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Address black + isort fbsource linter warnings

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

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
This commit is contained in:
Patrick Labatut
2020-03-29 14:46:33 -07:00
committed by Facebook GitHub Bot
parent eb512ffde3
commit d57daa6f85
110 changed files with 705 additions and 1850 deletions
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54
tests/test_vert_align.py
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@@ -2,22 +2,18 @@
import unittest
import torch
import torch.nn.functional as F
from common_testing import TestCaseMixin
from pytorch3d.ops.vert_align import vert_align
from pytorch3d.structures.meshes import Meshes
from common_testing import TestCaseMixin
class TestVertAlign(TestCaseMixin, unittest.TestCase):
@staticmethod
def vert_align_naive(
feats,
verts_or_meshes,
return_packed: bool = False,
align_corners: bool = True,
feats, verts_or_meshes, return_packed: bool = False, align_corners: bool = True
):
"""
Naive implementation of vert_align.
@@ -60,16 +56,13 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
return out_feats
@staticmethod
def init_meshes(
num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
):
def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
device = torch.device("cuda:0")
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = (
torch.rand((num_verts, 3), dtype=torch.float32, device=device)
* 2.0
torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
- 1.0
) # verts in the space of [-1, 1]
faces = torch.randint(
@@ -82,15 +75,11 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
return meshes
@staticmethod
def init_feats(
batch_size: int = 10, num_channels: int = 256, device: str = "cuda"
):
def init_feats(batch_size: int = 10, num_channels: int = 256, device: str = "cuda"):
H, W = [14, 28], [14, 28]
feats = []
for (h, w) in zip(H, W):
feats.append(
torch.rand((batch_size, num_channels, h, w), device=device)
)
feats.append(torch.rand((batch_size, num_channels, h, w), device=device))
return feats
def test_vert_align_with_meshes(self):
@@ -102,16 +91,12 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
# feats in list
out = vert_align(feats, meshes, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats, meshes, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats, meshes, return_packed=True)
self.assertClose(out, naive_out)
# feats as tensor
out = vert_align(feats[0], meshes, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats[0], meshes, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats[0], meshes, return_packed=True)
self.assertClose(out, naive_out)
def test_vert_align_with_verts(self):
@@ -120,30 +105,21 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
"""
feats = TestVertAlign.init_feats(10, 256)
verts = (
torch.rand(
(10, 100, 3), dtype=torch.float32, device=feats[0].device
)
* 2.0
torch.rand((10, 100, 3), dtype=torch.float32, device=feats[0].device) * 2.0
- 1.0
)
# feats in list
out = vert_align(feats, verts, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats, verts, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats, verts, return_packed=True)
self.assertClose(out, naive_out)
# feats as tensor
out = vert_align(feats[0], verts, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats[0], verts, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats[0], verts, return_packed=True)
self.assertClose(out, naive_out)
out2 = vert_align(
feats[0], verts, return_packed=True, align_corners=False
)
out2 = vert_align(feats[0], verts, return_packed=True, align_corners=False)
naive_out2 = TestVertAlign.vert_align_naive(
feats[0], verts, return_packed=True, align_corners=False
)
@@ -158,9 +134,7 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)