SE注意力机制问题，在Squeeze操作.GlobalAveragePooling2D之后维度发生改变，导致输入下面的卷积出现问题，怎么解决呢

Question

x = keras.layers.Conv2D(64,(3,3),padding='same')(input_tensor)

in_channels = K.int_shape(x)[-1]
###注意力机制
in_tensor = x

#Squeeze操作
x = layers.GlobalAveragePooling2D()(x)

ratio1 = 0.5

#Exicitation
out = Conv2D(filters=in_channels//ratio1, kernel_size=(1,1))(x)
out = Activation("relu")(out)
out = Conv2D(filters=in_channels, kernel_size=(1,1))(out)
out = Activation('sigmoid')(out)
out = layers.Reshape((1, in_channels))(out)

scale = tf.multiply(in_tensor, out)

Answer 1

在您提到的SE（Squeeze-and-Excitation）注意力机制中，GlobalAveragePooling2D操作确实会改变张量的维度。GlobalAveragePooling2D将每个特征图的维度从(height, width, channels)压缩到(channels,)，这就是所谓的Squeeze操作。但是，在Excitation步骤之后，您需要将输出张量的维度调整回原来的形状，以便与原始输入in_tensor进行元素乘法（tf.multiply）操作。

问题在于，out = layers.Reshape((1, in_channels))(out)这一行尝试重新塑形out，但是给出的新形状(1, in_channels)与in_tensor的形状不匹配。正确的做法是将out的形状调整为与in_tensor相同的空间维度(height, width)，同时保留通道数in_channels。

解决方案是在Excitation步骤之后，使用Reshape将输出张量的形状调整为(batch_size, height, width, channels)，这样就可以与in_tensor进行元素乘法操作了。由于GlobalAveragePooling2D操作之后，我们失去了height和width的信息，因此需要从in_tensor中获取这些信息。

修改后的代码如下：

from tensorflow.keras import layers, models
import tensorflow as tf
import keras.backend as K
from tensorflow.keras.layers import Conv2D, Activation

input_tensor = layers.Input(shape=(224, 224, 3))
x = layers.Conv2D(64, (3, 3), padding='same')(input_tensor)

in_channels = K.int_shape(x)[-1]
# 注意力机制
in_tensor = x

# Squeeze操作
x = layers.GlobalAveragePooling2D()(x)
x = layers.Reshape((1, 1, in_channels))(x)  # 重塑形状以匹配卷积操作的需求

ratio1 = 0.5

# Excitation
out = Conv2D(filters=int(in_channels//ratio1), kernel_size=(1, 1))(x)
out = Activation("relu")(out)
out = Conv2D(filters=in_channels, kernel_size=(1, 1))(out)
out = Activation('sigmoid')(out)

# 获取原始输入的空间维度
height, width = K.int_shape(in_tensor)[1], K.int_shape(in_tensor)[2]
# 重塑out以匹配in_tensor的形状
out = layers.Reshape((height, width, in_channels))(out)

# 执行缩放操作
scale = tf.multiply(in_tensor, out)

这里使用Reshape层将out的形状调整为与in_tensor相同的空间维度，从而使得两者可以进行逐元素的乘法操作。这样就解决了由于维度不匹配而导致的问题。