# Copyright OTT-JAX
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from types import MappingProxyType
from typing import Any, Mapping, Optional, Tuple
import jax.numpy as jnp
from ott.geometry import costs, pointcloud, segment
from ott.problems.linear import linear_problem
from ott.solvers.linear import sinkhorn
[docs]
def segment_sinkhorn(
x: jnp.ndarray,
y: jnp.ndarray,
num_segments: Optional[int] = None,
max_measure_size: Optional[int] = None,
cost_fn: Optional[costs.CostFn] = None,
segment_ids_x: Optional[jnp.ndarray] = None,
segment_ids_y: Optional[jnp.ndarray] = None,
indices_are_sorted: bool = False,
num_per_segment_x: Optional[Tuple[int, ...]] = None,
num_per_segment_y: Optional[Tuple[int, ...]] = None,
weights_x: Optional[jnp.ndarray] = None,
weights_y: Optional[jnp.ndarray] = None,
sinkhorn_kwargs: Mapping[str, Any] = MappingProxyType({}),
**kwargs: Any
) -> jnp.ndarray:
"""Compute regularized OT cost between subsets of vectors in `x` and `y`.
Helper function designed to compute Sinkhorn regularized OT cost between
several point clouds of varying size, in parallel, using padding.
In practice, The inputs `x` and `y` (and their weight vectors `weights_x` and
`weights_y`) are assumed to be large weighted point clouds, that describe
points taken from multiple measures. To extract several subsets of points, we
provide two interfaces. The first interface assumes that a vector of id's is
passed, describing for each point of `x` (resp. `y`) to which measure the
point belongs to. The second interface assumes that `x` and `y` were simply
formed by concatenating several measures contiguously, and that only indices
that segment these groups are needed to recover them.
For both interfaces, both `x` and `y` should contain the same total number of
segments. Each segment will be padded as necessary, all segments rearranged as
a tensor, and :func:`jax.vmap` used to evaluate Sinkhorn divergences in
parallel.
Args:
x: Array of input points, of shape `[num_x, feature]`.
Multiple segments are held in this single array.
y: Array of target points, of shape `[num_y, feature]`.
num_segments: Number of segments contained in `x` and `y`.
Providing this is required for JIT compilation to work,
see also :func:`~ott.geometry.segment.segment_point_cloud`.
max_measure_size: Total size of measures after padding. Should ideally be
set to an upper bound on points clouds processed with the segment
interface. Providing this is required for JIT compilation to work.
cost_fn: Cost function, defaults to
:class:`~ott.geometry.costs.SqEuclidean`.
segment_ids_x: **1st interface** The segment ID for which each row of `x`
belongs. This is a similar interface to `jax.ops.segment_sum`.
segment_ids_y: **1st interface** The segment ID for which each row of `y`
belongs.
indices_are_sorted: **1st interface** Whether `segment_ids_x` and
`segment_ids_y` are sorted.
num_per_segment_x: **2nd interface** Number of points in each segment in
`x`. For example, [100, 20, 30] would imply that `x` is segmented into
three arrays of length `[100]`, `[20]`, and `[30]` respectively.
num_per_segment_y: **2nd interface** Number of points in each segment in
`y`.
weights_x: Weights of each input points, arranged in the same segmented
order as `x`.
weights_y: Weights of each input points, arranged in the same segmented
order as `y`.
sinkhorn_kwargs: Optionally a dict containing the keywords arguments for
calls for the :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` solver,
called three times to evaluate for each segment the Sinkhorn regularized
OT cost between `x`/`y`, `x`/`x`, and `y`/`y` (except when `static_b` is
`True`, in which case `y`/`y` is not evaluated).
kwargs: keywords arguments passed to form
:class:`~ott.geometry.pointcloud.PointCloud` geometry objects from the
subsets of points and masses selected in `x` and `y`, possibly a
:class:`~ott.geometry.costs.CostFn` or an entropy regularizer.
Returns:
An array of Sinkhorn regularized OT costs for each segment.
"""
# instantiate padding vector
dim = x.shape[1]
if cost_fn is None:
# default padder
padding_vector = costs.CostFn._padder(dim=dim)
else:
padding_vector = cost_fn._padder(dim=dim)
def eval_fn(
padded_x: jnp.ndarray,
padded_y: jnp.ndarray,
padded_weight_x: jnp.ndarray,
padded_weight_y: jnp.ndarray,
) -> float:
mask_x = padded_weight_x > 0.0
mask_y = padded_weight_y > 0.0
geom = pointcloud.PointCloud(
padded_x,
padded_y,
cost_fn=cost_fn,
src_mask=mask_x,
tgt_mask=mask_y,
**kwargs,
)
prob = linear_problem.LinearProblem(
geom, a=padded_weight_x, b=padded_weight_y
)
solver = sinkhorn.Sinkhorn(**sinkhorn_kwargs)
return solver(prob).reg_ot_cost
return segment._segment_interface(
x,
y,
eval_fn,
num_segments=num_segments,
max_measure_size=max_measure_size,
segment_ids_x=segment_ids_x,
segment_ids_y=segment_ids_y,
indices_are_sorted=indices_are_sorted,
num_per_segment_x=num_per_segment_x,
num_per_segment_y=num_per_segment_y,
weights_x=weights_x,
weights_y=weights_y,
padding_vector=padding_vector
)
