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,我该如何解决?请提供详细的解决代码
时间: 2023-11-12 08:09:03 浏览: 26
从错误提示来看,你输入的张量 `x` 的 shape 是 `[batch_size, in_channels, n]`,其中 `in_channels=512`,但是你的第一个卷积层 `self.fc1` 的输入通道数是 `in_channels=1024`,因此报错了。
根据你的代码,我猜测你的输入张量 `x` 是从另一个模块传递过来的,而这个模块的输出通道数是 `in_channels=512`。因此,你需要修改 `self.fc1` 的输入通道数为 `in_channels=512`,如下所示:
```
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)
```
如果你的输入张量 `x` 的 shape 不是 `[batch_size, in_channels, n]`,而是 `[batch_size, n, in_channels]`,则需要将 `nn.Conv1d` 替换为 `nn.Conv2d`,并修改相应的参数。
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