Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze(-2)

这行代码使用了 PyTorch 的 `matmul` 函数来进行张量的矩阵乘法操作。

首先，`Q.unsqueeze(-2)` 会在倒数第二个维度上插入一个新的维度，假设原始张量 Q 的形状为 `(B, H, L_Q, E)`，则插入新维度后的形状为 `(B, H, 1, L_Q, E)`。

然后，`K_sample.transpose(-2, -1)` 会将 K_sample 的倒数第二个维度和倒数第一个维度进行交换，假设 K_sample 的形状为 `(B, H, L_K, E)`，则交换后的形状为 `(B, H, E, L_K)`。

接下来，`torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1))` 利用矩阵乘法操作对两个张量进行相乘，得到的结果形状为 `(B, H, 1, L_Q, L_K)`。

最后，`squeeze(-2)` 会将结果张量的倒数第二个维度大小为 1 的维度删除，得到最终的结果张量形状为 `(B, H, L_Q, L_K)`。

综上所述，这行代码的作用是计算 Q 和 K_sample 的注意力分数矩阵，其中 Q 是查询张量，K_sample 是键张量的样本，注意力分数矩阵的形状为 `(B, H, L_Q, L_K)`。

相关问题

class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, n = x.size() y = self.avg_pool(x) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x) def get_model(input_channels=6, use_xyz=True): return Pointnet2MSG(input_channels=input_channels, use_xyz=use_xyz) class Pointnet2MSG(nn.Module): def __init__(self, input_channels=6, use_xyz=True): super().__init__() self.SA_modules = nn.ModuleList() channel_in = input_channels skip_channel_list = [input_channels] for k in range(cfg.RPN.SA_CONFIG.NPOINTS.len()): mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy() channel_out = 0 for idx in range(mlps.len()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] mlps.append(channel_out) self.SA_modules.append( nn.Sequential( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN ), SelfAttention(channel_out) ) ) skip_channel_list.append(channel_out) channel_in = channel_out self.FP_modules = nn.ModuleList() for k in range(cfg.RPN.FP_MLPS.len()): pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out self.FP_modules.append( PointnetFPModule( mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k] ) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor): xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return l_xyz[0], l_features[0]在forward函数中，如果我要使用channel_out变量传入SA_modules中，我该如何在forward函数中计算并得到它，再传入SA_modules中，你可以给我详细的代码吗？

在 `Pointnet2MSG` 的构造函数中，我们可以看到 SA_modules 是一个 `nn.ModuleList()`，其中每个元素都是一个 `nn.Sequential()`，包含了一个 `PointnetSAModuleMSG` 和一个 `SelfAttention`。

在 `PointnetSAModuleMSG` 中，我们可以看到最后一个 MLP 输出的通道数即为 `channel_out`。因此，我们可以在每个 `PointnetSAModuleMSG` 的定义中记录 `channel_out`，并将其传递给下一个 `PointnetSAModuleMSG` 的输入。

以下是修改后的代码：

class Pointnet2MSG(nn.Module):
    def __init__(self, input_channels=6, use_xyz=True):
        super().__init__()

        self.SA_modules = nn.ModuleList()
        channel_in = input_channels
        skip_channel_list = [input_channels]

        for k in range(cfg.RPN.SA_CONFIG.NPOINTS.len()):
            mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy()
            channel_out = 0
            for idx in range(mlps.len()):
                mlps[idx] = [channel_in] + mlps[idx]
                channel_out += mlps[idx][-1]
            mlps.append(channel_out)
            sa_module = nn.Sequential(
                PointnetSAModuleMSG(
                    npoint=cfg.RPN.SA_CONFIG.NPOINTS[k],
                    radii=cfg.RPN.SA_CONFIG.RADIUS[k],
                    nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k],
                    mlps=mlps,
                    use_xyz=use_xyz,
                    bn=cfg.RPN.USE_BN
                )
            )
            self.SA_modules.append(sa_module)
            skip_channel_list.append(channel_out)
            channel_in = channel_out

        self.FP_modules = nn.ModuleList()

        for k in range(cfg.RPN.FP_MLPS.len()):
            pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out
            self.FP_modules.append(
                PointnetFPModule(
                    mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k]
                )
            )

    def _break_up_pc(self, pc):
        xyz = pc[..., 0:3].contiguous()
        features = (
            pc[..., 3:].transpose(1, 2).contiguous()
            if pc.size(-1) > 3 else None
        )
        return xyz, features

    def forward(self, pointcloud: torch.cuda.FloatTensor):
        xyz, features = self._break_up_pc(pointcloud)
        l_xyz, l_features = [xyz], [features]

