语义分割系列6-Unet++（pytorch实现）(语义分割用途)

语义分割系列6-Unet++（pytorch实现）

目录

Unet++网络

Dense connection

deep supervision

模型复现

Unet++

数据集准备

模型训练

训练结果

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Unet++：《UNet++: A Nested U-Net Architecture for Medical Image Segmentation》

作者对Unet和Unet++的理解：研习U-Net 

延续前文：语义分割系列2-Unet（pytorch实现）

本文将介绍Unet++网络，在pytorch框架上复现Unet++，并在Camvid数据集上进行训练。

Unet++网络Dense connection

Unet++继承了Unet的结构，同时又借鉴了DenseNet的稠密连接方式（图1中各种分支）。

图1 Unet++网络结构

作者通过各层之间的稠密连接，互相连接起来，就像Denset那样，前前后后每一个模块互相作用，每一个模块都能看到彼此，那对彼此互相熟悉，分割效果自然就会变好。

在实际分割中，一次次的下采样自然会丢掉一些细节特征，在Unet中是使用skip connection来恢复这些细节，但能否做的更好呢？Unet++就给出了答案，这种稠密连接的方式，每一层都尽量多的保存这种细节信息和全局信息，一层层之间架起桥梁互相沟通，最后共享给最后一层，实现全局信息和局部信息的保留和重构。 

deep supervision

当然，简单的将各个模块连接起来是会实现很好的效果。而我们又能发现，一个Unet++其实是很多个不同深度的Unet++叠加。那么，每一个深度的Unet++是不是就都可以输出一个loss？答案自然是可以的。

所以，作者提出了deep supervision，也就是监督每一个深度的Unet++的输出，通过一定的方式来叠加Loss（比如加权的方式），这样就得到了一个经由1、2、3、4层的Unet++的加权Loss（图2 不同深度Unet++融合）。

图2 不同深度Unet++融合

那么，deep supervision又有什么用呢？-剪枝

既然Unet++由多个不同深度的Unet++叠加起来，那么随意去掉一层，前向传播的梯度不会受到任何变化，但你发现Unet++第三个输出的效果和第四个输出效果差不多时，那就可以毫不犹豫删去4层深度的Unet++。比如，直接删去图3中棕色部分，就可以实现剪枝。这样，就得到了更加轻量化的网络。

