模型

import torch
from torch import nn__all__ = ['UNet', 'NestedUNet']class VGGBlock(nn.Module):def __init__(self, in_channels, middle_channels, out_channels):super().__init__()self.relu = nn.ReLU(inplace=True)self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)self.bn1 = nn.BatchNorm2d(middle_channels)self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)self.bn2 = nn.BatchNorm2d(out_channels)def forward(self, x):out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)return outclass UNet(nn.Module):def __init__(self, num_classes, input_channels=3, **kwargs):super().__init__()nb_filter = [32, 64, 128, 256, 512]self.pool = nn.MaxPool2d(2, 2)self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)#scale_factor:放大的倍数  插值self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)def forward(self, input):x0_0 = self.conv0_0(input)x1_0 = self.conv1_0(self.pool(x0_0))x2_0 = self.conv2_0(self.pool(x1_0))x3_0 = self.conv3_0(self.pool(x2_0))x4_0 = self.conv4_0(self.pool(x3_0))x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))output = self.final(x0_4)return outputclass NestedUNet(nn.Module):def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):super().__init__()nb_filter = [32, 64, 128, 256, 512]self.deep_supervision = deep_supervisionself.pool = nn.MaxPool2d(2, 2)self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2])self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1])self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0])if self.deep_supervision:self.final1 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final2 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final3 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final4 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)else:self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)def forward(self, input):# print('input:',input.shape)x0_0 = self.conv0_0(input)# print('x0_0:',x0_0.shape)x1_0 = self.conv1_0(self.pool(x0_0))# print('x1_0:',x1_0.shape)x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))# print('x0_1:',x0_1.shape)x2_0 = self.conv2_0(self.pool(x1_0))# print('x2_0:',x2_0.shape)x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))# print('x1_1:',x1_1.shape)x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))# print('x0_2:',x0_2.shape)x3_0 = self.conv3_0(self.pool(x2_0))# print('x3_0:',x3_0.shape)x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))# print('x2_1:',x2_1.shape)x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))# print('x1_2:',x1_2.shape)x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))# print('x0_3:',x0_3.shape)x4_0 = self.conv4_0(self.pool(x3_0))# print('x4_0:',x4_0.shape)x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))# print('x3_1:',x3_1.shape)x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))# print('x2_2:',x2_2.shape)x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))# print('x1_3:',x1_3.shape)x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))# print('x0_4:',x0_4.shape)if self.deep_supervision:output1 = self.final1(x0_1)output2 = self.final2(x0_2)output3 = self.final3(x0_3)output4 = self.final4(x0_4)return [output1, output2, output3, output4]else:output = self.final(x0_4)return output

损失函数

BCEDiceLoss：

• 这个损失函数结合了二元交叉熵损失（Binary Cross Entropy, BCE）和 Dice Loss。
• BCE 于衡量模型输出和真实标签之间的二值化像素级别匹配情况。
• Dice Loss 用于量模型输出和真实标签之间的相似度，但这里采用了一种稍微不同的计算方式，即将 Dice Loss 作为 1 减去 Dice 相似度的平均值，这样得到的损失越小，说明相似度越高。

LovaszHingeLoss：

• 这个损失函数采用的是 Lovasz-Hinge Loss，它是一种用于处理不平衡数据集的损失函数，尤其适用于像素级别的分类任务。
• Lovasz-Hinge Loss 能够更好地处理类别不平衡和边界情况，相比于交叉熵损失，在处理不平衡数据时更加稳定。
  LovaszHingeLoss相关介绍

