def maybe_move_axis_to_last(a, axis):
def move_axis_to_last(a, axis):
return tf.transpose(
a, tf.concat(
[tf.range(axis), tf.range(axis + 1, tf.rank(a)), [axis]],
axis=0))
return utils.cond(
axis == utils.subtract(tf.rank(a), 1),
lambda: a,
lambda: move_axis_to_last(a, axis))
def f(a, b): # pylint: disable=missing-docstring
# We can't assign to captured variable `axisa`, so make a new variable
axis_a = axisa
axis_b = axisb
axis_c = axisc
if axis is not None:
axis_a = axis
axis_b = axis
axis_c = axis
if axis_a < 0:
axis_a = utils.add(axis_a, tf.rank(a))
if axis_b < 0:
axis_b = utils.add(axis_b, tf.rank(b))
def maybe_move_axis_to_last(a, axis):
def move_axis_to_last(a, axis):
return tf.transpose(
a, tf.concat(
[tf.range(axis), tf.range(axis + 1, tf.rank(a)), [axis]],
axis=0))
return utils.cond(
axis == utils.subtract(tf.rank(a), 1),
lambda: a,
lambda: move_axis_to_last(a, axis))
a = maybe_move_axis_to_last(a, axis_a)
b = maybe_move_axis_to_last(b, axis_b)
a_dim = utils.getitem(tf.shape(a), -1)
b_dim = utils.getitem(tf.shape(b), -1)
def maybe_pad_0(a, size_of_last_dim):
def pad_0(a):
return tf.pad(a, tf.concat([tf.zeros([tf.rank(a) - 1, 2], tf.int32),
tf.constant([[0, 1]], tf.int32)], axis=0))
return utils.cond(size_of_last_dim == 2,
lambda: pad_0(a),
lambda: a)
a = maybe_pad_0(a, a_dim)
b = maybe_pad_0(b, b_dim)
c = tf.linalg.cross(*utils.tf_broadcast(a, b))
if axis_c < 0:
axis_c = utils.add(axis_c, tf.rank(c))
def move_last_to_axis(a, axis):
r = tf.rank(a)
return tf.transpose(
a, tf.concat(
[tf.range(axis), [r - 1], tf.range(axis, r - 1)], axis=0))
c = utils.cond(
(a_dim == 2) & (b_dim == 2),
lambda: c[..., 2],
lambda: utils.cond( # pylint: disable=g-long-lambda
axis_c == utils.subtract(tf.rank(c), 1),
lambda: c,
lambda: move_last_to_axis(c, axis_c)))
return c
