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 ?
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')
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)
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
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With