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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : [email protected] | |
| @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: # py3k | |
| 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) # B * H * W, C = P, 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) | |