python可变形卷积

# 定义可变形卷积层
class DeformableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding):
        super(DeformableConv2d, self).__init__()
        self.offset_conv = nn.Conv2d(in_channels, 2, kernel_size=3, padding=1)
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)

    def forward(self, x):
        # print('x.shape', x.shape)
        if x.shape[2]==1:
            x = x.permute(0, 1, 3, 2)
            x = torch.cat((x, x.clone()), dim=3)
        offset = self.offset_conv(x)
        offset = offset.view(offset.size(0), -1, offset.size(2), offset.size(3))
        # print('offset.shape', offset.shape)

        grid = torch.arange(x.size(2) * x.size(3)).view(1, 1, x.size(2), x.size(3)).float().to(x.device)
        grid = grid + offset

        x_offset = F.grid_sample(x, grid, mode='bilinear', padding_mode='zeros')
        output = self.conv(x_offset)
        output = output.permute(0, 1, 3, 2)
        return output
self.deformable_conv = DeformableConv2d(32, 32, kernel_size=3, padding=1)