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| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from math import exp | |
| class FocalLoss(nn.Module): | |
| """ | |
| copy from: https://github.com/Hsuxu/Loss_ToolBox-PyTorch/blob/master/FocalLoss/FocalLoss.py | |
| This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in | |
| 'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)' | |
| Focal_Loss= -1*alpha*(1-pt)*log(pt) | |
| :param alpha: (tensor) 3D or 4D the scalar factor for this criterion | |
| :param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more | |
| focus on hard misclassified example | |
| :param smooth: (float,double) smooth value when cross entropy | |
| :param balance_index: (int) balance class index, should be specific when alpha is float | |
| :param size_average: (bool, optional) By default, the losses are averaged over each loss element in the batch. | |
| """ | |
| def __init__(self, apply_nonlin=None, alpha=None, gamma=2, balance_index=0, smooth=1e-5, size_average=True): | |
| super(FocalLoss, self).__init__() | |
| self.apply_nonlin = apply_nonlin | |
| self.alpha = alpha | |
| self.gamma = gamma | |
| self.balance_index = balance_index | |
| self.smooth = smooth | |
| self.size_average = size_average | |
| if self.smooth is not None: | |
| if self.smooth < 0 or self.smooth > 1.0: | |
| raise ValueError('smooth value should be in [0,1]') | |
| def forward(self, logit, target): | |
| # logit: [B, 2, 224, 224] | |
| # target:[B, 1, 224, 224] | |
| if self.apply_nonlin is not None: | |
| logit = self.apply_nonlin(logit) | |
| # 2 | |
| num_class = logit.shape[1] | |
| if logit.dim() > 2: | |
| # N,C,d1,d2 -> N,C,m (m=d1*d2*...) | |
| # [B, 2, 224*224] | |
| logit = logit.view(logit.size(0), logit.size(1), -1) | |
| # [B, 224*224, 2] | |
| logit = logit.permute(0, 2, 1).contiguous() | |
| # [B*224*224, 2] | |
| logit = logit.view(-1, logit.size(-1)) | |
| target = torch.squeeze(target, 1) | |
| # [B*224*224, 1] | |
| target = target.view(-1, 1) | |
| alpha = self.alpha | |
| if alpha is None: | |
| alpha = torch.ones(num_class, 1) | |
| elif isinstance(alpha, (list, np.ndarray)): | |
| assert len(alpha) == num_class | |
| alpha = torch.FloatTensor(alpha).view(num_class, 1) | |
| alpha = alpha / alpha.sum() | |
| elif isinstance(alpha, float): | |
| alpha = torch.ones(num_class, 1) | |
| alpha = alpha * (1 - self.alpha) | |
| alpha[self.balance_index] = self.alpha | |
| else: | |
| raise TypeError('Not support alpha type') | |
| if alpha.device != logit.device: | |
| alpha = alpha.to(logit.device) | |
| # [B*224*224, 1] | |
| idx = target.cpu().long() | |
| # [B*224*224, 2] | |
| one_hot_key = torch.FloatTensor(target.size(0), num_class).zero_() | |
| one_hot_key = one_hot_key.scatter_(1, idx, 1) | |
| if one_hot_key.device != logit.device: | |
| one_hot_key = one_hot_key.to(logit.device) | |
| if self.smooth: | |
| one_hot_key = torch.clamp( | |
| one_hot_key, self.smooth / (num_class - 1), 1.0 - self.smooth) | |
| pt = (one_hot_key * logit).sum(1) + self.smooth | |
| logpt = pt.log() | |
| gamma = self.gamma | |
| alpha = alpha[idx] | |
| alpha = torch.squeeze(alpha) | |
| loss = -1 * alpha * torch.pow((1 - pt), gamma) * logpt | |
| if self.size_average: | |
| loss = loss.mean() | |
| return loss | |
| class BinaryDiceLoss(nn.Module): | |
| def __init__(self): | |
| super(BinaryDiceLoss, self).__init__() | |
| def forward(self, input, targets): | |
| # 获取每个批次的大小 N | |
| N = targets.size()[0] | |
| # 平滑变量 | |
| smooth = 1 | |
| # 将宽高 reshape 到同一纬度 | |
| input_flat = input.view(N, -1) | |
| targets_flat = targets.view(N, -1) | |
| # 计算交集 | |
| intersection = input_flat * targets_flat | |
| N_dice_eff = (2 * intersection.sum(1) + smooth) / (input_flat.sum(1) + targets_flat.sum(1) + smooth) | |
| # 计算一个批次中平均每张图的损失 | |
| loss = 1 - N_dice_eff.sum() / N | |
| return loss |