自校准卷积keras代码

以下是一个示例自校准卷积的Keras代码：

import tensorflow as tf
from tensorflow.keras.layers import Layer


class SelfCalibratedConv2D(Layer):
    def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', activation=None, **kwargs):
        super(SelfCalibratedConv2D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = kernel_size
        self.strides = strides
        self.padding = padding
        self.activation = tf.keras.activations.get(activation)

    def build(self, input_shape):
        self.conv_weights = self.add_weight(name='conv_weights',
                                             shape=(self.kernel_size[0], self.kernel_size[1], input_shape[3], self.filters),
                                             initializer='glorot_uniform',
                                             trainable=True)
        self.scale = self.add_weight(name='scale',
                                     shape=(self.filters,),
                                     initializer='ones',
                                     trainable=True)
        self.bias = self.add_weight(name='bias',
                                    shape=(self.filters,),
                                    initializer='zeros',
                                    trainable=True)
        super(SelfCalibratedConv2D, self).build(input_shape)

    def call(self, inputs):
        x = tf.nn.conv2d(inputs, self.conv_weights, strides=[1, self.strides[0], self.strides[1], 1], padding=self.padding)
        x = tf.nn.bias_add(x, self.bias)
        x = tf.keras.activations.relu(x)
        # 计算self-attention
        batch_size, height, width, filters = x.shape
        f = tf.reshape(x, [batch_size, -1, filters])  
        g = tf.reshape(x, [batch_size, -1, filters])  
        h = tf.reshape(x, [batch_size, -1, filters])  
        s = tf.matmul(g, h, transpose_b=True)  
        beta = tf.nn.softmax(s, axis=-1)  
        o = tf.matmul(beta, f) 
        o = tf.reshape(o, [batch_size, height, width, filters])
        # 计算scale
        o = o * self.scale
        if self.activation is not None:
            o = self.activation(o)
        return o

    def compute_output_shape(self, input_shape):
        output_shape = list(input_shape)
        output_shape[-1] = self.filters
        return tuple(output_shape)

    def get_config(self):
        config = super(SelfCalibratedConv2D, self).get_config()
        config.update({'filters': self.filters,
                       'kernel_size': self.kernel_size,
                       'strides': self.strides,
                       'padding': self.padding,
                       'activation': tf.keras.activations.serialize(self.activation)})
        return config

此代码定义了一个名为SelfCalibratedConv2D的自校准卷积类，它继承了Keras的Layer类。在build方法中，我们定义了需要训练的权重(conv_weights, scale, bias)，并在call方法中使用这些权重执行卷积操作和self-attention操作。最后，我们将结果乘以一个scale向量，并应用激活函数。该代码还实现了get_config方法，以便我们可以序列化该层并将其保存到磁盘上。