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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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16
pytorch3d/ops/points_alignment.py
View File

@ -1,12 +1,12 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import warnings
from typing import List, Optional, Tuple, Union
import torch
from typing import List, Tuple, Union
from pytorch3d.structures.pointclouds import Pointclouds
from pytorch3d.structures import utils as strutil
import torch
from pytorch3d.ops import utils as oputil
from pytorch3d.structures import utils as strutil
from pytorch3d.structures.pointclouds import Pointclouds
def corresponding_points_alignment(
@ -77,9 +77,7 @@ def corresponding_points_alignment(
weights = strutil.list_to_padded(weights)[..., 0]
if Xt.shape[:2] != weights.shape:
raise ValueError(
"weights should have the same first two dimensions as X."
)
raise ValueError("weights should have the same first two dimensions as X.")
b, n, dim = Xt.shape
@ -120,9 +118,7 @@ def corresponding_points_alignment(
U, S, V = torch.svd(XYcov)
# identity matrix used for fixing reflections
E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(
b, 1, 1
)
E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(b, 1, 1)
if not allow_reflection:
# reflection test:

7
pytorch3d/ops/utils.py
View File

@ -27,7 +27,7 @@ def wmean(
* if `weights` is None => `mean(x, dim)`,
* otherwise => `sum(x*w, dim) / max{sum(w, dim), eps}`.
"""
args = dict(dim=dim, keepdim=keepdim)
args = {"dim": dim, "keepdim": keepdim}
if weight is None:
return x.mean(**args)
@ -38,7 +38,6 @@ def wmean(
):
raise ValueError("wmean: weights are not compatible with the tensor")
return (
(x * weight[..., None]).sum(**args)
/ weight[..., None].sum(**args).clamp(eps)
return (x * weight[..., None]).sum(**args) / weight[..., None].sum(**args).clamp(
eps
)

2
tests/bm_points_alignment.py
View File

@ -3,8 +3,8 @@
from copy import deepcopy
from itertools import product
from fvcore.common.benchmark import benchmark
from fvcore.common.benchmark import benchmark
from test_points_alignment import TestCorrespondingPointsAlignment

7
tests/common_testing.py
View File

@ -1,8 +1,7 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Optional
import unittest
from typing import Optional
import numpy as np
import torch
@ -57,7 +56,5 @@ class TestCaseMixin(unittest.TestCase):
input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
)
else:
close = np.allclose(
input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
)
close = np.allclose(input, other, rtol=rtol, atol=atol, equal_nan=equal_nan)
self.assertTrue(close, msg)

6
tests/test_ops_utils.py
View File

@ -3,11 +3,10 @@ import unittest
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import utils as oputil
class TestOpsUtils(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
@ -62,8 +61,7 @@ class TestOpsUtils(TestCaseMixin, unittest.TestCase):
# test dim
weight = torch.rand(x.shape[0], n_points, device=device)
weight_np = np.tile(
weight[:, :, None].cpu().data.numpy(),
(1, 1, x_np.shape[-1]),
weight[:, :, None].cpu().data.numpy(), (1, 1, x_np.shape[-1])
)
mean = oputil.wmean(x, dim=0, weight=weight, keepdim=False)
mean_gt = np.average(x_np, axis=0, weights=weight_np)

51
tests/test_points_alignment.py
View File

@ -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)