Created
December 18, 2020 05:28
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| 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() | |
| } |
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