Python 3.x 检查失败：iz%kz==0（256对0）输入通道256不是过滤器通道512的倍数

我正在实现以下代码，即网络模块中的CBAM模块，但是，我得到以下错误：

F tensorflow/core/grappler/costs/op_level_cost_estimator.cc:654]检查失败：iz%kz==0（256对0）输入通道256不是滤波器通道512的倍数

这是CBAM块的代码

import tensorflow as tf

def CBAM(input, reduction):
    """
    @Convolutional Block Attention Module
    """

    _, channel, width, height = input.get_shape()  # (B, W, H, C)

    input = tf.transpose(input, perm=[0, 3, 2, 1]) 
    # channel attention
    x_mean = tf.reduce_mean(input, axis=(1, 2), keepdims=True)   # (B, 1, 1, C)
    x_mean = tf.layers.conv2d(x_mean, channel // reduction, 1, activation=tf.nn.relu, name='CA1', reuse=tf.AUTO_REUSE)  # (B, 1, 1, C // r)
    x_mean = tf.layers.conv2d(x_mean, channel, 1, name='CA2', reuse=tf.AUTO_REUSE)   # (B, 1, 1, C)

    x_max = tf.reduce_max(input, axis=(1, 2), keepdims=True)  # (B, 1, 1, C)
    x_max = tf.layers.conv2d(x_max, channel // reduction, 1, activation=tf.nn.relu, name='CA1', reuse=tf.AUTO_REUSE)
    # (B, 1, 1, C // r)
    x_max = tf.layers.conv2d(x_max, channel, 1, name='CA2', reuse=tf.AUTO_REUSE)  # (B, 1, 1, C)

    x = tf.add(x_mean, x_max)   # (B, 1, 1, C)
    x = tf.nn.sigmoid(x)        # (B, 1, 1, C)
    x = tf.multiply(input, x)   # (B, W, H, C)

    # spatial attention
    y_mean = tf.reduce_mean(x, axis=3, keepdims=True)  # (B, W, H, 1)
    y_max = tf.reduce_max(x, axis=3, keepdims=True)  # (B, W, H, 1)
    y = tf.concat([y_mean, y_max], axis=-1)     # (B, W, H, 2)
    y = tf.layers.conv2d(y, 1, 7, padding='same', activation=tf.nn.sigmoid)    # (B, W, H, 1)
    y = tf.multiply(x, y)  # (B, W, H, C)

    y = tf.transpose(y, perm=[0, 3, 2, 1])
    return y

这里我调用了下面给出的网络中的CBAM（）模块

layer17 = tf.layers.conv2d(layer16, 512, 3,
                           padding="same",
                           activation=tf.nn.relu,
                           dilation_rate=2,
                           data_format=self._data_format,
                           name="conv5/conv5_3")

layer17=CBAM(layer17, 8)
layer18 = tf.layers.max_pooling2d(layer17, 2, 1,
                                  padding="same",
                                  data_format=self._data_format) 

   

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如果有人能在以上代码中提供帮助，请！！！！