2020-07-10 16:27:22 +08:00

46 lines
1.6 KiB
Python

# encoding: utf-8
"""
@authorr: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from fastreid.modeling.losses import *
from fastreid.modeling.meta_arch import Baseline
from fastreid.modeling.meta_arch.build import META_ARCH_REGISTRY
@META_ARCH_REGISTRY.register()
class PartialBaseline(Baseline):
def forward(self, batched_inputs):
images = self.preprocess_image(batched_inputs)
features = self.backbone(images)
if self.training:
assert "targets" in batched_inputs, "person ID annotation are missing in training!"
targets = batched_inputs["targets"].long().to(self.device)
if targets.sum() < 0: targets.zero_()
cls_outputs, global_feat, fore_cls_outputs, fore_feat = self.heads(features, targets)
return cls_outputs, global_feat, fore_cls_outputs, fore_feat, targets
else:
pred_features = self.heads(features)
return pred_features
def losses(self, outputs):
cls_outputs, global_feat, fore_cls_outputs, fore_feat, gt_labels = outputs
loss_dict = {}
loss_names = self._cfg.MODEL.LOSSES.NAME
if "CrossEntropyLoss" in loss_names:
loss_dict['loss_avg_branch_cls'] = CrossEntropyLoss(self._cfg)(cls_outputs, gt_labels)
loss_dict['loss_fore_branch_cls'] = CrossEntropyLoss(self._cfg)(fore_cls_outputs, gt_labels)
if "TripletLoss" in loss_names:
loss_dict['loss_avg_branch_triplet'] = TripletLoss(self._cfg)(global_feat, gt_labels)
loss_dict['loss_fore_branch_triplet'] = TripletLoss(self._cfg)(fore_feat, gt_labels)
return loss_dict