneck(logits)
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# Evaluation
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if not self.training:
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cls_outptus = torch.sigmoid(logits)
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return cls_outptus
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return {
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"cls_outputs": logits,
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}
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