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


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.y_dim), 0),
                           [batch_size_tensor, 1])
        xx_range = tf.expand_dims(xx_range, -1)
        xx_channel = tf.matmul(xx_range, xx_ones)
        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.x_dim), 0),
                           [batch_size_tensor, 1])
        yy_range = tf.expand_dims(yy_range, 1)
        yy_channel = tf.matmul(yy_ones, yy_range)
        yy_channel = tf.expand_dims(yy_channel, -1)

        xx_channel = tf.cast(xx_channel, 'float32') / \
            tf.cast((self.x_dim - 1), 'float32')
        yy_channel = tf.cast(yy_channel, 'float32') / \
            tf.cast((self.y_dim - 1), 'float32')
        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


if __name__ == '__main__':
    batch_size, h_dim, w_dim, channel = 20, 15, 10, 50
    input_tensor = tf.convert_to_tensor(
        np.random.rand(batch_size, h_dim, w_dim, 50).astype(np.float32)
    )
    ret = AddCoords(w_dim, h_dim)(input_tensor)
    sess = tf.Session()
    r = sess.run(ret)
    print r.shape
