bob.ip.binseg.configs.models.resunet

Residual U-Net for image segmentation

A semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters however better performance.

Reference: [ZHANG-2017]

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Residual U-Net for image segmentation

A semantic segmentation neural network which combines the strengths of residual
learning and U-Net is proposed for road area extraction.  The network is built
with residual units and has similar architecture to that of U-Net. The benefits
of this model is two-fold: first, residual units ease training of deep
networks. Second, the rich skip connections within the network could facilitate
information propagation, allowing us to design networks with fewer parameters
however better performance.

Reference: [ZHANG-2017]_
"""

from torch.optim.lr_scheduler import MultiStepLR

from bob.ip.binseg.engine.adabound import AdaBound
from bob.ip.binseg.models.losses import SoftJaccardBCELogitsLoss
from bob.ip.binseg.models.resunet import resunet50

# config
lr = 0.001
betas = (0.9, 0.999)
eps = 1e-08
weight_decay = 0
final_lr = 0.1
gamma = 1e-3
eps = 1e-8
amsbound = False

scheduler_milestones = [900]
scheduler_gamma = 0.1

model = resunet50()

# optimizer
optimizer = AdaBound(
    model.parameters(),
    lr=lr,
    betas=betas,
    final_lr=final_lr,
    gamma=gamma,
    eps=eps,
    weight_decay=weight_decay,
    amsbound=amsbound,
)

# criterion
criterion = SoftJaccardBCELogitsLoss(alpha=0.7)

# scheduler
scheduler = MultiStepLR(
    optimizer, milestones=scheduler_milestones, gamma=scheduler_gamma
)