多任务学习使用不确定性来权衡场景几何和语义的损失，多任务学习中的辅助任务_Python_下载.zip

# AutomaticWeightedLoss A PyTorch implementation of Liebel L, Körner M. [Auxiliary tasks in multi-task learning](https://arxiv.org/pdf/1805.06334)[J]. arXiv preprint arXiv:1805.06334, 2018. The above paper improves the paper "[Multi-task learning using uncertainty to weigh losses for scene geometry and semantics](http://openaccess.thecvf.com/content_cvpr_2018/html/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.html)" to avoid the loss of becoming negative during training. ## Requirements * Python * PyTorch ## How to Train with Your Model * Clone the repository ``` bash git clone [email protected]:Mikoto10032/AutomaticWeightedLoss.git ``` * Create an AutomaticWeightedLoss module ```python from AutomaticWeightedLoss import AutomaticWeightedLoss awl = AutomaticWeightedLoss(2) # we have 2 losses loss1 = 1 loss2 = 2 loss_sum = awl(loss1, loss2) ``` * Create an optimizer to learn weight coefficients ```python from torch import optim model = Model() optimizer = optim.Adam([ {'params': model.parameters()}, {'params': awl.parameters(), 'weight_decay': 0} ]) ``` * A complete example ```python from torch import optim from AutomaticWeightedLoss import AutomaticWeightedLoss model = Model() awl = AutomaticWeightedLoss(2) # we have 2 losses loss_1 = ... loss_2 = ... # learnable parameters optimizer = optim.Adam([ {'params': model.parameters()}, {'params': awl.parameters(), 'weight_decay': 0} ]) for i in range(epoch): for data, label1, label2 in data_loader: # forward pred1, pred2 = Model(data) # calculate losses loss1 = loss_1(pred1, label1) loss2 = loss_2(pred2, label2) # weigh losses loss_sum = awl(loss1, loss2) # backward optimizer.zero_grad() loss_sum.backward() optimizer.step() ``` ## Something to Say Actually, it is not always effective, but I hope it can help you.