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https://github.com/facebookresearch/pytorch3d.git
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767d68a3af
Summary: Revisions to Poincloud data structure with added normals The biggest changes form the previous version include: a) If the user provides tensor inputs, we make no assumption about padding. Padding is only for internal use for us to convert from list to padded b) If features are not provided or if the poincloud is empty, all forms of features are None. This is so that we don't waste memory on holding dummy tensors. Reviewed By: nikhilaravi Differential Revision: D19791851 fbshipit-source-id: 7e182f7bb14395cb966531653f6dd6b328fd999c
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import numpy as np
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import unittest
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import torch
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class TestCaseMixin(unittest.TestCase):
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def assertSeparate(self, tensor1, tensor2) -> None:
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"""
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Verify that tensor1 and tensor2 have their data in distinct locations.
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"""
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self.assertNotEqual(
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tensor1.storage().data_ptr(), tensor2.storage().data_ptr()
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)
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def assertNotSeparate(self, tensor1, tensor2) -> None:
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"""
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Verify that tensor1 and tensor2 have their data in the same locations.
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"""
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self.assertEqual(
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tensor1.storage().data_ptr(), tensor2.storage().data_ptr()
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)
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def assertAllSeparate(self, tensor_list) -> None:
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"""
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Verify that all tensors in tensor_list have their data in
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distinct locations.
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"""
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ptrs = [i.storage().data_ptr() for i in tensor_list]
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self.assertCountEqual(ptrs, set(ptrs))
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def assertClose(
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self,
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input,
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other,
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*,
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rtol: float = 1e-05,
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atol: float = 1e-08,
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equal_nan: bool = False
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) -> None:
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"""
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Verify that two tensors or arrays are the same shape and close.
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Args:
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input, other: two tensors or two arrays.
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rtol, atol, equal_nan: as for torch.allclose.
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Note:
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Optional arguments here are all keyword-only, to avoid confusion
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with msg arguments on other assert functions.
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"""
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self.assertEqual(np.shape(input), np.shape(other))
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if torch.is_tensor(input):
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close = torch.allclose(
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input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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else:
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close = np.allclose(
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input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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self.assertTrue(close)
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