torch 实现unet 医学分割 - CSDN文库

UNet是一种常用于医学图像分割的卷积神经网络，可以用于分割出医学图像中感兴趣的区域，比如肿瘤、血管等。下面是使用PyTorch实现UNet的步骤： 1. 加载数据集首先需要加载医学图像数据集，可以使用PyTorch提供的`torchvision.datasets`模块，或自己编写代码读取数据。 2. 定义网络结构 UNet的结构可以分为编码器和解码器两部分，中间是连接两部分的跨层连接。可以使用PyTorch提供的`nn.Module`类来定义网络结构。 3. 定义损失函数常用的医学图像分割损失函数有Dice系数和交叉熵损失函数，可以使用PyTorch提供的`nn.CrossEntropyLoss`类来定义交叉熵损失函数。 4. 定义优化器可以使用PyTorch提供的优化器，如`torch.optim.Adam`。 5. 训练模型使用定义好的网络结构、损失函数和优化器来训练模型。 6. 测试模型使用训练好的模型来对测试集进行预测，并计算预测结果的准确率、召回率、F1值等指标。以上是使用PyTorch实现UNet医学图像分割的基本步骤，需要注意的是，在实际操作中，还需要对数据进行预处理和后处理等步骤，以提高模型的准确率和稳定性。

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torch 实现unet 医学分割代码

以下是使用PyTorch实现UNet医学图像分割的示例代码： ```python import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms from torch.utils.data import DataLoader from torchvision.datasets import ImageFolder class DoubleConv(nn.Module): def __init__(self, in_channels, out_channels): super(DoubleConv, self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self.conv(x) class UNet(nn.Module): def __init__(self, in_channels=1, out_channels=1, features=[64, 128, 256, 512]): super(UNet, self).__init__() self.ups = nn.ModuleList() self.downs = nn.ModuleList() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # 定义编码器 for feature in features: self.downs.append(DoubleConv(in_channels, feature)) in_channels = feature # 定义解码器 for feature in reversed(features): self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2)) self.ups.append(DoubleConv(feature*2, feature)) self.bottleneck = DoubleConv(features[-1], features[-1]*2) self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) def forward(self, x): skip_connections = [] # 编码器 for down in self.downs: x = down(x) skip_connections.append(x) x = self.pool(x) # 中间层 x = self.bottleneck(x) # 解码器 skip_connections = skip_connections[::-1] for idx in range(0, len(self.ups), 2): x = self.ups[idx](x) skip_connection = skip_connections[idx//2] if x.shape != skip_connection.shape: x = transforms.CenterCrop([skip_connection.shape[2], skip_connection.shape[3]])(x) x = torch.cat((skip_connection, x), dim=1) x = self.ups[idx+1](x) return self.final_conv(x) # 加载数据集 train_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor() ]) train_dataset = ImageFolder("data/train", transform=train_transforms) train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True) # 定义模型 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = UNet().to(device) # 定义损失函数和优化器 criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=1e-4) # 训练模型 num_epochs = 10 for epoch in range(num_epochs): for data, target in train_loader: data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() # 测试模型 test_transforms = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor() ]) test_dataset = ImageFolder("data/test", transform=test_transforms) test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False) with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) binary_output = torch.sigmoid(output) # 计算预测结果的准确率、召回率、F1值等指标 ``` 以上是使用PyTorch实现UNet医学图像分割的代码示例，其中包含了数据预处理、模型定义、损失函数和优化器的定义、模型训练和测试等步骤。需要根据实际情况进行修改和调整。

