可变形离卷积 pytorch

可变形卷积是一种在卷积神经网络中引入空间变形的操作。在PyTorch中，可以使用torchvision库中的torchvision.ops.DeformConv2d来实现可变形卷积。

以下是一个使用可变形卷积的PyTorch示例代码：

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torchvision.models as models
import torchvision.ops as ops

# 定义一个简单的卷积神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.deform_conv = ops.DeformConv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 10, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.deform_conv(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        return x

# 加载数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

# 初始化网络和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练网络
for epoch in range(2):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

这段代码定义了一个简单的卷积神经网络，其中使用了可变形卷积（DeformConv2d）。在训练过程中，网络将使用CIFAR10数据集进行训练。