pytorch手提包分割全部代码

以下是使用PyTorch进行手提包语义分割的完整代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import Cityscapes
from torchvision.models import vgg16

# 定义FCN模型
class FCN(nn.Module):
    def __init__(self, num_classes):
        super(FCN, self).__init__()
        self.features = vgg16(pretrained=True).features
        self.conv1 = nn.Conv2d(512, 4096, kernel_size=7)
        self.relu1 = nn.ReLU(inplace=True)
        self.drop1 = nn.Dropout2d()
        self.conv2 = nn.Conv2d(4096, 4096, kernel_size=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.drop2 = nn.Dropout2d()
        self.score = nn.Conv2d(4096, num_classes, kernel_size=1)

    def forward(self, x):
        x = self.features(x)
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.drop1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.drop2(x)
        x = self.score(x)
        x = F.interpolate(x, scale_factor=32, mode='bilinear', align_corners=True)
        return x

# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.cross_entropy(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()))

# 定义测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.cross_entropy(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize((512, 1024)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_set = Cityscapes('./data', split='train', mode='fine', target_type='semantic', transform=transform)
test_set = Cityscapes('./data', split='val', mode='fine', target_type='semantic', transform=transform)
train_loader = DataLoader(train_set, batch_size=4, shuffle=True)
test_loader = DataLoader(test_set, batch_size=4, shuffle=False)

# 初始化模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = FCN(num_classes=34).to(device)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练和测试模型
for epoch in range(1, 11):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)