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Remove pytorch3d's wrappers for eigh, solve, lstsq, qr

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Summary: Remove the compat functions eigh, solve, lstsq, and qr. Migrate callers to use torch.linalg directly.

Reviewed By: bottler

Differential Revision: D39172949

fbshipit-source-id: 484230a553237808f06ee5cdfde64651cba91c4c
This commit is contained in:
Chris Lambert
2022-08-31 13:04:07 -07:00
committed by Facebook GitHub Bot
parent 9a1213e0e5
commit d4a1051e0f
9 changed files with 11 additions and 67 deletions
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3
tests/test_acos_linear_extrapolation.py
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@@ -9,7 +9,6 @@ import unittest
import numpy as np
import torch
from pytorch3d.common.compat import lstsq
from pytorch3d.transforms import acos_linear_extrapolation
from .common_testing import TestCaseMixin
@@ -66,7 +65,7 @@ class TestAcosLinearExtrapolation(TestCaseMixin, unittest.TestCase):
bound_t = torch.tensor(bound, device=x.device, dtype=x.dtype)
# fit a line: slope * x + bias = y
x_1 = torch.stack([x, torch.ones_like(x)], dim=-1)
slope, bias = lstsq(x_1, y[:, None]).view(-1)[:2]
slope, bias = torch.linalg.lstsq(x_1, y[:, None]).solution.view(-1)[:2]
desired_slope = (-1.0) / torch.sqrt(1.0 - bound_t**2)
# test that the desired slope is the same as the fitted one
self.assertClose(desired_slope.view(1), slope.view(1), atol=1e-2)

3
tests/test_se3.py
View File

@@ -9,7 +9,6 @@ import unittest
import numpy as np
import torch
from pytorch3d.common.compat import qr
from pytorch3d.transforms.rotation_conversions import random_rotations
from pytorch3d.transforms.se3 import se3_exp_map, se3_log_map
from pytorch3d.transforms.so3 import so3_exp_map, so3_log_map, so3_rotation_angle
@@ -199,7 +198,7 @@ class TestSE3(TestCaseMixin, unittest.TestCase):
r = [identity, rot180]
r.extend(
[
qr(identity + torch.randn_like(identity) * 1e-6)[0]
torch.linalg.qr(identity + torch.randn_like(identity) * 1e-6)[0]
+ float(i > batch_size // 2) * (0.5 - torch.rand_like(identity)) * 1e-8
# this adds random noise to the second half
# of the random orthogonal matrices to generate

7
tests/test_so3.py
View File

@@ -11,7 +11,6 @@ from distutils.version import LooseVersion
import numpy as np
import torch
from pytorch3d.common.compat import qr
from pytorch3d.transforms.so3 import (
hat,
so3_exp_map,
@@ -49,7 +48,7 @@ class TestSO3(TestCaseMixin, unittest.TestCase):
# TODO(dnovotny): replace with random_rotation from random_rotation.py
rot = []
for _ in range(batch_size):
r = qr(torch.randn((3, 3), device=device))[0]
r = torch.linalg.qr(torch.randn((3, 3), device=device))[0]
f = torch.randint(2, (3,), device=device, dtype=torch.float32)
if f.sum() % 2 == 0:
f = 1 - f
@@ -145,7 +144,7 @@ class TestSO3(TestCaseMixin, unittest.TestCase):
# add random rotations and random almost orthonormal matrices
r.extend(
[
qr(identity + torch.randn_like(identity) * 1e-4)[0]
torch.linalg.qr(identity + torch.randn_like(identity) * 1e-4)[0]
+ float(i > batch_size // 2) * (0.5 - torch.rand_like(identity)) * 1e-3
# this adds random noise to the second half
# of the random orthogonal matrices to generate
@@ -245,7 +244,7 @@ class TestSO3(TestCaseMixin, unittest.TestCase):
r = [identity, rot180]
r.extend(
[
qr(identity + torch.randn_like(identity) * 1e-4)[0]
torch.linalg.qr(identity + torch.randn_like(identity) * 1e-4)[0]
for _ in range(batch_size - 2)
]
)