1#!/usr/bin/env python 

2# -*- coding: utf-8 -*- 

3 

4 

5"""HED Network for image segmentation 

6 

7Holistically-nested edge detection (HED), turns pixel-wise edge classification 

8into image-to-image prediction by means of a deep learning model that leverages 

9fully convolutional neural networks and deeply-supervised nets. 

10 

11Reference: [XIE-2015]_ 

12""" 

13 

14 

15from torch.optim.lr_scheduler import MultiStepLR 

16 

17from bob.ip.binseg.engine.adabound import AdaBound 

18from bob.ip.binseg.models.hed import hed 

19from bob.ip.binseg.models.losses import MultiSoftJaccardBCELogitsLoss 

20 

21# config 

22lr = 0.001 

23betas = (0.9, 0.999) 

24eps = 1e-08 

25weight_decay = 0 

26final_lr = 0.1 

27gamma = 1e-3 

28eps = 1e-8 

29amsbound = False 

30 

31scheduler_milestones = [900] 

32scheduler_gamma = 0.1 

33 

34model = hed() 

35 

36# optimizer 

37optimizer = AdaBound( 

38 model.parameters(), 

39 lr=lr, 

40 betas=betas, 

41 final_lr=final_lr, 

42 gamma=gamma, 

43 eps=eps, 

44 weight_decay=weight_decay, 

45 amsbound=amsbound, 

46) 

47# criterion 

48criterion = MultiSoftJaccardBCELogitsLoss(alpha=0.7) 

49 

50# scheduler 

51scheduler = MultiStepLR( 

52 optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma 

53)