深度混合可变形卷积 代码

### 关于深度混合可变形卷积的代码实现

对于深度混合可变形卷积网络的设计，该结构允许模型学习空间变换参数来增强特征提取能力[^1]。下面提供了一个基于PyTorch框架下简单的深度混合可变形卷积层的实现方式。

import torch
from torch import nn
import torchvision.ops.deform_conv as deform_conv


class DeepHybridDeformConv(nn.Module):
    def __init__(self, inc, outc, kernel_size=3, stride=1, padding=1, bias=None):
        super(DeepHybridDeformConv, self).__init__()
        
        # Offset generation network (OGN), which predicts the offsets.
        self.offset_net = nn.Sequential(
            nn.Conv2d(in_channels=inc,
                      out_channels=kernel_size * kernel_size * 2,  # 2 indicates that there are two coordinates per point.
                      kernel_size=kernel_size,
                      stride=stride,
                      padding=padding),
            nn.ReLU(inplace=True)
        )
    
        # Deformable Convolution Layer
        self.conv_op = deform_conv.DeformConv2d(in_channels=inc,
                                                out_channels=outc,
                                                kernel_size=kernel_size,
                                                stride=stride,
                                                padding=padding,
                                                bias=bias)

    def forward(self, input_tensor):
        offset = self.offset_net(input_tensor)
        output = self.conv_op(input=input_tensor, offset=offset)
        return output

此段代码定义了一种新的卷积操作——即通过引入偏移量预测子网（Offset Generation Network），使得标准卷积核能够自适应调整其采样位置，从而更好地捕捉目标物体形状变化带来的影响。