可变形卷积(Deformable Conv)原理解析与torch代码实现

import torch
from torch import nn
 
class DeformableConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(DeformableConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.offset = nn.Conv2d(in_channels, kernel_size[0] * kernel_size[1] * 2, 1, 1, 0, bias=True)
 
    def forward(self, x):
        offset = self.offset(x)
        y = self.conv(x, offset)
        return y
 
# 示例使用
deformable_conv = DeformableConv(3, 64, 3, stride=2, padding=1)
input_tensor = torch.randn(1, 3, 224, 224)
output_tensor = deformable_conv(input_tensor)
print(output_tensor.shape)

这段代码定义了一个名为DeformableConv的类，它继承自nn.Module。它包含一个普通的卷积层和一个偏移量卷积层，偏移量卷积层用于提供每个卷积操作的偏移量。在前向传播中，它使用偏移量进行可变形卷积操作。这个例子展示了如何使用可变形卷积来进行图像识别任务。