class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) # print("in_channels", in_channels) print("in_channels:", in_channels) print("reduction:", reduction) 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() print("x.shape=", x.shape) y = self.avg_pool(x) print("y.shape=", y.shape) # print("channel_out", channel_out) 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(Pointnet2MSG, self).__init__() self.SA_modules = nn.ModuleList() channel_in = input_channels # print("channel_in=", channel_in) 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 # print("channel_in=", channel_in) for idx in range(mlps.__len__()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] mlps.append(channel_out) # print(mlps) print("channel_out", 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[:2], use_xyz=use_xyz, bn=cfg.RPN.USE_BN, ), SelfAttention(channel_out) ) ) skip_channel_list.append(channel_out) channel_in = channel_out根据以上代码块,自注意力机制模块中forward函数所接受的x参数的值到底是什么?
时间: 2023-11-20 13:05:53 浏览: 42
根据代码块中的描述,自注意力机制模块中forward函数所接受的x参数应该是输入的特征张量,其形状为(b, c, n),其中b表示batch size,c表示通道数,n表示点云中点的数目。在代码块中,x首先通过avg_pool进行自适应平均池化操作得到y,然后经过fc1、relu、fc2和sigmoid层得到注意力权重,最后将输入特征张量x与注意力权重y相乘得到加权特征张量作为输出。
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) # print("in_channels", in_channels) 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) print("y.shape=", y.shape) # print("in_channels", in_channels) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)我想在这个类中实现将传入的参数进行打印,你可以给我详细的代码吗?
你可以在 `__init__` 方法中添加以下代码:
```
print("in_channels:", in_channels)
print("reduction:", reduction)
```
在 `forward` 方法中添加以下代码:
```
print("x.shape:", x.shape)
```
这样就可以在打印的信息中看到传入的参数和输入的张量形状了。完整的代码如下:
```
class SelfAttention(nn.Module):
def __init__(self, in_channels, reduction=4):
super(SelfAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool1d(1) #
print("in_channels:", in_channels)
print("reduction:", reduction)
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):
print("x.shape:", x.shape)
b, c, n = x.size()
y = self.avg_pool(x)
print("y.shape=", y.shape) #
y = self.fc1(y)
y = self.relu(y)
y = self.fc2(y)
y = self.sigmoid(y)
return x * y.expand_as(x)
```
这是类的定义,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),而这是使用该类的位置: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, channel_out=channel_out ), SelfAttention(channel_out=channel_out) ) ),但是运行时报错:File "/root/autodl-tmp/project/tools/../lib/net/pointnet2_msg.py", line 125, in __init__ SelfAttention(channel_out=channel_out) TypeError: __init__() got an unexpected keyword argument 'channel_out',为什么呢?如何解决
在你的代码中,SelfAttention类的初始化方法中只接收两个参数in_channels和reduction,而你在使用该类时传递了一个名为channel_out的参数,导致了TypeError。
解决方法是修改SelfAttention类的定义,为其增加一个名为channel_out的参数,并在初始化方法中接收和使用该参数。修改后的类定义如下:
```
class SelfAttention(nn.Module):
def __init__(self, in_channels, channel_out, 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, channel_out, 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)
```
然后在使用SelfAttention类时,需要传递in_channels和channel_out参数,例如:
```
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,
channel_out=channel_out
),
SelfAttention(in_channels=channel_out, channel_out=channel_out)
)
)
```