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https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
fix recent lint
Summary: lint clean again Reviewed By: patricklabatut Differential Revision: D20868775 fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9
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@ -1,12 +1,12 @@
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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from typing import List, Tuple, Union
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from pytorch3d.structures.pointclouds import Pointclouds
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from pytorch3d.structures import utils as strutil
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import torch
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from pytorch3d.ops import utils as oputil
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from pytorch3d.structures import utils as strutil
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from pytorch3d.structures.pointclouds import Pointclouds
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def corresponding_points_alignment(
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@ -77,9 +77,7 @@ def corresponding_points_alignment(
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weights = strutil.list_to_padded(weights)[..., 0]
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if Xt.shape[:2] != weights.shape:
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raise ValueError(
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"weights should have the same first two dimensions as X."
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)
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raise ValueError("weights should have the same first two dimensions as X.")
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b, n, dim = Xt.shape
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@ -120,9 +118,7 @@ def corresponding_points_alignment(
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U, S, V = torch.svd(XYcov)
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# identity matrix used for fixing reflections
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E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(
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b, 1, 1
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)
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E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(b, 1, 1)
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if not allow_reflection:
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# reflection test:
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@ -27,7 +27,7 @@ def wmean(
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* if `weights` is None => `mean(x, dim)`,
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* otherwise => `sum(x*w, dim) / max{sum(w, dim), eps}`.
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"""
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args = dict(dim=dim, keepdim=keepdim)
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args = {"dim": dim, "keepdim": keepdim}
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if weight is None:
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return x.mean(**args)
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@ -38,7 +38,6 @@ def wmean(
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):
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raise ValueError("wmean: weights are not compatible with the tensor")
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return (
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(x * weight[..., None]).sum(**args)
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/ weight[..., None].sum(**args).clamp(eps)
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return (x * weight[..., None]).sum(**args) / weight[..., None].sum(**args).clamp(
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eps
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)
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@ -3,8 +3,8 @@
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from copy import deepcopy
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from itertools import product
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from fvcore.common.benchmark import benchmark
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from fvcore.common.benchmark import benchmark
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from test_points_alignment import TestCorrespondingPointsAlignment
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@ -1,8 +1,7 @@
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from typing import Optional
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import unittest
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from typing import Optional
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import numpy as np
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import torch
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@ -57,7 +56,5 @@ class TestCaseMixin(unittest.TestCase):
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input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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else:
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close = np.allclose(
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input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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close = np.allclose(input, other, rtol=rtol, atol=atol, equal_nan=equal_nan)
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self.assertTrue(close, msg)
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@ -3,11 +3,10 @@ import unittest
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import numpy as np
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.ops import utils as oputil
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class TestOpsUtils(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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@ -62,8 +61,7 @@ class TestOpsUtils(TestCaseMixin, unittest.TestCase):
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# test dim
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weight = torch.rand(x.shape[0], n_points, device=device)
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weight_np = np.tile(
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weight[:, :, None].cpu().data.numpy(),
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(1, 1, x_np.shape[-1]),
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weight[:, :, None].cpu().data.numpy(), (1, 1, x_np.shape[-1])
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)
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mean = oputil.wmean(x, dim=0, weight=weight, keepdim=False)
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mean_gt = np.average(x_np, axis=0, weights=weight_np)
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@ -2,12 +2,11 @@
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import numpy as np
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import unittest
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import numpy as np
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.ops import points_alignment
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from pytorch3d.structures.pointclouds import Pointclouds
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from pytorch3d.transforms import rotation_conversions
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@ -54,18 +53,14 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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# generate random rotation matrices with orthogonalization of
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# random normal square matrices, followed by a transformation
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# that ensures determinant(R)==1
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H = torch.randn(
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batch_size, dim, dim, dtype=torch.float32, device=device
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)
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H = torch.randn(batch_size, dim, dim, dtype=torch.float32, device=device)
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U, _, V = torch.svd(H)
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E = torch.eye(dim, dtype=torch.float32, device=device)[None].repeat(
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batch_size, 1, 1
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)
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E[:, -1, -1] = torch.det(torch.bmm(U, V.transpose(2, 1)))
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R = torch.bmm(torch.bmm(U, E), V.transpose(2, 1))
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assert torch.allclose(
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torch.det(R), R.new_ones(batch_size), atol=1e-4
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)
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assert torch.allclose(torch.det(R), R.new_ones(batch_size), atol=1e-4)
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return R
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@ -94,19 +89,13 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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dtype=torch.int64,
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)
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X_list = [
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torch.randn(
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int(n_pt), dim, device=device, dtype=torch.float32
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)
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torch.randn(int(n_pt), dim, device=device, dtype=torch.float32)
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for n_pt in n_points_per_batch
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]
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X = Pointclouds(X_list)
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else:
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X = torch.randn(
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batch_size,
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n_points,
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dim,
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device=device,
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dtype=torch.float32,
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batch_size, n_points, dim, device=device, dtype=torch.float32
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)
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X = Pointclouds(list(X))
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else:
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@ -143,11 +132,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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# randomly select one of the dimensions to reflect for each
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# element in the batch
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dim_to_reflect = torch.randint(
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low=0,
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high=dim,
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size=(batch_size,),
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device=device,
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dtype=torch.int64,
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low=0, high=dim, size=(batch_size,), device=device, dtype=torch.int64
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)
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# convert dim_to_reflect to a batch of reflection matrices M
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@ -211,8 +196,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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weights *= (weights * template.size()[1] > 0.3).to(weights)
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if use_pointclouds: # convert to List[Tensor]
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weights = [
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w[:npts]
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for w, npts in zip(weights, X.num_points_per_cloud())
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w[:npts] for w, npts in zip(weights, X.num_points_per_cloud())
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]
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torch.cuda.synchronize()
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@ -255,7 +239,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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use_point_clouds_cases = (
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(True, False) if dim == 3 and n_points > 3 else (False,)
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)
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for random_weights in (False, True,):
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for random_weights in (False, True):
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for use_pointclouds in use_point_clouds_cases:
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for estimate_scale in (False, True):
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for reflect in (False, True):
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@ -325,8 +309,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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weights *= (weights * template.size()[1] > 0.3).to(weights)
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if use_pointclouds: # convert to List[Tensor]
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weights = [
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w[:npts]
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for w, npts in zip(weights, X.num_points_per_cloud())
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w[:npts] for w, npts in zip(weights, X.num_points_per_cloud())
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]
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# apply the generated transformation to the generated
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@ -374,9 +357,9 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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X_t_est = _apply_pcl_transformation(X_noisy, R_n, T_n, s=s_n)
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return (
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((X_t_est - X_t) * weights[..., None]) ** 2
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).sum(dim=(1, 2)) / weights.sum(dim=-1)
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return (((X_t_est - X_t) * weights[..., None]) ** 2).sum(
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dim=(1, 2)
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) / weights.sum(dim=-1)
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# check that using weights leads to lower weighted_MSE(X_noisy, X_t)
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self.assertTrue(
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@ -386,9 +369,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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if reflect and not allow_reflection:
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# check that all rotations have det=1
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self._assert_all_close(
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torch.det(R_est),
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R_est.new_ones(batch_size),
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assert_error_message,
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torch.det(R_est), R_est.new_ones(batch_size), assert_error_message
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)
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else:
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@ -430,6 +411,4 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
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if weights is None:
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self.assertClose(a_, b_, atol=atol, msg=err_message)
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else:
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self.assertClose(
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a_ * weights, b_ * weights, atol=atol, msg=err_message
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)
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self.assertClose(a_ * weights, b_ * weights, atol=atol, msg=err_message)
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