423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-08-02 11:52:50 +08:00
Code Issues Packages Projects Releases Wiki Activity

run lint

Browse Source 
Summary: Run `/dev/linter.sh` to fix linting

Reviewed By: nikhilaravi

Differential Revision: D20584037

fbshipit-source-id: 69e45b33d22e3d54b6d37c3c35580bb3e9dc50a5
This commit is contained in:
Georgia Gkioxari 2020-03-21 17:55:21 -07:00 committed by Facebook GitHub Bot
parent 6d34e1c60d
commit 03f441e7ca
4 changed files with 18 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
pytorch3d/renderer/cameras.py
View File

@ -2,7 +2,7 @@
import math
import numpy as np
from typing import Tuple, Optional, Sequence
from typing import Optional, Sequence, Tuple
import torch
import torch.nn.functional as F
@ -1039,15 +1039,18 @@ def look_at_view_transform(
if eye is not None:
broadcasted_args = convert_to_tensors_and_broadcast(
eye, at, up, device=device)
eye, at, up, device=device
)
eye, at, up = broadcasted_args
C = eye
else:
broadcasted_args = convert_to_tensors_and_broadcast(
dist, elev, azim, at, up, device=device)
dist, elev, azim, at, up, device=device
)
dist, elev, azim, at, up = broadcasted_args
C = camera_position_from_spherical_angles(
dist, elev, azim, degrees=degrees, device=device)
dist, elev, azim, degrees=degrees, device=device
)
R = look_at_rotation(C, at, up, device=device)
T = -torch.bmm(R.transpose(1, 2), C[:, :, None])[:, :, 0]

9
tests/test_sample_points_from_meshes.py
View File

@ -303,11 +303,16 @@ class TestSamplePoints(unittest.TestCase):
# randomly assign an invalid type
verts[torch.randperm(num_verts)[:10]] = float(invalid)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
num_verts,
size=(num_faces, 3),
dtype=torch.int64,
device=device,
)
meshes = Meshes(verts=[verts], faces=[faces])
with self.assertRaisesRegex(ValueError, "Meshes contain nan or inf."):
with self.assertRaisesRegex(
ValueError, "Meshes contain nan or inf."
):
sample_points_from_meshes(
meshes, num_samples=100, return_normals=True
)