类的定义如下: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),运行时报错提示: File "/root/autodl-tmp/project/tools/../lib/net/pointnet2_msg.py", line 88, in forward b, c, n = x.size() AttributeError: 'NoneType' object has no attribute 'size'
时间: 2023-06-27 18:07:56 浏览: 52
这个错误提示意味着输入到 `forward` 函数中的 `x` 是 `None`,也就是没有被正确传递。请检查一下在调用 `forward` 函数时是否正确地传递了输入参数。可能需要检查一下代码中数据的处理过程以及调用 `forward` 函数的部分,看看是否有数据丢失或者未正确传递的情况。另外,也可以尝试打印一下 `x` 的值,以便更好地了解问题所在。
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);而这是使用该类时的语句SelfAttention(channel_out),所以该语句是将channel_out传递给类中的in_channels吗?
是的,你的理解是正确的。在实例化`SelfAttention`类时,你需要传递一个`in_channels`参数,它将被用作输入张量`x`的通道数。在你的代码中,你使用`channel_out`来实例化`SelfAttention`类,因此`channel_out`将被传递给`in_channels`。在`forward`函数中,`x`表示输入张量,其大小为(b, c, n),其中b是批次大小,c是通道数,n是序列长度。