Reflection padding Conv2D

问题

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)

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

回答1:

The accepted answer above is not working in the current Keras version. Here is the version that's working:

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 compute_output_shape(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')

回答2:

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)

回答3:

As you can check in the documentation there is no such 'reflect' padding. Only 'same' and 'valid' are implemented in keras.

You maybe try to implemented on your own or find if somebody already did it. You should base yourself in the Conv2D class and check where self.padding member variable is used.

回答4:

import tensorflow as tf
from keras.layers import Lambda

inp_padded = Lambda(lambda x: tf.pad(x, [[0,0], [27,27], [27,27], [0,0]], 'REFLECT'))(inp)

The solution from Akihiko did not work with the new keras version, so I came up with my own. The snippet pads a batch of 202x202x3 images to 256x256x3

来源：https://stackoverflow.com/questions/50677544/reflection-padding-conv2d

标签

python

keras

padding

convolutional-neural-network

zero-padding