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 print("channel_in=", channel_in) skip_channel_list = [input_channels]为什么我运行程序打印出来的channel_in的值不是6
时间: 2023-11-28 13:49:28 浏览: 32
可能是因为你没有正确调用 `__init__` 函数。在 `Pointnet2MSG` 类中,你需要将 `__init__` 函数的定义改为:
```python
def __init__(self, input_channels=6, use_xyz=True):
super(Pointnet2MSG, self).__init__()
```
注意,`super()` 中需要传入 `Pointnet2MSG` 和 `self`,并使用 `__init__()` 函数来初始化父类。现在再次运行程序,应该可以正确打印出 `channel_in` 的值为 6。
相关问题
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(Pointnet2MSG, self).init() self.SA_modules = nn.ModuleList() channel_in = input_channels print("channel_in=", channel_in)在以上代码块中,明明channel_in 的初始值为 input_channels,即应该为6,为什么打印出来的值却为0,你可以给我详细的解决代码吗?
根据你提供的代码,我发现 `Pointnet2MSG` 的 `__init__` 方法中存在一个拼写错误,应该是 `def __init__` 而不是 `def init`。这个错误导致 `__init__` 方法没有被正确地初始化,所以 `channel_in` 的初始值为0。
修改后的代码如下:
```
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(Pointnet2MSG, self).__init__()
self.SA_modules = nn.ModuleList()
channel_in = input_channels
print("channel_in=", channel_in)
```
现在 `channel_in` 的初始值应该为6了。
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] ) )根据如上代码,如果要在Pointnet2MSG类中的forward函数调用SA_modules的话需要传入哪些参数,几个参数?
在Pointnet2MSG类中的forward函数调用SA_modules时,需要传入点云数据x以及额外的参数,具体如下:
```python
def forward(self, x):
l_xyz, l_features = [None] * self.num_layers, [None] * self.num_layers
# SA_modules
for i in range(self.num_layers):
# SA_modules需要传入的参数包括:输入点云数据x,输入点云xyz坐标l_xyz[i-1],输入点云特征向量l_features[i-1]
xyz, features = self.SA_modules[i][0](x if i == 0 else l_xyz[i-1], l_features[i-1])
l_xyz[i], l_features[i] = xyz, features
# SelfAttention模块需要传入的参数:输入特征向量l_features[i]
l_features[i] = self.SA_modules[i][1](l_features[i])
# FP_modules
for i in range(self.num_layers-1, -1, -1):
# FP_modules需要传入的参数包括:上一层的输出xyz坐标l_xyz[i+1],上一层的输出特征向量l_features[i+1],当前层的输入xyz坐标l_xyz[i],当前层的输入特征向量l_features[i]
l_features[i] = self.FP_modules[i](l_xyz[i+1], l_xyz[i], l_features[i+1], l_features[i])
return l_features[0]
```
其中,SA_modules需要传入的参数包括:输入点云数据x,输入点云xyz坐标l_xyz[i-1],输入点云特征向量l_features[i-1],SelfAttention模块需要传入的参数为输入特征向量l_features[i]。FP_modules需要传入的参数包括:上一层的输出xyz坐标l_xyz[i+1],上一层的输出特征向量l_features[i+1],当前层的输入xyz坐标l_xyz[i],当前层的输入特征向量l_features[i]。