neighborhood attention复现
时间: 2023-10-16 17:12:06 浏览: 64
要复现neighborhood attention,需要先了解它的原理。neighborhood attention是一种基于图神经网络的注意力机制,它可以在图上进行节点分类、链接预测等任务。其核心思想是将每个节点的邻居节点作为上下文信息,通过注意力机制来计算每个邻居节点对当前节点的重要性,从而得到当前节点的表示。
具体实现上,可以使用图卷积神经网络(GCN)来对邻居节点进行聚合,然后再通过注意力机制来计算每个邻居节点的权重。最终得到的表示可以用于节点分类、链接预测等任务。
如果要复现neighborhood attention,可以参考相关论文和代码实现。其中比较经典的是《Semi-Supervised Classification with Graph Convolutional Networks》这篇论文,代码实现可以参考DGL库中的示例代码。
相关问题
Dilated Neighborhood Attention Transformer
Dilated Neighborhood Attention Transformer是一种基于Neighborhood Attention Transformer的改进模型,它通过引入空洞卷积(Dilated Convolution)来扩大感受野,从而提高模型的性能。具体来说,Dilated Neighborhood Attention Transformer在每个层级中使用了多个不同的空洞卷积核,这些卷积核的空洞率逐渐增加,从而使得每个query的感受野逐渐扩大。这种方法可以在不增加计算复杂度的情况下提高模型的性能,特别是在处理长序列数据时效果更为明显。
以下是Dilated Neighborhood Attention Transformer的实现代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class DilatedNeighborhoodAttention(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation_rate):
super(DilatedNeighborhoodAttention, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=dilation_rate*(kernel_size-1), dilation=dilation_rate)
self.norm = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
x = self.relu(x)
return x
class DilatedNeighborhoodAttentionTransformer(nn.Module):
def __init__(self, num_layers, num_heads, d_model, d_ff, dropout):
super(DilatedNeighborhoodAttentionTransformer, self).__init__()
self.num_layers = num_layers
self.self_attentions = nn.ModuleList([nn.MultiheadAttention(d_model, num_heads, dropout=dropout) for _ in range(num_layers)])
self.dilated_attentions = nn.ModuleList([DilatedNeighborhoodAttention(d_model, d_model, kernel_size=3, dilation_rate=2**i) for i in range(num_layers)])
self.ffns = nn.ModuleList([nn.Sequential(nn.Linear(d_model, d_ff), nn.ReLU(inplace=True), nn.Linear(d_ff, d_model)) for _ in range(num_layers)])
self.norms1 = nn.ModuleList([nn.LayerNorm(d_model) for _ in range(num_layers)])
self.norms2 = nn.ModuleList([nn.LayerNorm(d_model) for _ in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, x):
for i in range(self.num_layers):
residual = x
x, _ = self.self_attentions[i](x, x, x)
x = self.norms1[i](residual + self.dropout(x))
residual = x
x = self.dilated_attentions[i](x)
x = self.norms2[i](residual + self.dropout(x))
residual = x
x = self.ffns[i](x)
x = self.norms2[i](residual + self.dropout(x))
return x
```
neighborhood attention代码
以下是一个使用PyTorch实现的邻域注意力(neighborhood attention)的示例代码:
```python
import torch
import torch.nn as nn
class NeighborhoodAttention(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1):
super(NeighborhoodAttention, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
# Convolutional layer
x = self.conv(x)
# Batch normalization
x = self.bn(x)
# ReLU activation
x = self.relu(x)
# Neighborhood attention
B, C, H, W = x.size()
x = x.view(B, C, H*W)
x = torch.bmm(x, x.transpose(1, 2))
x = nn.functional.softmax(x, dim=-1)
x = torch.bmm(x.transpose(1, 2), x)
x = x.view(B, C, H, W)
return x
```
在这个模块中,我们首先使用了一个卷积层对输入进行特征提取,然后进行批归一化和ReLU激活。接下来,我们使用一个邻域注意力机制,在特征图的每个位置上计算它与所有其他位置的相似度,并使用softmax函数将这些相似度转换为权重。最后,我们使用这些权重进行加权求和,得到最终的特征图。
需要注意的是,这个实现中的邻域注意力是在特征图的空间维度上计算的,而不是通道维度上。如果需要在通道维度上计算注意力,可以先使用一个卷积层将输入特征图的通道数降到1,然后使用类似的方法计算注意力。
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