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loss.py
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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):
if self.apply_nonlin is not None:
logit = self.apply_nonlin(logit)
num_class = logit.shape[1]
if logit.dim() > 2:
# N,C,d1,d2 -> N,C,m (m=d1*d2*...)
logit = logit.view(logit.size(0), logit.size(1), -1)
logit = logit.permute(0, 2, 1).contiguous()
logit = logit.view(-1, logit.size(-1))
target = torch.squeeze(target, 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)
idx = target.cpu().long()
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
class ConADLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self, contrast_mode='all',random_anchors=10):
super(ConADLoss, self).__init__()
assert contrast_mode in ['all', 'mean', 'random']
self.contrast_mode = contrast_mode
self.random_anchors = random_anchors
def forward(self, features, labels):
"""Compute loss for model. If both `labels` and `mask` are None,
it degenerates to SimCLR unsupervised loss:
https://arxiv.org/pdf/2002.05709.pdf
Args:
features: hidden vector of shape [bsz, C, ...].
labels: ground truth of shape [bsz, 1, ...]., where 1 denotes to abnormal, and 0 denotes to normal
Returns:
A loss scalar.
"""
device = (torch.device('cuda')
if features.is_cuda
else torch.device('cpu'))
if len(features.shape) != len(labels.shape):
raise ValueError('`features` needs to have the same dimensions with labels')
if len(features.shape) < 3:
raise ValueError('`features` needs to be [bsz, C, ...],'
'at least 3 dimensions are required')
if len(features.shape) > 3:
features = features.view(features.shape[0], features.shape[1], -1)
labels = labels.view(labels.shape[0], labels.shape[1], -1)
labels = labels.squeeze()
batch_size = features.shape[0]
C = features.shape[1]
normal_feats = features[:, :, labels == 0]
abnormal_feats = features[:, :, labels == 1]
normal_feats = normal_feats.permute((1, 0, 2)).contiguous().view(C, -1)
abnormal_feats = abnormal_feats.permute((1, 0, 2)).contiguous().view(C, -1)
contrast_count = normal_feats.shape[1]
contrast_feature = normal_feats
if self.contrast_mode == 'mean':
anchor_feature = torch.mean(normal_feats, dim=1)
anchor_feature = F.normalize(anchor_feature, dim=0, p=2)
anchor_count = 1
elif self.contrast_mode == 'all':
anchor_feature = contrast_feature
anchor_count = contrast_count
elif self.contrast_mode == 'random':
dim_to_sample = 1
num_samples = min(self.random_anchors, contrast_count)
permuted_indices = torch.randperm(normal_feats.size(dim_to_sample)).to(normal_feats.device)
selected_indices = permuted_indices[:num_samples]
anchor_feature = normal_feats.index_select(dim_to_sample, selected_indices)
else:
raise ValueError('Unknown mode: {}'.format(self.contrast_mode))
# compute logits
# maximize similarity
anchor_dot_normal = torch.matmul(anchor_feature.T, normal_feats).mean()
# minimize similarity
anchor_dot_abnormal = torch.matmul(anchor_feature.T, abnormal_feats).mean()
loss = 0
if normal_feats.shape[1] > 0:
loss -= anchor_dot_normal
if abnormal_feats.shape[1] > 0:
loss += anchor_dot_abnormal
loss = torch.exp(loss)
return loss