| |
| |
|
|
| """ |
| @Author : Peike Li |
| @Contact : peike.li@yahoo.com |
| @File : soft_dice_loss.py |
| @Time : 8/13/19 5:09 PM |
| @Desc : |
| @License : This source code is licensed under the license found in the |
| LICENSE file in the root directory of this source tree. |
| """ |
|
|
| from __future__ import print_function, division |
|
|
| import torch |
| import torch.nn.functional as F |
| from torch import nn |
|
|
| try: |
| from itertools import ifilterfalse |
| except ImportError: |
| from itertools import filterfalse as ifilterfalse |
|
|
|
|
| def tversky_loss(probas, labels, alpha=0.5, beta=0.5, epsilon=1e-6): |
| ''' |
| Tversky loss function. |
| probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) |
| labels: [P] Tensor, ground truth labels (between 0 and C - 1) |
| |
| Same as soft dice loss when alpha=beta=0.5. |
| Same as Jaccord loss when alpha=beta=1.0. |
| See `Tversky loss function for image segmentation using 3D fully convolutional deep networks` |
| https://arxiv.org/pdf/1706.05721.pdf |
| ''' |
| C = probas.size(1) |
| losses = [] |
| for c in list(range(C)): |
| fg = (labels == c).float() |
| if fg.sum() == 0: |
| continue |
| class_pred = probas[:, c] |
| p0 = class_pred |
| p1 = 1 - class_pred |
| g0 = fg |
| g1 = 1 - fg |
| numerator = torch.sum(p0 * g0) |
| denominator = numerator + alpha * torch.sum(p0 * g1) + beta * torch.sum(p1 * g0) |
| losses.append(1 - ((numerator) / (denominator + epsilon))) |
| return mean(losses) |
|
|
|
|
| def flatten_probas(probas, labels, ignore=255): |
| """ |
| Flattens predictions in the batch |
| """ |
| B, C, H, W = probas.size() |
| probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) |
| labels = labels.view(-1) |
| if ignore is None: |
| return probas, labels |
| valid = (labels != ignore) |
| vprobas = probas[valid.nonzero().squeeze()] |
| vlabels = labels[valid] |
| return vprobas, vlabels |
|
|
|
|
| def isnan(x): |
| return x != x |
|
|
|
|
| def mean(l, ignore_nan=False, empty=0): |
| """ |
| nanmean compatible with generators. |
| """ |
| l = iter(l) |
| if ignore_nan: |
| l = ifilterfalse(isnan, l) |
| try: |
| n = 1 |
| acc = next(l) |
| except StopIteration: |
| if empty == 'raise': |
| raise ValueError('Empty mean') |
| return empty |
| for n, v in enumerate(l, 2): |
| acc += v |
| if n == 1: |
| return acc |
| return acc / n |
|
|
|
|
| class SoftDiceLoss(nn.Module): |
| def __init__(self, ignore_index=255): |
| super(SoftDiceLoss, self).__init__() |
| self.ignore_index = ignore_index |
|
|
| def forward(self, pred, label): |
| pred = F.softmax(pred, dim=1) |
| return tversky_loss(*flatten_probas(pred, label, ignore=self.ignore_index), alpha=0.5, beta=0.5) |
|
|
|
|
| class SoftJaccordLoss(nn.Module): |
| def __init__(self, ignore_index=255): |
| super(SoftJaccordLoss, self).__init__() |
| self.ignore_index = ignore_index |
|
|
| def forward(self, pred, label): |
| pred = F.softmax(pred, dim=1) |
| return tversky_loss(*flatten_probas(pred, label, ignore=self.ignore_index), alpha=1.0, beta=1.0) |
|
|
