Extracted CordConvs tensorflow implementation from (An intriguing failing of convolutional neural networks and the CoordConv solution) https://arxiv.org/pdf/1807.03247.pdf

CoordConvLayer.py

from tensorflow.python.layers import base
import tensorflow as tf

class AddCoords(base.Layer):
    
    """Add coords to a tensor"""
    def __init__(self, x_dim=64, y_dim=64, with_r=False):
        super(AddCoords, self).__init__()
        self.x_dim = x_dim
        self.y_dim = y_dim
        self.with_r = with_r
        
    def call(self, input_tensor):
        """
        input_tensor: (batch, x_dim, y_dim, c)
        """
        batch_size_tensor = tf.shape(input_tensor)[0]
        xx_ones = tf.ones([batch_size_tensor, self.x_dim], dtype=tf.int32)
        xx_ones = tf.expand_dims(xx_ones, -1)
        xx_range = tf.tile(tf.expand_dims(tf.range(self.x_dim), 0), [batch_size_tensor, 1])
        xx_range = tf.expand_dims(xx_range, 1)
        xx_channel = tf.matmul(xx_ones, xx_range)
        xx_channel = tf.expand_dims(xx_channel, -1)
        yy_ones = tf.ones([batch_size_tensor, self.y_dim], dtype=tf.int32)
        yy_ones = tf.expand_dims(yy_ones, 1)
        yy_range = tf.tile(tf.expand_dims(tf.range(self.y_dim), 0), [batch_size_tensor, 1])
        yy_range = tf.expand_dims(yy_range, -1)
        yy_channel = tf.matmul(yy_range, yy_ones)
        yy_channel = tf.expand_dims(yy_channel, -1)
        xx_channel = tf.cast(xx_channel, 'float32') / (self.x_dim - 1)
        yy_channel = tf.cast(yy_channel, 'float32') / (self.y_dim - 1)
        xx_channel = xx_channel*2 - 1
        yy_channel = yy_channel*2 - 1
        ret = tf.concat([input_tensor, xx_channel, yy_channel], axis=-1)
        if self.with_r:
            rr = tf.sqrt( tf.square(xx_channel-0.5) + tf.square(yy_channel-0.5))
            ret = tf.concat([ret, rr], axis=-1)
        return ret
    
class CoordConv(base.Layer):
    
    """CoordConv layer as in the paper."""
    def __init__(self, x_dim, y_dim, with_r, *args, **kwargs):
        super(CoordConv, self).__init__()
        self.addcoords = AddCoords(x_dim=x_dim, y_dim=y_dim, with_r=with_r)
        self.conv = tf.layers.Conv2D(*args, **kwargs)
        
    def call(self, input_tensor):
        ret = self.addcoords(input_tensor)
        ret = self.conv(ret)
        return ret  

Example of usage:

x = CoordConv(x_dim=16, y_dim=16, with_r=False,
                       filters = 64, kernel_size = (3, 3), strides = (1, 1), padding = 'same', activation = None)(x)

Also as I understand x_dim, y_dim size can be inferred from input tensor shape.