def forward(self, outputs, labels):
cross_entropy_loss = CrossEntropyLoss(num_classes=751)
triplet_loss = TripletLoss(margin=1.2)
Triplet_Loss = triplet_loss(outputs[:1], labels)
Triplet_Loss = sum(Triplet_Loss) / len(Triplet_Loss)
CrossEntropy_Loss = [cross_entropy_loss(output, labels) for output in outputs[4:]]
CrossEntropy_Loss = sum(CrossEntropy_Loss) / len(CrossEntropy_Loss)
loss_sum = Triplet_Loss + 2 * CrossEntropy_Loss
# print('\rtotal loss:%.2f Triplet_Loss:%.2f CrossEntropy_Loss:%.2f' % (
# loss_sum.data.cpu().numpy(),
# Triplet_Loss.data.cpu().numpy(),
# CrossEntropy_Loss.data.cpu().numpy()),
# end=' ')
return loss_sum
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True):
super(ImageTripletEngine, self).__init__(datamanager, model, optimizer,
scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)