bob.ip.binseg.configs.models.lwnet

Little W-Net for image segmentation

The Little W-Net architecture contains roughly around 70k parameters and closely matches (or outperforms) other more complex techniques.

Reference: [GALDRAN-2020]

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

"""Little W-Net for image segmentation

The Little W-Net architecture contains roughly around 70k parameters and
closely matches (or outperforms) other more complex techniques.

Reference: [GALDRAN-2020]_
"""

from torch.optim import Adam
from torch.optim.lr_scheduler import CosineAnnealingLR

from bob.ip.binseg.models.losses import MultiWeightedBCELogitsLoss
from bob.ip.binseg.models.lwnet import lwnet

# config
max_lr = 0.01  # start
min_lr = 1e-08  # valley
cycle = 50  # epochs for a complete scheduling cycle

model = lwnet()

criterion = MultiWeightedBCELogitsLoss()

optimizer = Adam(
    model.parameters(),
    lr=max_lr,
)

scheduler = CosineAnnealingLR(
    optimizer,
    T_max=cycle,
    eta_min=min_lr,
)