def expand_dims_(x):
"""Implementation of `expand_dims`."""
with tf.name_scope(name or 'expand_dims'):
x = tf.convert_to_tensor(x, name='x')
new_axis = tf.convert_to_tensor(axis,
dtype_hint=tf.int32,
name='axis')
nx = prefer_static.rank(x)
na = prefer_static.size(new_axis)
is_neg_axis = new_axis < 0
k = prefer_static.reduce_sum(
prefer_static.cast(is_neg_axis, new_axis.dtype))
new_axis = prefer_static.where(is_neg_axis, new_axis + nx,
new_axis)
new_axis = prefer_static.sort(new_axis)
axis_neg, axis_pos = prefer_static.split(new_axis, [k, -1])
idx = prefer_static.argsort(prefer_static.concat([
axis_pos,
prefer_static.range(nx),
axis_neg,
],
axis=0),
stable=True)
shape = prefer_static.pad(prefer_static.shape(x),
paddings=[[na - k, k]],
constant_values=1)
shape = prefer_static.gather(shape, idx)
return tf.reshape(x, shape)
def expand_dims(x, axis, name=None):
"""Like `tf.expand_dims` but accepts a vector of axes to expand."""
with tf.name_scope(name or 'expand_dims'):
x = tf.convert_to_tensor(x, name='x')
axis = tf.convert_to_tensor(axis, dtype_hint=tf.int32, name='axis')
nx = prefer_static.rank(x)
na = prefer_static.size(axis)
is_neg_axis = axis < 0
k = prefer_static.reduce_sum(
prefer_static.cast(is_neg_axis, axis.dtype))
axis = prefer_static.where(is_neg_axis, axis + nx, axis)
axis = prefer_static.sort(axis)
axis_neg, axis_pos = prefer_static.split(axis, [k, -1])
idx = prefer_static.argsort(prefer_static.concat([
axis_pos,
prefer_static.range(nx),
axis_neg,
],
axis=0),
stable=True)
shape = prefer_static.pad(prefer_static.shape(x),
paddings=[[na - k, k]],
constant_values=1)
shape = prefer_static.gather(shape, idx)
return tf.reshape(x, shape)