图3 剪枝模型模型复现Unet++

为了更直观一些，我把代码中的所有符号都和网络结构中对应上了。

import torchimport torch.nn as nnclass ContinusParalleConv(nn.Module): # 一个连续的卷积模块，包含BatchNorm 在前 和 在后 两种模式 def __init__(self, in_channels, out_channels, pre_Batch_Norm = True): super(ContinusParalleConv, self).__init__() self.in_channels = in_channels self.out_channels = out_channels if pre_Batch_Norm: self.Conv_forward = nn.Sequential( nn.BatchNorm2d(self.in_channels), nn.ReLU(), nn.Conv2d(self.in_channels, self.out_channels, 3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(), nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)) else: self.Conv_forward = nn.Sequential( nn.Conv2d(self.in_channels, self.out_channels, 3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(), nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1), nn.BatchNorm2d(self.out_channels), nn.ReLU()) def forward(self, x): x = self.Conv_forward(x) return xclass UnetPlusPlus(nn.Module): def __init__(self, num_classes, deep_supervision=False): super(UnetPlusPlus, self).__init__() self.num_classes = num_classes self.deep_supervision = deep_supervision self.filters = [64, 128, 256, 512, 1024] self.CONV3_1 = ContinusParalleConv(512*2, 512, pre_Batch_Norm = True) self.CONV2_2 = ContinusParalleConv(256*3, 256, pre_Batch_Norm = True) self.CONV2_1 = ContinusParalleConv(256*2, 256, pre_Batch_Norm = True) self.CONV1_1 = ContinusParalleConv(128*2, 128, pre_Batch_Norm = True) self.CONV1_2 = ContinusParalleConv(128*3, 128, pre_Batch_Norm = True) self.CONV1_3 = ContinusParalleConv(128*4, 128, pre_Batch_Norm = True) self.CONV0_1 = ContinusParalleConv(64*2, 64, pre_Batch_Norm = True) self.CONV0_2 = ContinusParalleConv(64*3, 64, pre_Batch_Norm = True) self.CONV0_3 = ContinusParalleConv(64*4, 64, pre_Batch_Norm = True) self.CONV0_4 = ContinusParalleConv(64*5, 64, pre_Batch_Norm = True) self.stage_0 = ContinusParalleConv(3, 64, pre_Batch_Norm = False) self.stage_1 = ContinusParalleConv(64, 128, pre_Batch_Norm = False) self.stage_2 = ContinusParalleConv(128, 256, pre_Batch_Norm = False) self.stage_3 = ContinusParalleConv(256, 512, pre_Batch_Norm = False) self.stage_4 = ContinusParalleConv(512, 1024, pre_Batch_Norm = False) self.pool = nn.MaxPool2d(2) self.upsample_3_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=4, stride=2, padding=1) self.upsample_2_1 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1) self.upsample_2_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1) self.upsample_1_1 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1) self.upsample_1_2 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1) self.upsample_1_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1) self.upsample_0_1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1) self.upsample_0_2 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1) self.upsample_0_3 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1) self.upsample_0_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1) # 分割头 self.final_super_0_1 = nn.Sequential( nn.BatchNorm2d(64), nn.ReLU(), nn.Conv2d(64, self.num_classes, 3, padding=1), ) self.final_super_0_2 = nn.Sequential( nn.BatchNorm2d(64), nn.ReLU(), nn.Conv2d(64, self.num_classes, 3, padding=1), ) self.final_super_0_3 = nn.Sequential( nn.BatchNorm2d(64), nn.ReLU(), nn.Conv2d(64, self.num_classes, 3, padding=1), ) self.final_super_0_4 = nn.Sequential( nn.BatchNorm2d(64), nn.ReLU(), nn.Conv2d(64, self.num_classes, 3, padding=1), ) def forward(self, x): x_0_0 = self.stage_0(x) x_1_0 = self.stage_1(self.pool(x_0_0)) x_2_0 = self.stage_2(self.pool(x_1_0)) x_3_0 = self.stage_3(self.pool(x_2_0)) x_4_0 = self.stage_4(self.pool(x_3_0)) x_0_1 = torch.cat([self.upsample_0_1(x_1_0) , x_0_0], 1) x_0_1 = self.CONV0_1(x_0_1) x_1_1 = torch.cat([self.upsample_1_1(x_2_0), x_1_0], 1) x_1_1 = self.CONV1_1(x_1_1) x_2_1 = torch.cat([self.upsample_2_1(x_3_0), x_2_0], 1) x_2_1 = self.CONV2_1(x_2_1) x_3_1 = torch.cat([self.upsample_3_1(x_4_0), x_3_0], 1) x_3_1 = self.CONV3_1(x_3_1) x_2_2 = torch.cat([self.upsample_2_2(x_3_1), x_2_0, x_2_1], 1) x_2_2 = self.CONV2_2(x_2_2) x_1_2 = torch.cat([self.upsample_1_2(x_2_1), x_1_0, x_1_1], 1) x_1_2 = self.CONV1_2(x_1_2) x_1_3 = torch.cat([self.upsample_1_3(x_2_2), x_1_0, x_1_1, x_1_2], 1) x_1_3 = self.CONV1_3(x_1_3) x_0_2 = torch.cat([self.upsample_0_2(x_1_1), x_0_0, x_0_1], 1) x_0_2 = self.CONV0_2(x_0_2) x_0_3 = torch.cat([self.upsample_0_3(x_1_2), x_0_0, x_0_1, x_0_2], 1) x_0_3 = self.CONV0_3(x_0_3) x_0_4 = torch.cat([self.upsample_0_4(x_1_3), x_0_0, x_0_1, x_0_2, x_0_3], 1) x_0_4 = self.CONV0_4(x_0_4) if self.deep_supervision: out_put1 = self.final_super_0_1(x_0_1) out_put2 = self.final_super_0_2(x_0_2) out_put3 = self.final_super_0_3(x_0_3) out_put4 = self.final_super_0_4(x_0_4) return [out_put1, out_put2, out_put3, out_put4] else: return self.final_super_0_4(x_0_4)if __name__ == "__main__": print("deep_supervision: False") deep_supervision = False device = torch.device('cpu') inputs = torch.randn((1, 3, 224, 224)).to(device) model = UnetPlusPlus(num_classes=3, deep_supervision=deep_supervision).to(device) outputs = model(inputs) print(outputs.shape) print("deep_supervision: True") deep_supervision = True model = UnetPlusPlus(num_classes=3, deep_supervision=deep_supervision).to(device) outputs = model(inputs) for out in outputs: print(out.shape)

