| ''' | |
| Author: Chris Xiao yl.xiao@mail.utoronto.ca | |
| Date: 2023-09-16 18:21:41 | |
| LastEditors: Chris Xiao yl.xiao@mail.utoronto.ca | |
| LastEditTime: 2023-12-12 16:19:16 | |
| FilePath: /EndoSAM/endoSAM/loss.py | |
| Description: loss functions | |
| I Love IU | |
| Copyright (c) 2023 by Chris Xiao yl.xiao@mail.utoronto.ca, All Rights Reserved. | |
| ''' | |
| import torch.nn as nn | |
| from torchmetrics.classification import JaccardIndex | |
| def mse_loss(gt, pred): | |
| mse = nn.MSELoss().to(pred.device) | |
| return mse(pred, gt) | |
| def ce_loss(gt, pred): | |
| ce = nn.CrossEntropyLoss().to(pred.device) | |
| return ce(pred, gt) | |
| def jaccard(gt, pred): | |
| jaccard = JaccardIndex(task='multiclass', num_classes=2, average='micro').to(pred.device) | |
| return jaccard(pred, gt) |