cost = (fea1 * fea2).view([B, num_groups, channels_per_group, H, W]).mean(dim=2)

这段代码的作用是将两个特征图进行逐元素相乘，然后将得到的结果视图变形为一个5维张量（batch size, 组数，每组通道数，高度，宽度），最后对每个组内的通道进行平均池化，得到一个大小为(batch size, 组数, 高度, 宽度)的特征图。其中，fea1和fea2的大小为(batch size, channels, H, W)，num_groups表示将通道数分成几组，channels_per_group表示每组中通道的数量。

相关问题

def build_gwc_volume_cos(refimg_fea, targetimg_fea, maxdisp, num_groups): refimg_fea = refimg_fea/(torch.sum(refimg_fea**2, dim=1,keepdim=True).pow(1/2)+1e-05) targetimg_fea = targetimg_fea/(torch.sum(targetimg_fea**2, dim=1,keepdim=True).pow(1/2)+1e-05) B, C, H, W = refimg_fea.shape volume = refimg_fea.new_zeros([B, num_groups, maxdisp, H, W]) for i in range(maxdisp): if i > 0: volume[:, :, i, :, i:] = groupwise_correlation(refimg_fea[:, :, :, i:], targetimg_fea[:, :, :, :-i], num_groups) else: volume[:, :, i, :, :] = groupwise_correlation(refimg_fea, targetimg_fea, num_groups) volume = volume.contiguous() return volume

这是一个构建视差体积的函数，输入参数包括参考图像特征(refimg_fea)、目标图像特征(targetimg_fea)、最大视差(maxdisp)和分组数(num_groups)。首先，对参考图像特征和目标图像特征进行归一化处理。然后，根据最大视差和分组数创建一个大小为[B, num_groups, maxdisp, H, W]的零张量(volume)。接下来，通过循环遍历每个视差值i，计算对应视差的分组相关性并将结果存储在volume张量中。最后，返回计算完成的volume张量。

修改下列模块代码，使其能够对三维模型的直线特征进行提取：class FaceKernelCorrelation(nn.Module): def __init__(self, num_kernel=64, sigma=0.2): super(FaceKernelCorrelation, self).__init__() self.num_kernel = num_kernel self.sigma = sigma self.weight_alpha = Parameter(torch.rand(1, num_kernel, 4) * np.pi) self.weight_beta = Parameter(torch.rand(1, num_kernel, 4) * 2 * np.pi) self.bn = nn.BatchNorm1d(num_kernel) self.relu = nn.ReLU() def forward(self, normals, neighbor_index): b, _, n = normals.size() center = normals.unsqueeze(2).expand(-1, -1, self.num_kernel, -1).unsqueeze(4) neighbor = torch.gather(normals.unsqueeze(3).expand(-1, -1, -1, 3), 2, neighbor_index.unsqueeze(1).expand(-1, 3, -1, -1)) neighbor = neighbor.unsqueeze(2).expand(-1, -1, self.num_kernel, -1, -1) fea = torch.cat([center, neighbor], 4) fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 4) weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta), torch.sin(self.weight_alpha) * torch.sin(self.weight_beta), torch.cos(self.weight_alpha)], 0) weight = weight.unsqueeze(0).expand(b, -1, -1, -1) weight = weight.unsqueeze(3).expand(-1, -1, -1, n, -1) weight = weight.unsqueeze(4).expand(-1, -1, -1, -1, 4, -1) dist = torch.sum((fea - weight)**2, 1) fea = torch.sum(torch.sum(np.e**(dist / (-2 * self.sigma**2)), 4), 3) / 16 return self.relu(self.bn(fea))

修改后的代码如下：

class FaceKernelCorrelation(nn.Module):
    def __init__(self, num_kernel=64, sigma=0.2):
        super(FaceKernelCorrelation, self).__init__()
        self.num_kernel = num_kernel
        self.sigma = sigma
        self.weight_alpha = Parameter(torch.rand(1, num_kernel, 6) * np.pi)
        self.weight_beta = Parameter(torch.rand(1, num_kernel, 6) * 2 * np.pi)
        self.bn = nn.BatchNorm1d(num_kernel)
        self.relu = nn.ReLU()

    def forward(self, vertices, neighbor_index):
        b, _, n = vertices.size()
        center = vertices.unsqueeze(2).expand(-1, -1, self.num_kernel, -1).unsqueeze(4)
        neighbor = torch.gather(vertices.unsqueeze(3).expand(-1, -1, -1, 3),
                                2, neighbor_index.unsqueeze(1).expand(-1, 3, -1, -1))
        neighbor = neighbor.unsqueeze(2).expand(-1, -1, self.num_kernel, -1, -1)

        # 计算直线特征
        line = neighbor - center
        length = torch.sqrt(torch.sum(line**2, dim=-1, keepdim=True))
        direction = line / (length + 1e-8)
        fea = torch.cat([center, direction, length], dim=4)
        fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 6)

        weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta),
                            torch.sin(self.weight_alpha) * torch.sin(self.weight_beta),
                            torch.cos(self.weight_alpha)], 0)
        weight = weight.unsqueeze(0).expand(b, -1, -1, -1)
        weight = weight.unsqueeze(3).expand(-1, -1, -1, n, -1)
        weight = weight.unsqueeze(4).expand(-1, -1, -1, -1, 6, -1)

        dist = torch.sum((fea - weight)**2, 1)
        fea = torch.sum(torch.sum(np.e**(dist / (-2 * self.sigma**2)), 4), 3) / 16

        return self.relu(self.bn(fea))

对比原有的代码，主要修改的地方如下：

1. 修改了 weight_alpha 和 weight_beta 的形状，将其从 4 改为 6，以便存储直线特征；
2. 在 forward 函数中，首先计算出所有点的邻居点，然后根据邻居点和中心点计算出直线特征（即方向向量和长度），并将其拼接在一起；
3. 将拼接后的直线特征与权重相减后，进行距离计算和高斯加权求和。