        for i in range(len(self.SA_modules)):
            sa_module = self.SA_modules[i]
            li_xyz, li_features = sa_module(l_xyz[i], l_features[i])
            l_xyz.append(li_xyz)
            l_features.append(li_features)
            channel_out = sa_module[-1].fc2.out_channels

        for i in range(-1, -(len(self.FP_modules) + 1), -1):
            l_features[i - 1] = self.FP_modules[i](
                l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
            )

        return l_xyz[0], l_features[0], channel_out

使用上述代码，我们可以在 forward 函数中得到 `channel_out`，并将其传递给下一个 `PointnetSAModuleMSG` 的输入。

for k in range(cfg.RPN.SA_CONFIG.NPOINTS.__len__()): mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy() channel_out = 0 for idx in range(mlps.__len__()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] self.SA_modules.append( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN ) ) skip_channel_list.append(channel_out) channel_in = channel_out self.FP_modules = nn.ModuleList() for k in range(cfg.RPN.FP_MLPS.__len__()): pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out self.FP_modules.append( PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k]) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor): xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return l_xyz[0], l_features[0]在forward函数中，如果我要使用channel_out变量传入SA_modules中，我该如何在forward函数中计算并得到它，再传入SA_modules中，你可以给我详细的代码吗？

在 forward 函数中，可以定义一个变量 channel_out，用于保存当前 SA_module 的输出通道数。然后在每一次循环时更新 channel_out 的值，并将其传入 SA_module 中。代码如下：

def forward(self, pointcloud: torch.cuda.FloatTensor):
    xyz, features = self._break_up_pc(pointcloud)
    
    l_xyz, l_features = [xyz], [features]
    channel_out = 0 # 初始化 channel_out
    for i in range(len(self.SA_modules)):
        mlps = self.SA_modules[i].mlps # 获取当前 SA_module 的 MLPs
        
        # 计算当前 SA_module 的输出通道数
        channel_out = sum(mlp[-1] for mlp in mlps)
        
        li_xyz, li_features = self.SA_modules[i](
            l_xyz[i], l_features[i], channel_out=channel_out # 传入 channel_out
        )
        l_xyz.append(li_xyz)
        l_features.append(li_features)
    
    for i in range(-1, -(len(self.FP_modules) + 1), -1):
        l_features[i - 1] = self.FP_modules[i](
            l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
        )
    
    return l_xyz[0], l_features[0]

在 SA_module 的 forward 函数中，需要修改函数签名，增加一个参数 channel_out，用于指定输出通道数。然后在函数中使用该参数来初始化 MLP 中的第一个元素。代码如下：

class PointnetSAModuleMSG(nn.Module):
    def __init__(self, npoint, radii, nsamples, mlps, use_xyz=True, bn=True):
        super(PointnetSAModuleMSG, self).__init__()
        self.npoint = npoint
        self.radii = radii
        self.nsamples = nsamples
        self.bn = bn
        self.use_xyz = use_xyz

        self.SA_modules = nn.ModuleList()
        for j in range(len(mlps)):
            in_channel = mlps[j][0]
            mlp = nn.Sequential()
            for k in range(len(mlps[j])-1):
                mlp.add_module('layer{}'.format(k),
                    pt_utils.Conv1d(in_channel, mlps[j][k+1], bn=self.bn))
                in_channel = mlps[j][k+1]
            self.SA_modules.append(mlp)
        
    def forward(self, xyz, features, channel_out):
        if self.use_xyz:
            new_features_list = [xyz.transpose(1, 2)]
        else:
            new_features_list = []
        for i in range(len(self.radii)):
            idx, pts_cnt = pt_utils.query_ball_point(self.radii[i], self.nsamples[i], xyz, xyz)
            grouped_xyz = pt_utils.grouping_operation(xyz, idx)
            grouped_xyz -= xyz.unsqueeze(-1)
            if features is not None:
                grouped_features = pt_utils.grouping_operation(features, idx)
                grouped_features = torch.cat([grouped_features, grouped_xyz], dim=1)
            else:
                grouped_features = grouped_xyz
            new_features = self.SA_modules[i](grouped_features)
            new_features = new_features.max(dim=-1)[0]
            new_features_list.append(new_features)
        
        new_features = torch.cat(new_features_list, dim=1)
        for i in range(len(self.SA_modules)):
            new_features[:, i:i+1] = self.SA_modules[i][0](new_features[:, i:i+1]) # 初始化 MLP 中的第一个元素
        
        if channel_out != new_features.size(1): # 如果输出通道数不一致，进行升维或降维
            new_features = pt_utils.Conv1d(new_features.size(1), channel_out, bn=self.bn)(new_features)
        
        new_xyz = pt_utils.gather_operation(xyz, pt_utils.furthest_point_sample(xyz, self.npoint))
        return new_xyz, new_features