多尺度注意力机制代码
时间: 2023-12-16 17:28:38 浏览: 36
```python
class MultiScaleAttention(nn.Module):
def __init__(self, in_channels, out_channels):
super(MultiScaleAttention, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=5, padding=2)
self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=7, padding=3)
self.fc = nn.Linear(3*out_channels, out_channels)
def forward(self, x):
feat1 = F.relu(self.conv1(x))
feat2 = F.relu(self.conv2(x))
feat3 = F.relu(self.conv3(x))
multi_scale_feat = torch.cat((feat1, feat2, feat3), dim=1)
attention_weight = F.softmax(multi_scale_feat, dim=1)
attention_out = torch.matmul(attention_weight, multi_scale_feat)
output = self.fc(attention_out)
return output
```
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