图像分割实战-系列教程5：unet医学细胞分割实战3（医学数据集、图像分割、语义分割、unet网络、代码逐行解读）

🍁🍁🍁图像分割实战-系列教程总目录

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本篇文章的代码运行界面均在Pycharm中进行
本篇文章配套的代码资源已经上传

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unet医学细胞分割实战2
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unet医学细胞分割实战4

5、损失函数类

import torch
import torch.nn as nn
import torch.nn.functional as Ftry:from LovaszSoftmax.pytorch.lovasz_losses import lovasz_hinge
except ImportError:pass__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 loss

6 模型架构类

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

6、单个epoch的训练函数解析

def train(config, train_loader, model, criterion, optimizer):avg_meters = {'loss': AverageMeter(), 'iou': AverageMeter()}model.train()pbar = tqdm(total=len(train_loader))for input, target, _ in train_loader:input = input.cuda()target = target.cuda()# compute outputif config['deep_supervision']:outputs = model(input)loss = 0for output in outputs:loss += criterion(output, target)loss /= len(outputs)iou = iou_score(outputs[-1], target)else:output = model(input)loss = criterion(output, target)iou = iou_score(output, target)# compute gradient and do optimizing stepoptimizer.zero_grad()loss.backward()optimizer.step()avg_meters['loss'].update(loss.item(), input.size(0))avg_meters['iou'].update(iou, input.size(0))postfix = OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg),])pbar.set_postfix(postfix)pbar.update(1)pbar.close()return OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg)])

7、单个epoch的验证函数

def validate(config, val_loader, model, criterion):avg_meters = {'loss': AverageMeter(),'iou': AverageMeter()}# switch to evaluate modemodel.eval()with torch.no_grad():pbar = tqdm(total=len(val_loader))for input, target, _ in val_loader:input = input.cuda()target = target.cuda()# compute outputif config['deep_supervision']:outputs = model(input)loss = 0for output in outputs:loss += criterion(output, target)loss /= len(outputs)iou = iou_score(outputs[-1], target)else:output = model(input)loss = criterion(output, target)iou = iou_score(output, target)avg_meters['loss'].update(loss.item(), input.size(0))avg_meters['iou'].update(iou, input.size(0))postfix = OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg),])pbar.set_postfix(postfix)pbar.update(1)pbar.close()return OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg)])