x = conv2d_bn(x, 80, 1, 1, padding='valid') x = conv2d_bn(x, 192, 3, 3, padding='valid') x = MaxPooling2D((3, 3), strides=(2, 2))(x)将1*1卷积用ghost_module代替

Sure! Here's the modified code using Ghost Module instead of the 1x1 convolutions:

import tensorflow as tf

def ghost_module(x, num_channels, kernel_size):
    # Split input feature map into two sub-feature maps
    ghost_channels = num_channels // 2
    
    # First sub-feature map using 1x1 convolution
    branch1x1 = tf.keras.layers.Conv2D(ghost_channels, 1)(x)
    
    # Second sub-feature map using smaller kernel size for convolution
    branch3x3 = tf.keras.layers.Conv2D(ghost_channels, kernel_size, padding='valid')(x)
    
    # Concatenate the two sub-feature maps
    out = tf.keras.layers.Concatenate()([branch1x1, branch3x3])
    
    return out

# Replace 1x1 convolutions with Ghost Module
x = ghost_module(x, 80, 1)
x = ghost_module(x, 192, 3)
x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(x)

In the modified code, the `ghost_module` function is used to replace the 1x1 convolutions. It splits the input feature map into two sub-feature maps and applies a 1x1 convolution to the first sub-feature map and a smaller kernel convolution to the second sub-feature map. Finally, it concatenates the two sub-feature maps to obtain the output.

Please note that this code assumes you have defined the `ghost_module` function correctly and have imported the necessary dependencies. Additionally, make sure to adjust the parameters according to your specific needs.