mmaction2 注意力机制
时间: 2023-06-30 13:24:42 浏览: 123
mmaction2是一个基于PyTorch的开源动作识别工具包,它支持多种经典的动作识别模型,如2D CNN、3D CNN、C3D、R(2+1)D等。在mmaction2中,可以通过添加注意力机制来提高模型的性能,常用的注意力机制包括SENet、CBAM、Non-local、Temporal Shift Attention等。
以SENet为例,下面是在mmaction2中添加SENet模块的代码示例:
```python
import torch.nn as nn
from mmcv.cnn import build_norm_layer
class SEBlock(nn.Module):
def __init__(self, channels, reduction=16, norm_cfg=None):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool3d(1)
self.fc = nn.Sequential(
nn.Linear(channels, channels // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channels // reduction, channels, bias=False),
nn.Sigmoid()
)
self.norm_name, self.norm = build_norm_layer(norm_cfg, channels)
def forward(self, x):
b, c, _, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1, 1)
x = x * y
x = self.norm(x)
return x
class ResNet3d(nn.Module):
def __init__(self, block, layers, num_classes, with_se=False, norm_cfg=None):
super(ResNet3d, self).__init__()
# define your network here
# add SEBlock to some layers
if with_se:
self.layer1[0].downsample[0] = nn.Sequential(
nn.Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False),
nn.BatchNorm3d(256),
SEBlock(channels=256, norm_cfg=norm_cfg),
)
self.layer2[0].downsample[0] = nn.Sequential(
nn.Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False),
nn.BatchNorm3d(512),
SEBlock(channels=512, norm_cfg=norm_cfg),
)
self.layer3[0].downsample[0] = nn.Sequential(
nn.Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False),
nn.BatchNorm3d(1024),
SEBlock(channels=1024, norm_cfg=norm_cfg),
)
def forward(self, x):
# forward pass of your network here
# add SEBlock to some layers
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
return x
```
在这个代码示例中,我们定义了一个名为`SEBlock`的SENet模块,它接受输入张量并通过全局平均池化和两个全连接层来计算通道注意力权重,最后使用Sigmoid函数将权重映射到[0, 1]范围内。然后,我们将`SEBlock`添加到了网络的某些层中,以实现注意力机制的引入。在`forward`方法中,我们首先对输入进行正向传递,然后在某些层上应用`SEBlock`。同时,我们还可以通过设置`with_se=True`参数来启用SENet模块。