mirror of
https://github.com/facebookresearch/pytorch3d.git
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34f648ede0
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import numpy as np
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import torch
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from .common_testing import TestCaseMixin
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class TestOpsUtils(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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torch.manual_seed(42)
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np.random.seed(42)
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def test_all_close(self):
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device = torch.device("cuda:0")
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n_points = 20
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noise_std = 1e-3
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msg = "tratata"
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# test absolute tolerance
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x = torch.rand(n_points, 3, device=device)
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x_noise = x + noise_std * torch.rand(n_points, 3, device=device)
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assert torch.allclose(x, x_noise, atol=10 * noise_std)
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assert not torch.allclose(x, x_noise, atol=0.1 * noise_std)
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self.assertClose(x, x_noise, atol=10 * noise_std)
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with self.assertRaises(AssertionError) as context:
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self.assertClose(x, x_noise, atol=0.1 * noise_std, msg=msg)
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self.assertTrue(msg in str(context.exception))
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# test numpy
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def to_np(t):
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return t.data.cpu().numpy()
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self.assertClose(to_np(x), to_np(x_noise), atol=10 * noise_std)
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with self.assertRaises(AssertionError) as context:
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self.assertClose(to_np(x), to_np(x_noise), atol=0.1 * noise_std, msg=msg)
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self.assertIn(msg, str(context.exception))
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self.assertIn("Not close", str(context.exception))
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# test relative tolerance
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assert torch.allclose(x, x_noise, rtol=100 * noise_std)
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assert not torch.allclose(x, x_noise, rtol=noise_std)
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self.assertClose(x, x_noise, rtol=100 * noise_std)
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with self.assertRaises(AssertionError) as context:
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self.assertClose(x, x_noise, rtol=noise_std, msg=msg)
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self.assertTrue(msg in str(context.exception))
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# test norm aggregation
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# if one of the spatial dimensions is small, norm aggregation helps
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x_noise[:, 0] = x_noise[:, 0] - x[:, 0]
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x[:, 0] = 0.0
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assert not torch.allclose(x, x_noise, rtol=100 * noise_std)
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self.assertNormsClose(
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x, x_noise, rtol=100 * noise_std, norm_fn=lambda t: t.norm(dim=-1)
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)
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