def losses(self, outputs):
logits, feats, targets = outputs
loss_dict = {}
loss_dict.update(
reid_losses(self._cfg, logits[0], feats[0], targets, 'b1_'))
loss_dict.update(
reid_losses(self._cfg, logits[1], feats[1], targets, 'b2_'))
loss_dict.update(
reid_losses(self._cfg, logits[2], feats[2], targets, 'b3_'))
loss_dict.update(
reid_losses(self._cfg, logits[3], feats[3], targets, 'b21_'))
loss_dict.update(
reid_losses(self._cfg, logits[5], feats[4], targets, 'b31_'))
part_ce_loss = [(CrossEntropyLoss(self._cfg)(logits[4], None,
targets), 'b22_'),
(CrossEntropyLoss(self._cfg)(logits[6], None,
targets), 'b32_'),
(CrossEntropyLoss(self._cfg)(logits[7], None,
targets), 'b33_')]
named_ce_loss = {}
for item in part_ce_loss:
named_ce_loss[item[1] + [*item[0]][0]] = [*item[0].values()][0]
loss_dict.update(named_ce_loss)
return loss_dict
def losses(self, outputs, gt_labels):
b1_logits, b2_logits, b21_logits, b22_logits, b3_logits, b31_logits, b32_logits, b33_logits, \
b1_pool_feat, b2_pool_feat, b3_pool_feat, b22_pool_feat, b33_pool_feat, pred_class_logits = outputs
loss_dict = {}
loss_names = self._cfg.MODEL.LOSSES.NAME
# Log prediction accuracy
if "CrossEntropyLoss" in loss_names:
loss_dict['loss_cls_b1'] = CrossEntropyLoss(self._cfg)(b1_logits,
gt_labels)
loss_dict['loss_cls_b2'] = CrossEntropyLoss(self._cfg)(b2_logits,
gt_labels)
loss_dict['loss_cls_b21'] = CrossEntropyLoss(self._cfg)(b21_logits,
gt_labels)
loss_dict['loss_cls_b22'] = CrossEntropyLoss(self._cfg)(b22_logits,
gt_labels)
loss_dict['loss_cls_b3'] = CrossEntropyLoss(self._cfg)(b3_logits,
gt_labels)
loss_dict['loss_cls_b31'] = CrossEntropyLoss(self._cfg)(b31_logits,
gt_labels)
loss_dict['loss_cls_b32'] = CrossEntropyLoss(self._cfg)(b32_logits,
gt_labels)
loss_dict['loss_cls_b33'] = CrossEntropyLoss(self._cfg)(b33_logits,
gt_labels)
CrossEntropyLoss.log_accuracy(pred_class_logits.detach(),
gt_labels)
if "TripletLoss" in loss_names:
loss_dict['loss_triplet_b1'] = TripletLoss(self._cfg)(b1_pool_feat,
gt_labels)
loss_dict['loss_triplet_b2'] = TripletLoss(self._cfg)(b2_pool_feat,
gt_labels)
loss_dict['loss_triplet_b3'] = TripletLoss(self._cfg)(b3_pool_feat,
gt_labels)
loss_dict['loss_triplet_b22'] = TripletLoss(self._cfg)(
b22_pool_feat, gt_labels)
loss_dict['loss_triplet_b33'] = TripletLoss(self._cfg)(
b33_pool_feat, gt_labels)
if "NpairLoss" in loss_names:
loss_dict['loss_npair_b1'] = NpairLoss(self._cfg)(b1_pool_feat,
gt_labels)
loss_dict['loss_npair_b2'] = NpairLoss(self._cfg)(b2_pool_feat,
gt_labels)
loss_dict['loss_npair_b3'] = NpairLoss(self._cfg)(b3_pool_feat,
gt_labels)
loss_dict['loss_npair_b22'] = NpairLoss(self._cfg)(b22_pool_feat,
gt_labels)
loss_dict['loss_npair_b33'] = NpairLoss(self._cfg)(b33_pool_feat,
gt_labels)
return loss_dict
def losses(self, b1_logits, b2_logits, b21_logits, b22_logits, b3_logits, b31_logits, b32_logits, b33_logits,
b1_pool_feat, b2_pool_feat, b3_pool_feat, b22_pool_feat, b33_pool_feat, gt_labels):
loss_dict = {}
loss_names = self._cfg.MODEL.LOSSES.NAME
if "CrossEntropyLoss" in loss_names:
loss_dict['loss_cls_b1'] = CrossEntropyLoss(self._cfg)(b1_logits, gt_labels)
loss_dict['loss_cls_b2'] = CrossEntropyLoss(self._cfg)(b2_logits, gt_labels)
loss_dict['loss_cls_b21'] = CrossEntropyLoss(self._cfg)(b21_logits, gt_labels)
loss_dict['loss_cls_b22'] = CrossEntropyLoss(self._cfg)(b22_logits, gt_labels)
loss_dict['loss_cls_b3'] = CrossEntropyLoss(self._cfg)(b3_logits, gt_labels)
loss_dict['loss_cls_b31'] = CrossEntropyLoss(self._cfg)(b31_logits, gt_labels)
loss_dict['loss_cls_b32'] = CrossEntropyLoss(self._cfg)(b32_logits, gt_labels)
loss_dict['loss_cls_b33'] = CrossEntropyLoss(self._cfg)(b33_logits, gt_labels)
if "TripletLoss" in loss_names:
loss_dict['loss_triplet_b1'] = TripletLoss(self._cfg)(b1_pool_feat, gt_labels)
loss_dict['loss_triplet_b2'] = TripletLoss(self._cfg)(b2_pool_feat, gt_labels)
loss_dict['loss_triplet_b3'] = TripletLoss(self._cfg)(b3_pool_feat, gt_labels)
loss_dict['loss_triplet_b22'] = TripletLoss(self._cfg)(b22_pool_feat, gt_labels)
loss_dict['loss_triplet_b33'] = TripletLoss(self._cfg)(b33_pool_feat, gt_labels)
return loss_dict