pytorch语义分割代码
时间: 2023-09-23 22:11:35 浏览: 44
以下是一个简单的 PyTorch 语义分割代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
# 定义数据预处理
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载数据集
train_dataset = datasets.Cityscapes('path/to/dataset', split='train', mode='fine', target_type='semantic', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# 定义模型
class SegNet(nn.Module):
def __init__(self):
super(SegNet, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 19, kernel_size=2, stride=2)
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
# 定义训练器
model = SegNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
# 开始训练
for epoch in range(1, 11):
train(epoch)
```
这是一个简单的语义分割模型,使用了 Cityscapes 数据集,使用了 SegNet 架构。你可以根据自己的需求修改代码。