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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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21
tests/test_mesh_edge_loss.py
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@@ -1,12 +1,11 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.loss import mesh_edge_loss
from pytorch3d.structures import Meshes
from common_testing import TestCaseMixin
from test_sample_points_from_meshes import TestSamplePoints
@@ -27,9 +26,7 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
mesh = Meshes(verts=verts_list, faces=faces_list)
loss = mesh_edge_loss(mesh, target_length=target_length)
self.assertClose(
loss, torch.tensor([0.0], dtype=torch.float32, device=device)
)
self.assertClose(loss, torch.tensor([0.0], dtype=torch.float32, device=device))
self.assertTrue(loss.requires_grad)
@staticmethod
@@ -53,9 +50,7 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
num_edges = mesh_edges.size(0)
for e in range(num_edges):
v0, v1 = verts_edges[e, 0], verts_edges[e, 1]
predlosses[b] += (
(v0 - v1).norm(dim=0, p=2) - target_length
) ** 2.0
predlosses[b] += ((v0 - v1).norm(dim=0, p=2) - target_length) ** 2.0
if num_edges > 0:
predlosses[b] = predlosses[b] / num_edges
@@ -96,12 +91,8 @@ class TestMeshEdgeLoss(TestCaseMixin, unittest.TestCase):
self.assertClose(loss, predloss)
@staticmethod
def mesh_edge_loss(
num_meshes: int = 10, max_v: int = 100, max_f: int = 300
):
meshes = TestSamplePoints.init_meshes(
num_meshes, max_v, max_f, device="cuda:0"
)
def mesh_edge_loss(num_meshes: int = 10, max_v: int = 100, max_f: int = 300):
meshes = TestSamplePoints.init_meshes(num_meshes, max_v, max_f, device="cuda:0")
torch.cuda.synchronize()
def compute_loss():