测试用例：

lovasz_losses.py 相关内容

"""
Lovasz-Softmax and Jaccard hinge loss in PyTorch
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""from __future__ import print_function, divisionimport torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as nptry:from itertools import ifilterfalse
except ImportError:  # py3kfrom itertools import filterfalse as ifilterfalsedef lovasz_grad(gt_sorted):"""Computes gradient of the Lovasz extension w.r.t sorted errorsSee Alg. 1 in paper"""p = len(gt_sorted)gts = gt_sorted.sum()intersection = gts - gt_sorted.float().cumsum(0)union = gts + (1 - gt_sorted).float().cumsum(0)jaccard = 1. - intersection / unionif p > 1:  # cover 1-pixel casejaccard[1:p] = jaccard[1:p] - jaccard[0:-1]return jaccarddef iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):"""IoU for foreground classbinary: 1 foreground, 0 background"""if not per_image:preds, labels = (preds,), (labels,)ious = []for pred, label in zip(preds, labels):intersection = ((label == 1) & (pred == 1)).sum()union = ((label == 1) | ((pred == 1) & (label != ignore))).sum()if not union:iou = EMPTYelse:iou = float(intersection) / float(union)ious.append(iou)iou = mean(ious)  # mean accross images if per_imagereturn 100 * ioudef iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):"""Array of IoU for each (non ignored) class"""if not per_image:preds, labels = (preds,), (labels,)ious = []for pred, label in zip(preds, labels):iou = []for i in range(C):if i != ignore:  # The ignored label is sometimes among predicted classes (ENet - CityScapes)intersection = ((label == i) & (pred == i)).sum()union = ((label == i) | ((pred == i) & (label != ignore))).sum()if not union:iou.append(EMPTY)else:iou.append(float(intersection) / float(union))ious.append(iou)ious = [mean(iou) for iou in zip(*ious)]  # mean accross images if per_imagereturn 100 * np.array(ious)# --------------------------- BINARY LOSSES ---------------------------def lovasz_hinge(logits, labels, per_image=True, ignore=None):"""Binary Lovasz hinge losslogits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)per_image: compute the loss per image instead of per batchignore: void class id"""if per_image:loss = mean(lovasz_hinge_flat(*flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore))for log, lab in zip(logits, labels))else:loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))return lossdef lovasz_hinge_flat(logits, labels):"""Binary Lovasz hinge losslogits: [P] Variable, logits at each prediction (between -\infty and +\infty)labels: [P] Tensor, binary ground truth labels (0 or 1)ignore: label to ignore"""if len(labels) == 0:# only void pixels, the gradients should be 0return logits.sum() * 0.signs = 2. * labels.float() - 1.errors = (1. - logits * Variable(signs))errors_sorted, perm = torch.sort(errors, dim=0, descending=True)perm = perm.datagt_sorted = labels[perm]grad = lovasz_grad(gt_sorted)loss = torch.dot(F.relu(errors_sorted), Variable(grad))return lossdef flatten_binary_scores(scores, labels, ignore=None):"""Flattens predictions in the batch (binary case)Remove labels equal to 'ignore'"""scores = scores.view(-1)labels = labels.view(-1)if ignore is None:return scores, labelsvalid = (labels != ignore)vscores = scores[valid]vlabels = labels[valid]return vscores, vlabelsclass StableBCELoss(torch.nn.modules.Module):def __init__(self):super(StableBCELoss, self).__init__()def forward(self, input, target):neg_abs = - input.abs()loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()return loss.mean()def binary_xloss(logits, labels, ignore=None):"""Binary Cross entropy losslogits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)ignore: void class id"""logits, labels = flatten_binary_scores(logits, labels, ignore)loss = StableBCELoss()(logits, Variable(labels.float()))return loss# --------------------------- MULTICLASS LOSSES ---------------------------def lovasz_softmax(probas, labels, classes='present', per_image=False, ignore=None):"""Multi-class Lovasz-Softmax lossprobas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).Interpreted as binary (sigmoid) output with outputs of size [B, H, W].labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.per_image: compute the loss per image instead of per batchignore: void class labels"""if per_image:loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes)for prob, lab in zip(probas, labels))else:loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), classes=classes)return lossdef lovasz_softmax_flat(probas, labels, classes='present'):"""Multi-class Lovasz-Softmax lossprobas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)labels: [P] Tensor, ground truth labels (between 0 and C - 1)classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average."""if probas.numel() == 0:# only void pixels, the gradients should be 0return probas * 0.C = probas.size(1)losses = []class_to_sum = list(range(C)) if classes in ['all', 'present'] else classesfor c in class_to_sum:fg = (labels == c).float()  # foreground for class cif (classes == 'present' and fg.sum() == 0):continueif C == 1:if len(classes) > 1:raise ValueError('Sigmoid output possible only with 1 class')class_pred = probas[:, 0]else:class_pred = probas[:, c]errors = (Variable(fg) - class_pred).abs()errors_sorted, perm = torch.sort(errors, 0, descending=True)perm = perm.datafg_sorted = fg[perm]losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))return mean(losses)def flatten_probas(probas, labels, ignore=None):"""Flattens predictions in the batch"""if probas.dim() == 3:# assumes output of a sigmoid layerB, H, W = probas.size()probas = probas.view(B, 1, H, W)B, C, H, W = probas.size()probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C)  # B * H * W, C = P, Clabels = labels.view(-1)if ignore is None:return probas, labelsvalid = (labels != ignore)vprobas = probas[valid.nonzero().squeeze()]vlabels = labels[valid]return vprobas, vlabelsdef xloss(logits, labels, ignore=None):"""Cross entropy loss"""return F.cross_entropy(logits, Variable(labels), ignore_index=255)# --------------------------- HELPER FUNCTIONS ---------------------------
def isnan(x):return x != xdef mean(l, ignore_nan=False, empty=0):"""nanmean compatible with generators."""l = iter(l)if ignore_nan:l = ifilterfalse(isnan, l)try:n = 1acc = next(l)except StopIteration:if empty == 'raise':raise ValueError('Empty mean')return emptyfor n, v in enumerate(l, 2):acc += vif n == 1:return accreturn acc / n

