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# Copyright The Lightning team.
#
# 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 typing import Union
import torch
from torch import Tensor, linalg
from torchmetrics.utilities.checks import _check_same_shape
from torchmetrics.utilities.prints import rank_zero_warn
def procrustes_disparity(
point_cloud1: Tensor, point_cloud2: Tensor, return_all: bool = False
) -> Union[Tensor, tuple[Tensor, Tensor, Tensor]]:
"""Runs procrustrus analysis on a batch of data points.
Works similar ``scipy.spatial.procrustes`` but for batches of data points.
Args:
point_cloud1: The first set of data points
point_cloud2: The second set of data points
return_all: If True, returns the scale and rotation matrices along with the disparity
"""
_check_same_shape(point_cloud1, point_cloud2)
if point_cloud1.ndim != 3:
raise ValueError(
"Expected both datasets to be 3D tensors of shape (N, M, D), where N is the batch size, M is the number of"
f" data points and D is the dimensionality of the data points, but got {point_cloud1.ndim} dimensions."
)
point_cloud1 = point_cloud1 - point_cloud1.mean(dim=1, keepdim=True)
point_cloud2 = point_cloud2 - point_cloud2.mean(dim=1, keepdim=True)
point_cloud1 /= linalg.norm(point_cloud1, dim=[1, 2], keepdim=True)
point_cloud2 /= linalg.norm(point_cloud2, dim=[1, 2], keepdim=True)
try:
u, w, v = linalg.svd(
torch.matmul(point_cloud2.transpose(1, 2), point_cloud1).transpose(1, 2), full_matrices=False
)
except Exception as ex:
rank_zero_warn(
f"SVD calculation in procrustes_disparity failed with exception {ex}. Returning 0 disparity and identity"
" scale/rotation.",
UserWarning,
)
return torch.tensor(0.0), torch.ones(point_cloud1.shape[0]), torch.eye(point_cloud1.shape[2])
rotation = torch.matmul(u, v)
scale = w.sum(1, keepdim=True)
point_cloud2 = scale[:, None] * torch.matmul(point_cloud2, rotation.transpose(1, 2))
disparity = (point_cloud1 - point_cloud2).square().sum(dim=[1, 2])
if return_all:
return disparity, scale, rotation
return disparity
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