make_model.py

import torch
import segmentation_models_pytorch as smp
import numpy as np
import matplotlib.pyplot as plt
from catalyst import dl, metrics, core, contrib, utils
import torch.nn as nn
from skimage.io import imread
import os
from sklearn.model_selection import train_test_split
from catalyst.dl import  CriterionCallback, MetricAggregationCallback
encoder = 'timm-regnety_004'
model = smp.UnetPlusPlus(encoder, classes=1, in_channels=1)
#model.cuda()
learning_rate = 5e-3
encoder_learning_rate = 5e-3 / 10
layerwise_params = {"encoder*": dict(lr=encoder_learning_rate, weight_decay=0.00003)}
model_params = utils.process_model_params(model, layerwise_params=layerwise_params)
base_optimizer = contrib.nn.RAdam(model_params, lr=learning_rate, weight_decay=0.0003)
optimizer = contrib.nn.Lookahead(base_optimizer)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.25, patience=10)
criterion = {
    "dice": DiceLoss(mode='binary'),
    "bce": nn.BCEWithLogitsLoss()
}