PyTorch 1.4 compat

Summary: Restore compatibility with PyTorch 1.4 and 1.5, and a few lint fixes.

Reviewed By: patricklabatut

Differential Revision: D30048115

fbshipit-source-id: ee05efa7c625f6079fb06a3cc23be93e48df9433

pytorch3d/common/compat.py

@@ -19,7 +19,7 @@ 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, "solve"):
+    if hasattr(torch, "linalg") and hasattr(torch.linalg, "solve"):
         # PyTorch version >= 1.8.0
         return torch.linalg.solve(A, B)
 
@@ -31,7 +31,7 @@ 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, "lstsq"):
+    if hasattr(torch, "linalg") and hasattr(torch.linalg, "lstsq"):
         # PyTorch version >= 1.9
         return torch.linalg.lstsq(A, B).solution
 
@@ -45,7 +45,7 @@ def qr(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # pragma: no cove
     """
     Like torch.linalg.qr.
     """
-    if hasattr(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)

pytorch3d/renderer/cameras.py

@@ -6,7 +6,7 @@
 
 import math
 import warnings
-from typing import Optional, Sequence, Tuple, Union, List
+from typing import List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -259,8 +259,9 @@ class CamerasBase(TensorProperties):
             # users might might have to implement the screen to NDC transform based
             # on the definition of the camera parameters.
             # See PerspectiveCameras/OrthographicCameras for an example.
-            # We don't flip xy because we assume that world points are in PyTorch3D coodrinates
-            # and thus conversion from screen to ndc is a mere scaling from image to [-1, 1] scale.
+            # We don't flip xy because we assume that world points are in
+            # PyTorch3D coordinates, and thus conversion from screen to ndc
+            # is a mere scaling from image to [-1, 1] scale.
             return get_screen_to_ndc_transform(self, with_xyflip=False, **kwargs)
 
     def transform_points_ndc(

pytorch3d/renderer/points/pulsar/unified.py

@@ -551,7 +551,6 @@ class PulsarPointsRenderer(nn.Module):
                 otherargs["bg_col"] = bg_col
             # Go!
             images.append(
-                torch.flipud(
                 self.renderer(
                     vert_pos=vert_pos,
                     vert_col=vert_col,
@@ -561,7 +560,6 @@ class PulsarPointsRenderer(nn.Module):
                     max_depth=zfar,
                     min_depth=znear,
                     **otherargs,
-                    )
-                )
+                ).flip(dims=[0])
             )
         return torch.stack(images, dim=0)

pytorch3d/transforms/rotation_conversions.py

@@ -140,8 +140,10 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
         dim=-2,
     )
 
-    # clipping is not important here; if q_abs is small, the candidate won't be picked
-    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].clip(0.1))
+    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
+    # the candidate won't be picked.
+    # pyre-ignore [16]: `torch.Tensor` has no attribute `new_tensor`.
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
 
     # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
     # forall i; we pick the best-conditioned one (with the largest denominator)