(self, net)
Adds one-hot encoding of coordinates to different views in the networks.

    For each "pixel" of a feature map it adds a onehot encoded x and y
    coordinates.

    Args:
      net: a tensor of shape=[batch_size, height, width, num_features]

    Returns:
      a tensor with the same height and width, but altered feature_size.
Adds one-hot encoding of coordinates to different views in the networks.
Adds one - hot encoding of coordinates to different views in the networks .
def encode_coordinates_fn(self, net):
    """Adds one-hot encoding of coordinates to different views in the networks.

    For each "pixel" of a feature map it adds a onehot encoded x and y
    coordinates.

    Args:
      net: a tensor of shape=[batch_size, height, width, num_features]

    Returns:
      a tensor with the same height and width, but altered feature_size.
    """
    mparams = self._mparams['encode_coordinates_fn']
    if mparams.enabled:
      batch_size, h, w, _ = net.shape.as_list()
      x, y = tf.meshgrid(tf.range(w), tf.range(h))
      w_loc = slim.one_hot_encoding(x, num_classes=w)
      h_loc = slim.one_hot_encoding(y, num_classes=h)
      loc = tf.concat([h_loc, w_loc], 2)
      loc = tf.tile(tf.expand_dims(loc, 0), [batch_size, 1, 1, 1])
      return tf.concat([net, loc], 3)
    else:
      return net
def encode_coordinates_fn ( self , net ) : mparams = self . _mparams [ 'encode_coordinates_fn' ] if mparams . enabled : batch_size , h , w , _ = net . shape . as_list ( ) x , y = tf . meshgrid ( tf . range ( w ) , tf . range ( h ) ) w_loc = slim . one_hot_encoding ( x , num_classes = w ) h_loc = slim . one_hot_encoding ( y , num_classes = h ) loc = tf . concat ( [ h_loc , w_loc ] , 2 ) loc = tf . tile ( tf . expand_dims ( loc , 0 ) , [ batch_size , 1 , 1 , 1 ] ) return tf . concat ( [ net , loc ] , 3 ) else : return net