mirror of
https://github.com/JDAI-CV/fast-reid.git
synced 2025-06-03 14:50:47 +08:00
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
|
# encoding: utf-8
|
||
|
|
"""
|
||
|
|
@author: liaoxingyu
|
||
|
|
@contact: sherlockliao01@gmail.com
|
||
|
|
"""
|
||
|
|
|
||
|
|
from fastreid.modeling.meta_arch.baseline import Baseline
|
||
|
|
from fastreid.modeling.meta_arch.build import META_ARCH_REGISTRY
|
||
|
|
from .bce_loss import cross_entropy_sigmoid_loss
|
||
|
|
|
||
|
|
|
||
|
|
@META_ARCH_REGISTRY.register()
|
||
|
|
class AttrBaseline(Baseline):
|
||
|
|
|
||
|
|
def losses(self, outs, sample_weight=None):
|
||
|
|
r"""
|
||
|
|
Compute loss from modeling's outputs, the loss function input arguments
|
||
|
|
must be the same as the outputs of the model forwarding.
|
||
|
|
"""
|
||
|
|
# fmt: off
|
||
|
|
outputs = outs["outputs"]
|
||
|
|
gt_labels = outs["targets"]
|
||
|
|
# model predictions
|
||
|
|
# pred_class_logits = outputs['pred_class_logits'].detach()
|
||
|
|
cls_outputs = outputs['cls_outputs']
|
||
|
|
# fmt: on
|
||
|
|
|
||
|
|
# Log prediction accuracy
|
||
|
|
# log_accuracy(pred_class_logits, gt_labels)
|
||
|
|
|
||
|
|
loss_dict = {}
|
||
|
|
loss_names = self._cfg.MODEL.LOSSES.NAME
|
||
|
|
|
||
|
|
if "BinaryCrossEntropyLoss" in loss_names:
|
||
|
|
loss_dict['loss_bce'] = cross_entropy_sigmoid_loss(
|
||
|
|
cls_outputs,
|
||
|
|
gt_labels,
|
||
|
|
sample_weight,
|
||
|
|
) * self._cfg.MODEL.LOSSES.BCE.SCALE
|
||
|
|
|
||
|
|
return loss_dict
|