pytorch实现unet图像分割

UNet是一种用于图像分割的卷积神经网络结构，它可以用于医学图像分割、自然图像分割等领域。下面我来介绍如何使用PyTorch实现UNet图像分割。 1. 准备数据首先，你需要准备好图像分割的数据集。这个数据集需要包括原始图像和对应的分割掩码图像。你可以使用任何你熟悉的数据集，比如Kaggle上的数据集，或者自己制作的数据集。 2. 定义UNet模型接下来，你需要定义UNet模型。UNet模型由编码器和解码器两部分组成，编码器用于提取图像特征，解码器用于将特征映射回分割掩码图像。下面是一个简单的UNet模型实现： ```python import torch import torch.nn as nn class UNet(nn.Module): def __init__(self): super(UNet, self).__init__() # 编码器 self.conv1 = nn.Conv2d(3, 64, 3, padding=1) self.bn1 = nn.BatchNorm2d(64) self.relu1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(64, 64, 3, padding=1) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d(2, 2) self.conv3 = nn.Conv2d(64, 128, 3, padding=1) self.bn3 = nn.BatchNorm2d(128) self.relu3 = nn.ReLU(inplace=True) self.conv4 = nn.Conv2d(128, 128, 3, padding=1) self.bn4 = nn.BatchNorm2d(128) self.relu4 = nn.ReLU(inplace=True) self.pool2 = nn.MaxPool2d(2, 2) self.conv5 = nn.Conv2d(128, 256, 3, padding=1) self.bn5 = nn.BatchNorm2d(256) self.relu5 = nn.ReLU(inplace=True) self.conv6 = nn.Conv2d(256, 256, 3, padding=1) self.bn6 = nn.BatchNorm2d(256) self.relu6 = nn.ReLU(inplace=True) self.pool3 = nn.MaxPool2d(2, 2) self.conv7 = nn.Conv2d(256, 512, 3, padding=1) self.bn7 = nn.BatchNorm2d(512) self.relu7 = nn.ReLU(inplace=True) self.conv8 = nn.Conv2d(512, 512, 3, padding=1) self.bn8 = nn.BatchNorm2d(512) self.relu8 = nn.ReLU(inplace=True) self.pool4 = nn.MaxPool2d(2, 2) self.conv9 = nn.Conv2d(512, 1024, 3, padding=1) self.bn9 = nn.BatchNorm2d(1024) self.relu9 = nn.ReLU(inplace=True) self.conv10 = nn.Conv2d(1024, 1024, 3, padding=1) self.bn10 = nn.BatchNorm2d(1024) self.relu10 = nn.ReLU(inplace=True) # 解码器 self.upconv1 = nn.ConvTranspose2d(1024, 512, 2, stride=2) self.conv11 = nn.Conv2d(1024, 512, 3, padding=1) self.bn11 = nn.BatchNorm2d(512) self.relu11 = nn.ReLU(inplace=True) self.conv12 = nn.Conv2d(512, 512, 3, padding=1) self.bn12 = nn.BatchNorm2d(512) self.relu12 = nn.ReLU(inplace=True) self.upconv2 = nn.ConvTranspose2d(512, 256, 2, stride=2) self.conv13 = nn.Conv2d(512, 256, 3, padding=1) self.bn13 = nn.BatchNorm2d(256) self.relu13 = nn.ReLU(inplace=True) self.conv14 = nn.Conv2d(256, 256, 3, padding=1) self.bn14 = nn.BatchNorm2d(256) self.relu14 = nn.ReLU(inplace=True) self.upconv3 = nn.ConvTranspose2d(256, 128, 2, stride=2) self.conv15 = nn.Conv2d(256, 128, 3, padding=1) self.bn15 = nn.BatchNorm2d(128) self.relu15 = nn.ReLU(inplace=True) self.conv16 = nn.Conv2d(128, 128, 3, padding=1) self.bn16 = nn.BatchNorm2d(128) self.relu16 = nn.ReLU(inplace=True) self.upconv4 = nn.ConvTranspose2d(128, 64, 2, stride=2) self.conv17 = nn.Conv2d(128, 64, 3, padding=1) self.bn17 = nn.BatchNorm2d(64) self.relu17 = nn.ReLU(inplace=True) self.conv18 = nn.Conv2d(64, 64, 3, padding=1) self.bn18 = nn.BatchNorm2d(64) self.relu18 = nn.ReLU(inplace=True) self.conv19 = nn.Conv2d(64, 1, 1) def forward(self, x): # 编码器 x1 = self.relu1(self.bn1(self.conv1(x))) x2 = self.relu2(self.bn2(self.conv2(x1))) x3 = self.relu3(self.bn3(self.conv3(self.pool1(x2)))) x4 = self.relu4(self.bn4(self.conv4(x3))) x5 = self.relu5(self.bn5(self.conv5(self.pool2(x4)))) x6 = self.relu6(self.bn6(self.conv6(x5))) x7 = self.relu7(self.bn7(self.conv7(self.pool3(x6)))) x8 = self.relu8(self.bn8(self.conv8(x7))) x9 = self.relu9(self.bn9(self.conv9(self.pool4(x8)))) x10 = self.relu10(self.bn10(self.conv10(x9))) # 解码器 x = self.relu11(self.bn11(self.conv11(torch.cat([x8, self.upconv1(x10)], 1)))) x = self.relu12(self.bn12(self.conv12(x))) x = self.relu13(self.bn13(self.conv13(torch.cat([x6, self.upconv2(x)], 1)))) x = self.relu14(self.bn14(self.conv14(x))) x = self.relu15(self.bn15(self.conv15(torch.cat([x4, self.upconv3(x)], 1)))) x = self.relu16(self.bn16(self.conv16(x))) x = self.relu17(self.bn17(self.conv17(torch.cat([x2, self.upconv4(x)], 1)))) x = self.relu18(self.bn18(self.conv18(x))) x = self.conv19(x) return x ``` 在这个模型中，UNet有5个下采样层和5个上采样层。每个下采样层由两个卷积层和一个最大池化层组成，每个上采样层由一个转置卷积层和两个卷积层组成。 3. 定义损失函数和优化器接下来，你需要定义损失函数和优化器。在图像分割任务中，我们通常使用交叉熵损失函数。优化器可以选择Adam、SGD等。 ```python import torch.optim as optim criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) ``` 4. 训练模型最后，你可以开始训练模型了。你需要将数据集分成训练集和验证集，然后使用PyTorch的DataLoader加载数据集，并在每个epoch训练模型。 ```python from torch.utils.data import DataLoader train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=4, shuffle=True) for epoch in range(num_epochs): train_loss = 0 val_loss = 0 # 训练模型 model.train() for images, masks in train_loader: optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, masks) loss.backward() optimizer.step() train_loss += loss.item() # 验证模型 model.eval() with torch.no_grad(): for images, masks in val_loader: outputs = model(images) loss = criterion(outputs, masks) val_loss += loss.item() train_loss /= len(train_loader) val_loss /= len(val_loader) print('Epoch: {}, Train Loss: {}, Val Loss: {}'.format(epoch+1, train_loss, val_loss)) ``` 在训练过程中，你可以在每个epoch后计算训练集和验证集的损失，并输出训练结果。训练完成后，你可以保存模型并在测试集上进行测试。这就是使用PyTorch实现UNet图像分割的基本流程。当然，你可以根据自己的需求调整模型结构、损失函数和优化器等。

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