基于keras搭建的交叉注意力机制python代码

以下是基于Keras搭建的交叉注意力机制的Python代码示例：

from keras.layers import Input, Dense, Reshape, Permute, Lambda, Concatenate, multiply
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, UpSampling2D
from keras.layers import Activation, BatchNormalization, Dropout
from keras.models import Model
from keras import backend as K

def squeeze_excite_block(input, ratio=16):
    ''' Squeeze and Excitation block
    '''
    init = input
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = init._keras_shape[channel_axis]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', use_bias=False)(se)

    if K.image_data_format() == 'channels_first':
        se = Permute((3, 1, 2))(se)

    x = multiply([init, se])
    return x

def spatial_attention_block(input):
    ''' Spatial Attention block
    '''
    conv = Conv2D(1, (3, 3), padding="same", activation='sigmoid')(input)
    return multiply([input, conv])

def cross_attention_block(input1, input2):
    ''' Cross Attention block
    '''
    filters = input1._keras_shape[-1]

    g1 = Conv2D(filters // 8, (1, 1), padding='same')(input1)
    x1 = Conv2D(filters // 8, (1, 1), padding='same')(input2)
    g1_x1 = add([g1, x1])
    psi = Activation('relu')(g1_x1)
    psi = Conv2D(1, (1, 1), padding='same')(psi)
    psi = Activation('sigmoid')(psi)
    x1_psi = multiply([x1, psi])
    return x1_psi

# define input
inputs = Input(shape=(224, 224, 3))

# define model
x = Conv2D(64, (3, 3), padding='same', activation='relu')(inputs)
x = BatchNormalization()(x)
x = squeeze_excite_block(x)
x = spatial_attention_block(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)

x = Conv2D(128, (3, 3), padding='same', activation='relu')(x)
x = BatchNormalization()(x)
x = squeeze_excite_block(x)
x = spatial_attention_block(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)

x = cross_attention_block(x, x)

x = Conv2D(256, (3, 3), padding='same', activation='relu')(x)
x = BatchNormalization()(x)
x = squeeze_excite_block(x)
x = spatial_attention_block(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)

x = Conv2D(512, (3, 3), padding='same', activation='relu')(x)
x = BatchNormalization()(x)
x = squeeze_excite_block(x)
x = spatial_attention_block(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)

x = GlobalAveragePooling2D()(x)
x = Dense(128, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.25)(x)
outputs = Dense(10, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

该代码实现了一个基于Keras的交叉注意力机制模型，包括Squeeze and Excitation block（压缩和激发块）、Spatial Attention block（空间注意块）和Cross Attention block（交叉注意块）。其中，Squeeze and Excitation block用于增强通道特征的表达能力，Spatial Attention block用于增强空间特征的表达能力，Cross Attention block用于增强不同特征图之间的交互作用。