测试结果如下 

数据集准备

数据集使用Camvid数据集，可在CamVid数据集的创建和使用-pytorch中参考构建方法。

# 导入库import osos.environ['CUDA_VISIBLE_DEVICES'] = '0'import torchimport torch.nn as nnimport torch.optim as optimimport torch.nn.functional as Ffrom torch import optimfrom torch.utils.data import Dataset, DataLoader, random_splitfrom tqdm import tqdmimport warningswarnings.filterwarnings("ignore")import os.path as ospimport matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport albumentations as Afrom albumentations.pytorch.transforms import ToTensorV2torch.manual_seed(17)# 自定义数据集CamVidDatasetclass CamVidDataset(torch.utils.data.Dataset): """CamVid Dataset. Read images, apply augmentation and preprocessing transformations. Args: images_dir (str): path to images folder masks_dir (str): path to segmentation masks folder class_values (list): values of classes to extract from segmentation mask augmentation (albumentations.Compose): data transfromation pipeline (e.g. flip, scale, etc.) preprocessing (albumentations.Compose): data preprocessing (e.g. noralization, shape manipulation, etc.) """ def __init__(self, images_dir, masks_dir): self.transform = A.Compose([ A.Resize(224, 224), A.HorizontalFlip(), A.VerticalFlip(), A.Normalize(), ToTensorV2(), ]) self.ids = os.listdir(images_dir) self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids] self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids] def __getitem__(self, i): # read data image = np.array(Image.open(self.images_fps[i]).convert('RGB')) mask = np.array( Image.open(self.masks_fps[i]).convert('RGB')) image = self.transform(image=image,mask=mask) return image['image'], image['mask'][:,:,0] def __len__(self): return len(self.ids)# 设置数据集路径DATA_DIR = r'dataset\camvid' # 根据自己的路径来设置x_train_dir = os.path.join(DATA_DIR, 'train_images')y_train_dir = os.path.join(DATA_DIR, 'train_labels')x_valid_dir = os.path.join(DATA_DIR, 'valid_images')y_valid_dir = os.path.join(DATA_DIR, 'valid_labels')train_dataset = CamVidDataset( x_train_dir, y_train_dir, )val_dataset = CamVidDataset( x_valid_dir, y_valid_dir, )train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True,drop_last=True)val_loader = DataLoader(val_dataset, batch_size=8, shuffle=True,drop_last=True)模型训练model = UnetPlusPlus(num_classes=33).cuda()#载入预训练模型#model.load_state_dict(torch.load(r"checkpoints/Unet++_25.pth"),strict=False)from d2l import torch as d2lfrom tqdm import tqdmimport pandas as pd#损失函数选用多分类交叉熵损失函数lossf = nn.CrossEntropyLoss(ignore_index=255)#选用adam优化器来训练optimizer = optim.SGD(model.parameters(),lr=0.1)scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1, last_epoch=-1)#训练50轮epochs_num = 50def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,scheduler, devices=d2l.try_all_gpus()): timer, num_batches = d2l.Timer(), len(train_iter) animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1], legend=['train loss', 'train acc', 'test acc']) net = nn.DataParallel(net, device_ids=devices).to(devices[0]) loss_list = [] train_acc_list = [] test_acc_list = [] epochs_list = [] time_list = [] for epoch in range(num_epochs): # Sum of training loss, sum of training accuracy, no. of examples, # no. of predictions metric = d2l.Accumulator(4) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = d2l.train_batch_ch13( net, features, labels.long(), loss, trainer, devices) metric.add(l, acc, labels.shape[0], labels.numel()) timer.stop() if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (metric[0] / metric[2], metric[1] / metric[3], None)) test_acc = d2l.evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) scheduler.step() print(f"epoch {epoch+1} --- loss {metric[0] / metric[2]:.3f} --- train acc {metric[1] / metric[3]:.3f} --- test acc {test_acc:.3f} --- cost time {timer.sum()}") #---------保存训练数据--------------- df = pd.DataFrame() loss_list.append(metric[0] / metric[2]) train_acc_list.append(metric[1] / metric[3]) test_acc_list.append(test_acc) epochs_list.append(epoch) time_list.append(timer.sum()) df['epoch'] = epochs_list df['loss'] = loss_list df['train_acc'] = train_acc_list df['test_acc'] = test_acc_list df['time'] = time_list df.to_excel("savefile/Unet++_camvid1.xlsx") #----------------保存模型------------------- if np.mod(epoch+1, 5) == 0: torch.save(model.state_dict(), f'checkpoints/Unet++_{epoch+1}.pth')

开始训练

train_ch13(model, train_loader, val_loader, lossf, optimizer, epochs_num,scheduler)训练结果Unet++训练结果