这是我所加的注意力机制模块: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): # print("x=", 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),然后运行训练程序时报错:File "/root/autodl-tmp/project/tools/../lib/net/pointnet2_msg.py", line 91, in forward y = self.fc1(y) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 258, in forward return F.conv1d(input, self.weight, self.bias, self.stride, RuntimeError: Given groups=1, weight of size [256, 1024, 1], expected input[16, 512, 1] to have 1024 channels, but got 512 channels instead你知道是为什么吗,我该如何重新编写该注意力机制模块使得问题解决?请提供详细的解决代码并加入注释吗?
时间: 2023-07-16 12:15:42 浏览: 41
根据错误信息,我们可以发现问题是在 self.fc1(y) 这一行,提示输入的通道数应该是 1024,但实际上只有 512。这是因为输入的向量维度是 [16, 512, 1],而在 forward 函数中我们使用了 AdaptiveAvgPool1d 将其降维至 [16, 512, 1],导致后续的卷积层参数不匹配。
因此,我们需要将 AdaptiveAvgPool1d 替换为 AdaptiveAvgPool2d,并将卷积层的维度修改为二维([in_channels, in_channels // reduction, 1, 1] 和 [in_channels // reduction, in_channels, 1, 1])。
以下是修改后的 SelfAttention 类代码:
```python
class SelfAttention(nn.Module):
def __init__(self, in_channels, reduction=4):
super(SelfAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc1 = nn.Conv2d(in_channels, in_channels // reduction, 1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(in_channels // reduction, in_channels, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, h, w = 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)
```
这样就可以解决输入通道数不匹配的错误了。
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