DCNv2的代码

以下是使用PyTorch实现的DCNv2的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class DeformConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, deform_groups=1, bias=False):
        super(DeformConv2d, self).__init__()
        self.offset_conv = nn.Conv2d(in_channels, deform_groups * 2 * kernel_size * kernel_size, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)
        self.scale_conv = nn.Conv2d(in_channels, deform_groups * kernel_size * kernel_size, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)

        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)

        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.deform_groups = deform_groups

    def forward(self, x):
        offset = self.offset_conv(x)
        scale = self.scale_conv(x)

        B, C, H, W = x.size()
        kernel_size = self.kernel_size
        stride = self.stride
        padding = self.padding
        dilation = self.dilation
        groups = self.groups
        deform_groups = self.deform_groups

        # reshape offset and scale
        offset = offset.view(B, deform_groups, 2 * kernel_size * kernel_size, H, W).contiguous()
        scale = scale.view(B, deform_groups, kernel_size * kernel_size, H, W).contiguous()

        # compute output
        output = deform_conv2d(x, offset, scale, self.conv.weight, self.conv.bias, kernel_size, stride, padding, dilation, groups, deform_groups)

        return output

def deform_conv2d(input, offset, scale, weight, bias=None, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, deform_groups=1):
    B, C, H, W = input.size()
    out_channels, in_channels, kernel_h, kernel_w = weight.size()
    assert in_channels % groups == 0
    assert out_channels % groups == 0

    out_h = (H + 2 * padding - dilation * (kernel_h - 1) - 1) // stride + 1
    out_w = (W + 2 * padding - dilation * (kernel_w - 1) - 1) // stride + 1

    # pad input and offset
    input = F.pad(input, (padding, padding, padding, padding))
    offset = F.pad(offset, (padding, padding, padding, padding))

    # compute output
    output = torch.zeros(B, out_channels, out_h, out_w, dtype=input.dtype, layout=input.layout, device=input.device)
    deform_conv2d_cuda.deform_conv2d_cuda_forward(input, offset, scale, weight, bias, output, kernel_size, stride, padding, dilation, groups, deform_groups)

    return output

这个代码实现了一个`DeformConv2d`类，该类继承自PyTorch的`nn.Module`类，并实现了一个可变形卷积层。其中`deform_conv2d`函数是实现可变形卷积操作的核心代码部分，使用CUDA实现了可变形卷积的前向传播过程。