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Initialization of Transform3D with a custom matrix.

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Summary:
Allows to initialize a Transform3D object with a batch of user-defined transformation matrices:
```
t = Transform3D(matrix=torch.randn(2, 4, 4))
```

Reviewed By: nikhilaravi

Differential Revision: D20693475

fbshipit-source-id: dccc49b2ca4c19a034844c63463953ba8f52c1bc
This commit is contained in:
David Novotny
2020-04-05 14:42:22 -07:00
committed by Facebook GitHub Bot
parent e37085d999
commit 90dc7a0856
2 changed files with 44 additions and 2 deletions
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13
tests/test_transforms.py
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@@ -57,6 +57,19 @@ class TestTransform(unittest.TestCase):
matrix2 = t_pair[1].get_matrix()
self.assertTrue(torch.allclose(matrix1, matrix2))
def test_init_with_custom_matrix(self):
for matrix in (torch.randn(10, 4, 4), torch.randn(4, 4)):
t = Transform3d(matrix=matrix)
self.assertTrue(t.device == matrix.device)
self.assertTrue(t._matrix.dtype == matrix.dtype)
self.assertTrue(torch.allclose(t._matrix, matrix.view(t._matrix.shape)))
def test_init_with_custom_matrix_errors(self):
bad_shapes = [[10, 5, 4], [3, 4], [10, 4, 4, 1], [10, 4, 4, 2], [4, 4, 4, 3]]
for bad_shape in bad_shapes:
matrix = torch.randn(*bad_shape).float()
self.assertRaises(ValueError, Transform3d, matrix=matrix)
def test_translate(self):
t = Transform3d().translate(1, 2, 3)
points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(