双通道注意力机制代码
时间: 2023-08-21 12:08:29 浏览: 52
Sure! Here's an example code for a dual-channel attention mechanism in Python using PyTorch:
```python
import torch
import torch.nn as nn
class DualAttention(nn.Module):
def __init__(self, input_size, hidden_size):
super(DualAttention, self).__init__()
self.hidden_size = hidden_size
# Query projection
self.query_projection = nn.Linear(input_size, hidden_size)
# Key and Value projections for channel 1
self.key_projection_1 = nn.Linear(input_size, hidden_size)
self.value_projection_1 = nn.Linear(input_size, hidden_size)
# Key and Value projections for channel 2
self.key_projection_2 = nn.Linear(input_size, hidden_size)
self.value_projection_2 = nn.Linear(input_size, hidden_size)
# Output projection
self.output_projection = nn.Linear(hidden_size * 2, input_size)
def forward(self, input_1, input_2):
# Compute queries
query = self.query_projection(input_1)
# Compute keys and values for channel 1
key_1 = self.key_projection_1(input_1)
value_1 = self.value_projection_1(input_1)
# Compute keys and values for channel 2
key_2 = self.key_projection_2(input_2)
value_2 = self.value_projection_2(input_2)
# Compute attention weights for channel 1
scores_1 = torch.matmul(query, key_1.transpose(-2, -1))
attention_weights_1 = nn.functional.softmax(scores_1, dim=-1)
# Compute attention weights for channel 2
scores_2 = torch.matmul(query, key_2.transpose(-2, -1))
attention_weights_2 = nn.functional.softmax(scores_2, dim=-1)
# Apply attention weights to values
context_1 = torch.matmul(attention_weights_1, value_1)
context_2 = torch.matmul(attention_weights_2, value_2)
# Concatenate the context vectors
context = torch.cat([context_1, context_2], dim=-1)
# Apply output projection
output = self.output_projection(context)
return output
```
In this code, we define a `DualAttention` module that takes input from two different channels (`input_1` and `input_2`). It projects the inputs into query, key, and value vectors using linear layers. Then, it computes attention weights for each channel separately and applies them to the corresponding values. Finally, it concatenates the context vectors and applies an output projection.