基于Kears实现CA注意力机制的python程序代码
时间: 2023-07-06 15:35:52 浏览: 69
下面是一个基于Keras实现CA注意力机制的Python程序代码示例:
```python
from keras.layers import Conv2D, Activation, Multiply, Lambda
def channel_attention(input_feature, ratio=8):
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
channel = input_feature._keras_shape[channel_axis]
shared_layer_one = Dense(channel // ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
shared_layer_two = Dense(channel, kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
avg_pool = GlobalAveragePooling2D()(input_feature)
avg_pool = Reshape((1,1,channel))(avg_pool)
avg_pool = shared_layer_one(avg_pool)
avg_pool = shared_layer_two(avg_pool)
max_pool = GlobalMaxPooling2D()(input_feature)
max_pool = Reshape((1,1,channel))(max_pool)
max_pool = shared_layer_one(max_pool)
max_pool = shared_layer_two(max_pool)
cbam_feature = Add()([avg_pool,max_pool])
cbam_feature = Activation('sigmoid')(cbam_feature)
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return multiply([input_feature, cbam_feature])
def spatial_attention(input_feature):
kernel_size = 7
if K.image_data_format() == "channels_first":
channel = input_feature._keras_shape[1]
cbam_feature = Permute((2,3,1))(input_feature)
else:
channel = input_feature._keras_shape[-1]
cbam_feature = input_feature
avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
concat = Concatenate(axis=3)([avg_pool, max_pool])
cbam_feature = Conv2D(filters = 1,
kernel_size=kernel_size,
strides=1,
padding='same',
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(concat)
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return multiply([input_feature, cbam_feature])
def cbam_block(cbam_feature,ratio=8):
cbam_feature = channel_attention(cbam_feature,ratio)
cbam_feature = spatial_attention(cbam_feature)
return cbam_feature
```
注意:这只是一个示例,具体实现可能需要根据具体应用进行修改。
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