flownet中correlation代码

以下是使用PyTorch实现的FlowNet中的correlation代码：

import torch
import torch.nn.functional as F

class CorrelationLayer(torch.nn.Module):
    def __init__(self, max_disp=40):
        super(CorrelationLayer, self).__init__()
        self.max_disp = max_disp
        
    def forward(self, x1, x2):
        b, c, h, w = x1.shape
        
        # pad input tensors
        padding = self.max_disp
        x1 = F.pad(x1, (padding, padding, padding, padding))
        x2 = F.pad(x2, (padding, padding, padding, padding))
        
        # create empty output tensor
        out = torch.zeros((b, (2 * self.max_disp + 1)**2, h, w)).to(x1.device)
        
        # loop over all displacements
        for i in range(-self.max_disp, self.max_disp+1):
            for j in range(-self.max_disp, self.max_disp+1):
                # shift x2 by displacement
                x2_shifted = x2[:, :, padding+i:h+padding+i, padding+j:w+padding+j]
                
                # compute correlation between x1 and shifted x2
                corr = torch.sum(x1 * x2_shifted, dim=1)
                
                # set correlation values outside of valid range to 0
                corr[:, :, :i+padding, :] = 0
                corr[:, :, :, :j+padding] = 0
                corr[:, :, -i-padding:, :] = 0
                corr[:, :, :, -j-padding:] = 0
                
                # add correlation values to output tensor
                out[:, (i+self.max_disp)*(2*self.max_disp+1) + (j+self.max_disp), :, :] = corr
        
        return out

该代码定义了一个名为CorrelationLayer的PyTorch模块，它接受两个输入张量x1和x2，并计算它们之间的相关性。为了处理边缘情况，输入张量被填充了一些额外的像素。输出张量的形状为(b, (2 * max_disp + 1)^2, h, w)，其中b是批量大小，h和w是输入张量的高度和宽度，max_disp是允许的最大位移。输出张量的每个通道对应于x1和x2之间的一个位移。在计算相关性时，超出有效范围的值被设置为0。