Resize or interpolate tensors to any spatial dimension in Keras

Upsample.py

import tensorflow as tf
import keras.backend as K
from keras.layers import Lambda

#
# Keras + TensorFlow
# Resize 'tensorA' to be of the same size as 'tensorB' using 'tf.image.resize_bilinear'
# Very useful for skip-connections and 'Concatenate' layers that might complain about being off by one column
# Only works when dimensions are provided since we use ' K.int_shape' that returns the static shape
#
def ResizeLayerLike(tensorA, tensorB):
    sB = K.int_shape(tensorB)
    def resize_like(tensor, sB): return tf.image.resize_bilinear(tensor, sB[1:3], align_corners=True)
    return Lambda(resize_like, arguments={'sB':sB})(tensorA)

Thanks for this gist, only change I had to make was instead of tf.image.resize_bilinear I needed to use tf.compat.v1.image.resize_bilinear. Weird that this isn't already a standard layer in tensorflow/keras.