在深度网络中添加CBAM的优势
时间: 2024-04-06 07:29:24 浏览: 15
在深度神经网络中添加CBAM(Convolutional Block Attention Module)有以下优势:
1. 提高模型的准确性:CBAM可以学习到不同通道和空间位置的相关性,从而使得网络能够更好地理解输入数据的特征,提高模型的准确性。
2. 增强模型的泛化能力:CBAM能够自适应地对不同通道和空间位置进行注意力加权,使得网络更加关注重要的特征,从而增强模型的泛化能力。
3. 减少模型的参数量:CBAM只需要添加少量的参数就可以增强网络的表达能力,从而减少模型的参数量,降低模型的复杂度。
4. 适用范围广:CBAM可以与各种类型的深度神经网络结构相结合,如卷积神经网络、循环神经网络和注意力机制等,适用范围广泛。
相关问题
用theano库在ResNet中添加CBAM
CBAM是一种用于卷积神经网络的注意力机制,可以帮助网络更好地关注重要的特征,提高网络性能。在ResNet中添加CBAM需要进行以下步骤:
1. 在导入库的部分,添加以下代码:
```python
from theano import tensor as T
from theano import function
```
2. 定义CBAM的注意力模块,代码如下:
```python
def cbam(inputs, reduction_ratio=0.5, name=None):
# Channel attention module
pool = T.mean(inputs, axis=[2, 3], keepdims=True)
pool = T.flatten(pool, outdim=2)
shared_layer_one = T.nnet.relu(T.dot(pool, T.alloc(1.0, inputs.shape[1], int(inputs.shape[1]*reduction_ratio))))
shared_layer_two = T.dot(shared_layer_one, T.alloc(1.0, int(inputs.shape[1]*reduction_ratio), inputs.shape[1]))
attention = T.nnet.sigmoid(shared_layer_two)
attention = T.reshape(attention, (inputs.shape[0], inputs.shape[1], 1, 1))
weighted_input = inputs * attention
# Spatial attention module
filters = inputs.shape[1]
conv_output = T.nnet.relu(T.nnet.conv2d(inputs, T.alloc(1.0, filters, 1, 1), border_mode='same'))
pool_output = T.nnet.relu(T.nnet.max_pool_2d(conv_output, (1, 1), ignore_border=True))
shared_layer_one = T.nnet.relu(T.nnet.conv2d(pool_output, T.alloc(1.0, filters, 1, 1), border_mode='same'))
shared_layer_two = T.nnet.conv2d(shared_layer_one, T.alloc(1.0, filters, 1, 1), border_mode='same')
attention = T.nnet.sigmoid(shared_layer_two)
attention = T.addbroadcast(attention, 1, 2)
attention = T.addbroadcast(attention, 1, 3)
weighted_input = inputs * attention
return weighted_input
```
3. 在ResNet的每个残差块中,添加CBAM模块,代码如下:
```python
def residual_block(input, filters, reduction_ratio=0.5, stride=1, name=None):
shortcut = input
if stride != 1 or input.shape[1] != filters * 4:
shortcut = T.nnet.relu(T.nnet.conv2d(input, filters * 4, (1, 1), subsample=(stride, stride), border_mode='same', name=name + '_shortcut_conv'))
conv1 = T.nnet.relu(T.nnet.conv2d(input, filters, (1, 1), subsample=(stride, stride), border_mode='same', name=name + '_conv1'))
conv2 = T.nnet.relu(cbam(T.nnet.conv2d(conv1, filters, (3, 3), border_mode='same', name=name + '_conv2'), reduction_ratio=reduction_ratio, name=name + '_cbam'))
conv3 = T.nnet.conv2d(conv2, filters * 4, (1, 1), border_mode='same', name=name + '_conv3')
output = T.nnet.relu(conv3 + shortcut)
return output
```
4. 在ResNet的网络结构中,调用上面定义的残差块函数,代码如下:
```python
def resnet(input_shape=(3, 224, 224), num_classes=1000, block_sizes=[64, 128, 256, 512], reduction_ratio=0.5):
input = T.tensor4('input')
output = input - T.alloc(0.5, input.shape[0], input.shape[1], input.shape[2], input.shape[3])
output = output * T.alloc(2.0, input.shape[0], input.shape[1], input.shape[2], input.shape[3])
output = T.nnet.relu(T.nnet.conv2d(output, block_sizes[0], (7, 7), subsample=(2, 2), border_mode='same', name='conv1'))
output = T.nnet.max_pool_2d(output, (3, 3), subsample=(2, 2), ignore_border=False)
output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2a')
output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2b')
output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2c')
output = residual_block(output, block_sizes[1], stride=2, reduction_ratio=reduction_ratio, name='3a')
output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3b')
output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3c')
output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3d')
output = residual_block(output, block_sizes[2], stride=2, reduction_ratio=reduction_ratio, name='4a')
output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4b')
output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4c')
output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4d')
output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4e')
output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4f')
output = residual_block(output, block_sizes[3], stride=2, reduction_ratio=reduction_ratio, name='5a')
output = residual_block(output, block_sizes[3], reduction_ratio=reduction_ratio, name='5b')
output = residual_block(output, block_sizes[3], reduction_ratio=reduction_ratio, name='5c')
output = T.nnet.relu(T.nnet.mean(T.nnet.relu(output), axis=(2, 3)))
output = T.nnet.softmax(T.dot(output, T.alloc(0.0, num_classes, block_sizes[3]*4)), axis=-1)
f = function([input], output)
return f
```
以上就是用theano库在ResNet中添加CBAM的步骤。
在yolov8中添加注意力CBAM
在YOLOv8中添加注意力CBAM的步骤如下:
1. 在yolov8_cbam.yaml文件中添加CBAM注意力机制的配置信息。
2. 在common.py文件中配置CBAM注意力机制的参数。
3. 在yolo.py文件中添加CBAM注意力机制的代码。
4. 训练模型并测试效果。
具体步骤如下:
. 在yolov8_cbam.yaml文件中添加CBAM注意力机制的配置信息:
```yaml
model:
...
attention: CBAM
...
```
2. 在common.py文件中配置CBAM注意力机制的参数:
```python
class CBAM(nn.Module):
def __init__(self, channels, reduction=16):
super(CBAM, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(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.conv = nn.Conv2d(channels, channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
avg_out = self.avg_pool(x)
max_out = self.max_pool(x)
avg_out = self.fc(avg_out.squeeze()).unsqueeze(2).unsqueeze(3)
max_out = self.fc(max_out.squeeze()).unsqueeze(2).unsqueeze(3)
out = avg_out + max_out
out = self.conv(out)
out = torch.sigmoid(out)
out = x * out
return out
```
3. 在yolo.py文件中添加CBAM注意力机制的代码:
```python
from models.common import CBAM
class YOLOLayer(nn.Module):
def __init__(self, ...):
...
self.cbam = CBAM(channels)
def forward(self, ...):
...
x = self.cbam(x)
...
return x
```
4. 训练模型并测试效果。