使用tensorflow实现CDCN

Question

最近，我想使用tensorflow2.8+python3.9在CVPR2020中实现CDCN。这需要我的自定义层获取当前conv2d层的权重。但是，当我尝试将自定义层添加到序列模型中时，出现了错误：NotImplementedError:numpy((仅在启用急切执行时可用。这是我的密码。有人能帮我吗？我曾尝试添加tf.compat.v1.enable_eager_execution((，但它不起作用。

import numpy as np
import tensorflow.keras as tfk
import tensorflow as tf

class CDC(tfk.layers.Layer):
def __init__(self, output_dim, kernel_size=(3, 3), padding='same', activation=None, theta=0.7, **kwargs):
super(CDC, self).__init__()
self.theta = theta
self.activation = None
self.output_dim = output_dim
self.kernel_size = kernel_size
self.padding = padding
if activation is not None:
self.activation = tfk.activations.get(activation)
def build(self, input_shape):
self.conv = tfk.layers.Conv2D(self.output_dim, self.kernel_size, padding=self.padding, input_shape=input_shape)
self.conv.build(input_shape=input_shape)
self._kernel = self.conv.kernel
super(CDC, self).build(input_shape)
self.built = True

def call(self, inputs, training=None, mask=None):
vanillaOutput = self.conv(inputs)
weightSum = self.conv.kernel.numpy().sum(axis=0).sum(axis=0).sum(axis=0)
weightSum = np.reshape(weightSum, (1, 1, 1, self.output_dim))
weightSum = tf.constant(weightSum, dtype=tf.float32)
cDiff = tf.nn.conv2d(inputs, filters=weightSum, strides=self.conv.strides, padding=self.conv.padding.upper())
result = vanillaOutput - self.theta * cDiff
if self.activation is not None:
return self.activation(result)
return vanillaOutput

Answer 1

如果您只想要内核中所有元素的总和

tf.math.reduce_sum()

同时更换线路

weightSum = self.conv.kernel.numpy().sum(axis=0).sum(axis=0).sum(axis=0)
weightSum = np.reshape(weightSum, (1, 1, 1, self.output_dim))
weightSum = tf.constant(weightSum, dtype=tf.float32)

weightSum = tf.math.reduce_sum(tf.math.reduce_sum(tf.math.reduce_sum(self.conv.kernel,axis=0),axis=0),axis=0)
weightSum = tf.reshape(weightSum, (1, 1, 1, self.output_dim))