Address black + isort fbsource linter warnings

Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8

pytorch3d/transforms/__init__.py

@@ -22,4 +22,5 @@ from .so3 import (
 )
 from .transform3d import Rotate, RotateAxisAngle, Scale, Transform3d, Translate
 
+
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/transforms/rotation_conversions.py

@@ -2,6 +2,7 @@
 
 import functools
 from typing import Optional
+
 import torch
 
 
@@ -155,9 +156,7 @@ def euler_angles_to_matrix(euler_angles, convention: str):
     for letter in convention:
         if letter not in ("X", "Y", "Z"):
             raise ValueError(f"Invalid letter {letter} in convention string.")
-    matrices = map(
-        _axis_angle_rotation, convention, torch.unbind(euler_angles, -1)
-    )
+    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
     return functools.reduce(torch.matmul, matrices)
 
 
@@ -246,10 +245,7 @@ def matrix_to_euler_angles(matrix, convention: str):
 
 
 def random_quaternions(
-    n: int,
-    dtype: Optional[torch.dtype] = None,
-    device=None,
-    requires_grad=False,
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
 ):
     """
     Generate random quaternions representing rotations,
@@ -266,19 +262,14 @@ def random_quaternions(
     Returns:
         Quaternions as tensor of shape (N, 4).
     """
-    o = torch.randn(
-        (n, 4), dtype=dtype, device=device, requires_grad=requires_grad
-    )
+    o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
     s = (o * o).sum(1)
     o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
     return o
 
 
 def random_rotations(
-    n: int,
-    dtype: Optional[torch.dtype] = None,
-    device=None,
-    requires_grad=False,
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
 ):
     """
     Generate random rotations as 3x3 rotation matrices.

pytorch3d/transforms/so3.py

@@ -3,6 +3,7 @@
 
 import torch
 
+
 HAT_INV_SKEW_SYMMETRIC_TOL = 1e-5
 
 
@@ -65,9 +66,7 @@ def so3_rotation_angle(R, eps: float = 1e-4, cos_angle: bool = False):
     rot_trace = R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2]
 
     if ((rot_trace < -1.0 - eps) + (rot_trace > 3.0 + eps)).any():
-        raise ValueError(
-            "A matrix has trace outside valid range [-1-eps,3+eps]."
-        )
+        raise ValueError("A matrix has trace outside valid range [-1-eps,3+eps].")
 
     # clamp to valid range
     rot_trace = torch.clamp(rot_trace, -1.0, 3.0)

pytorch3d/transforms/transform3d.py

@@ -3,6 +3,7 @@
 import math
 import warnings
 from typing import Optional
+
 import torch
 
 from .rotation_conversions import _axis_angle_rotation
@@ -230,9 +231,7 @@ class Transform3d:
                 # the transformations with get_matrix(), this correctly
                 # right-multiplies by the inverse of self._matrix
                 # at the end of the composition.
-                tinv._transforms = [
-                    t.inverse() for t in reversed(self._transforms)
-                ]
+                tinv._transforms = [t.inverse() for t in reversed(self._transforms)]
                 last = Transform3d(device=self.device)
                 last._matrix = i_matrix
                 tinv._transforms.append(last)
@@ -334,9 +333,7 @@ class Transform3d:
         return self.compose(Scale(device=self.device, *args, **kwargs))
 
     def rotate_axis_angle(self, *args, **kwargs):
-        return self.compose(
-            RotateAxisAngle(device=self.device, *args, **kwargs)
-        )
+        return self.compose(RotateAxisAngle(device=self.device, *args, **kwargs))
 
     def clone(self):
         """
@@ -388,9 +385,7 @@ class Transform3d:
 
 
 class Translate(Transform3d):
-    def __init__(
-        self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"
-    ):
+    def __init__(self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"):
         """
         Create a new Transform3d representing 3D translations.
 
@@ -424,9 +419,7 @@ class Translate(Transform3d):
 
 
 class Scale(Transform3d):
-    def __init__(
-        self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"
-    ):
+    def __init__(self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"):
         """
         A Transform3d representing a scaling operation, with different scale
         factors along each coordinate axis.
@@ -444,9 +437,7 @@ class Scale(Transform3d):
                 - 1D torch tensor
         """
         super().__init__(device=device)
-        xyz = _handle_input(
-            x, y, z, dtype, device, "scale", allow_singleton=True
-        )
+        xyz = _handle_input(x, y, z, dtype, device, "scale", allow_singleton=True)
         N = xyz.shape[0]
 
         # TODO: Can we do this all in one go somehow?
@@ -469,11 +460,7 @@ class Scale(Transform3d):
 
 class Rotate(Transform3d):
     def __init__(
-        self,
-        R,
-        dtype=torch.float32,
-        device: str = "cpu",
-        orthogonal_tol: float = 1e-5,
+        self, R, dtype=torch.float32, device: str = "cpu", orthogonal_tol: float = 1e-5
     ):
         """
         Create a new Transform3d representing 3D rotation using a rotation
@@ -562,9 +549,7 @@ def _handle_coord(c, dtype, device):
     return c
 
 
-def _handle_input(
-    x, y, z, dtype, device, name: str, allow_singleton: bool = False
-):
+def _handle_input(x, y, z, dtype, device, name: str, allow_singleton: bool = False):
     """
     Helper function to handle parsing logic for building transforms. The output
     is always a tensor of shape (N, 3), but there are several types of allowed