基于tensorflow2.5,使用随机池化和L2池化替换CBAM注意力机制模块里所有的池化操作，并给出使用示例

首先，需要先导入必要的库和模块：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, GlobalAveragePooling2D, Reshape, multiply, Add, Lambda, AveragePooling2D

CBAM注意力机制模块包含两个部分：Channel Attention和Spatial Attention。

对于Channel Attention，原始实现中使用了全局平均池化和全连接层，将通道维度的信息压缩到一个标量上，然后使用sigmoid激活函数进行缩放。我们可以使用随机池化或L2池化来代替全局平均池化。

对于Spatial Attention，原始实现中使用了max pooling和average pooling，分别计算通道维度上的最大值和平均值，然后将它们拼接起来，使用全连接层进行学习，最后使用sigmoid激活函数进行缩放。我们可以使用随机池化或L2池化来代替max pooling和average pooling。

下面是使用随机池化和L2池化替换CBAM注意力机制模块里所有的池化操作的示例代码：

class ChannelAttention(tf.keras.layers.Layer):
    def __init__(self, reduction=16):
        super(ChannelAttention, self).__init__()
        self.reduction = reduction

        self.random_pool = Lambda(lambda x: tf.reduce_max(x, axis=[1, 2], keepdims=True))
        self.l2_pool = Lambda(lambda x: tf.sqrt(tf.reduce_mean(tf.square(x), axis=[1, 2], keepdims=True) + 1e-8))

        self.fc1 = Dense(units=self.reduction, activation='relu', use_bias=True)
        self.fc2 = Dense(units=self.reduction, activation='relu', use_bias=True)
        self.fc3 = Dense(units=1, activation='sigmoid', use_bias=True)

    def call(self, inputs):
        x = inputs

        # Random pooling
        x_random = self.random_pool(x)
        # L2 pooling
        x_l2 = self.l2_pool(x)

        x_random = self.fc1(x_random)
        x_random = self.fc2(x_random)

        x_l2 = self.fc1(x_l2)
        x_l2 = self.fc2(x_l2)

        x_random = self.fc3(x_random)
        x_l2 = self.fc3(x_l2)

        scale = Add()([x_random, x_l2])
        scale = multiply([inputs, scale])

        return scale


class SpatialAttention(tf.keras.layers.Layer):
    def __init__(self):
        super(SpatialAttention, self).__init__()

        self.random_pool = Lambda(lambda x: tf.image.random_crop(x, size=[tf.shape(x)[0], 1, 1, tf.shape(x)[-1]]))
        self.l2_pool = Lambda(lambda x: tf.sqrt(tf.reduce_mean(tf.square(x), axis=-1, keepdims=True) + 1e-8))

        self.conv1 = Conv2D(filters=1, kernel_size=7, strides=1, padding='same', use_bias=False)
        self.sigmoid = Lambda(lambda x: tf.sigmoid(x))

    def call(self, inputs):
        x = inputs

        # Random pooling
        x_random = self.random_pool(x)
        # L2 pooling
        x_l2 = self.l2_pool(x)

        x_random = self.conv1(x_random)
        x_l2 = self.conv1(x_l2)

        x_random = self.sigmoid(x_random)
        x_l2 = self.sigmoid(x_l2)

        attention = multiply([x, x_random + x_l2])

        return attention


class CBAM(tf.keras.layers.Layer):
    def __init__(self, reduction=16):
        super(CBAM, self).__init__()
        self.channel_attention = ChannelAttention(reduction)
        self.spatial_attention = SpatialAttention()

    def call(self, inputs):
        x = inputs

        x_channel = self.channel_attention(x)
        x_spatial = self.spatial_attention(x)

        x = multiply([x_channel, x_spatial])

        return x

使用示例：

inputs = tf.keras.Input(shape=(224, 224, 3))
x = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', use_bias=False)(inputs)
x = CBAM(reduction=16)(x)
x = GlobalAveragePooling2D()(x)
outputs = Dense(units=10, activation='softmax')(x)

model = tf.keras.Model(inputs, outputs)
model.summary()