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Address black + isort fbsource linter warnings
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
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
eb512ffde3
commit
d57daa6f85
@@ -3,8 +3,8 @@
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import math
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import unittest
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import torch
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import torch
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from pytorch3d.transforms.so3 import so3_exponential_map
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from pytorch3d.transforms.transform3d import (
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Rotate,
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@@ -18,9 +18,7 @@ from pytorch3d.transforms.transform3d import (
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class TestTransform(unittest.TestCase):
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def test_to(self):
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tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
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R = torch.FloatTensor(
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[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
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)
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R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
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R = Rotate(R)
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t = Transform3d().compose(R, tr)
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for _ in range(3):
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@@ -36,9 +34,7 @@ class TestTransform(unittest.TestCase):
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the same as composition of clones of translation and rotation.
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"""
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tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
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R = torch.FloatTensor(
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[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
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)
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R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
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R = Rotate(R)
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# check that the _matrix property of clones of
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@@ -63,9 +59,9 @@ class TestTransform(unittest.TestCase):
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def test_translate(self):
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t = Transform3d().translate(1, 2, 3)
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points = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
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).view(1, 3, 3)
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points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
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1, 3, 3
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)
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normals = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
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).view(1, 3, 3)
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@@ -82,9 +78,9 @@ class TestTransform(unittest.TestCase):
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def test_scale(self):
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t = Transform3d().scale(2.0).scale(0.5, 0.25, 1.0)
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points = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
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).view(1, 3, 3)
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points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
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1, 3, 3
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)
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normals = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
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).view(1, 3, 3)
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@@ -101,9 +97,9 @@ class TestTransform(unittest.TestCase):
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def test_scale_translate(self):
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t = Transform3d().scale(2, 1, 3).translate(1, 2, 3)
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points = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
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).view(1, 3, 3)
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points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
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1, 3, 3
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)
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normals = torch.tensor(
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[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
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).view(1, 3, 3)
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@@ -120,9 +116,9 @@ class TestTransform(unittest.TestCase):
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def test_rotate_axis_angle(self):
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t = Transform3d().rotate_axis_angle(90.0, axis="Z")
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points = torch.tensor(
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[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]
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).view(1, 3, 3)
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points = torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]).view(
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1, 3, 3
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)
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normals = torch.tensor(
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[[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]
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).view(1, 3, 3)
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@@ -194,9 +190,7 @@ class TestTransform(unittest.TestCase):
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t_ = Rotate(
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so3_exponential_map(
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torch.randn(
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(batch_size, 3),
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dtype=torch.float32,
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device=device,
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(batch_size, 3), dtype=torch.float32, device=device
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)
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),
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device=device,
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@@ -717,9 +711,7 @@ class TestRotate(unittest.TestCase):
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def test_inverse(self, batch_size=5):
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device = torch.device("cuda:0")
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log_rot = torch.randn(
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(batch_size, 3), dtype=torch.float32, device=device
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)
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log_rot = torch.randn((batch_size, 3), dtype=torch.float32, device=device)
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R = so3_exponential_map(log_rot)
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t = Rotate(R)
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im = t.inverse()._matrix
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@@ -749,9 +741,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 0.0, 1.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix))
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@@ -775,9 +765,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 0.0, 1.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
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@@ -835,9 +823,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 0.0, -1.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
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@@ -866,9 +852,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 0.0, -1.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
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@@ -923,9 +907,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 1.0, 0.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
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@@ -949,9 +931,7 @@ class TestRotateAxisAngle(unittest.TestCase):
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transformed_points = t.transform_points(points)
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expected_points = torch.tensor([0.0, 1.0, 0.0])
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self.assertTrue(
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torch.allclose(
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transformed_points.squeeze(), expected_points, atol=1e-7
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)
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torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
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)
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self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
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