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https://github.com/JDAI-CV/fast-reid.git
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41 lines
1.2 KiB
Python
41 lines
1.2 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 fastreid.modeling.meta_arch.baseline import Baseline
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from fastreid.modeling.meta_arch.build import META_ARCH_REGISTRY
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from .bce_loss import cross_entropy_sigmoid_loss
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@META_ARCH_REGISTRY.register()
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class AttrBaseline(Baseline):
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def __init__(self, cfg, sample_weights):
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super(AttrBaseline, self).__init__(cfg)
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bce_weight_enabled = cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED
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if bce_weight_enabled:
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self.register_buffer("sample_weight", sample_weights)
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else:
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self.sample_weights = None
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def losses(self, outputs, gt_labels):
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r"""
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Compute loss from modeling's outputs, the loss function input arguments
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must be the same as the outputs of the model forwarding.
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"""
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# model predictions
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cls_outputs = outputs['cls_outputs']
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loss_dict = {}
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loss_names = self._cfg.MODEL.LOSSES.NAME
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if "BinaryCrossEntropyLoss" in loss_names:
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loss_dict["loss_bce"] = cross_entropy_sigmoid_loss(
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cls_outputs,
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gt_labels,
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self.sample_weight,
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) * self._cfg.MODEL.LOSSES.BCE.SCALE
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return loss_dict
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