当在unet当中加入cbam时，cbam与卷积层池化层的位置分布应该是怎样的

在将CBAM添加到UNet中，通常可以将CBAM应用于UNet的编码器和解码器部分的每个卷积层之后。以下是一种常见的CBAM与卷积层和池化层的位置分布示例：

import tensorflow as tf
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, concatenate, Activation

def cbam_block(inputs, reduction_ratio=16):
    # Channel attention
    channels = inputs.shape[-1]
    avg_pool = tf.reduce_mean(inputs, axis=[1, 2], keepdims=True)
    fc1 = Conv2D(channels // reduction_ratio, kernel_size=(1, 1))(avg_pool)
    fc1 = Activation('relu')(fc1)
    fc2 = Conv2D(channels, kernel_size=(1, 1))(fc1)
    channel_attention = tf.sigmoid(fc2) * inputs

    # Spatial attention
    max_pool = tf.reduce_max(inputs, axis=3, keepdims=True)
    conv = Conv2D(1, kernel_size=(7, 7), padding='same')(max_pool)
    spatial_attention = tf.sigmoid(conv) * inputs

    # Combine channel and spatial attention
    outputs = tf.add(channel_attention, spatial_attention)

    return outputs

def unet_cbam(input_shape):
    inputs = Input(input_shape)
    
    # Encoder
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    conv1 = cbam_block(conv1)  # CBAM block added
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    # Decoder
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    conv2 = cbam_block(conv2)  # CBAM block added
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    # Add more encoder and decoder layers with CBAM block
    
    # Output
    outputs = Conv2D(num_classes, 1, activation='softmax')(pool2)
    
    model = Model(inputs=inputs, outputs=outputs)
    
    return model

在这个示例中，CBAM块被插入到了每个卷积层之后，以增强模型的注意力机制。你可以根据需要添加更多的编码器和解码器层，并在每层之后应用CBAM块。这里的示例代码仅供参考，你可以根据自己的具体需求进行修改和优化。