反射填充Conv2D [英] Reflection padding Conv2D

问题描述

我正在使用keras构建用于图像分割的卷积神经网络，我想使用反射填充"而不是填充相同"，但是我找不到在keras中做到这一点的方法.

I'm using keras to build a convolutional neural network for image segmentation and I want to use "reflection padding" instead of padding "same" but I cannot find a way to to do it in keras.

inputs = Input((num_channels, img_rows, img_cols))
conv1=Conv2D(32,3,padding='same',kernel_initializer='he_uniform',data_format='channels_first')(inputs)

是否可以实现反射层并将其插入keras模型中?

Is there a way to implement a reflection layer and insert it in a keras model ?

推荐答案

找到了解决方案！我们只需要创建一个将图层作为输入的新类，并使用tensorflow预定义函数即可做到这一点.

Found the solution! We have only to create a new class that takes a layer as input and use tensorflow predefined function to do it.

import tensorflow as tf
from keras.engine.topology import Layer
from keras.engine import InputSpec

class ReflectionPadding2D(Layer):
    def __init__(self, padding=(1, 1), **kwargs):
        self.padding = tuple(padding)
        self.input_spec = [InputSpec(ndim=4)]
        super(ReflectionPadding2D, self).__init__(**kwargs)

    def get_output_shape_for(self, s):
        """ If you are using "channels_last" configuration"""
        return (s[0], s[1] + 2 * self.padding[0], s[2] + 2 * self.padding[1], s[3])

    def call(self, x, mask=None):
        w_pad,h_pad = self.padding
        return tf.pad(x, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')

# a little Demo
inputs = Input((img_rows, img_cols, num_channels))
padded_inputs= ReflectionPadding2D(padding=(1,1))(inputs)
conv1 = Conv2D(32, 3, padding='valid', kernel_initializer='he_uniform',
               data_format='channels_last')(padded_inputs)

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