# -*- coding:utf-8 -*-
from __future__ import absolute_import, print_function, division
import tensorflow as tf
'''
all of these ops are derived from tenosrflow Object Detection API
'''
def indices_to_dense_vector(indices,
size,
indices_value=1.,
default_value=0,
dtype=tf.float32):
"""Creates dense vector with indices set to specific (the para "indices_value" ) and rest to zeros.
This function exists because it is unclear if it is safe to use
tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
with indices which are not ordered.
This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
Args:
indices: 1d Tensor with integer indices which are to be set to
indices_values.
size: scalar with size (integer) of output Tensor.
indices_value: values of elements specified by indices in the output vector
default_value: values of other elements in the output vector.
dtype: data type.
Returns:
dense 1D Tensor of shape [size] with indices set to indices_values and the
rest set to default_value.
"""
size = tf.to_int32(size)
zeros = tf.ones([size], dtype=dtype) * default_value
values = tf.ones_like(indices, dtype=dtype) * indices_value
return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)],
[zeros, values])
