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

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

pytorch3d/common/compat.py

@@ -14,53 +14,6 @@ Some functions which depend on PyTorch or Python versions.
 """
 
 
-def solve(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:  # pragma: no cover
-    """
-    Like torch.linalg.solve, tries to return X
-    such that AX=B, with A square.
-    """
-    if hasattr(torch, "linalg") and hasattr(torch.linalg, "solve"):
-        # PyTorch version >= 1.8.0
-        return torch.linalg.solve(A, B)
-
-    # pyre-fixme[16]: `Tuple` has no attribute `solution`.
-    return torch.solve(B, A).solution
-
-
-def lstsq(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:  # pragma: no cover
-    """
-    Like torch.linalg.lstsq, tries to return X
-    such that AX=B.
-    """
-    if hasattr(torch, "linalg") and hasattr(torch.linalg, "lstsq"):
-        # PyTorch version >= 1.9
-        return torch.linalg.lstsq(A, B).solution
-
-    solution = torch.lstsq(B, A).solution
-    if A.shape[1] < A.shape[0]:
-        return solution[: A.shape[1]]
-    return solution
-
-
-def qr(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # pragma: no cover
-    """
-    Like torch.linalg.qr.
-    """
-    if hasattr(torch, "linalg") and hasattr(torch.linalg, "qr"):
-        # PyTorch version >= 1.9
-        return torch.linalg.qr(A)
-    return torch.qr(A)
-
-
-def eigh(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # pragma: no cover
-    """
-    Like torch.linalg.eigh, assuming the argument is a symmetric real matrix.
-    """
-    if hasattr(torch, "linalg") and hasattr(torch.linalg, "eigh"):
-        return torch.linalg.eigh(A)
-    return torch.symeig(A, eigenvectors=True)
-
-
 def meshgrid_ij(
     *A: Union[torch.Tensor, Sequence[torch.Tensor]]
 ) -> Tuple[torch.Tensor, ...]:  # pragma: no cover

pytorch3d/implicitron/tools/circle_fitting.py

@@ -10,7 +10,6 @@ from math import pi
 from typing import Optional
 
 import torch
-from pytorch3d.common.compat import eigh, lstsq
 
 
 def _get_rotation_to_best_fit_xy(
@@ -28,7 +27,7 @@ def _get_rotation_to_best_fit_xy(
         (3,3) tensor rotation matrix
     """
     points_centered = points - centroid[None]
-    return eigh(points_centered.t() @ points_centered)[1][:, [1, 2, 0]]
+    return torch.linalg.eigh(points_centered.t() @ points_centered)[1][:, [1, 2, 0]]
 
 
 def _signed_area(path: torch.Tensor) -> torch.Tensor:
@@ -106,9 +105,8 @@ def fit_circle_in_2d(
     n_provided = points2d.shape[0]
     if n_provided < 3:
         raise ValueError(f"{n_provided} points are not enough to determine a circle")
-    solution = lstsq(design, rhs[:, None])
+    solution = torch.linalg.lstsq(design, rhs[:, None]).solution
     center = solution[:2, 0] / 2
-    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     radius = torch.sqrt(solution[2, 0] + (center**2).sum())
     if n_points > 0:
         if angles is not None:

pytorch3d/implicitron/tools/eval_video_trajectory.py

@@ -9,7 +9,6 @@ import math
 from typing import Optional, Tuple
 
 import torch
-from pytorch3d.common.compat import eigh
 from pytorch3d.implicitron.tools import utils
 from pytorch3d.implicitron.tools.circle_fitting import fit_circle_in_3d
 from pytorch3d.renderer import look_at_view_transform, PerspectiveCameras
@@ -205,7 +204,7 @@ def _disambiguate_normal(normal, up):
 def _fit_plane(x):
     x = x - x.mean(dim=0)[None]
     cov = (x.t() @ x) / x.shape[0]
-    _, e_vec = eigh(cov)
+    _, e_vec = torch.linalg.eigh(cov)
     return e_vec
 
 

pytorch3d/ops/perspective_n_points.py

@@ -16,7 +16,6 @@ from typing import NamedTuple, Optional
 
 import torch
 import torch.nn.functional as F
-from pytorch3d.common.compat import eigh
 from pytorch3d.ops import points_alignment, utils as oputil
 
 
@@ -106,7 +105,7 @@ def _null_space(m, kernel_dim):
             kernel vectors, of size B x kernel_dim
     """
     mTm = torch.bmm(m.transpose(1, 2), m)
-    s, v = eigh(mTm)
+    s, v = torch.linalg.eigh(mTm)
     return v[:, :, :kernel_dim].reshape(-1, 4, 3, kernel_dim), s[:, :kernel_dim]
 
 

pytorch3d/ops/points_normals.py

@@ -7,7 +7,6 @@
 from typing import Tuple, TYPE_CHECKING, Union
 
 import torch
-from pytorch3d.common.compat import eigh
 from pytorch3d.common.workaround import symeig3x3
 
 from .utils import convert_pointclouds_to_tensor, get_point_covariances
@@ -147,7 +146,7 @@ def estimate_pointcloud_local_coord_frames(
     if use_symeig_workaround:
         curvatures, local_coord_frames = symeig3x3(cov, eigenvectors=True)
     else:
-        curvatures, local_coord_frames = eigh(cov)
+        curvatures, local_coord_frames = torch.linalg.eigh(cov)
 
     # disambiguate the directions of individual principal vectors
     if disambiguate_directions:

pytorch3d/transforms/se3.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from pytorch3d.common.compat import solve
 
 from .so3 import _so3_exp_map, hat, so3_log_map
 
@@ -174,7 +173,7 @@ def se3_log_map(
     # log_translation is V^-1 @ T
     T = transform[:, 3, :3]
     V = _se3_V_matrix(*_get_se3_V_input(log_rotation), eps=eps)
-    log_translation = solve(V, T[:, :, None])[:, :, 0]
+    log_translation = torch.linalg.solve(V, T[:, :, None])[:, :, 0]
 
     return torch.cat((log_translation, log_rotation), dim=1)
 

tests/test_acos_linear_extrapolation.py

@@ -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)

tests/test_se3.py

@@ -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

tests/test_so3.py

@@ -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)
             ]
         )