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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the BSD-style license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from .common_testing import TestCaseMixin | |
| class TestOpsUtils(TestCaseMixin, unittest.TestCase): | |
| def setUp(self) -> None: | |
| super().setUp() | |
| torch.manual_seed(42) | |
| np.random.seed(42) | |
| def test_all_close(self): | |
| device = torch.device("cuda:0") | |
| n_points = 20 | |
| noise_std = 1e-3 | |
| msg = "tratata" | |
| # test absolute tolerance | |
| x = torch.rand(n_points, 3, device=device) | |
| x_noise = x + noise_std * torch.rand(n_points, 3, device=device) | |
| assert torch.allclose(x, x_noise, atol=10 * noise_std) | |
| assert not torch.allclose(x, x_noise, atol=0.1 * noise_std) | |
| self.assertClose(x, x_noise, atol=10 * noise_std) | |
| with self.assertRaises(AssertionError) as context: | |
| self.assertClose(x, x_noise, atol=0.1 * noise_std, msg=msg) | |
| self.assertTrue(msg in str(context.exception)) | |
| # test numpy | |
| def to_np(t): | |
| return t.data.cpu().numpy() | |
| self.assertClose(to_np(x), to_np(x_noise), atol=10 * noise_std) | |
| with self.assertRaises(AssertionError) as context: | |
| self.assertClose(to_np(x), to_np(x_noise), atol=0.1 * noise_std, msg=msg) | |
| self.assertIn(msg, str(context.exception)) | |
| self.assertIn("Not close", str(context.exception)) | |
| # test relative tolerance | |
| assert torch.allclose(x, x_noise, rtol=100 * noise_std) | |
| assert not torch.allclose(x, x_noise, rtol=noise_std) | |
| self.assertClose(x, x_noise, rtol=100 * noise_std) | |
| with self.assertRaises(AssertionError) as context: | |
| self.assertClose(x, x_noise, rtol=noise_std, msg=msg) | |
| self.assertTrue(msg in str(context.exception)) | |
| # test norm aggregation | |
| # if one of the spatial dimensions is small, norm aggregation helps | |
| x_noise[:, 0] = x_noise[:, 0] - x[:, 0] | |
| x[:, 0] = 0.0 | |
| assert not torch.allclose(x, x_noise, rtol=100 * noise_std) | |
| self.assertNormsClose( | |
| x, x_noise, rtol=100 * noise_std, norm_fn=lambda t: t.norm(dim=-1) | |
| ) | |