import torch
import torch.nn as nn
import torch.nn.functional as F
from lovasz_losses import lovasz_hinge# __all__ = ['BCEDiceLoss', 'LovaszHingeLoss']class BCEDiceLoss(nn.Module):def __init__(self):super().__init__()def forward(self, input, target):bce = F.binary_cross_entropy_with_logits(input, target)smooth = 1e-5input = torch.sigmoid(input)num = target.size(0)input = input.view(num, -1)target = target.view(num, -1)intersection = (input * target)dice = (2. * intersection.sum(1) + smooth) / (input.sum(1) + target.sum(1) + smooth)dice = 1 - dice.sum() / numreturn 0.5 * bce + diceclass LovaszHingeLoss(nn.Module):def __init__(self):super().__init__()def forward(self, input, target):input = input.squeeze(1)target = target.squeeze(1)loss = lovasz_hinge(input, target, per_image=True)return lossif __name__ == '__main__':import torch# 假设模型输出和真实标签都是二值化的图像，大小为(1, H, W)output = torch.tensor([[[0.3, 0.7], [0.8, 0.6]]])  # 模型输出# output = output.round().long()target = torch.tensor([[[0, 1], [1, 0]]],dtype=torch.float)  # 真实标签bce_dice_loss = BCEDiceLoss()bce_dice = bce_dice_loss(output, target)lovasz_hinge_loss = LovaszHingeLoss()lovasz_hinge = lovasz_hinge_loss(output, target)print("BCE Dice Loss:", bce_dice)print("Lovasz Hinge Loss:", lovasz_hinge)

原理解释和数学公式：

BCEDiceLoss 原理：

• BCE Dice Loss 结合了二元交叉熵损失和 Dice Loss。其数学表达式如下：

$$BCE\_Dice\_Loss=0.5\times BCE+(1-Dice)$$

其中，$BCE$ 表示二元交叉熵损失，$Dice$ 表示 Dice 相似度。这个损失函数的目标是最小化二元交叉熵损失和最大化 Dice 相似度，以达到更好的模型训练效果。

LovaszHingeLoss 原理：

• Lovasz-Hinge Loss 是一种非平衡数据集上的损失函数，用于像素级别的分类任务。其数学表达式如下：

$$Lovasz\_Hinge\_Loss=\text{lovasz\_hinge}(input,target)$$

这里的 $\text{lovasz\_hinge}$ 是一个函数，用于计算 Lovasz-Hinge Loss。

训练

评估函数

metrics.py

import numpy as np
import torch
import torch.nn.functional as Fdef iou_score(output, target):smooth = 1e-5if torch.is_tensor(output):output = torch.sigmoid(output).data.cpu().numpy()if torch.is_tensor(target):target = target.data.cpu().numpy()output_ = output > 0.5target_ = target > 0.5intersection = (output_ & target_).sum()union = (output_ | target_).sum()return (intersection + smooth) / (union + smooth)def dice_coef(output, target):smooth = 1e-5output = torch.sigmoid(output).view(-1).data.cpu().numpy()target = target.view(-1).data.cpu().numpy()intersection = (output * target).sum()return (2. * intersection + smooth) / \(output.sum() + target.sum() + smooth)if __name__ == '__main__':import numpy as npimport torch# 假设模型输出和真实标签都是二值化的图像，大小为(1, H, W)output = torch.tensor([[[0.3, 0.7], [0.8, 0.6]]])  # 模型输出target = torch.tensor([[[0, 1], [1, 0]]])  # 真实标签iou = iou_score(output, target)dice = dice_coef(output, target)print("IoU Score:", iou)print("Dice Coefficient:", dice)

IoU（Intersection over Union）评分函数原理

IoU 是一种常用的图像分割评价指标，它衡量了模型输出与真实标签之间的重程度。其数学公式如下：

$$IoU=\frac{TP}{TP+FP+FN}$$

其中，$TP$ 表示真正例（模型正确预测为正样本的数量），$FP$ 表示假正例（模型错误预测为正样本的数量），$FN$ 表示假负例（模型错误预测为负样本的数量）。

Dice Coefficient评分函数原理

Dice Coefficient 也是一种常用的图像分割评价指标，衡量模型输出和真实标签之间的相似度。其数学公式如下：

$$Dice=\frac{2\times TP}{2\times TP+FP+FN}$$

其中，$TP$ 表示真正例，$FP$ 表示假正例，$FN$ 表示假负例，与 IoU 公式中的定义相同。

这两个评分函数都以模型的真正例为分子，而分母则是真正例、假正例和假负例的总和，以此来衡量模型预测结果与真实标签的相似程度。公式中的平滑因子用于避免分母为零的情况，增加了数值稳定性。