########################################################################################
#
# Hierarchical Attentive Recurrent Tracking
# Copyright (C) 2017 Adam R. Kosiorek, Oxford Robotics Institute, University of Oxford
# email: adamk@robots.ox.ac.uk
# webpage: http://ori.ox.ac.uk
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
########################################################################################
import numpy as np
import tensorflow as tf
def convert_shape(shape, dtype=np.int32):
"""Converts `shape` of type tf.TensorShape to a np.array of numbers
:param shape: tf.TensorShape
:param dtype: output dtype
:return: np.array
"""
if not isinstance(shape, tf.TensorShape):
return shape
ss = map(lambda x: x if x is not None else -1, shape.as_list())
return np.asarray(ss, dtype=dtype)
def broadcast_against(tensor, against_expr):
"""Adds trailing dimensions to mask to enable broadcasting against data
:param tensor: tensor to be broadcasted
:param against_expr: tensor will be broadcasted against it
:return: mask expr with tf.rank(mask) == tf.rank(data)
"""
def cond(data, tensor):
return tf.less(tf.rank(tensor), tf.rank(data))
def body(data, tensor):
return data, tf.expand_dims(tensor, -1)
shape_invariants = [against_expr.get_shape(), tf.TensorShape(None)]
_, tensor = tf.while_loop(cond, body, [against_expr, tensor], shape_invariants)
return tensor
