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fix recent lint

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Summary: lint clean again

Reviewed By: patricklabatut

Differential Revision: D20868775

fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9
This commit is contained in:
Jeremy Reizenstein
2020-04-06 06:38:50 -07:00
committed by Facebook GitHub Bot
parent 90dc7a0856
commit b87058c62a
6 changed files with 29 additions and 60 deletions
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51
tests/test_points_alignment.py
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@@ -2,12 +2,11 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import numpy as np
import unittest
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import points_alignment
from pytorch3d.structures.pointclouds import Pointclouds
from pytorch3d.transforms import rotation_conversions
@@ -54,18 +53,14 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
# generate random rotation matrices with orthogonalization of
# random normal square matrices, followed by a transformation
# that ensures determinant(R)==1
H = torch.randn(
batch_size, dim, dim, dtype=torch.float32, device=device
)
H = torch.randn(batch_size, dim, dim, dtype=torch.float32, device=device)
U, _, V = torch.svd(H)
E = torch.eye(dim, dtype=torch.float32, device=device)[None].repeat(
batch_size, 1, 1
)
E[:, -1, -1] = torch.det(torch.bmm(U, V.transpose(2, 1)))
R = torch.bmm(torch.bmm(U, E), V.transpose(2, 1))
assert torch.allclose(
torch.det(R), R.new_ones(batch_size), atol=1e-4
)
assert torch.allclose(torch.det(R), R.new_ones(batch_size), atol=1e-4)
return R
@@ -94,19 +89,13 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
dtype=torch.int64,
)
X_list = [
torch.randn(
int(n_pt), dim, device=device, dtype=torch.float32
)
torch.randn(int(n_pt), dim, device=device, dtype=torch.float32)
for n_pt in n_points_per_batch
]
X = Pointclouds(X_list)
else:
X = torch.randn(
batch_size,
n_points,
dim,
device=device,
dtype=torch.float32,
batch_size, n_points, dim, device=device, dtype=torch.float32
)
X = Pointclouds(list(X))
else:
@@ -143,11 +132,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
# randomly select one of the dimensions to reflect for each
# element in the batch
dim_to_reflect = torch.randint(
low=0,
high=dim,
size=(batch_size,),
device=device,
dtype=torch.int64,
low=0, high=dim, size=(batch_size,), device=device, dtype=torch.int64
)
# convert dim_to_reflect to a batch of reflection matrices M
@@ -211,8 +196,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
weights *= (weights * template.size()[1] > 0.3).to(weights)
if use_pointclouds: # convert to List[Tensor]
weights = [
w[:npts]
for w, npts in zip(weights, X.num_points_per_cloud())
w[:npts] for w, npts in zip(weights, X.num_points_per_cloud())
]
torch.cuda.synchronize()
@@ -255,7 +239,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
use_point_clouds_cases = (
(True, False) if dim == 3 and n_points > 3 else (False,)
)
for random_weights in (False, True,):
for random_weights in (False, True):
for use_pointclouds in use_point_clouds_cases:
for estimate_scale in (False, True):
for reflect in (False, True):
@@ -325,8 +309,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
weights *= (weights * template.size()[1] > 0.3).to(weights)
if use_pointclouds: # convert to List[Tensor]
weights = [
w[:npts]
for w, npts in zip(weights, X.num_points_per_cloud())
w[:npts] for w, npts in zip(weights, X.num_points_per_cloud())
]
# apply the generated transformation to the generated
@@ -374,9 +357,9 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
X_t_est = _apply_pcl_transformation(X_noisy, R_n, T_n, s=s_n)
return (
((X_t_est - X_t) * weights[..., None]) ** 2
).sum(dim=(1, 2)) / weights.sum(dim=-1)
return (((X_t_est - X_t) * weights[..., None]) ** 2).sum(
dim=(1, 2)
) / weights.sum(dim=-1)
# check that using weights leads to lower weighted_MSE(X_noisy, X_t)
self.assertTrue(
@@ -386,9 +369,7 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
if reflect and not allow_reflection:
# check that all rotations have det=1
self._assert_all_close(
torch.det(R_est),
R_est.new_ones(batch_size),
assert_error_message,
torch.det(R_est), R_est.new_ones(batch_size), assert_error_message
)
else:
@@ -430,6 +411,4 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
if weights is None:
self.assertClose(a_, b_, atol=atol, msg=err_message)
else:
self.assertClose(
a_ * weights, b_ * weights, atol=atol, msg=err_message
)
self.assertClose(a_ * weights, b_ * weights, atol=atol, msg=err_message)